Apparatus and methods for monitoring a polypeptide type concentration in a multi-step biologic production process

Abstract

Methods for monitoring a polypeptide type concentration comprise obtaining a first signal from first measurements from a first substrate-based sensor exposed to a biologic sample. The first signal includes a resonance contribution for each first measurement, for each of a first plurality of polypeptide types, based on polypeptide type concentration in the sample and first substrate binding affinity for the polypeptide type. A second signal is obtained from second measurements from a second substrate-based sensor exposed to the sample. The second signal includes a resonance contribution for each second measurement, for each of a second plurality of polypeptide types, based on polypeptide type concentration in the sample and second substrate binding affinity for the polypeptide type. The first and second substrate binding affinity for a first polypeptide type in biologic sample differ. The first polypeptide type concentration is determined using the first and second signals.

Description

APPARATUS AND METHODS FOR MONITORING A POLYPEPTIDE TYPE CONCENTRATION IN A MULTI-STEP BIOLOGIC PRODUCTION PROCESSCROSS-REFERENCE TO RELATED APPLICATIONS

[0001] This application claims priority to U.S. Provisional Patent Application No. 63 / 747,181, filed on January 20, 2025, and U.S. Provisional Patent Application No. 63 / 657,518, filed on June 7, 2024, which are hereby incorporated by reference in their entirety.BACKGROUND

[0002] Multi-step production processes for producing a biologic often involve steps such as fractionation (e.g., through precipitation), chromatography, and filtration. For instance, immunoglobulin products from human plasma are used to treat immune deficiency. While initially, intramuscular or subcutaneous administration of IgG were the methods of choice, effective treatment of many diseases requires larger amounts of IgG provided by intravenous IgG products. Similarly, several multi-step production processes have been described for the manufacture of other plasma proteins such as Factor H from by-products formed during the manufacture of IgG immunoglobulins, including those described in WO 2008 / 113589 and WO 2011 / 011753.

[0003] To ensure quality control during the multi-step production processes for producing a biologic such as IgG, precising monitoring of concentration of one or more polypeptide types is desired. For instance, in the case of immunoglobulin based biologies, measurement of the concentration of particular IgG subclass, such as IgGl, lgG2, IgG3, or IgG4, or combinations thereof is desirable after one or more steps of the multi-step production process (e.g., after precipitation, chromatography and / or filtration). In many instances, such concentration measurements need to be performed by offline methods, such as by nephelometric (light scattering) or enzyme-linked immunosorbent assay (ELISA) approaches, which are time and resource consuming.

[0004] Process conditions and process times for producing a biological are often based on development and historical data. Conventional testing systems often do not allow real time control of the production process or the customization of process steps. For instance, conventional testing systems often do not allow for real time monitoring of the concentration of a first polypeptide type, such as IgG3, during or after a chromatographic step such that process parameters of the chromatographic step, process parameters of a preceding precipitation step, or process parameters of a downstream filtration step can be adjusted responsive to real time IgGs concentration measurement during or after the chromatographic step.

[0005] What is needed in the art is real time monitoring (e. ., inline, online, etc.) of the concentration of specific proteins during the manufacture of a biologic. Such monitoring would have advantages for real-time release (of intermediates), process efficiency, safety (by avoidance of sample taking and increased process control), and real-time adjustment of step parameters.SUMMARY

[0006] The present disclosure addresses the need in the art by providing real time methods for monitoring the concentration of one or more proteins (e.g., first polypeptide type) in a multi-step production process. The present disclosure makes use of first and second substrate-based sensors that have differential binding affinities and / or kinetics for the one or more proteins for which measurement is sought. In some embodiments the first and second substrate-based sensors are incorporated into a flow cell thereby allowing for incorporation of the sensor atline or online to the multi-step production process. By analyzing the differential signal produced by the first and second substrate-based sensors, in view of the differential binding affinities and / or kinetics for the one or more proteins it is possible to determine the concentration of the one or more proteins. In some embodiments, a machine learning model that makes use of the signal from the first and second substrate-based sensors determines the concentration of the one or more proteins. In some embodiments, the machine learning model additionally uses relevant process parameters such as pH, temperature, conductivity, flow rate, etc. to increase the accuracy of the determination of the concentration of the one or more proteins in the multi-step productionprocess. In some embodiments, the method enables a process optimization by modifying process parameters influencing the concentration of one or more proteins in the multi-step production process.

[0007] In one aspect of the present disclosure, methods for monitoring a concentration of a first polypeptide type in a multi-step production process of a biologic are provided. In some embodiments, the biologic is plasma-derived. In some embodiments, the biologic is immunoglobulin G purified from human plasma from a plurality of donors.

[0008] In some embodiments, the first polypeptide type is immunoglobulin IgG3. In some embodiments, the first polypeptide type is immunoglobulin IgAl or immunoglobulin IgA2. In some embodiments, the biologic comprises the first polypeptide type. In some embodiments, the biologic consists of the first polypeptide type.

[0009] A first signal is obtained from a plurality of first time-resolved measurements, occurring over a time period, from a first optical sensor in optical communication with a first substratebased sensor exposed to a sample during the time period, where the sample includes the biologic. The sample is associated with a first step in the multi-step production process. The first substrate-based sensor has a first plurality of binding affinities and / or kinetics. Each respective binding affinity and / or binding kinetic in the first plurality of binding affinities and / or kinetics is for a corresponding polypeptide type in a first plurality of polypeptide types that includes a first polypeptide type. The first polypeptide type is present in the sample. The first signal includes a resonance contribution, for each respective first time-resolved measurement in the plurality of first time-resolved measurements, from each respective polypeptide type in the first plurality of polypeptide types as a function of (i) a concentration of the respective polypeptide type in the sample during the respective first time-resolved measurement and (ii) the respective binding affinity and / or binding kinetics for the respective polypeptide type in the first plurality of binding affinities and / or kinetics.

[0010] In some embodiments, the time period is between thirty seconds and ten minutes.

[0011] In some embodiments, the time period is between one minute and six minutes.

[0012] A second signal is obtained from a plurality of second time-resolved measurements, occurring over the time period, from a second optical sensor in optical communication with a second substrate-based sensor exposed to the sample during the time period. The second substrate-based sensor has a second plurality of binding affinities and / or kinetics. Each respective binding affinity and / or kinetics in the second plurality of binding affinities and / or kinetics is for a corresponding polypeptide type in a second plurality of polypeptide types that includes the first polypeptide type. The second signal includes a resonance contribution, for each respective second time-resolved measurement in the plurality of second time-resolved measurements, from each respective polypeptide type in the second plurality of polypeptide types as a function of (i) a concentration of the respective polypeptide type in the sample during the respective second time-resolved measurement and (ii) the respective binding affinity and / or kinetics for the respective polypeptide type in the second plurality of binding affinities and / or kinetics. The respective binding affinity and / or kinetics in the first plurality of binding affinities and / or kinetics for the first polypeptide type is other than the respective binding affinity and / or kinetics in the second plurality of binding affinities and / or kinetics for the first polypeptide type.

[0013] In some embodiments, the first signal and the second signal are obtained concurrently.

[0014] In some embodiments, the first plurality of polypeptide types and the second plurality of polypeptide types differ by at least one polypeptide type.

[0015] In some embodiments, the first plurality of polypeptide types is identical to the second plurality of polypeptide types.

[0016] In some embodiments, the first plurality of polypeptide types comprises two, three, four, or more polypeptide types, and the second plurality of polypeptide types comprises two, three, four, or more polypeptide types.

[0017] In some embodiments, at least one of IgGl, IgG2, and IgG4 is present in the sample, and the first plurality of polypeptide types and the second plurality of polypeptide types each comprise at least one of IgGl, IgG2, and IgG4 that contributes to the first and second signal.

[0018] In some embodiments, at least two of IgGl, IgG2, and IgG4 is present in the sample, and the first plurality of polypeptide types and the second plurality of polypeptide types each comprise at least two of IgGl, IgG2, and IgG4 that each contribute to first and second signal.

[0019] In some embodiments, IgGl, IgG2, and IgG4 are each present in the sample, and the first plurality of polypeptide types and the second plurality of polypeptide types each comprise IgGl, IgG2, and IgG4 and each contribute to the first and second signal.

[0020] In some embodiments, the sample comprises IgG3 (IgG3) and IgGl (IgGl) at a percent weight IgG3 to IgGl ratio of between 0.056 to 0.16, and at least the first plurality of polypeptide types or the second plurality of polypeptide types comprises IgGl and IgG2.

[0021] In some embodiments, the sample comprises IgG3 (IgG3) and IgG2 (IgG2) at a percent weight IgG3 to IgG2 ratio of between 0.10 and 0.34, and at least the first plurality of polypeptide types or the second plurality of polypeptide types comprises IgG2 and IgG3.

[0022] In some embodiments, the sample comprises IgG3 and IgG4 at a percent weight IgG3 to IgG4 ratio of between 0.80 and 3.3, and at least the first plurality of polypeptide types or the second plurality of polypeptide types comprises IgG3 and IgG4.

[0023] In some embodiments, the first plurality of polypeptide types and the second plurality of polypeptide types each comprise at least one of, at least 2 of, or at least 3 of the group consisting of: immunoglobulin IgA, immunoglobulin IgM, immunoglobulin IgE, albumin, protein C, complement component Cl, protein S, anti-A hemagglutinin antibody, anti-B hemagglutinin antibody, anti-D hemagglutinin antibody, complement component 3, complement component C2a, complement component C3a, complement component C4a, complement component C5a, complement component C2b, complement component C3b, complement component C4b, complement component C5b, hemoglobin, hemopexin, parvo-19 antibody, an antibody to the polio virus, an antibody to the measles virus, a diphtheria antibody, alpha-2 macroglobulin, transferrin, fibrinogen, ceruloplasmin, plasmin, tissue thromboplastin (CD142), an apolipoprotein, alpha- 1 -antitrypsin, anti-thrombin, factor Xia, factor Xlla, prothrombin (factor two), factor XII, factor VIII, factor IX, factor X, factor XI, Von Willebrand factor, antithrombin III, Cl -esterase inhibitor, and haptoglobin.

[0024] In some embodiments, the first plurality of polypeptide types and the second plurality of polypeptide types each comprise at least one of, at least 2 of, or at least 3 of the group consisting of: immunoglobulins, viruses, proteins, and peptides.

[0025] In some embodiments, the first and second plurality of polypeptide types have at least two or three polypeptide types, present in the sample, in common and contributing to the first and second signal.

[0026] In some embodiments, the respective binding affinity in the first plurality of binding affinities for the first polypeptide type is negligible, and the respective binding affinity in the second plurality of binding affinities for the first polypeptide type is other than negligible.

[0027] In some embodiments, the respective binding affinity in the first plurality of binding affinities for the first polypeptide type is less than half of the respective binding affinity in the second plurality of binding affinities for the first polypeptide type.

[0028] In some embodiments, at least one polypeptide type in the first plurality of polypeptide types, present in the sample and contributing to the first signal, is not represented in the second plurality of polypeptide types.

[0029] In some embodiments, at least one polypeptide type in the first plurality of polypeptide types, present in the sample and contributing to the first signal, does not contribute to the second signal.

[0030] In some embodiments, at least two polypeptide types in the first plurality of polypeptide types, present in the sample and contributing to the first signal, are not represented in the second plurality of polypeptide types and do not contribute to the second signal.

[0031] In some embodiments, the sample further comprises ethanol.

[0032] In some embodiments, the sample is at a pH of between 5 and 6. In some embodiments, the sample is at a pH of between 4 and 8.7.

[0033] In some embodiments, the sample has a conductivity of between 0 mS / cm and 20 mS / cm.

[0034] In some embodiments, the first substrate-based sensor and the second substrate-based sensor are inline or atline with respect to the first step.

[0035] In some embodiments, the first substrate-based sensor and the second substrate-based sensor are off-line with respect to the first step.

[0036] In some embodiments, each step of the multi-step production process is inline.

[0037] In some embodiments, a first aliquot of the sample is used in the obtaining the first signal, and a second aliquot of the sample is used in the obtaining the second signal.

[0038] In some embodiments, the same aliquot of the sample is used in the obtaining the first signal and the second signal.

[0039] In some embodiments, the first signal is modulated by first Kon, Koff, or Ka from the first set of time-resolved measurements arising from the interaction of the first plurality of polypeptide types, in the sample, with a first functionalized solid surface of the first substratebased sensor, and the second signal is modulated by second Kon, Koff, or Ka from the second set of time-resolved measurements arising from the interaction of the second plurality of polypeptide types with a second functionalized solid surface of the second substrate-based sensor.

[0040] In some embodiments, the method further comprises injecting the sample into a flow cell in fluid communication with the first substrate-based sensor and the second substrate-based sensor. The first signal further comprises one or more first respective auxiliary measurements, accompanying each respective first time-resolved measurement in the plurality of first time- resolved measurements, that is a conductivity of the sample, a flow rate of the first step, a flow rate of the sample in the flow cell, a volume of the sample in the flow cell, a pressure of the sample in the flow cell, a pH of the sample, a temperature of the sample, an identity of a buffer in the sample, an ionic strength of the sample, or any combination thereof, representative of the sample during the respective first time-resolved measurement. The second signal further comprises one or more respective auxiliary measurements, accompanying each respective second time-resolved measurement in the plurality of second time-resolved measurements, that is a conductivity of the sample, a flow rate of the first step, a flow rate of the sample in the flow cell, a volume of the sample, a pressure of the sample in the flow cell, a pH of the sample, atemperature of the sample, an identity of a buffer in the sample, an ionic strength of the sample, or any combination thereof, representative of the sample during the respective second time- resolved measurement.

[0041] In some embodiments, the flow cell has a void volume of 1 mL or less. In some embodiments, the flow cell has a void volume of between 0.25 mL and 0.9 mL.

[0042] In some embodiments, the method further comprises acquiring an injection time in which the sample is injected into a flow cell containing the first substrate-based sensor and the second substrate-based sensor. Each respective first time-resolved measurement in the plurality of first time-resolved measurements is a baseline corrected median localized surface plasmon resonance signal or extinction signal, after the injection time, from a first functionalized solid surface of the first substrate-based sensor that is exposed to the sample. Each respective second time-resolved measurement in the plurality of second time-resolved measurements is a baseline corrected median localized surface plasmon resonance signal or extinction signal, after the injection time, from a second functionalized solid surface of the second substrate-based sensor that is exposed to the sample. In some embodiments, the flow cell has a void volume of 1 mL or less. In some embodiments, the flow cell has a void volume of between 0.25 mL and 0.9 mL.

[0043] In some embodiments, the first polypeptide type is immunoglobulin IgG3, the first solid surface is functionalized with protein A, and the second solid surface is functionalized with protein G.

[0044] In some embodiments, the first functionalized solid surface comprises a first plurality of metal nanoparticles (eg. gold nanoparticles, silver nanoparticles, or copper nanoparticles) coated with protein A that are fixed to a first substrate exposed to the sample (e.g., ligand bound via linker to gold particles), and the second functionalized solid surface comprises a second plurality of metal nanoparticles coated with protein G that are fixed to second substrate exposed to the sample.

[0045] In some embodiments, the sample passes through the flow cell at a predetermined flow velocity during the time period.

[0046] In some embodiments, the first step is associated with an injection time in which the sample is injected into a flow cell containing the first substrate-based sensor and the second substrate-based sensor. Each respective first time-resolved measurement in the plurality of first time-resolved measurements is a baseline corrected median localized surface plasmon resonance signal or extinction signal, around the injection time, from a first functionalized solid surface of the first substrate-based sensor that is exposed to the sample. Each respective second time- resolved measurement in the plurality of second time-resolved measurements is a baseline corrected median localized surface plasmon resonance signal or extinction signal, around the injection time, from a second functionalized solid surface of the second substrate-based sensor that is exposed to the sample. In some embodiments, the flow cell has a void volume of 1 mL or less. In some embodiments, the flow cell has a void volume of between 0.25 mL and 0.9 mL.

[0047] In some embodiments, the first polypeptide type is immunoglobulin IgG3, the first solid surface is functionalized with protein A, and the second solid surface is functionalized with protein G.

[0048] In some embodiments, the first functionalized solid surface comprises a first plurality of metal nanoparticles (e.g. gold nanoparticles, silver nanoparticles, or copper nanoparticles) coated with protein A that are fixed to a first substrate exposed to the sample (e.g., ligand bound via linker to gold particles), and the second functionalized solid surface comprises a second plurality of metal nanoparticles coated with protein G that are fixed to second substrate exposed to the sample.

[0049] In some embodiments, the sample passes through the flow cell at a predetermined flow velocity during the time period.

[0050] In some embodiments, the plurality of first time-resolved measurements are ultra-violet light measurements of the sample, and the plurality of second time-resolved measurements are ultra-violet light measurements of the sample.

[0051] In some embodiments, the first step comprises a fractionation of a plurality of plasma units from a plurality of donors.

[0052] In some embodiments, the time period occurs during the fractionation. In some embodiments, the time period occurs, at least in part, during the fractionation. In some embodiments, the time period occurs upon completion of the fractionation. In some embodiments, the plurality of first time-resolved measurements and the plurality of second time- resolved measurements are taken within an hour of completion of the fractionation. In some embodiments, the plurality of first time-resolved measurements and the plurality of second time- resolved measurements are taken within 12 hours of completion of the fractionation. In some embodiments, the plurality of first time-resolved measurements and the plurality of second time- resolved measurements are taken within a day of completion of the fractionation.

[0053] In some embodiments, the first step comprises a precipitation of a plasma component. In some embodiments, the precipitation is ethanol precipitation, ammonium sulfate precipitation, polyethylene glycol precipitation, citrate precipitation, or acid precipitation. In some embodiments, the time period occurs during the precipitation. In some embodiments, the time period occurs, at least in part, during the precipitation. In some embodiments, the time period occurs upon completion of the precipitation. In some embodiments, the plurality of first time- resolved measurements and the plurality of second time-resolved measurements are taken within an hour of completion of the precipitation. In some embodiments, the plurality of first time- resolved measurements and the plurality of second time-resolved measurements are taken within 12 hours of completion of the precipitation. In some embodiments, the plurality of first time- resolved measurements and the plurality of second time-resolved measurements of time-resolved measurements are taken within a day of completion of the precipitation.

[0054] In some embodiments, the first step comprises application of a chromatographic step on the sample. In some such embodiments, the chromatographic step is application of protein A affinity chromatography, protein G affinity chromatography, ion exchange chromatography, size exclusion chromatography, and / or hydrophobic interaction chromatography on the sample.

[0055] In some embodiments, the time period occurs during the chromatographic step. In some embodiments, the time period occurs, at least in part, during the chromatographic step. In some embodiments, the time period occurs upon completion of the chromatographic step. In some embodiments, the plurality of first time-resolved measurements and the plurality of second time-resolved measurements are taken within an hour of completion of the chromatographic step. In some embodiments, the plurality of first time-resolved measurements and the plurality of second time-resolved measurements are taken within 12 hours of completion of the chromatographic step. In some embodiments, the plurality of first time-resolved measurements and the plurality of second time-resolved measurements are taken within a day of completion of the chromatographic step.

[0056] In some embodiments, the first step comprises a filtration of the sample. In some embodiments, the filtration comprises microfiltration using a membrane with pores between 0.1 and 10 micrometers in size. In some embodiments, the filtration comprises ultrafiltration using a membrane with pores between 1 nanometer and 100 nanometers in size. In some embodiments, the filtration comprises nanofiltration with a membrane with pores between 1 and 10 nanometers in size. In some embodiments, the filtration comprises a depth filtration. In some embodiments, the filtration comprises a tangential flow filtration. In some embodiments, the time period occurs during the filtration. In some embodiments, the time period occurs, at least in part, during the filtration.

[0057] In some embodiments, the time period occurs upon completion of the filtration. In some embodiments, the plurality of first time-resolved measurements and the plurality of second time- resolved measurements are taken within an hour of completion of the filtration. In some embodiments, the plurality of first time-resolved measurements and the plurality of second time- resolved measurements are taken within 12 hours of completion of the filtration. In some embodiments, the plurality of first time-resolved measurements and the plurality of second time- resolved measurements are taken within a day of completion of the filtration.

[0058] Responsive to an analysis of the first and second signal, a calculated concentration of the first polypeptide type in the sample is obtained.

[0059] In some embodiments, the first step is associated with a first process parameter and a corresponding first validated range, and the method further comprises adjusting the first process parameter from a first value to a second value in the corresponding first validated range responsive to the calculated concentration of the first polypeptide type in the sample.

[0060] In some embodiments, the first process parameter is a pH, a salt concentration, a loading velocity, a protein density, a temperature, or a flow rate of the first step.

[0061] In some embodiments, a second step precedes the first step in the multi-step production process, the second step is associated with a second process parameter and a corresponding second validated range, and the method further comprises adjusting the second process parameter from a first value to a second value in the corresponding second validated range responsive to the calculated concentration of the first polypeptide type in the sample.

[0062] In some embodiments, the second process parameter is a pH, a salt concentration, a loading velocity, a protein density, a temperature, or a flow rate of the second step.

[0063] In some embodiments, a second step is after the first step in the multi-step production process, the second step is associated with a second process parameter and a corresponding second validated range, and the method further comprises adjusting the second process parameter from a first value to a second value in the corresponding second validated range responsive to the calculated concentration of the first polypeptide type in the sample.

[0064] In some embodiments, the analysis of the first and second signal comprises inputting at least the first and second signal into a model comprising a plurality of parameters thereby obtaining the calculated concentration of the first polypeptide type in the sample through interaction of the first plurality of parameters with the first and second signal.

[0065] In some embodiments, the model is an ElasticNet model, a random forest model, or a light gradient boosting machine (LightGBM) model.

[0066] In some embodiments, the model is a regression model.

[0067] In some embodiments, the model is a neural network model, a support vector machine model, a Naive Bayes model, a nearest neighbor model, a boosted trees model, a random forest model, a decision tree model, a multinomial logistic regression model, a linear model, or a linear regression model.

[0068] In some embodiments, the analysis of the first and second signal comprises a polynomial fitting of the first and second signal.

[0069] In some embodiments, the analysis of the first and second signal comprises a finite or infinite impulse response evaluation of the first and second signal.

[0070] In some embodiments, the analysis of the first and second signal comprises a Z- Transform analysis of the first and second signal.

[0071] In some embodiments, the analysis of the first and second signal comprises a mechanistic modeling of the first and second signal.

[0072] In some embodiments, the mechanistic modeling is a Scatchard model, a higher order Scatchard model, a steric mass action chromatography model, a colloidal particle adsorption chromatography model, manifold learning, or application of a convolutional neural network.

[0073] In some embodiments, the analysis determines that the concentration of the first polypeptide type is in a validated range associated with the first step and the method further comprises releasing the biologic for use in treating a condition of a species.

[0074] In some embodiments, the biologic is a human immunoglobulin biologic and the condition is a human condition (e.g., the human condition is primary immunodeficiency, multifocal motor neuropathy, humoral immunodeficiency secondary to myeloma, chronic lymphocytic leukemia, chronic inflammatory demyelinating polyneuropathy, a bacterial infection, peritonitis, or sepsis).

[0075] In some embodiments, the first step comprises a fractionation of a plurality of plasma units from a plurality of donors, the sample is from a first fraction arising from the fractionation, and the method further comprises further purifying the biologic from the first fraction after the analysis determines that the concentration of the first polypeptide type is in a predetermined range.

[0076] In some embodiments, , when the analysis determines that the concentration of the first polypeptide type is in a predetermined range the method further comprises releasing the human immunoglobulin biologic for use in treating a human condition, and when the analysis determines that the concentration of the first polypeptide type is outside the predetermined rangethe method further comprises adjusting a first process parameter, within a validated range, of the first step.

[0077] In some embodiments, the process parameter is a pH, a salt concentration, a loading velocity, a protein density, a temperature, or a flow rate associated with a purification process occurring upstream of the first step in the multi-step production process.

[0078] In some embodiments, the method further comprises performing a regeneration cycle on the first substrate-based sensor and the second substrate-based sensor.

[0079] In some embodiments, the regenerating cycle has a duration of between one minute and five minutes.

[0080] Another aspect of the present disclosure provides a computer system for monitoring a concentration of a first polypeptide type in a multi-step production process of a biologic. The computer system comprises one or more processors and memory addressable by the one or more processors. The memory stores at least one program for execution by the one or more processors.

[0081] The at least one program comprises instructions for obtaining a first signal from a plurality of first time-resolved measurements, occurring over a time period, from a first optical sensor in optical communication with a first substrate-based sensor exposed to a sample, which includes the biologic, during the time period. The sample is associated with a first step in the multi-step production process. The first substrate-based sensor has a first plurality of binding affinities and / or kinetics. Each respective binding affinity and / or kinetics in the first plurality of binding affinities and / or kinetics is for a corresponding polypeptide type in a first plurality of polypeptide types that includes the first polypeptide type. The first polypeptide type is present in the sample. The first signal includes a resonance contribution, for each respective first time- resolved measurement in the plurality of first time-resolved measurements, from each respective polypeptide type in the first plurality of polypeptide types as a function of (i) a concentration of the respective polypeptide type in the sample during the respective first time-resolved measurement and (ii) the respective binding affinity and / or kinetics for the respective polypeptide type in the first plurality of binding affinities and / or kinetics.

[0082] The at least one program further comprises instructions for obtaining a second signal from a second plurality of second time-resolved measurements, occurring over the time period, from a second optical sensor in optical communication with a second substrate-based sensor exposed to the sample during the time period. The second substrate-based sensor has a second plurality of binding affinities and / or kinetics. Each respective binding affinity and / or kinetics in the second plurality of binding affinities and / or kinetics is for a corresponding polypeptide type in a second plurality of polypeptide types that includes the first polypeptide type. The second signal includes a resonance contribution, for each respective second time-resolved measurement in the plurality of second time-resolved measurements, from each respective polypeptide type in the second plurality of polypeptide types as a function of (i) a concentration of the respective polypeptide type in the sample during the respective second time-resolved measurement and (ii) the respective binding affinity and / or kinetics for the respective polypeptide type in the second plurality of binding affinities and / or kinetics. The respective binding affinity and / or kinetics in the first plurality of binding affinities and / or kinetics for the first polypeptide type is other than the respective binding affinity and / or kinetics in the second plurality of binding affinities and / or kinetics for the first polypeptide type.

[0083] The at least one program further comprises instructions for obtaining, responsive to an analysis of the first and second signal, a calculated concentration of the first polypeptide type in the sample.

[0084] Another aspect of the present disclosure provides a computer system for monitoring a concentration of a first polypeptide type in a multi-step production process of a biologic. The computer system comprises one or more processors and memory addressable by the one or more processors. The memory stores at least one program for execution by the one or more processors. The at least one program comprises instructions for performing any of the methods provided in the present disclosure.

[0085] Another aspect of the present disclosure provides a non-transitory computer readable storage medium. The non-transitory computer readable storage medium stores instructions, which when executed by a computer system, cause the computer system to perform a method of monitoring a concentration of the first polypeptide type in a multi-step production process of abiologic. The method comprises obtaining a first signal from a plurality of first time-resolved measurements, occurring over a time period, from a first optical sensor in optical communication with a first substrate-based sensor exposed to a sample, where the sample includes the biologic, during the time period. The sample is associated with a first step in the multi-step production process. The first substrate-based sensor has a first plurality of binding affinities and / or kinetics. Each respective binding affinity and / or kinetics in the first plurality of binding affinities and / or kinetics is for a corresponding polypeptide type in a first plurality of polypeptide types that includes the first polypeptide type. The first polypeptide type is present in the sample. The first signal includes a resonance contribution, for each respective first time-resolved measurement in the plurality of first time-resolved measurements, from each respective polypeptide type in the first plurality of polypeptide types as a function of (i) a concentration of the respective polypeptide type in the sample during the respective first time-resolved measurement and (ii) the respective binding affinity and / or kinetics for the respective polypeptide type in the first plurality of binding affinities and / or kinetics.

[0086] The method further comprises obtaining a second signal from a second plurality of second time-resolved measurements, occurring over the time period, from a second optical sensor in optical communication with a second substrate-based sensor exposed to the sample during the time period. The second substrate-based sensor has a second plurality of binding affinities and / or kinetics, each respective binding affinity and / or kinetics in the second plurality of binding affinities and / or kinetics is for a corresponding polypeptide type in a second plurality of polypeptide types that includes the first polypeptide type. The second signal includes a resonance contribution, for each respective second time-resolved measurement in the plurality of second time-resolved measurements, from each respective polypeptide type in the second plurality of polypeptide types as a function of (i) a concentration of the respective polypeptide type in the sample during the respective second time-resolved measurement and (ii) the respective binding affinity and / or kinetics for the respective polypeptide type in the second plurality of binding affinities and / or kinetics. The respective binding affinity and / or kinetics in the first plurality of binding affinities and / or kinetics for the first polypeptide type is other than the respective binding affinity and / or kinetics in the second plurality of binding affinities and / or kinetics for the first polypeptide type.

[0087] The method further comprises, responsive to an analysis of the first and second signal, obtaining a calculated concentration of the first polypeptide type in the sample.

[0088] Another aspect of the represent disclosure provides a non-transitory computer readable storage medium, where the non-transitory computer readable storage medium stores instructions, which when executed by a computer system, cause the computer system to perform a method of monitoring a concentration of a first polypeptide type in a multi-step production process of a biologic. The method comprises any of the methods provided in the present disclosure.

[0089] Another aspect of the present disclosure provides an apparatus for a multi-step production process of a biologic. The apparatus comprises a first substrate-based sensor. The first substrate-based sensor has a first plurality of binding affinities and / or kinetics. Each respective binding affinity and / or kinetics in the first plurality of binding affinities and / or kinetics is for a corresponding polypeptide type in a first plurality of polypeptide types that includes a first polypeptide type.

[0090] The apparatus further comprises a first optical sensor in optical communication with the first substrate-based sensor.

[0091] The apparatus further comprises a second substrate-based sensor. The second substratebased sensor has a second plurality of binding affinities and / or kinetics. Each respective binding affinity and / or kinetics in the second plurality of binding affinities and / or kinetics is for a corresponding polypeptide type in a second plurality of polypeptide types that includes the first polypeptide type. The respective binding affinity and / or kinetics in the first plurality of binding affinities and / or kinetics for the first polypeptide type is other than the respective binding affinity and / or kinetics in the second plurality of binding affinities and / or kinetics for the first polypeptide type.

[0092] The apparatus further comprises a second optical sensor in optical communication with the second substrate-based sensor.

[0093] In some embodiments the apparatus further comprises a flow through that houses the first substrate-based sensor and the second substrate-based sensor. In some embodiments a first flowthrough houses the first substrate-based sensor and a second flow through houses the second substrate-based sensor.

[0094] The apparatus further comprises a processing module. The processing module comprises instructions for acquiring a first signal that includes a resonance contribution, for each respective first time-resolved measurement in a plurality of first time-resolved measurements, from each respective polypeptide type in the first plurality of polypeptide types as a function of (i) a concentration of the respective polypeptide type in a sample exposed to the first substrate-based sensor during the respective first time-resolved measurement and (ii) the respective binding affinity and / or kinetics for the respective polypeptide type in the first plurality of binding affinities and / or kinetics, wherein the sample is associated with a first step in the multi-step production process.

[0095] The processing module further comprises instructions for acquiring a second signal that includes a resonance contribution, for each respective second time-resolved measurement in a plurality of second time-resolved measurements, from each respective polypeptide type in the second plurality of polypeptide types as a function of (i) a concentration of the respective polypeptide type in the sample exposed to the second substrate-based sensor during the respective second time-resolved measurement and (ii) the respective binding affinity and / or kinetics for the respective polypeptide type in the second plurality of binding affinities and / or kinetics.

[0096] The processing module further comprises instructions for obtaining a calculated concentration of the first polypeptide type in the sample through an analysis of the first and second signal.

[0097] In some embodiments the first plurality of polypeptide types and the second plurality of polypeptide types differ by at least one polypeptide type.

[0098] In some embodiments, the first plurality of polypeptide types is identical to the second plurality of polypeptide types.

[0099] In some embodiments, the respective binding affinity in the first plurality of binding affinities for the first polypeptide type is negligible, and the respective binding affinity in the second plurality of binding affinities for the first polypeptide type is other than negligible.

[0100] In some embodiments the respective binding affinity in the first plurality of binding affinities for the first polypeptide type is less than half of the respective binding affinity in the second plurality of binding affinities for the first polypeptide type.

[0101] In some embodiments, the first polypeptide type is immunoglobulin IgG .

[0102] In some embodiments, at least one of IgGl, IgG2, and IgG4 is present in the sample, and the first plurality of polypeptide types and the second plurality of polypeptide types each comprise at least one of IgGl, IgG2, and IgG4 that contributes to the first and second signal.

[0103] In some embodiments, at least two of IgGl, IgG2, and IgG4 is present in the sample, and the first plurality of polypeptide types and the second plurality of polypeptide types each comprise at least two of IgGl, IgG2, and IgG4 that each contribute to first and second signal.

[0104] In some embodiments, IgGl, IgG2, and IgG4 are each present in the sample, and the first plurality of polypeptide types and the second plurality of polypeptide types each comprise IgGl, IgG2, and IgG4 and each contribute to the first and second signal.

[0105] In some embodiments, the first and second plurality of polypeptide types have at least two polypeptide types, present in the sample, in common and contributing to the first and second signal.

[0106] In some embodiments, the first and second plurality of polypeptide types have at least three polypeptide types, present in the sample, in common and contributing to the first signal and the second signal.

[0107] In some embodiments, at least one polypeptide type in the first plurality of polypeptide types, present in the sample and contributing to the first signal, is not represented in the first plurality of polypeptide types.

[0108] In some embodiments, at least one polypeptide type in the first plurality of polypeptide types, present in the sample and contributing to the first signal, is not represented in the second plurality of polypeptide types and does not contribute to the second signal.

[0109] In some embodiments, at least two polypeptide types in the first plurality of polypeptide types, present in the sample and contributing to the first signal, are not represented in the second plurality of polypeptide types and do not contribute to the second signal.

[0110] In some embodiments, the first plurality of polypeptide types and the second plurality of polypeptide types each comprise at least one of, at least 2 of, or at least 3 of the group consisting of: immunoglobulin IgA, immunoglobulin IgM, immunoglobulin IgE, albumin, protein C, complement component Cl, protein S, anti-A hemagglutinin antibody, anti-B hemagglutinin antibody, anti-D hemagglutinin antibody, complement component 3, complement component C2a, complement component C3a, complement component C4a, complement component C5a, complement component C2b, complement component C3b, complement component C4b, complement component C5b, hemoglobin, hemopexin, parvo-19 antibody, an antibody to the polio virus, an antibody to the measles virus, a diphtheria antibody, alpha-2 macroglobulin, transferrin, fibrinogen, ceruloplasmin, plasmin, tissue thromboplastin (CD142), an apolipoprotein, alpha- 1 -antitrypsin, anti-thrombin, factor Xia, factor Xlla, factor Xlla, prothrombin (factor two), factor XII, factor VIII, factor IX, factor X, factor XI, Von Willebrand factor, antithrombin III, Cl -esterase inhibitor, and haptoglobin.[0U1] In some embodiments, the first polypeptide type is immunoglobulin IgAl or immunoglobulin IgA2.

[0112] In some embodiments, the first signal is modulated by first Kon, Koff, or Ka from the first set of time-resolved measurements arising from the interaction of the first plurality of polypeptide types, in the sample, with a first functionalized solid surface of the first substratebased sensor, and the second signal is modulated by second Kon, Koff, or Ka from the second set of time-resolved measurements arising from the interaction of the second plurality of polypeptide types with a second functionalized solid surface of the second substrate-based sensor.

[0113] In some embodiments, the processing module further comprises instructions for acquiring an injection time in which the sample is injected into the flow through, and where each respective first time-resolved measurement in the plurality of first time-resolved measurements is a baseline corrected median localized surface plasmon resonance signal or extinction signal, after the injection time, from a first functionalized solid surface of the first substrate-based sensor that is exposed to the sample, and each respective second time-resolved measurement in the plurality of second time-resolved measurements is a baseline corrected median localized surface plasmon resonance signal or extinction signal, after the injection time, from a second functionalized solid surface of the second substrate-based sensor that is exposed to the sample.

[0114] In some embodiments, the processing module further comprises: instructions for acquiring an injection time in which the sample is injected into the flow through, and where each respective first time-resolved measurement in the plurality of first time-resolved measurements is a baseline corrected median localized surface plasmon resonance signal or extinction signal, around the injection time, from a first functionalized solid surface of the first substrate-based sensor that is exposed to the sample, and each respective second time-resolved measurement in the plurality of second time-resolved measurements is a baseline corrected median localized surface plasmon resonance signal or extinction signal, around the injection time, from a second functionalized solid surface of the second substrate- substrate sensor that is exposed to the sample.

[0115] In some embodiments, the first polypeptide type is immunoglobulin IgG , the first solid surface is functionalized with protein A, and the second solid surface is functionalized with protein G.

[0116] In some embodiments, the first functionalized solid surface comprises a first plurality of metal nanoparticles coated with protein A that are fixed to a first substrate exposed to the sample and the second functionalized solid surface comprises a second plurality of metal nanoparticles coated with protein G that are fixed to second substrate exposed to the sample.

[0117] In some embodiments, the first plurality of metal nanoparticles and the second plurality of metal nanoparticles are gold nanoparticles, silver nanoparticles, or copper nanoparticles.

[0118] In some embodiments, the plurality of first time-resolved measurements are ultra-violet light measurements of the sample, and the plurality of second time-resolved measurements are ultra-violet light measurements of the sample.

[0119] In some embodiments, the analysis of the first and second signal comprises inputting at least the first and second signal into a model comprising a plurality of parameters thereby obtaining the calculated concentration of the first polypeptide type in the sample through interaction of the first plurality of parameters with the first and second signal.

[0120] In some embodiments, the model is an ElasticNet model, a random forest model, or a light gradient boosting machine (LightGBM) model.

[0121] In some embodiments, the model is a regression model.

[0122] In some embodiments, model is a neural network model, a support vector machine model, a Naive Bayes model, a nearest neighbor model, a boosted trees model, a random forest model, a decision tree model, a multinomial logistic regression model, a linear model, or a linear regression model.

[0123] In some embodiments, the analysis of the first and second signal comprises a polynomial fitting of the first and second signal.

[0124] In some embodiments, the analysis of the first and second signal comprises a finite or infinite impulse response evaluation of the first and second signal.

[0125] In some embodiments, the analysis of the first and second signal comprises a Z- Transform analysis of the first and second signal.

[0126] In some embodiments, the analysis of the first and second signal comprises a mechanistic modeling of the first and second signal. In some such embodiments, the mechanistic modeling is a Scatchard model, a higher order Scatchard model, a steric mass action chromatography model, a colloidal particle adsorption chromatography model, manifold learning, or application of a convolutional neural network.

[0127] In some embodiments, the first plurality of polypeptide types comprises two or more polypeptide types, and the second plurality of polypeptide types comprises two or more polypeptide types.

[0128] In some embodiments, the first plurality of polypeptide types comprises three or more polypeptide types, and the second plurality of polypeptide types comprises three or more polypeptide types.

[0129] In some embodiments, the first plurality of polypeptide types comprises four or more polypeptide types, and the second plurality of polypeptide types comprises four or more polypeptide types.

[0130] In some embodiments, the sample comprises IgG3 and IgGl at a percent weight IgG3 to IgGl ratio of between 0.056 to 0.16, and at least the first plurality of polypeptide types or the second plurality of polypeptide types comprises IgGl and IgG2.

[0131] In some embodiments, the sample comprises IgG3 and IgG4 at a percent weight IgG3 to IgG4 ratio of between 1.1 to 2.5, preferably 1.8, and at least the first plurality of polypeptide types or the second plurality of polypeptide types comprises IgGl and IgG2.

[0132] In some embodiments, the time period is between thirty seconds and ten minutes. In some embodiments, the time period is between one minute and six minutes.

[0133] In some embodiments, the first substrate-based sensor and the second substrate-based sensor are atline with respect to the first step.

[0134] In some embodiments, the first substrate-based sensor and the second substrate-based sensor are inline with respect to the first step.

[0135] In some embodiments, the first substrate-based sensor and the second substrate-based sensor are off-line with respect to the first step.

[0136] In some embodiments, the first signal and the second signal are obtained concurrently by the processing module.

[0137] In some embodiments, the flow through has a void volume of 1 mL or less.

[0138] In some embodiments, the flow through has a void volume of between 0.25 mL and 0.9 mL.

[0139] In some embodiments, a first aliquot of the sample is used in acquiring the first signal, and a second aliquot of the sample is used in acquiring the second signal.

[0140] In some embodiments, the same aliquot of the sample is used in acquiring the first and second signal.

[0141] Other aspects and advantages of the invention will be apparent from the following detailed description, figures and the appended claims.BRIEF DESCRIPTION OF THE DRAWINGS

[0142] FIGs. 1A and IB illustrate a system for monitoring a concentration of a first polypeptide type in accordance with an embodiment of the present disclosure.

[0143] FIGs. 2A, 2B, 2C, 2D, 2E, 2F, 2G, 2H, 21, 2J, 2K, 2L, 2M, 2N, 20, 2P, 2Q, 2R, and 2S illustrate methods for monitoring a concentration of a polypeptide in a multi-step production process, in which optional elements are indicated by dashed boxes, in accordance with an embodiment of the present disclosure.

[0144] Fig. 3 illustrates the use of Protein A and Protein G concurrent chips for IgG3 concentration estimation using a localized surface plasmon resonance system in accordance with an embodiment of the present disclosure.

[0145] Fig. 4 illustrates multi-line equipment in accordance with an embodiment of the present disclosure.

[0146] Figs. 5A and 5B illustrate nanoplasmonic sensing based on localized surface plasmon resonance (LSPR). (A) Molecular interactions that take place at the surface of a metal nanoparticle change the conditions for the LSPR excitation (B) that result in a measurable shift in extinction coefficient.

[0147] Figs. 6A and 6B illustrate an LSPR signal of IgG of fractionation on surrogate plasma (4% Albumin, 0.3% IgG, 0.9% NaCl, pH 6.9). (A) time series of the injection-regeneration- equilibration cycles of the experiment. (B) The absorption maximum of the gold beads is set as baseline (0 pm) for the equilibration buffer (PBS). The regeneration buffer (0.05 M NaOH) has a strong hypsochromic effect (blue shift) of around 4000 pm. The binding of IgG has a bathochromic effect (red-shift) of up to 1000 pm, proportional to the amount of IgG bound.

[0148] Figs. 7A and 7B illustrate a change of binding response of samples during fractionation. The response is derived from the timeseries signal 115 seconds after sample injection. (A) Aggregate’s suspension samples (raw). (B) Supernatant samples.

[0149] Fig 8 illustrates a setup for IgG measurement in accordance with an embodiment of the present disclosure.

[0150] Fig. 9 illustrates LSPR signal over time after injection of serial dilutions of IgG, in accordance with an embodiment of the present disclosure.

[0151] Fig. 10 illustrates non-linear regression of the calculated concentrations for dilutions and the process signal from LSPR at higher concentrations, in accordance with an embodiment of the present disclosure.

[0152] Figs. 11A, 1 IB, 11C, and 1 ID illustrate signal data from time-resolved measurements, occurring over a time period, from an optical sensor in optical communication with a substratebased sensor exposed to a sample, in accordance with an embodiment of the present disclosure.

[0153] Fig. 12 illustrates an apparatus for measuring first and second signal in accordance with an embodiment of the present disclosure.

[0154] Fig. 13 illustrates an L-SPR shift signal in time-series data in accordance with an embodiment of the present disclosure.

[0155] Like reference numerals refer to corresponding parts throughout the several views of the drawings.DETAILED DESCRIPTION OF THE INVENTION

[0156] A. Introduction

[0157] IgG3 is very effective at engaging effector mechanisms but represents a relatively small percentage of circulating IgG in human serum. The dominant feature of IgG3 is the long hinge connecting the Fab domains to the Fc portion of the molecule. The hinge of IgG3 is 62 amino acids long, more than four times that of IgGl and containing 11 disulfide bonds.

[0158] Measurement of IgG3 in human plasma solutions is important since established limits must be met to avoid complement activation in human when delivering IgG product. In present production processes, limited offline data is available at the time being and information on the distribution of IgG3 (removal and concentration) during plasma fractionation is not available.

[0159] The present disclosure uses optical measurement, such as spectroscopic LSPR, for the estimation of IgG (all idiotypes), IgG3 and IgA using substrate-sensors that have differential binding affinity for one or more of the idiotypes. In some embodiments the present disclosure uses a first substrate-sensor that uses protein A as a binding and second substrate-sensor that used protein G, on the basis that protein A and protein G have differing binding affinity for IgG3. By using chips coated with gold nanoparticles treated with specific binders, such as protein A and G, that are selective towards the target proteins (first polypeptide type), a signal proportional to the concentration of the target protein is generated and transmitted to a detector caused by the light absorption spectral shift when the substrate-based sensor binder is bound to the target protein.

[0160] As illustrated in Fig. 3, protein G binds all human idiotypes of IgG while protein A does not bind IgG3 and partially binds IgA and with different kinetics. The present disclosure, by way of example, take advantage of the difference between the measured concentrations using substrate-based protein G and protein A sensors, and of their binding kinetics, so that an algorithm combining the two translated signals allows for the estimate of not only IgG3, but also IgA, as well as all IgG combined.

[0161] In order to obtain this result, it is necessary a pair of sensors (multi-line) is installed on the multi-step production process equipment. For instance, various installation points in the process of human plasma fractionation, carried out with the Cohn process, its modifications, orothers, is done in order to monitor concentration of a first polypeptide type at a select step in which processes.

[0162] In one embodiment, the present disclosure discloses a multi-line system that facilitates testing of upstream and downstream (through inclusion of the final product biologic) fractions of the IgG purification process as illustrated in Fig. 4. In instances where the first and second substrate-based sensors are pH or component sensitive, a desalting column can be applied to assure a constant matrix. This decreases data bias and increases accuracy of the measured samples. The disclosed apparatus allows for real-time IgG (IgG3 among others) evaluations of upstream and downstream IgG fractions.

[0163] It will be appreciated that the designed multi-line equipment is not limited to LSPR and can be applied for further analytical methods such as ELISA, capillary zone electrophoresis among others. In one embodiment, the design comprises a modular mechatronic system that combines actuators (pumps and valves), sensors, and controllers to preprocess samples for condition-sensitive analytical sensors. As illustrated in Fig. 4, in some embodiments the system comprises one or more different valves, one or more pumps, different kinds of sensors in series or parallel (including desalting columns where necessary). In some embodiments, the parts illustrated in Fig. 4 are connected with rigid or flexible tubing that can be made of different material suitable for the liquid that is passed in the device.

[0164] In some embodiments, the liquid flow path is controlled with an independent computer (e g., control module 101 of Fig. 1A, control unit 484 of Fig. 4), digital and analog I / O, and / or power drivers, controlled with software. In some embodiments the disclosed apparatus is designed to offer multiple defined-volume injections to increase the detection range of the sensors (high dynamic range, HDR) by using two or more injection valves in series or in parallel. In some embodiments, the disclosed apparatus provides an in-situ buffer exchange option using a desalting column for analytical systems sensitive to buffer and other parameters considered relevant for the process.

[0165] In some embodiments, the disclosed apparatus has the option to connect additional preprocessing steps including an inline filter, an inline mixing chamber, and / or temperature incubation with Peltier elements, or others able to achieve the same purpose.

[0166] In some embodiments, the disclosed apparatus can be operated in inline, online, or atline / offline mode and is therefore considered as multi-line equipment.

[0167] In some embodiments, the disclosed apparatus requires only one calibration solution and self-calibrates with up to n2calibration points (including blank), for n-injection valve setup.

[0168] In some embodiments, the disclosed apparatus is multi-line equipment that facilitates inline first polypeptide (e.g., IgG3) measurement (among other attributes). Inline concentration measurement of first polypeptide type (e.g., IgG3) is detectable and quantifiable. The apparatus and methods of the present disclosure It can be applied for process knowledge increase, input evaluation and real-time release of the final biologic product.

[0169] B. Definitions

[0170] While the terms used herein are believed to be well understood by one of ordinary skill in the art, definitions are set forth herein to facilitate explanation of the subject matter disclosed herein.

[0171] Unless defined otherwise, all technical and scientific terms used herein have the same meaning as commonly understood by one of ordinary skill in the art to which the subject matter disclosed herein belongs. Although any methods, devices, and materials similar or equivalent to those described herein can be used in the practice or testing of the presently disclosed subject matter, representative methods, devices, and materials are described herein.

[0172] The terms “a,” “an,” and “the” refer to “one or more” when used in this application, including the claims. The use of the word “a” or “an” when used in conjunction with the term “comprising” in the claims and / or the specification may mean “one,” but it is also consistent with the meaning of “one or more,” “at least one,” and “one or more than one.”

[0173] All references to singular characteristics or limitations of the present disclosure shall include the corresponding plural characteristic(s) or limitation(s) and vice versa, unless otherwise specified or clearly implied to the contrary by the context in which the reference is made.

[0174] All combinations of method or process steps as used herein can be performed in any order, unless otherwise specified or clearly implied to the contrary by the context in which the referenced combination is made.

[0175] The methods and devices of the present disclosure, including components thereof, can comprise, consist of, or consist essentially of the essential elements and limitations of the embodiments described herein, as well as any additional or optional components or limitations described herein or otherwise useful.

[0176] Unless otherwise indicated, all numbers expressing physical dimensions, quantities of ingredients, properties such as reaction conditions, and so forth used in the specification and claims are to be understood as being modified in all instances by the term “about”. Accordingly, unless indicated to the contrary, the numerical parameters set forth in this specification and claims are approximations that can vary depending upon the desired properties sought to be obtained by the presently disclosed subject matter.

[0177] As used herein, ranges can be expressed as from “about” one particular value, and / or to “about” another particular value. It is also understood that there are a number of values disclosed herein, and that each value is also herein disclosed as “about” that particular value in addition to the value itself. For example, if the value “10” is disclosed, then “about 10” is also disclosed. It is also understood that each unit between two particular units are also disclosed. For example, if 10 and 15 are disclosed, then 11, 12, 13, and 14 are also disclosed.

[0178] In the interest of clarity, not all of the routine features of the implementations described herein are shown and described. It will be appreciated that, in the development of any such actual implementation, numerous implementation-specific decisions are made in order to achieve the developer’s specific goals, such as compliance with application- and business-related constraints, and that these specific goals will vary from one implementation to another and from one developer to another. Moreover, it will be appreciated that such a development effort might be complex and time-consuming, but would nevertheless be a routine undertaking of engineering for those of ordinary skill in the art having the benefit of this disclosure.

[0179] Many modifications and variations of the exemplary embodiments set forth in this disclosure can be made without departing from the spirit and scope of the exemplary embodiments, as will be apparent to those skilled in the art. The specific exemplary embodiments described herein are offered by way of example only, and the disclosure is to be limited only by the terms of the appended claims, along with the full scope of equivalents to which such claims are entitled.

[0180] Unless defined otherwise, all technical and scientific terms used herein have the same meaning as commonly understood by one of ordinary skill in the art (e.g., in cell culture, molecular genetics, nucleic acid chemistry, hybridization techniques and biochemistry). Standard techniques are used for molecular, genetic and biochemical methods (see generally, Sambrook et al., Molecular Cloning: A Laboratory Manual, 2d ed. (1989) Cold Spring Harbor Laboratory Press, Cold Spring Harbor, N.Y. and Ausubel et al., Short Protocols in Molecular Biology (1999) 4thEd, John Wiley & Sons, Inc. which are incorporated herein by reference) and chemical methods. In addition, Harlow & Lane, A Laboratory Manual, Cold Spring Harbor, N.Y., is referred to for standard Immunological Techniques.

[0181] The term “affinity chromatography” refers to a protein separation technique in which a protein of interest (e.g., an Fc region containing protein of interest or antibody) is specifically bound to a ligand which is specific for the protein of interest. Such a ligand is generally referred to as an affinity or a biospecific ligand. In some embodiments, the affinity ligand (e.g., Protein A or a functional variant thereof) is covalently attached to a chromatography matrix material and is accessible to the protein of interest in solution as the solution contacts the chromatography matrix. The protein of interest generally retains its specific binding affinity for the affinity ligand during the chromatographic steps, while other solutes and / or proteins in the mixture do not bind appreciably or specifically to the ligand. Binding of the protein of interest to the immobilized ligand allows contaminating proteins or protein impurities to be passed through the chromatography matrix while the protein of interest remains specifically bound to the immobilized ligand on the solid phase material. The specifically bound protein of interest is then removed in active form from the immobilized ligand under suitable conditions (e.g., low pH, high pH, high salt, competing ligand etc.), and passed through the chromatographic column withthe elution buffer, free of the contaminating proteins or protein impurities that were earlier allowed to pass through the column. Any component can be used as a ligand for purifying its respective specific binding protein, e.g., antibody.

[0182] The term “affinity ligand” refers to a ligand capable of capturing one or more IgG species. An exemplary affinity ligand is selective from one IgG species in a mixture of species and use of a column, filter or other medium comprising the affinity ligand provides a method by which one or more IgG species can be purified from a mixture containing the one or more IgG species. An exemplary affinity ligand is a VHH antibody or a fragment thereof.

[0183] The term “antibody” and “immunoglobulin” are used interchangeably to refer, in some embodiments, to a protein comprising at least two heavy (H) chains and two light (L) chains inter-connected by disulfide bonds. Each heavy chain is comprised of a heavy chain variable region (abbreviated herein as VH) and a heavy chain constant region (abbreviated herein as CH). In some antibodies, e.g., naturally occurring IgG antibodies, the heavy chain constant region is comprised of a hinge and three domains, CHL CH2 and CH3. In some antibodies, e.g., naturally occurring IgG antibodies, each light chain is comprised of a light chain variable region (abbreviated herein as VL) and a light chain constant region. The light chain constant region is comprised of one domain (abbreviated herein as CL). The VH and VL regions can be further subdivided into regions of hypervariability, termed complementarity determining regions (CDR), interspersed with regions that are more conserved, termed framework regions (FR). Each VH and VL is composed of three CDRs and four FRs, arranged from amino-terminus to carboxyterminus in the following order: FR1, CDR1, FR2, CDR2, FR3, CDR3, and FR4. The variable regions of the heavy and light chains contain a binding domain that interacts with an antigen. A heavy chain may have the C-terminal lysine or not. The term “antibody” can include a bispecific antibody or a multi-specific antibody. An exemplary affinity ligand is an antibody or a fragment thereof. An exemplary antibody (immunoglobulin) includes a human IgA, IgD, IgG, IgE or IgM. The antibody can be an IgGl, IgG2, IgG3 and IgG4 antibody. As used herein an “IgG” has, in some embodiments, the structure of a naturally occurring IgG antibody, e.g., it has the same number of heavy and light chains and disulfide bonds as a naturally occurring IgG antibody of the same subclass. For example, an IgG antibody may consist of two heavy chains (HCs) andtwo light chains (LCs), where the two HCs and LCs are linked by the same number and location of disulfide bridges that occur in naturally occurring Ig antibodies, respectively (unless the antibody has been mutated to modify the disulfide bridges).

[0184] An immunoglobulin can be from any of the commonly known idiotypes, including but not limited to IgA, secretory IgA, IgG and IgM. The IgG idiotype is divided in subclasses in certain species: IgGl, IgG2, IgG3 and IgG4 in humans, and IgGl, IgG2a, IgG2b and IgG3 in mice. Immunoglobulins, e.g., IgGl, exist in several idiotypes, which differ from each other in at most a few amino acids. "Antibody" includes, by way of example, both naturally occurring and non-naturally occurring antibodies; monoclonal and polyclonal antibodies; chimeric and humanized antibodies; human and nonhuman antibodies and wholly synthetic antibodies.

[0185] The term “buffer” as used herein, refers to a substance which, by its presence in solution, increases the amount of acid or alkali that must be added to cause unit change in pH. A buffered solution resists changes in pH by the action of its acid-base conjugate components. Buffered solutions for use with biological reagents are generally capable of maintaining a constant concentration of hydrogen ions such that the pH of the solution is within a physiological range. Traditional buffer components include, but are not limited to, organic and inorganic salts, acids and bases.

[0186] The term “chromatography” refers to a technique separating a protein of interest (e.g., an antibody) from other molecules (e.g., contaminants) present in a mixture. Usually, the protein of interest is separated from other molecules (e.g., contaminants) as a result of differences in rates at which the individual molecules of the mixture migrate through a stationary medium under the influence of a moving phase, or in bind and elute processes. The term “matrix” or “chromatography matrix” are used interchangeably herein and refer to any kind of sorbent, resin or solid phase which in a separation process separates a protein of interest (e.g., an Fc region containing protein such as an immunoglobulin) from other molecules present in a mixture. Nonlimiting examples include particulate, monolithic or fibrous resins as well as membranes that can be put in columns or cartridges. Examples of materials for forming the matrix include polysaccharides (such as agarose and cellulose); and other mechanically stable matrices such as silica (e.g., controlled pore glass), poly(styrenedivinyl)benzene, polyacrylamide, ceramicparticles and derivatives of any of the above. Examples for typical matrix types suitable for the method of the present disclosure are cation exchange resins, affinity resins, anion exchange resins or mixed mode resins. A “ligand” is a functional group that is attached to the chromatography matrix and that determines the binding properties of the matrix. Examples of “ligands” include, but are not limited to, ion exchange groups, hydrophobic interaction groups, hydrophilic interaction groups, thiophilic interactions groups, metal affinity groups, affinity ligands, bioaffinity groups, and mixed mode groups (combinations of the aforementioned). Some preferred ligands that can be used herein include, but are not limited to, strong cation exchange groups, such as sulphopropyl, sulfonic acid; strong anion exchange groups, such as trimethylammonium chloride; weak cation exchange groups, such as carboxylic acid; weak anion exchange groups, such as N5N diethylamino or DEAE; hydrophobic interaction groups, such as phenyl, butyl, propyl, hexyl; and affinity ligands, such as Protein A, Protein G, and Protein L.

[0187] The term “chromatography column” or “column” in connection with chromatography as used herein, refers to a container, frequently in the form of a cylinder or a hollow pillar which is filled with the chromatography matrix or resin. The chromatography matrix or resin is the material which provides the physical and / or chemical properties that are employed for purification. For example, immunoglobulin can be purified by the removal of contaminating non-immunoglobulin proteins; they are also purified by the removal of immunoglobulin other than IgG. The removal of non-immunoglobulin proteins and / or the removal of immunoglobulin other than IgG results in an increase in the percent of desired IgG in the feedstock. Purity can be measured by standard assays known in the art or described herein, examples of which include SDS-PAGE followed by Coomassie blue staining as well as chromatographic methods (e.g., size exclusion chromatography (SEC) on a HPLC system). Purity of the IgG sample can be calculated from an SDS PAGE gel after scanning, e.g. using a Kodak Image Station 1000 or equivalent system, or by analysis of SEC chromatogram by software on a Shimadzu HPLC system. A sample is considered pure if it is at least 90%, 95%, or 99% free of components other than the desired product (e.g., polypeptide type).

[0188] As used interchangeably herein, the terms “classifier,” “model,” or “regressor” interchangeably refer to a machine learning model. In some embodiments, a model is anunsupervised learning model. In some embodiments, a model includes supervised machine learning. Nonlimiting examples of supervised learning models include, but are not limited to, logistic regression models, neural networks, support vector machines, Naive Bayes models, nearest neighbors models, random forest models, decision trees, boosted trees, multinomial logistic regression models, linear models, linear regression models, Gradient Boosting models, mixture models, hidden Markov models, Gaussian NB models, linear discriminant analysis models, or any combinations thereof.

[0189] As used herein, the term “parameter” refers to any coefficient or, similarly, any value of an internal or external element (e.g., a weight and / or a hyperparameter) in a model, regressor, and / or classifier that can affect (e.g., modify, tailor, and / or adjust) one or more inputs, outputs, and / or functions in the model, regressor and / or classifier. For example, in some embodiments, a parameter refers to any coefficient, weight, and / or hyperparameter that can be used to control, modify, tailor, and / or adjust the behavior, learning, and / or performance of a model, regressor, and / or classifier. In some instances, a parameter is used to increase or decrease the influence of an input (e.g., a feature) to the model, regressor, and / or classifier. As a nonlimiting example, in some embodiments, a parameter is used to increase or decrease the influence of a node (e.g., of a neural network), where the node includes one or more activation functions. Assignment of parameters to specific inputs, outputs, and / or functions is not limited to any one paradigm for a given model, regressor, and / or classifier but can be used in any suitable model, regressor, and / or classifier architecture for a desired performance. In some embodiments, a parameter has a fixed value. In some embodiments, a value of a parameter is manually and / or automatically adjustable. In some embodiments, a value of a parameter is modified by a validation and / or training process for an algorithm, model, regressor, and / or classifier (e.g., by error minimization and / or backpropagation methods). In some embodiments, a model, regressor, and / or classifier of the present disclosure includes a plurality of parameters. In some embodiments, the plurality of parameters is n parameters, where: n > 2; n > 5; n > 10; n > 25; n > 40; n > 50; n > 75; n > 100; n > 125; n > 150; n > 200; n > 225; n > 250; n > 350; n > 500; n > 600; n > 750; n > 1,000; n > 2,000; n > 4,000; n > 5,000; n > 7,500; n > 10,000; n > 20,000; n > 40,000; n > 75,000; n > 100,000; n > 200,000; n > 500,000, n > 1 x 106, n > 5 x 106, or n > 1 x 107. As such, some embodiments of the models, regressors, and / or classifiers of the present disclosure cannot bementally performed. In some embodiments n is between 10,000 and 1 x 10', between 100,000 and 5 x 106, or between 500,000 and 1 x 106. In some embodiments, the models, regressors, and / or classifiers of the present disclosure operate in a k-dimensional space, where k is a positive integer of 5 or greater (e.g., 5, 6, 7, 8, 9, 10, etc.). As such, some embodiments of the models, regressors, and / or classifiers of the present disclosure cannot be mentally performed.

[0190] “Polymer”, as used herein, refers to molecules composed of repeating monomers, connected to each other in chain linked fashion by covalent chemical bonds. Examples of polymers include proteins, nucleic acids, peptides, peptoids, and cellulose. In the case of polymers that are proteins or peptides, each monomer is an amino acid residue and the covalent chemical bonds linking the amino acid residues are peptide bonds. A polymer, such as a protein or peptide, may also have any number of posttranslational modifications. Thus, a polymer includes those that are modified by acylation, alkylation, amidation, biotinylation, formylation, glutamyl ati on, glycosylation, glycylation, hydroxylation, iodination, isoprenylation, lipoylation, cofactor addition (for example, of a heme, flavin, metal, etc. , addition of nucleosides and their derivatives, oxidation, reduction, pegylation, phosphatidylinositol addition, phosphopantetheinylation, phosphorylation, pyroglutamate formation, racemization, addition of amino acids by tRNA (for example, arginylation), sulfation, selenoylation, ISGylation, SUMOylation, ubiquitination, chemical modifications (for example, citrullination and deamidation), and treatment with other enzymes (for example, proteases, phosphotases and kinases). Other types of posttranslational modifications are known in the art and are also included within the scope of polymers as used herein.

[0191] In some embodiments, an “amino acid residue” refers a residue of any of the twenty standard naturally occurring amino acids known in the art, which include imino acids, such as proline and hydroxyproline. Amino acids also include D, L, R and S. Moreover, amino acids also include nonnatural amino acids. Thus, selenocysteine, pyrrolysine, lanthionine, 2- aminoisobutyric acid, gamma-aminobutyric acid, dehydroalanine, ornithine, ceratine, citrulline and homocysteine are all considered amino acids. Other variants or analogs of the amino acids are known in the art. Thus, a polymer may include synthetic peptidomimetic structures such as peptoids and peptides. See Simon et al., 1992, Proceedings of the National Academy ofSciences USA, 89, 9367, which is hereby incorporated by reference herein in its entirety. See also Chin et al., 2003, Science 301, 964; and Chin et al., 2003, Chemistry & Biology 10, 511, each of which is incorporated by reference herein in its entirety.

[0192] “Protein A”, as used herein, refers to an affinity ligand, which is a 49 kDa surface protein originally found in the cell wall of the bacteria Staphylococcus aureus. This protein binds immunoglobulins, and comprises five homologous Ig-binding domains. Affinity chromatographic media including immobilized Protein A are known in the art. As will appreciated by those of skill in the art, other affinity ligands can be utilized in place of or in addition to Protein A, e.g., Protein G, Protein A / G and Protein L, in variations on the method disclosed herein, which are considered within the scope of the current invention. Those portions of the current disclosure expressly disclosing Protein A are also relevant to embodiments in which one or more of Protein G, Protein A / G and / or Protein L are utilized instead of or in addition to Protein A.

[0193] The terms “purifying,” “separating,” or “isolating,” as used interchangeably herein, refer to increasing the degree of purity of a polypeptide type from a composition or sample comprising the polypeptide type and one or more impurities. Typically, the degree of purity of the polypeptide type is increased by removing (completely or partially) at least one impurity from the composition. “Purifying” and its equivalents refer to one or more step performed to isolate a polypeptide type from one or more other impurities (e.g., bulk impurities) or components present in a fluid containing a polypeptide type e.g., plasma, Cohn fraction, liquid culture medium proteins or one or more other components (e.g., DNA, RNA, other proteins, endotoxins, viruses, etc.) present in or secreted from a mammalian cell). For example, purifying can be performed during or after an initial capturing step. Purification can be performed using a resin, membrane, or any other solid support that binds either a therapeutic protein or contaminants (e.g., through the use of affinity chromatography, hydrophobic interaction chromatography, anion or cation exchange chromatography, or molecular sieve chromatography). A polypeptide type can be purified from a fluid containing the polypeptide type using at least one chromatography column and / or chromatographic membrane (e.g., any of the chromatography columns or chromatographic membranes described herein).

[0194] “Therapeutic drug substance,” as used herein, refers to a substance including a protein, e.g., an IgG, that is sufficiently enriched, purified or isolated by a method of the invention from contaminating proteins, lipids, and nucleic acids (e.g., contaminating proteins, lipids, and nucleic acids present in a liquid culture medium or from a host cell (e.g., from a mammalian, yeast, or bacterial host cell) and biological contaminants (e.g., viral and bacterial contaminants)). An exemplary therapeutic drug substance can be formulated into a pharmaceutical agent without any further substantial purification and / or decontamination step.

[0195] C. Abbreviations

[0196] CMS Carboxymethyl Sepharose

[0197] CSP Capto - Sulphopropyl

[0198] CV column volumes

[0199] ELISA Enzyme-linked immunosorbent assay

[0200] EtOH Ethanol

[0201] FC Final Container

[0202] IgA Immunoglobulin A

[0203] IGI Immune Globulin Infusion

[0204] IGSC Immune Globulin Subcutaneous

[0205] IgG Immunoglobulin G

[0206] IgM Immunoglobulin M

[0207] LoD / LoQ Limit of Detection / Limit of Quantification

[0208] LSPR Localized surface-plasmon resonance

[0209] MEK Methyl Ethyl Ketone

[0210] mAU milli Absorbance Units

[0211] OPC Open Platform Communication

[0212] PAT Process Analytical Technology

[0213] pm picometer

[0214] Ppt G Precipitate G intermediate

[0215] QC Quality Control

[0216] RoD Recovery of Detection

[0217] S / D Solvent / Detergent

[0218] SOP Standard Operating Procedure

[0219] TP Total Protein

[0220] UV Ultraviolet

[0221] D. Exemplary systems for monitoring a concentration of a first polypeptide type.

[0222] FIGs. 1A and IB illustrate a computer system 100 monitoring a concentration of a first polypeptide type in a multi-step production process of a biologic. Referring to FIGs. 1A and IB, in typical embodiments, computer system 100 comprises one or more computers. For purposes of illustration in FIGs. 1A and IB, the computer system 100 is represented as a single computer that includes all of the functionality of the disclosed computer system 100. However, the present disclosure is not so limited. The functionality of the computer system 100 may be spread across any number of networked computers and / or reside on each of several networked computers and / or virtual machines. One of skill in the art will appreciate that a wide array of different computer topologies is possible for the computer system 100 and all such topologies are within the scope of the present disclosure.

[0223] Turning to FIGs. 1A and IB with the foregoing in mind, the computer system 100 comprises one or more central processing units (CPUs) 64, optionally one or more graphic processing units (GPUs) 74, a network or other communications interface 76, a user interface 68 (e.g., including an optional display 70 and optional keyboard 72 or other form of input device), a memory 58 (e.g, random access memory, persistent memory, or combination thereof), one or more magnetic disk storage and / or persistent devices 60 optionally accessed by one or more controllers 62, one or more communication busses 12 for interconnecting the aforementioned components, and a power supply 66 for powering the aforementioned components. To the extent that components of memory 58 are not persistent, data in memory 58 can be seamlessly shared with non-volatile memory 60 using known computing techniques such as caching. Memory 60 can include mass storage that is remotely located with respect to the central processing unit(s) 64. In other words, some data stored in memory 58 and / or memory 60 may in fact be hosted oncomputers that are external to computer system 100 but that can be electronically accessed by the computer system 100 over an Internet, intranet, or other form of network or electronic cable using network interface 76.

[0224] The memory 58 of the computer system 100 stores:• a sample start time 102 associated with a sample that includes a biologic;• an optional sample injection time 104 that indicates a time when the sample is injected into an injection loop 478;• an end time 106 associated with the sample;• a sample measuring window 108;• sample parameters { 110-1, . .. , 110-K}, where K is a positive integer associated with the sample (e.g., pH, conductivity, temperature, pressure, EtOH concentration, amount of detergent, flow rate, etc.);• first signal data 112 associated with the sample, the first signal data including: o parameters 114 for a first substrate-based sensor that has a first plurality of binding affinities {sensor one binding affinity for polypeptide 1 (116-1), ..., sensor one binding affinity for polypeptide M (116-M)}, where M is a positive integer of 2 or greater, o first substrate-based sensor time series data 118 for the sample that includes a plurality of first time-resolved measurements {time-resolved measurement at time step 1 (120-1), . time-resolved measurement at time step Q (120-Q); o a sample event time 122 for sensor one; o a medial signal 124 after the event; o a baseline signal 126; o a baseline corrected signal 128;• second signal data 130 associated with the sample, the second signal data including: o parameters 132 for a first substrate-based sensor that has a first plurality of binding affinities {sensor two binding affinity for polypeptide 1 (134-1), ..., sensor two binding affinity for polypeptide M (134-M)}, where M is a positive integer of 2 or greater,o second substrate-based sensor time series data 136 for the sample that includes a plurality of second time-resolved measurements {time-resolved measurement at time step 1 (138-1), time-resolved measurement at time step Q (138-Q); o a sample event time 140 for sensor two; o a medial signal 142 after the event; o a baseline signal 144; o a baseline corrected signal 146;• a machine learning model comprising: o machine learning parameters {machine learning parameter 1 (150-1), . . ., machine learning parameter P (150-P)}, where P is a positive integer, and o a calculated concentration of a first polypeptide type 152 in the sample.

[0225] Fig. 11. illustrates first signal data 112 from a plurality of first time-resolved measurements, occurring over a time period, from a first optical sensor in optical communication with a first substrate-based sensor exposed to a sample during the time period. The sample has as a start time 102 and an end time 106. In some embodiments for quantifying a concentration of a first polypeptide type, first, a measuring window is selected (T). In Fig. 11 A, the measuring window begins at line 1102 and ends at the end time 106. Second, referring to Figs. 1 IB and 11C, in some embodiments, the first sensor sample event 122 is detected, at the maximum increase rate 1150 of the curve 112 defined by the first signal data 112 ) and (3). In some embodiments, the median value before (baseline signal 126 illustrated in Fig. 1 ID) and the median value 124 after the event 122 is measuredIn some embodiments, referring to Fig.1 ID, the value before the event (baseline signal 126) is subtracted from the median value 124 to calculate the first sensor baseline corrected event signal 128In some alternative embodiments, the baseline estimator is performed with the minimum of the 10 percentile of the before-event signal. Also, in some embodiments the derivative of 112 is used to estimate the corrected event signal 128, for instance in cases where the concentration of the first polypeptide type in the sample is high.

[0226] In some implementations, one or more of the above identified data elements or modules of the computer system 100 are stored in one or more of the previously mentioned memorydevices and correspond to a set of instructions for performing a function described above. The above identified data, modules or programs (eg sets of instructions) need not be implemented as separate software programs, procedures or modules, and thus various subsets of these modules may be combined or otherwise re-arranged in various implementations. In some implementations, the memory 60 (and optionally memory 58) optionally stores a subset of the modules and data structures identified above. Furthermore, in some embodiments the memory 60 (and optionally memory 58) stores additional modules and data structures not described above.

[0227] E. Exemplary Methods

[0228] Now that an apparatus for monitoring a concentration of a first polypeptide type in a multi-step production process of a biologic has been described in conjunction with Figs. 1 A and IB, various methods for monitoring a concentration of a first polypeptide type in a multi-step production process of a biologic are described below in conjunction with Figs. 2A through 2S.

[0229] Referring to block 200, in some embodiments, methods for monitoring a concentration of a first polymer type in a multi-step production process of a biologic are provided.

[0230] Referring to block 202, in some embodiments, the biologic is plasma-derived.

[0231] Alternatively, in some embodiments, the biologic is of recombinant origin. In some such embodiments, the first polymer type is indicative of the presence of a virus in the sample. In some embodiments, the concentration of the first polymer type is indicative of an amount of a virus in the sample.

[0232] In some embodiments the measurement of a concentration of the first polypeptide in the sample is used to detect a host cell protein, where the host cell is used in the product of the biologic.

[0233] In some embodiments, the biologic is purified from human plasma from 2, 3, 4, 5, 6, 7, 8, 9, or 10 or more donors. In some embodiments, the biologic is purified from human plasma from 10, 20, 30, 40, 50, 60, 70, 80, 90, or 100 or more donors. In some embodiments, thebiologic is purified from human plasma from 200, 500, 750, 1000, 1250, 1500, 2000, 2500, 3000, or 4000 or more donors.

[0234] Referring to block 204, in some embodiments, the biologic is immunoglobulin G purified from human plasma from a plurality of donors. In some embodiments, the biologic is immunoglobulin G purified from human plasma from 2, 3, 4, 5, 6, 7, 8, 9, or 10 or more donors. In some embodiments, the biologic is immunoglobulin G purified from human plasma from 10, 20, 30, 40, 50, 60, 70, 80, 90, or 100 or more donors. In some embodiments, the biologic is immunoglobulin G purified from human plasma from 200, 500, 750, 1000, 1250, 1500, 2000, 2500, 3000, or 4000 or more donors.

[0235] In some embodiments the biologic is immunoglobulin in intravenous form. Usually intravenous immunoglobulin (IVIG) contains the pooled immunoglobulin G (IgG) immunoglobulins from the plasma of more than a thousand blood donors. Typically containing more than 95% unmodified IgG, which has intact Fc-dependent effector functions, and only trace amounts of immunoglobulin A (IgA) or immunoglobulin M (IgM), IVIGs are sterile, purified IgG products primarily used in treating three main categories of medical conditions: (i) immune deficiencies such as X-linked agammaglobulinemia, hypogammaglobulinemia (primary immune deficiencies), and acquired compromised immunity conditions (secondary immune deficiencies), featuring low antibody levels; (ii) inflammatory and autoimmune diseases; and (iii) acute infections.

[0236] In some embodiments the biologic comprises a plasma protein or a plurality of plasma proteins from human plasma from a plurality of donors. In some embodiments, the biologic is purified from human plasma from 2, 3, 4, 5, 6, 7, 8, 9, or 10 or more donors. In some embodiments, the biologic is purified from human plasma from 10, 20, 30, 40, 50, 60, 70, 80, 90, or 100 or more donors. In some embodiments, such collection of plasma involves screening, blood testing, donor referral, quarantine and / or investigation as described in Chapters 44, 55, and 56 of, eds. Simon et al. 2016, John Wiley & Sons Inc., Hoboken, NJ, which is hereby incorporated by reference.

[0237] In some embodiments the plasma is collected from whole blood collection. In some embodiments the plasma is collected by plasmapheresis. In some embodiments the plasma isfrozen within 24 hours of collection, and the rate of freezing is such that -25 °C is attained in 12 hours of being placed in a freezing apparatus.

[0238] Referring to block 206, in some embodiments, the first polymer type is immunoglobulin IgG3.

[0239] Referring to block 208, in some embodiments, the first polymer type is immunoglobulin IgAl or immunoglobulin IgA2.

[0240] Referring to block 210, in some embodiments, the biologic comprises the first polymer type.

[0241] Referring to block 212, in some embodiments, the biologic consists of the first polymer type.

[0242] Referring to block 214, in some embodiments, a first signal 112 is obtained from a plurality of first time-resolved measurements 118, occurring over a time period, from a first optical sensor in optical communication with a first substrate-based sensor exposed to a sample during the time period, where the sample includes the biologic. Fig. 11 illustrates the first signal 112. In some embodiments, the plurality of first time-resolved measurements comprises 10, 20, 30, 40, 50, 60, 70, 80, 100, 200, 300, 400, 500, or 1000 first time-resolved measurements 120.

[0243] Examples of a substrate-based sensor are the Protein A sensor chip and the protein G sensor chip illustrated in Fig. 3 and described in further detail in Example 1. As illustrated in Fig. 3, in some embodiments the substrate-based sensor includes a support scaffold on which gold particles are covalently bound to binders (e.g., protein A, protein G, etc.) that have varying affinity for the polymer types that occur in the sample. A nonlimiting example arrangement of a first optical sensor in optical communication with a first substrate-based sensor is described in Tran etal., 2022, “Nanoplasmonic Avidity -Based Detection and Quantification of IgG Aggregates,” Anal. Chem. 94, 15754-15762, which is hereby incorporated by reference. As illustrated in Fig. 3, in some embodiments, gold nanobeads are deposited on a stainless-steel support rod, collectively called a substrate-based sensor, and put in contact with the sample inside a flow cell. In Fig. 3, the gold nanobeads are coated with Protein A, or Protein G, and the substrate-based sensor exhibits a peak of absorption at around 500 nm due to an L-SPR effect,with a red shift when the Protein A, or Protein G, are bound to IgG, caused by a change in the refractive index of the IgG-dense local environment of the bead. Opposite to the substrate-based sensor through the flow cell, an optical element with white light illumination and an emission capturing fiber collects the reflected light off the substrate-based sensor and its spectrum is decomposed to find the maximum absorption peak and its IgG-induced red shift.

[0244] The sample is associated with a first step in the multi-step production process. Multi-step production processes and the various steps that can be found in them are described in further detail below.

[0245] The first substrate-based sensor has a first plurality of binding affinities. For instance, in Fig. 1 A, the parameters of substrate based sensor one 114 include a different binding affinity 116 for each of a first plurality of polymer types (e.g., binding affinity 116-1 for a first polymer type, . . ., binding affinity 116-M for an M111polymer type). Each respective binding affinity 116 in the first plurality of binding affinities is for a corresponding polymer type in a first plurality of polymer types that includes a first polymer type. For example, as illustrated in Figure 3, the Protein A sensor chip has appreciable binding affinity for IgA, IgG4, IgG2, and IgGl, but not IgG3.

[0246] In some embodiments the first plurality of polymer types comprises 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, or 12 or more polymer types. In some embodiments, the binding affinity of the first substrate-based sensor for each polymer type is the same or different. For example, in some embodiments the first substrate-based sensor has a high binding affinity for a first subset of the first plurality of polymer types and a low binding affinity for a second subset of the first plurality of polymer types, where the first and second subsets do not overlap.

[0247] In some embodiments, the first polymer type is present in the sample. In some embodiments, the first polymer type is not present in the sample.

[0248] The first signal 112 includes a resonance contribution, for each respective first time- resolved measurement 120 in the plurality of first time-resolved measurements, from each respective polymer type in the first plurality of polymer types as a function of (i) a concentration of the respective polymer type in the sample during the respective first time-resolvedmeasurement 120 and (ii) the respective binding affinity 116 for the respective polymer type in the first plurality of binding affinities. For instance, in the case of the Protein A sensor chip, each time-resolved measurement 120-1, . .. , 120-Q in the plurality of first time-resolved measurements includes a separate resonance contribution from each respective polymer type in the first plurality of polymer types as a function of (i) a concentration of the respective polymer type in the sample during the measurement and (ii) the respective binding affinity 116 for the respective polymer type in the first plurality of binding affinities. For example, in the Protein A sensor chip of Fig. 3, each respective time-resolved measurement is a function of the concentration of IgA in the sample at the respective time step, the binding affinity of the protein A sensor chip for IgA, the concentration of IgG4 in the sample at the respective time step, the binding affinity of the protein A sensor chip for IgG4, the concentration of IgG2 in the sample at the respective time step, the binding affinity of the protein A sensor chip for IgG2, the concentration of IgGl in the sample at the respective time step and the binding affinity of the protein A sensor chip for IgGl . In typical embodiments, when the first sensor chip does not have a binding affinity for a given polymer type, that polymer type does not contribute to the time-resolved measurements for the first sensor chip even in the case where the given polymer type is present in the sample. It will be appreciated that the contribution each polypeptide makes to the time-resolved measurement may be affected by various parameters such as sample flow rate, sample temperature, sample pH, sample ethanol concentration, presence and concentration of detergent in the sample, to name a few.

[0249] Referring to block 216, in some embodiments, the time period is between thirty seconds and ten minutes. Referring to block 218, in some embodiments, the time period is between one minute and six minutes. In some embodiments the time period is between thirty seconds and four hours, between one minute and three hours, between two minutes and two hours, or between three minutes and one hour. In some embodiments the time period is between 1 minute and 15 minutes, between two minutes and 12 minutes, or between three minutes and ten minutes. In some embodiments the time period is greater than 30 seconds, greater than one minute, greater than two minutes, or greater than three minutes. In some embodiments the time period is less than one hour, less than thirty minutes, less than fifteen minutes, less than ten minutes, or less than five minutes.

[0250] Referring to block 220, in some embodiments, a second signal 130 is obtained from a plurality of second time-resolved measurements 136, occurring over the time period, from a second optical sensor in optical communication with a second substrate-based sensor exposed to the sample during the time period. The second signal 130 is similar to the first signal 112 illustrated in Fig. 11. Fig 3 provides an illustration of both the first signal 112 and the second signal 130. In some embodiments, the plurality of second time-resolved measurements comprises 10, 20, 30, 40, 50, 60, 70, 80, 100, 200, 300, 400, 500, or 1000 second time-resolved measurements 138.

[0251] The second substrate-based sensor has a second plurality of binding affinities. For instance, in Fig. 1 A, the parameters of substrate based sensor two 114 include a different binding affinity 134 for each of a plurality of polymer types (e. ., binding affinity 134-1 for a first polymer type, .. ., binding affinity 134-M for an M111polymer type). Each respective binding affinity 134 in the second plurality of binding affinities is for a corresponding polymer type in a second plurality of polymer types that includes the first polymer type. For example, as illustrated in Fig. 3, the Protein G sensor chip has appreciable binding affinity for IgG4, IgG3, IgG2, and IgGl, but not IgA.

[0252] In some embodiments the second plurality of polymer types comprises 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, or 12 or more polymer types. In some embodiments, the binding affinity of the second substrate-based sensor for each polymer type is the same or different. For example, in some embodiments the second substrate-based sensor has a high binding affinity for a first subset of the second plurality of polymer types and a low binding affinity for a second subset of the second plurality of polymer types, where the first and second subsets do not overlap.

[0253] In some embodiments, the first plurality and the second plurality of polymer types is the same. In some embodiments, the first plurality and the second plurality of polymer types differ by 1, 2, 3, 4, 5, or 6 different polymer types.

[0254] The second signal 130 includes a resonance contribution, for each respective second time-resolved measurement 138 in the plurality of second time-resolved measurements, from each respective polymer type in the second plurality of polymer types as a function of (i) a concentration of the respective polymer type in the sample during the respective second time-resolved measurement 138 and (ii) the respective binding affinity 134 for the respective polymer type in the second plurality of binding affinities.

[0255] The respective binding affinity 116 in the first plurality of binding affinities for the first polymer type is other than the respective binding affinity 134 in the second plurality of binding affinities for the first polymer type. In some embodiments, the respective binding affinity 116 in the first plurality of binding affinities for the first polymer type is negligible and the respective binding affinity 134 in the first plurality of binding affinities for the first polymer type is high.

[0256] In some embodiments, the respective binding affinity 116 in the first plurality of binding affinities for the first polymer type is twice, three time, four times, 10 times, 100 times, or 1000 times greater than the respective binding affinity 134 in the first plurality of binding affinities for the first polymer type.

[0257] In some embodiments, the respective binding affinity 134 in the second plurality of binding affinities for the first polymer type is twice, three time, four times, 10 times, 100 times, or 1000 times greater than the respective binding affinity 116 in the first plurality of binding affinities for the first polymer type.

[0258] In some embodiments, the first and second plurality of binding affinities differ for more than just the first polymer type. For example, in some embodiments, in addition to differing in binding affinity for the first polymer type, the respective binding affinity 116 in the first plurality of binding affinities for a second polymer type is twice, three time, four times, 10 times, 100 times, or 1000 times greater than the respective binding affinity 134 in the first plurality of binding affinities for the second polymer type. As another example, in some embodiments, in addition to differing in binding affinity for the first polymer type, the respective binding affinity 134 in the second plurality of binding affinities for a second polymer type is twice, three time, four times, 10 times, 100 times, or 1000 times greater than the respective binding affinity 116 in the first plurality of binding affinities for the second polymer type.

[0259] In some embodiments, the first and second plurality of binding affinities differ by more than just the first polymer type and the second polymer type. For example, in some embodiments, in addition to differing in binding affinity for the first and second polymer types,the respective binding affinity 116 in the first plurality of binding affinities for a third polymer type is twice, three time, four times, 10 times, 100 times, or 1000 times greater than the respective binding affinity 134 in the first plurality of binding affinities for the third polymer type. As another example, in some embodiments, in addition to differing in binding affinity for the first and second polymer type, the respective binding affinity 134 in the second plurality of binding affinities for a third polymer type is twice, three time, four times, 10 times, 100 times, or 1000 times greater than the respective binding affinity 116 in the first plurality of binding affinities for the third polymer type.

[0260] Referring to block 222, in some embodiments, the first signal and the second signal are obtained concurrently. For instance, in some embodiments the apparatus for measuring the signal is illustrated in Fig. 4. In particular, in some embodiments the apparatus for measuring the signal is illustrated in Fig. 12. In some embodiments the first substrate-based sensor is in analytical inline device 482-1 and the second substrate-based sensor is in analytical inline device 482-2. In some embodiments analytical inline devices 482-1 and 482-2 are each flow cells that contain the respective substrate-based sensors. In one specific example each analytical inline device is part of an AugaOne system (ArgusEye, Sweden). The AugaOne system includes an optical sensor (first or second optical sensor) in optical communication with a flow cell 482 made of stainless steel housing the corresponding substrate based sensor. In some embodiments flow cell 482 supports a flowrate of between 0.01 and 200 mL / min. In some embodiments each substrate-based sensor has a dynamic range of 0.01 1 10 mg / ML for first polymer type detection. In some embodiments, rather than being connected in series as illustrated in Figure 12, analytical inline devices 482-1 and 482-2 are in parallel.

[0261] Referring to block 224, in some embodiments, the first plurality of polymer types and the second plurality of polymer types differ by at least one polymer type. In other words, in the case where the first and second plurality of polymer types differ by one polymer type, one of the first and second substrate-based sensors has measurable binding affinity to this one polymer type but the other of the first and second substrate-based sensors does not have measurable binding affinity to this one polymer type. In some embodiments, the first plurality of polymer types and the second plurality of polymer types differ by one, two, three, four, or five polymer types.

[0262] Referring to block 226, in some embodiments, the first plurality of polymer types is identical to the second plurality of polymer types. In other words, the first and second substratebased sensors have measurable binding affinity the same set of polymer types. However, it remains that the first and second substrate-based sensors have different binding affinity for the first polymer type.

[0263] Referring to block 228, in some embodiments, the first plurality of polymer types comprises two, three, four, or more polymer types. In such embodiments, the first substratebased sensor has measurable binding affinity for two, three, four, or more polymer types. In some embodiments the second plurality of polymer types comprises two, three, four, or more polymer types. In such embodiments, the second substrate-based sensor has measurable binding affinity for two, three, four, or more polymer types.

[0264] Referring to block 230, in some embodiments, at least one of IgGl, IgG2, and IgG4 is present in the sample, and the first plurality of polymer types and the second plurality of polymer types each comprise at least one of IgGl, IgG2, and IgG4 that contributes to the first and second signal.

[0265] Referring to block 232, in some embodiments, at least two of IgGl, IgG2, and IgG4 is present in the sample, and the first plurality of polymer types and the second plurality of polymer types each comprise at least two of IgGl, IgG2, and IgG4 that each contribute to first and second signal.

[0266] In some embodiments IgG3 is present in the sample. In addition, at least one of IgGl, IgG2, and IgG4 is present in the sample, and the first plurality of polymer types and the second plurality of polymer types each comprise IgG3 as well as at least one of IgGl, IgG2, and IgG4 that contributes to the first and second signal.

[0267] Referring to block 234, in some embodiments, igGl, IgG2, and IgG4 are each present in the sample, and the first plurality of polymer types and the second plurality of polymer types each comprise IgGl, IgG2, and IgG4 and each contribute to the first and second signal.

[0268] Referring to block 236, in some embodiments, the sample comprises IgG3 (IgG3) andIgGl (IgGl) at a percent weight IgG3 to IgGl ratio of between 0.056 to 0. 16, and at least thefirst plurality of polymer types or the second plurality of polymer types comprises IgGl and IgG2.

[0269] In some embodiments, the sample comprises IgG3 (IgG3) and IgGl (IgGl) at a percent weight IgG3 to IgGl ratio of between 0.01 to 0.2, and at least the first plurality of polymer types or the second plurality of polymer types comprises IgGl and IgG2.

[0270] Referring to block 238, in some embodiments, the sample comprises IgG3 (IgG3) and IgG2 (IgG2) at a percent weight IgG3 to IgG2 ratio of between 0.10 and 0.34, and at least the first plurality of polymer types or the second plurality of polymer types comprises IgG2 and IgG3.

[0271] In some embodiments, the sample comprises IgG3 (IgG3) and IgG2 (IgG2) at a percent weight IgG3 to IgG2 ratio of between 0.05 and 0.40, and at least the first plurality of polymer types or the second plurality of polymer types comprises IgG2 and IgG3.

[0272] Referring to block 240, in some embodiments, the sample comprises IgG3 and IgG4 at a percent weight IgG3 to IgG4 ratio of between 0.80 and 3.3, and at least the first plurality of polymer types or the second plurality of polymer types comprises IgG3 and IgG4.

[0273] In some embodiments, the sample comprises IgG3 and IgG4 at a percent weight IgG3 to IgG4 ratio of between 0.60 and 5.0, and at least the first plurality of polymer types or the second plurality of polymer types comprises IgG3 and IgG4.

[0274] Referring to block 242, in some embodiments, the first plurality of polymer types and the second plurality of polymer types each comprise at least one of, at least 2 of, at least 3 of, at least 4 of, or at least 5 of the group consisting of: immunoglobulin IgA, immunoglobulin IgM, immunoglobulin IgE, albumin, protein C, complement component Cl, protein S, anti-A hemagglutinin antibody, anti-B hemagglutinin antibody, anti-D hemagglutinin antibody, complement component 3, complement component C2a, complement component C3a, complement component C4a, complement component C5a, complement component C2b, complement component C3b, complement component C4b, complement component C5b, hemoglobin, hemopexin, parvo-19 antibody, an antibody to the polio virus, an antibody to the measles virus, a diphtheria antibody, alpha-2 macroglobulin, transferrin, fibrinogen,ceruloplasmin, plasmin, tissue thromboplastin (CD142), an apolipoprotein, alpha- 1 -antitrypsin, anti-thrombin, factor Xia, factor Xlla, factor Xlla, prothrombin (factor two), factor XII, factor VIII, factor IX, factor X, factor XI, Von Willebrand factor, antithrombin III, Cl -esterase inhibitor, and haptoglobin.

[0275] In some embodiments, the first plurality of polymer types and the second plurality of polymer types each consists of no more than 10 of, 9 of, 8 of, 7 of, 6 of, 5 of, 4 of, 3 of or 2 of the group consisting of: immunoglobulin IgA, immunoglobulin IgM, immunoglobulin IgE, albumin, protein C, complement component Cl, protein S, anti -A hemagglutinin antibody, anti- 13 hemagglutinin antibody, anti-D hemagglutinin antibody, complement component 3, complement component C2a, complement component C3a, complement component C4a, complement component C5a, complement component C2b, complement component C3b, complement component C4b, complement component C5b, hemoglobin, hemopexin, parvo-19 antibody, an antibody to the polio virus, an antibody to the measles virus, a diphtheria antibody, alpha-2 macroglobulin, transferrin, fibrinogen, ceruloplasmin, plasmin, tissue thromboplastin (CD 142), an apolipoprotein, alpha- 1 -antitrypsin, anti-thrombin, factor Xia, factor Xlla, prothrombin (factor two), factor XII, factor VIII, factor IX, factor X, factor XI, Von Willebrand factor, antithrombin III, Cl -esterase inhibitor, and haptoglobin.

[0276] In some embodiments the first and second substrate based sensors have a different binding affinity for immunoglobulin IgA, immunoglobulin IgM, immunoglobulin IgE, albumin, protein C, complement component Cl, protein S, anti-A hemagglutinin antibody, anti-B hemagglutinin antibody, anti-D hemagglutinin antibody, complement component 3, complement component C2a, complement component C3a, complement component C4a, complement component C5a, complement component C2b, complement component C3b, complement component C4b, complement component C5b, hemoglobin, hemopexin, parvo-19 antibody, an antibody to the polio virus, an antibody to the measles virus, a diphtheria antibody, alpha-2 macroglobulin, transferrin, fibrinogen, ceruloplasmin, plasmin, tissue thromboplastin (CD142), an apolipoprotein, alpha- 1 -antitrypsin, anti-thrombin, factor Xia, factor Xlla, prothrombin (factor two), factor XII, factor VIII, factor IX, factor X, factor XI, Von Willebrand factor, antithrombin III, Cl -esterase inhibitor, and haptoglobin.

[0277] In some embodiments, the first plurality of polymer types and the second plurality of polymer types each comprise an immunoglobulin for which the first and second substrate-based sensors have a different binding affinity.

[0278] It will be appreciated by those of skill in the art that the present disclosure is also applicable to viruses as well. In such embodiments, rather than, or in addition to, the first and second plurality of polymer types, the first and second substrate-based sensors have different binding affinities for a particular virus type, such that the differential kinetics of these differing binding affinities can be used to determine a concentration of the virus in the sample.

[0279] In some embodiments, the first plurality of polymer types and the second plurality of polymer types each comprise a protein presented by a virus. In such embodiments, the first and second substrate-based sensors have different binding affinities for the particular protein presented by the virus, such that the differential kinetics of these differing binding affinities can be used to determine a concentration of the virus in the sample.

[0280] In some embodiments, the first plurality of polymer types and the second plurality of polymer types each comprise a protein for which the first and second substrate-based sensors have a different binding affinity.

[0281] In some embodiments, the first plurality of polymer types and the second plurality of polymer types each comprise a polypeptide for which the first and second substrate-based sensors have a different binding affinity. As used herein, the term “polypeptide” means two or more amino acids or residues linked by a peptide bond. The terms “polypeptide” and “protein” are used interchangeably herein and include oligopeptides and peptides. In some embodiments, an “amino acid” or “residue” refers to any of the twenty standard structural units of proteins as known in the art, which include imino acids, such as proline and hydroxyproline. The designation of an amino acid isomer may include D, L, R and S. Moreover, amino acids also include nonnatural amino acids. Thus, selenocysteine, pyrrolysine, lanthionine, 2- aminoisobutyric acid, gamma-aminobutyric acid, dehydroalanine, ornithine, citrulline and homocysteine are all considered amino acids. Other variants or analogs of the amino acids are known in the art. Thus, a polymer may include synthetic peptidomimetic structures such as peptoids and peptides. See Simon et al., 1992, Proceedings of the National Academy ofSciences USA, 89, 9367, which is hereby incorporated by reference herein in its entirety. See also Chin et al., 2003, Science 301, 964; and Chin et al., 2003, Chemistry & Biology 10, 511, each of which is incorporated by reference herein in its entirety. The polymer may also have any number of posttranslational modifications. Thus, a polymer includes those that are modified by acylation, alkylation, amidation, biotinylation, formylation, glutamylation, glycosylation, glycylation, hydroxylation, iodination, isoprenylation, lipoylation, cofactor addition (for example, of a heme, flavin, metal, etc.), addition of nucleosides and their derivatives, oxidation, reduction, pegylation, phosphatidylinositol addition, phosphopantetheinylation, phosphorylation, pyroglutamate formation, racemization, addition of amino acids by tRNA (for example, arginylation), sulfation, selenoylation, ISGylation, SUMOylation, ubiquitination, chemical modifications (for example, citrullination and deamidation), and treatment with other enzymes (for example, proteases, phosphotases and kinases). Other types of posttranslational modifications are known in the art and are also included.

[0282] In some embodiments, the first plurality of polymer types and the second plurality of polymer types each comprise a peptide for which the first and second substrate-based sensors have a different binding affinity.

[0283] In some embodiments, the first plurality of polymer types and the second plurality of polymer types each comprise a surfactant for which the first and second substrate-based sensors have a different binding affinity. Surfactants are compounds that lower the surface tension of a liquid, the interfacial tension between two liquids, or that between a liquid and a solid. Surfactants may act as detergents, wetting agents, emulsifiers, foaming agents, and dispersants. Surfactants are usually organic compounds that are amphiphilic, meaning they contain both hydrophobic groups (their tails) and hydrophilic groups (their heads). Therefore, a surfactant molecule contains both a water insoluble (or oil soluble) component and a water soluble component. Surfactant molecules will diffuse in water and adsorb at interfaces between air and water or at the interface between oil and water, in the case where water is mixed with oil. The insoluble hydrophobic group may extend out of the bulk water phase, into the air or into the oil phase, while the water soluble head group remains in the water phase. This alignment ofsurfactant molecules at the surface modifies the surface properties of water at the water / air or water / oil interface.

[0284] Examples of ionic surfactants include ionic surfactants such as anionic, cationic, or zwitterionic (ampoteric) surfactants. Anionic surfactants include (i) sulfates such as alkyl sulfates (e.g., ammonium lauryl sulfate, sodium lauryl sulfate), alkyl ether sulfates (e.g., sodium laureth sulfate, sodium myreth sulfate ), (ii) sulfonates such as docusates (e.g., dioctyl sodium sulfosuccinate), sulfonate fluorosurfactants (e g., perfluorooctanesulfonate and perfluorobutanesulfonate), and alkyl benzene sulfonates, (iii) phosphates such as alkyl aryl ether phosphate and alkyl ether phosphate , and (iv) carboxylates such as alkyl carboxylates (e.g., fatty acid salts (soaps) and sodium stearate), sodium lauroyl sarcosinate, and carboxylate fluorosurfactants (e.g., perfluorononanoate, perfluorooctanoate, etc.). Cationic surfactants include pH-dependent primary, secondary, or tertiary amines and permanently charged quaternary ammonium cations. Examples of quaternary ammonium cations include alkyltrimethylammonium salts (e.g., cetyl trimethylammonium bromide, cetyl trimethylammonium chloride), cetylpyridinium chloride (CPC), benzalkonium chloride (BAC), benzethonium chloride (BZT), 5-bromo-5-nitro- 1,3-di oxane , dimethyldioctadecylammonium chloride, and dioctadecyldimethylammonium bromide (DODAB) . Zwitterionic surfactants include sulfonates such as CHAPS (3-[(3-Cholamidopropyl)dimethylammonio]-l- propanesulfonate) and sultaines such as cocamidopropyl hydroxysultaine. Zwitterionic surfactants also include carboxylates and phosphates.

[0285] Nonionic surfactants include fatty alcohols such as cetyl alcohol, stearyl alcohol, cetostearyl alcohol, and oleyl alcohol. Nonionic surfactants also include polyoxyethylene glycol alkyl ethers (e.g., octaethylene glycol monododecyl ether, pentaethylene glycol monododecyl ether), polyoxypropylene glycol alkyl ethers, glucoside alkyl ethers (decyl glucoside, lauryl glucoside, octyl glucoside, etc.), polyoxyethylene glycol octylphenol ethers (CsHi7-(C6H4)-(O- C2H4)l-25-OH), polyoxyethylene glycol alkylphenol ethers (C9Hi9-(CeH4)-(O-C2H4)l-25-OH, glycerol alkyl esters (e.g., glyceryl laurate), polyoxyethylene glycol Sorbian alkyl esters, sorbitan alkyl esters, cocamide MEA, cocamide DEA, dodecyl dimethylamine oxideblock copolymers ofpolyethylene glycol and polypropylene glycol (poloxamers), and polyethoxylated tallow amine.In some embodiments, the polymer under study is a reverse micelle, or liposome.

[0286] In some embodiments, the first plurality of polymer types and the second plurality of polymer types each comprise a fullerene for which the first and second substrate-based sensors have a different binding affinity. A fullerene is any molecule composed entirely of carbon, in the form of a hollow sphere, ellipsoid or tube. Spherical fullerenes are also called buckyballs, and they resemble the balls used in association football. Cylindrical ones are called carbon nanotubes or buckytubes. Fullerenes are similar in structure to graphite, which is composed of stacked graphene sheets of linked hexagonal rings; but they may also contain pentagonal (or sometimes heptagonal) rings.

[0287] Referring to block 244, in some embodiments, the first and second plurality of polymer types have at least two or three polymer types, present in the sample, in common and contributing to the first and second signal.

[0288] Referring to block 246, in some embodiments, the respective binding affinity in the first plurality of binding affinities for the first polymer type is negligible (e.g. below a limit of detection)., and the respective binding affinity in the second plurality of binding affinities for the first polymer type is other than negligible (e.g. above a limit of detection).

[0289] Referring to block 248, in some embodiments, the respective binding affinity in the first plurality of binding affinities for the first polymer type is less than half of the respective binding affinity in the second plurality of binding affinities for the first polymer type.

[0290] Referring to block 248, in some embodiments, the respective binding affinity in the first plurality of binding affinities for the first polymer type is less than eight tenths, less than six tenths, less than four tenths, less than two tenths or less than one tenths of the respective binding affinity in the second plurality of binding affinities for the first polymer type.

[0291] Referring to block 250, in some embodiments, at least one polymer type in the first plurality of polymer types, present in the sample and contributing to the first signal, is not represented in the second plurality of polymer types.

[0292] Referring to block 252, in some embodiments, at least one polymer type in the first plurality of polymer types, present in the sample and contributing to the first signal, does not contribute to the second signal.

[0293] Referring to block 254, in some embodiments, at least two polymer types in the first plurality of polymer types, present in the sample and contributing to the first signal, are not represented in the second plurality of polymer types and do not contribute to the second signal.

[0294] Referring to block 256, in some embodiments, the sample further comprises ethanol. In some embodiments, the same comprises between 0.1 percent and 25 percent ethanol by volume, between 0.2 percent and 20 percent ethanol by volume, between 0.3 percent and 15 percent ethanol by volume, between 0.4 percent and 10 percent ethanol by volume, or between 0.5 percent and 5 percent ethanol by volume.

[0295] Referring to block 258, in some embodiments, the sample is at a pH of between 5 and 6. Referring to block 260, in some embodiments, the sample is at a pH of between 4 and 7. In some embodiments the sample is at a pH between 2 and 12. In some embodiments the sample is at a pH of greater than 2, 3, 4, 5, 6, 7, 8, 9, 10, or 11. In some embodiments the sample is at a pH of less than 12, 11, 10, 9, 8, 7, 6, 5, 4, or 3.

[0296] Referring to block 262, in some embodiments, the sample has a conductivity of between 0 milli siemens per centimeter (mS / cm) and 25 mS / cm. In some embodiments the sample has a conductivity of between 0.5 Siemens per meter (S / m) and 0.7 S / m. In some embodiments the sample has a conductivity of between 0.4 S / m and 0.8 S / m. In some embodiments the sample has a conductivity of between 0.3 S / m and 0.9 S / m. In some embodiments the sample has a conductivity of greater than 0. 1 S / m, greater than 0.2 S / m, greater than 0.3 S / m, greater than 0.4 S / m, greater than 0.5 S / m, greater than 0.6 S / m, greater than 0.7 S / m, or greater than 0.8 S / m. In some embodiments the sample has a conductivity of less than 1.0 S / m, less than 0.9 S / m, less than 0.8 S / m, less than 0.7 S / m, less than 0.6 S / m, less than 0.5 S / m, less than 0.4 S / m, or less than 0.3 S / m.

[0297] Referring to block 264, in some embodiments, the first substrate-based sensor and the second substrate-based sensor are atline with respect to the first step. As used herein “atline”refers to a type of analysis that occurs close to the multi-step production process, but not directly integrated into it. It falls between online and offline analysis in terms of immediacy and proximity to the process. For example, referring to Fig. 4, in some embodiments the sample is injected through circuit 1 valve 476.

[0298] Referring to block 266, in some embodiments, the first substrate-based sensor and the second substrate-based sensor are inline with respect to the first step. As used herein “inline” refers to a physical arrangement where the first substrate-based sensors are within the production line for the multi-step production process. For example, referring to Fig. 4, in some embodiments the sample is injected through circuit 3 valve 464.

[0299] Referring to block 268, in some embodiments, the first substrate-based sensor and the second substrate-based sensor are off-line with respect to the first step.

[0300] Referring to block 270, in some embodiments, each step of the multi-step production process is inline.

[0301] Referring to block 272, in some embodiments, a first aliquot of the sample is used in obtaining the first signal, and a second aliquot of the sample is used in obtaining the second signal. For example, in some such embodiments, a first flow cell houses the first substrate-based sensor and a second flow cell houses the second substrate-based sensor and the first and second flow cells run in parallel to each other as opposed to the serial arrangement illustrated for devices 482-1 and 482-2 of Fig. 12.

[0302] Referring to block 274, in some embodiments, the same aliquot of the sample is used in the obtaining the first signal and the second signal. For example, in some such embodiments a first device 482-1 is a flow cell housing the first substrate-based sensor and a second device 482- 2 is a flow cell housing the second substrate-based sensor and the first and second flow cells run in series to each other as illustrated in Fig. 12.

[0303] Referring to block 278, in some embodiments, the first signal is modulated by first Kon, Koff, or Kd from the first set of time-resolved measurements arising from the interaction of the first plurality of polymer types, in the sample, with a first functionalized solid surface of the first substrate-based sensor, and the second signal is modulated by second Kon, Koff, or Kd from thesecond set of time-resolved measurements arising from the interaction of the second plurality of polymer types with a second functionalized solid surface of the second substrate-based sensor. In some such embodiments this modulation is in the form of a binder, such as Protein A or Protein G, that functionalizes the surface of the solid surface as illustrated in Fig. 3. In Fig. 3, gold nanoparticles on the sensor chip surface are functionalized with Protein A or Protein G. Protein A and G each have distinct binding affinities for various polymer types (IgA, IgG4, etc.) as illustrated in Fig. 3 and thus modulate the first and second signals. For example, as illustrated in Fig. 3, protein A fails to bind to IgG3 and thus IgG3 does not contribute to the signal generated from the Protein A sensor chip of Fig. 3. By contract, protein A binds to IgA, IgG4, IgG2, and IgGl and thus IgG4, IgG2, and IgGl present in the sample contribute to the signal generated from the Protein A sensor chip of Fig. 3. Moreover, the specific Kon, Koff, or Kd value that protein A has for each of IgG4, IgG2, and IgGl affects the amount of signal that the protein A sensor contributes to the signal derived from the protein A sensor. Referring to Fig. 3, in some embodiments the gold nanobeads coated with Protein A, or Protein G, exhibit a peak of absorption at around 500 nm due to a L-SPR effect, with a red shift when the Protein A, or Protein G, are bound to IgG, caused by a change in the refractive index of the IgG-dense local environment of the bead.

[0304] Referring to block 280, in some embodiments, the method further comprises injecting the sample into a flow cell in fluid communication with the first substrate-based sensor and the second substrate-based sensor. The first signal further comprises one or more first respective auxiliary measurements, accompanying each respective first time-resolved measurement in the plurality of first time-resolved measurements, that is a conductivity of the sample, a flow rate of the first step, a flow rate of the sample in the flow cell, a volume of the sample in the flow cell, a pressure of the sample in the flow cell, a pH of the sample, a temperature of the sample, an identity of a buffer in the sample, an ionic strength of the sample, or any combination thereof, representative of the sample during the respective first time-resolved measurement.

[0305] The second signal further comprises one or more respective auxiliary measurements, accompanying each respective second time-resolved measurement in the plurality of second time-resolved measurements, that is a conductivity of the sample, a flow rate of the first step, aflow rate of the sample in the flow cell, a volume of the sample, a pressure of the sample in the flow cell, a pH of the sample, a temperature of the sample, an identity of a buffer in the sample, an ionic strength of the sample, or any combination thereof, representative of the sample during the respective second time-resolved measurement.

[0306] Referring to Fig. 12, in instances where the first and second time-revolved measurements are taken in series, the auxiliary measurement taken for the first and second signals may in fact be the same auxiliary measurement. For example, in Fig. 12, after passing through the first and second substrate-based sensors in respective flow cells 482-1 and 482-2, a pH of the sample is taken by the pH measuring device 1204 and this same pH measurement can be associated with a corresponding first (from the first substrate-based sensor) and second (from the second substratebased sensor) time-resolved measurements.

[0307] Referring to block 282, in some embodiments, the flow cell has a void volume of 1 mL or less. As used here, the void volume refers to the volume within the flow cell that is not occupied with sample in contact with the substrate-based sensor.

[0308] Referring to block 284, in some embodiments, the flow cell has a void volume of between 0.25 mL and 0.9 mL. In some embodiments, the flow cell has a void volume of between 0.1 mL and 5 mL In some embodiments, the flow cell has a void volume of between 0.2 mL and 4 mL In some embodiments, the flow cell has a void volume of between 0.3 mL and 3 mL. In some embodiments, the flow cell has a void volume of between 0.4 mL and 2 mL In some embodiments, the flow cell has a void volume of less than 10 mL, 9 mL, 8 mL, 7 mL, 6 mL, 5 mL, 4 mL, 3 mL, 2 mL, or 1 mL.

[0309] Referring to block 286, in some embodiments, the method further comprises acquiring an injection time in which the sample is injected into a flow cell containing the first substrate-based sensor and / or the second substrate-based sensor. In some embodiments, each respective first time-resolved measurement in the plurality of first time-resolved measurements is a baseline corrected median localized surface plasmon resonance signal or extinction signal, after the injection time, from a first functionalized solid surface of the first substrate-based sensor that is exposed to the sample. In some embodiments, each respective second time-resolved measurement in the plurality of second time-resolved measurements is a baseline correctedmedian localized surface plasmon resonance signal or extinction signal, after the injection time, from a second functionalized solid surface of the second substrate-based sensor that is exposed to the sample. For example, Fig. 13 illustrates an L-SPR shift signal from such time-series data. The top panel of Fig. 13 illustrates an L-SPR slice of about 30 minutes (multiple measurements). Black, full vertical lines identify the start of a sample. Dotted vertical lines identify the injection of a sample after regeneration and equilibration. Dash-dotted vertical lines identify the end of a sample. The bottom panel shows all the standalone measurements, expanding over 2 months. The top panel view is a small slice of the whole time series. In some embodiments, the first substrate-based sensor is in a first flow cell 482-1 and the second substrate-based sensor is in a second flow cell 482-2 in series, as illustrated in Fig. 12. In some embodiments, the first substrate-based sensor is in a first flow cell 482-1 and the second substrate-based sensor is in a second flow cell 482-2 in parallel, not illustrated. In some embodiments, the first substratebased sensor and the second substrate-based sensor are in the same second flow cell.

[0310] Referring to block 288, in some embodiments in which the first and second substratebased sensor are in the same flow cell, the flow cell has a void volume of 5 mL or less, 4 mL or less, 3 mL or less, 2 mL or less, 1 mL or less or 0.5 mL or less. In some embodiments in which the first and second substrate-based sensor are in the same flow cell, the flow cell has a void volume of between 0.25 mL and 10 mL, between 0.5 mL and 5 mL, or between 1 mL and 4 mL.

[0311] In some embodiments in which the first and second substrate-based sensor are each in a respective first and second flow cell, the first and second flow cell flow cell each have a void volume of 5 mL or less, 4 mL or less, 3 mL or less, 2 mL or less, 1 mL or less or 0.5 mL or less, in which the first and second substrate-based sensor are each in a respective first and second flow cell, the first and second flow cell flow cell each have a void volume of between 0.25 mL and 10 mL, between 0.5 mL and 5 mL, or between 1 mL and 4 mL.

[0312] Referring to block 290, in some embodiments in which the first and second substratebased sensor are in the same flow cell, the flow cell has a void volume of between 0.25 mL and 0.9 mL.

[0313] In some embodiments in which the first and second substrate-based sensor are each in a respective first and second flow cell, the first and second flow cell flow cell each have a void volume of between 0.25 mL and 0.9 mb.

[0314] Referring to block 292, in some embodiments, the first polymer type is immunoglobulin IgG3, the first solid surface is functionalized with protein A, and the second solid surface is functionalized with protein G. A first substrate-based sensor having a solid surface functionalized with protein A is illustrated in Fig. 3 A second substrate-based sensor having a solid surface functionalized with protein G is also illustrated in Fig. 3 In this capacity, the protein A and protein B serve as binders. Nonlimiting alternative binders to protein A and protein G include, but are not limited to, engineered Cyr receptors such as CD36, CD32, CD64, etc., which are the natural ligand of IgG. Nonlimiting alternative binders to protein A and protein G further include monoclonal antibodies, protein L, and engineered small molecules (e.g., peptides, aptamers, designed ankyrin repeat proteins (DARPins), and single-domain antibodies). DARPins are a class of engineered proteins composed of repeating units called ankyrin repeats. Ankyrin repeats are protein structural motifs known for their stability and versatility in binding to target molecules. By arranging these repeats in specific configurations, DARPins can be designed to bind to a wide range of target molecules with high affinity and specificity. See Stumpp el al, 2008, “ARPins, short for Designed Ankyrin Repeat Proteins,” Drug Discovery Today 13, pp. 695-701, which is hereby incorporated by reference.

[0315] Referring to block 294, in some embodiments, the first functionalized solid surface comprises a first plurality of metal nanoparticles e.g. gold nanoparticles, silver nanoparticles, or copper nanoparticles) coated with protein A that are fixed to a first substrate exposed to the sample (e.g., ligand bound via linker to gold particles), and the second functionalized solid surface comprises a second plurality of metal nanoparticles (e.g. gold nanoparticles, silver nanoparticles, or copper nanoparticles) coated with protein G that are fixed to second substrate exposed to the sample.

[0316] Referring to block 296, in some embodiments, the sample passes through the flow cell at a predetermined flow velocity during the time period. In some embodiments this flow velocity is associated with a flow rate. In some such embodiments, the flow rate is determined by pumpspeed and tubing size. As a nonlimiting example, in the case where a pump in the arrangement shown in Fig. 12 is a Watson-Marlow 120U peristaltic pump and 0.5 millimeter (mm) tubing is used, the flow rate can range from 0.2 milliliters / minute (ml / min) to 4.4 ml / min. In the case where a pump in the arrangement shown in Fig. 12 is a Watson-Marlow 120U peristaltic pump and 0.8 mm tubing is used, the flow rate can range from 0.4 ml / min to 8.8 ml / min. In the case where a pump in the arrangement shown in Fig. 12 is a Watson-Marlow 120U peristaltic pump and 1.6 mm tubing is used, the flow rate can range from 1.4 ml / min to 31.0 ml / min. In the case where a pump in the arrangement shown in Fig. 12 is a Watson-Marlow 120U peristaltic pump and 4.8 mm tubing is used, the flow rate can range from 8.5 ml / min to 190.0 ml / min. In some embodiments, the predetermined flow velocity is between 0.2 ml / min and 250.0 ml / min. In some embodiments, the predetermined flow velocity is between 1 ml / min and 10.0 ml / min. In some embodiments, the predetermined flow velocity is greater than 0.5 ml / min, 1 ml / min, or 2 ml / min.

[0317] Referring to block 298, in some embodiments, the first step is associated with an injection time in which the sample is injected into a flow cell containing the first substrate-based sensor and the second substrate-based sensor. In some such embodiments the plurality of first time-resolved measurements is used to calculate a baseline corrected median localized surface plasmon resonance signal or extinction signal, around the injection time, from a first functionalized solid surface of the first substrate-based sensor that is exposed to the sample. As illustrated in Fig. 1 ID, a median baseline measurement value 126 is determined using a mean of a first subset of the plurality of first time-resolved measurement from before an injection time of the sample. As further illustrated in Fig. 1 ID, a median (event) measurement value 124 is determined using a second subset of the plurality of first time-resolved measurement from after the injection time of the sample. The median baseline measurement value 126 is subtracted from the median measurement value observed after injection 124 to arrive at the baseline corrected median localized surface plasmon resonance signal or extinction signal 128 for the first substrate-based sensor. More generally, a first measure of central tendency (e.g., mean, median, mode, weighted mean, weighted median, and / or weighted mode) is computed using the first subset of the plurality of first time-resolved measurements from before the injection time of the sample, and a second measure of central tendency (e.g., mean, median, mode, weighted mean,weighted median, and / or weighted mode) is computed using a second subset of the plurality of first time-resolved measurement from after the injection time of the sample. The first measure of central tendency (baseline) is subtracted from the second measure of central tendency to arrive at the baseline corrected localized surface plasmon resonance signal or extinction signal for the first substrate-based sensor. In some embodiments, the plurality of first time-revolved measurements includes both an extinction measurement and a localized surface plasmon resonance signal from the first functionalized solid surface of the first substrate-based sensor that is exposed to the sample. In such embodiments, both a baseline corrected localized surface plasmon resonance signal and a baseline extinction signal are determined for the first functionalized solid surface. In some embodiments, the first subset of the plurality of first measurements includes at least 5, 10, 15, 20, 25, 30, or 40 measurements. In some embodiments, the first subset of the plurality of first measurements includes between 5 and 500 measurements. In some embodiments, the second subset of the plurality of first measurements includes at least 5, 10, 15, 20, 25, 30, or 40 measurements. In some embodiments, the second subset of the plurality of first measurements includes between 5 and 500 measurements.

[0318] Corresponding, each respective second time-resolved measurement in the plurality of second time-resolved measurements is a baseline corrected median localized surface plasmon resonance signal or extinction signal, around the injection time, from a second functionalized solid surface of the second substrate-based sensor that is exposed to the sample. In some such embodiments, the plurality of second time-resolved measurements is used to calculate a baseline corrected median localized surface plasmon resonance signal or extinction signal, around the injection time, from the second functionalized solid surface of the second substrate-based sensor that is exposed to the sample. In some such embodiments, a median baseline measurement value is determined using a mean of a first subset of the plurality of second time-resolved measurement from before the injection time of the sample. Also, a median (event) measurement value is determined using a second subset of the plurality of second time-resolved measurement from after the injection time of the sample. The median baseline measurement value is subtracted from the median measurement value observed after injection to arrive at the baseline corrected median localized surface plasmon resonance signal or extinction signal 128 for the second substrate-based sensor. More generally, a first measure of central tendency (e.g., mean, median,mode, weighted mean, weighted median, and / or weighted mode) is computed using the first subset of the plurality of second time-resolved measurements from before the injection time of the sample, and a second measure of central tendency (e.g., mean, median, mode, weighted mean, weighted median, and / or weighted mode) is computed using a second subset of the plurality of second time-resolved measurement from after the injection time of the sample. The first measure of central tendency (baseline) is subtracted from the second measure of central tendency to arrive at the baseline corrected localized surface plasmon resonance signal or extinction signal for the second substrate-based sensor. In some embodiments, the plurality of second time-revolved measurements includes both an extinction measurement and a localized surface plasmon resonance signal from the second functionalized solid surface of the second substrate-based sensor that is exposed to the sample. In such embodiments, both a baseline corrected localized surface plasmon resonance signal and a baseline extinction signal are determined for the second functionalized solid surface. In some embodiments, the first subset of the plurality of second measurements includes at least 5, 10, 15, 20, 25, 30, or 40 measurements. In some embodiments, the first subset of the plurality of second measurements includes between 5 and 500 measurements. In some embodiments, the second subset of the plurality of second measurements includes at least 5, 10, 15, 20, 25, 30, or 40 measurements. In some embodiments, the second subset of the plurality of second measurements includes between 5 and 500 measurements.

[0319] Referring to block 300, in some embodiments, the flow cell has a void volume of 1 mL or less. Additional flow cell volumes in certain embodiments of the present disclosure are discussed above in conjunction with block 288.

[0320] Referring to block 302, in some embodiments, the flow cell has a void volume of between 0.25 mL and 0.9 mL. Additional flow cell volumes in certain embodiments of the present disclosure are discussed above in conjunction with block 290.

[0321] Referring to block 304, in some embodiments, the first polymer type is immunoglobulinIgG3, the first solid surface is functionalized with protein A, and the second solid surface is functionalized with protein G. This embodiment is illustrated in Fig. 3.

[0322] Referring to block 306, in some embodiments, the first functionalized solid surface comprises a first plurality of metal nanoparticles (eg. gold nanoparticles, silver nanoparticles, or copper nanoparticles) coated with protein A that are fixed to a first substrate exposed to the sample (e.g., ligand bound via linker to gold particles), and the second functionalized solid surface comprises a second plurality of metal nanoparticles coated with protein G that are fixed to second substrate exposed to the sample. An embodiment in which the first functionalized solid surface comprises a first plurality of gold nanoparticles coated with protein A that are fixed to a first substrate exposed to the sample (e.g., ligand bound via linker to gold particles) and the second functionalized solid surface comprises a second plurality of gold nanoparticles coated with protein G that are fixed to second substrate exposed to the sample is illustrated in Fig. 3

[0323] Referring to block 308, in some embodiments, the sample passes through the flow cell at a predetermined flow velocity during the time period. Nonlimiting examples of flow velocity in accordance with the present disclosure are provided above in conjunction with block 296.

[0324] Referring to block 310, in some embodiments, the plurality of first time-resolved measurements are ultra-violet light measurements of the sample, and the plurality of second time-resolved measurements are ultra-violet light measurements of the sample. For example, in some embodiments ultra-violet light measurements at about 280 nm are measured. In some embodiments ultra-violet light measurements at about 295 nm are measured. In some embodiments ultra-violet light measurements at a wavelength in the range of between about 280 nm and about 320 nm is measured.

[0325] Referring to block 312, in some embodiments, the first step comprises a fractionation of a plurality of plasma units from a plurality of donors. For instance, in some embodiments, the plasma is obtained from voluntary donations and is stored at < -20 °C. Prior to fractionations, the plasma units are analyzed for potential biological risk parameters. See, for example, Chapters 44, 55, and 56 of Rossi ’s Principles of Transfusion Medicine, eds. Simon et al. 2016, John Wiley & Sons Inc., Hoboken, NJ, which is hereby incorporated by reference.

[0326] After this validation step, the plasma from the plurality of donors is thawed up and pooled, and the main protein components are split in crude fractions depending on their precipitation (salting-out effect) at different temperature, ethanol concentration, pH, ionicstrength, and media composition. For IgG, precipitation occurs between 8% and 25% ethanol and -7.5 °C, called fraction 11+111. In some embodiments the precipitate from this fraction is extracted by filtration or centrifugation, re-dissolved, filtered, and precipitated again under more stringent conditions, including filter aids, detergents, and fumed-silica adsorption of impurities. This second, more pure precipitate is called Precipitate G (PptG). Precipitate G, containing mostly IgG, is re-dissolved, filtered (deep filtration) and treated with a mix of mild detergent, solvent detergent (S / D) reagent as a viral inactivation step for lipid- viruses.

[0327] Referring to block 314, in some embodiments, the time period occurs during the fractionation. Referring to block 316, in some embodiments, the time period occurs, at least in part, during the fractionation. For example, in some such embodiments, the fractionation results in 2 or more, 3 or more, 5 or more, or 10 or more different crude fractions and the sample is one of these crude fractions. In some embodiments the fractionation is in accordance with the Cohn plasma fractionation process and the disclosed methods are used to determine the concentration of a first protein type in a Cohn fraction. Cohn fractions and supernatants are documented in Bertolini, “The purification of plasma proteins for therapeutic use,” Rossi ’s Principles of Transfusion Medicine, Chapter 27, eds. Simon et al. 2016, John Wiley & Sons Inc., Hoboken, NJ, which is hereby incorporated by reference. For example, in some embodiments the Cohn fraction is Cryoprecipitate. In some embodiments the Cohn fraction is Cryoprecipitate and the first polypeptide type is Factor VIII, vWF, or fibrinogen. In some embodiments the Cohn fraction is Fraction I. In some embodiments the Cohn fraction is Fraction I and the first polypeptide type is fibrinogen or factor XIII. In some embodiments the Cohn fraction is Fraction II + III. In some embodiments the Cohn fraction is Fraction II + III and the first polypeptide type is immunoglobulin, or plasminogen. In some embodiments the Cohn fraction is Fraction IV-I. In some embodiments the Cohn fraction is Fraction IV-I and the first polypeptide type is alpha-proteinase inhibitor or apolipoprotein A. In some embodiments the Cohn fraction is Fraction IV-4. In some embodiments the Cohn fraction is Fraction IV-4 and the first polypeptide type is alpha-proteinase inhibitor, apolipoprotein A, transferrin, ceruloplasmin, or haptoglobin. In some embodiments the Cohn fraction is Fraction V. In some embodiments the Cohn fraction is Fraction V and the first polypeptide type is albumin or haptoglobin.

[0328] In some embodiments the disclosed quantification methods for monitoring a concentration of a first polypeptide type in a multi-step production process of a biologic are used to monitor a concentration of a first polypeptide type in a Cohn supernatant. For example, in some embodiments the Cohn supernatant is Cohn Cry supernatant. In some embodiments the Cohn supernatant is Cohn Cry supernatant and the first polypeptide type is prothrombin complex, factor IX, antithrombin III, Cl -esterase inhibitor, activated prothrombin, factor IX, factor X, or factor II (thrombin). In some embodiments the Cohn supernatant is Supernatant I. In some embodiments the Cohn supernatant is Supernatant I and the first polypeptide type is antithrombin III, prothrombin complex, or factor IX. In some embodiments the Cohn supernatant is Supernatant II + III. In some embodiments the Cohn supernatant is Supernatant II + III and the first polypeptide type is albumin. In some embodiments the Cohn supernatant is Supernatant VI + I. In some embodiments the Cohn supernatant is Supernatant IV-4.

[0329] Referring to block 318, in some embodiments, the time period occurs upon completion of the fractionation. For example, in some embodiments the fraction is precipitate G.

[0330] Referring to block 320, in some embodiments, the plurality of first time-resolved measurements and the plurality of second time-resolved measurements are taken within an hour, two hours, three hours, four hours, five hours, six hours, seven hours, or eight hours of completion of the fractionation. In some such embodiments completion of the fractionation is when precipitate G formation is complete or substantially complete.

[0331] Referring to block 322, in some embodiments, the plurality of first time-resolved measurements and the plurality of second time-resolved measurements are taken within 12 hours of completion of the fractionation. In some embodiments the plurality of first time-resolved measurements and the plurality of second time-resolved measurements are taken within 2 to 24 hours of completion of the fractionation (e.g., upon completion of precipitate G formation).

[0332] Referring to block 324, in some embodiments, the plurality of first time-resolved measurements and the plurality of second time-resolved measurements are taken within a day, two days, three days or four days of completion of the fractionation. In some such embodiments completion of the fractionation is when precipitate G formation is complete or substantially complete.

[0333] Referring to block 326, in some embodiments, the first step comprises a precipitation of a plasma component. In some such embodiments this is fraction 11+111, which for IgG, occurs between 8% and 25% ethanol at -7.5 °C.

[0334] Referring to block 328, in some embodiments, the precipitation is ethanol precipitation, ammonium sulfate precipitation, polyethylene glycol precipitation, citrate precipitation, or acid precipitation. In some embodiments, temperature, ethanol concentration, pH, ionic strength, and / or media composition is used to cause the precipitation.

[0335] Referring to block 330, in some embodiments, the time period occurs during the precipitation. Referring to block 332, in some embodiments, the time period occurs, at least in part, during the precipitation.

[0336] Referring to block 334, in some embodiments, the time period occurs upon completion of the precipitation. In some such embodiments, the precipitant is redissolved and the disclosed systems and methods are used to determine the exact concentration of IgG in the precipitant. In some embodiments the precipitant is fraction II+III precipitant. In some embodiments the fraction II+III precipitant is extracted by filtration or centrifugation, re-dissolved, filtered, and precipitated again under more stringent conditions, including filter aids, detergents, and fumed- silica adsorption of impurities. This second, more pure precipitate is called Precipitate G. In some such embodiments, the Precipitate G is redissolved and the disclosed systems and methods are used to determine the exact concentration of IgG in the Precipitate G.

[0337] Referring to block 336, in some embodiments, the plurality of first time-resolved measurements and the plurality of second time-resolved measurements are taken within an hour, two hours, three hours, four hours, five hours, six hours, seven hours, or eight hours of completion of the precipitation. In some such embodiments completion of the precipitation is when precipitate from fraction II+III is complete or substantially complete. In some such embodiments completion of the precipitation is when precipitate G formation is complete or substantially complete.

[0338] Referring to block 338, in some embodiments, the plurality of first time-resolved measurements and the plurality of second time-resolved measurements are taken within 12 hoursof completion of the precipitation. In some embodiments the plurality of first time-resolved measurements and the plurality of second time-resolved measurements are taken within 2 to 24 hours of completion of the precipitation (e.g., upon completion of precipitate G formation).

[0339] Referring to block 340, in some embodiments, the plurality of first time-resolved measurements and the plurality of second time-resolved measurements are taken within a day of completion of the precipitation. In some embodiments the plurality of first time-resolved measurements and the plurality of second time-resolved measurements are taken within 2 to 24 hours of completion of the precipitation (e.g., upon completion of precipitate G formation, upon completion of the precipitation of fraction 11+111).

[0340] In some embodiments Precipitate G, containing mostly IgG, is re-dissolved, filtered (deep filtration) and treated with a mix of mild detergent, solvent detergent (S / D) reagent as a viral inactivation step for lipid- viruses. In some embodiments this filtrate is then passed through two chromatography columns. First, a cation exchange chromatography in bind-elute mode, is used to wash out the detergents and other impurities in the flow through (S / D Removal), and then elute IgG with a low-to-high pH gradient. In some embodiments the eluate of the first chromatography is corrected for pH and passed through an anion exchange resin in flow-through mode, where the IgG does not bind, but the other contaminants do, including IgA (IgG Polishing steps).

[0341] Referring to block 348, in some embodiments, the first step comprises application of a chromatographic step on the sample. While Cohn fractionation can generate highly purified albumin and immunoglobulin products, production of immunoglobulin products typically requires application of chromatographic techniques to a Cohn fraction. Indeed, many multi-step biologic production processes involve some form of chromatography, in which molecules in solution (mobile phase) are separated based on differences in chemical or physical interaction with a solid material (stationary phase). Gel filtration (also called size-exclusion chromatography or SEC) uses a porous resin material to separate molecules based on size (e.g., physical exclusion). In ion exchange chromatography, molecules are separated according to the strength of their overall ionic interaction with a solid phase material (e.g., nonspecific interactions). Affinity chromatographic methods are widely used in multi-step biologicproduction processes. Affinity chromatography (also called affinity purification) relies on specific binding interactions between molecules. A particular ligand is chemically immobilized or “coupled” to a solid support so that when a complex mixture is passed over the column, those molecules having specific binding affinity to the ligand become bound. After other sample components are washed away, the bound molecule is stripped from the support, resulting in its purification from the original sample. A range of affinity ligands useful for producing and purifying proteins is known in the art. In some embodiments, Protein A and protein G are used to capture proteins in the purification of human antibodies.

[0342] Referring to block 350, in some such embodiments, the application of the chromatographic step is affinity chromatography (e.g., protein A affinity chromatography, protein G affinity chromatography, etc. ion exchange chromatography, size exclusion chromatography, or hydrophobic interaction chromatography on the sample (e.g. Cohn fraction). In such embodiments, the sample is passed through a corresponding chromatographic column. In some embodiments the chromatographic step is performed with a column packed with resin beads of between about 50 pm and about 100 pm (e.g., about 80 pm ) in diameter and derivatized with particular functional groups to allow the particular chromatographic separation to be performed.

[0343] Ion exchange chromatographic resins exist in either the anion (positively charged) or cation (negatively charged) forms. Typical ion exchange ligands include dietlylaminoethyl (DEAE), quaternary aminoethyl (QAE), quaternary ammonium (Q), carboxymethyl (CM), sulfopropyl (SP), and methyl sulfonate (S) for anion and cation exchange chromatography, respectively.

[0344] Referring to block 352, in some embodiments in accordance with block 350, the time period occurs during the chromatographic step. For example, in some such embodiments the flow through of the column is sampled using the methods of the present disclosure to quantify the concentration of in the first polypeptide type in the flow through while the sample is being run through the column.

[0345] Referring to block 354, in some embodiments, the time period occurs, at least in part, during the chromatographic step. Accordingly, in some embodiments, the plurality of first time-resolved measurements and the plurality of second time-resolved measurements are taken during the chromatographic step. In one nonlimiting example, in the embodiment described above in which the Precipitate G is passed through two columns, the plurality of first time-resolved measurements and the plurality of second time-resolved measurements are taken of the sample after is passes the first column but before it passes through the second column.

[0346] Referring to block 356, in some embodiments, the time period occurs upon completion of the chromatographic step. In one nonlimiting example, in the embodiment described above in which the Precipitate G is passed through two columns, the time period for measurement of the concentration of the first polypeptide type in accordance with block 342 occurs after the sample has passed through the second column.

[0347] Referring to block 358, in some embodiments, the plurality of first time-resolved measurements and the plurality of second time-resolved measurements are taken within an hour, two hours, three hours, four hours, five hours, six hours, seven hours, or eight hours of completion of the chromatographic step.

[0348] Referring to block 360, in some embodiments, the plurality of first time-resolved measurements and the plurality of second time-resolved measurements are taken within 12 hours of completion of the chromatography.

[0349] Referring to block 362, in some embodiments, the plurality of first time-resolved measurements and the plurality of second time-resolved measurements are taken within a day of completion of the chromatography. In some embodiments, the plurality of first time-resolved measurements and the plurality of second time-resolved measurements are taken within two days, three days or four days of completion of the chromatographic step.

[0350] In some embodiments, for example in the case where the biologic comprises IgG, upon completion of the chromatographic step, further purification includes additional virus removal filtration, concentration (e.g., to 100 g / 1) with tangential ultra / diafiltration, and / or sterile filtration.

[0351] Referring to block 364, in some embodiments, the first step comprises filtration of the sample. In some embodiments this filtration is a viral filtration. In some embodiments the viralfiltration removes parvovirus Bl 9, hepatitis A, hepatitis B, hepatitis C, and human immunodeficiency virus (HIV).

[0352] Referring to block 366, in some embodiments in accordance with block 364, the filtration comprises microfiltration using a membrane with pores between 0.1 and 10 micrometers in size.

[0353] In some embodiments the filtration comprises filtration using a membrane with pores between 15 nm and 20 nm in size.

[0354] In some embodiments the filtration comprises filtration using a membrane with pores between 15 nm and 20 nm in size. In some embodiments the first polypeptide type has a molecular weight of 150,000 Daltons or less and the filtration comprises filtration using a membrane with pores between 15 nm and 20 nm in size.

[0355] In some embodiments the first polypeptide type has a molecular weight of greater than 150,000 Daltons or greater (e.g., von Willebrand factor which has a molecular weight of 500,000 Daltons) and the filtration comprises filtration using a membrane with pores between 30 nm and 40 nm in size, such as 35 nm.

[0356] Referring to block 368, in some embodiments, the filtration comprises ultrafiltration using a membrane with pores between 1 nanometer and 100 nanometers in size.

[0357] Referring to block 370, in some embodiments, the filtration comprises nanofiltration with a membrane with pores between 1 and 10 nanometers in size.

[0358] In some embodiments in accordance with block 364, the filtration is virus removal filtration, concentration (e.g., to 100 g / 1) with tangential ultra / diafiltration, and / or sterile filtration.

[0359] Referring to block 372, in some embodiments, the filtration comprises a depth filtration.

[0360] Referring to block 374, in some embodiments, the filtration comprises a tangential flow filtration.

[0361] Referring to block 376, in some embodiments, the time period occurs during the filtration.

[0362] Referring to block 378, in some embodiments, the time period occurs, at least in part, during the filtration.

[0363] Referring to block 380, in some embodiments, the time period occurs upon completion of the filtration.

[0364] Referring to block 382, in some embodiments, the plurality of first time-resolved measurements and the plurality of second time-resolved measurements are taken within an hour, two hours, three hours, four hours, five hours, six hours, seven hours, or eight hours of completion of the filtration.

[0365] Referring to block 384, in some embodiments, the plurality of first time-resolved measurements and the plurality of second time-resolved measurements are taken within 12 hours of completion of the filtration. In some embodiments the plurality of first time-resolved measurements and the plurality of second time-resolved measurements are taken within 2 to 24 hours of completion of the filtration.

[0366] Referring to block 386, in some embodiments, the plurality of first time-resolved measurements and the plurality of second time-resolved measurements are taken within a day, two days, three days or four days of completion of the filtration.

[0367] Referring to block 388, in some embodiments, responsive to an analysis of the first and second signal, a calculated concentration of the first polymer type in the sample is obtained. Fig. 3 illustrates how the binding affinity in the first plurality of binding affinities of the first substrate-based sensor being other than the binding affinity in the second plurality of binding affinities of the second substrate-based sensor for the first polypeptide type leads to differential kinetics that can be used to ascertain the relative contribution the first polymer type makes to the first and second signal, and from this, the concentration of the first polymer type in the sample. In Fig. 3, the Protein A substrate-based sensor does not have a measurable binding affinity for IgG3 while the protein G substrate-based sensor has a strong binding affinity for IgG3. Thus, IgG3 present in the sample does not contribute to the signal measured from the Protein A substrate-based sensor but does contribute to the signal measured from the Protein G substrate-based sensor. Analysis of this differential contribution of IgG3 to the two signals is used to ascertain the concentration of IgG3 in the sample.

[0368] Fig. 11. illustrates first signal data from a plurality of first time-resolved measurements, occurring over a time period, from a first optical sensor in optical communication with a first substrate-based sensor exposed to a sample during the time period. While not illustrated in Fig. 11, second signal data from a plurality of second time-resolved measurements, occurring over the time period, from a second optical sensor in optical communication with the second substrate-based sensor exposed to the sample during the time period would have a similar form, as illustrated in Fig. 3.

[0369] In some embodiments, the first signal data is evaluated to obtain a first single value, in the form of a first substrate-based sensor baseline corrected event signal, for the analysis of the first polymer type concentration. For instance, in some embodiments, a measuring window is selected (T). In Fig. 11A, the measuring window begins at line 1102 and ends at the end time 106. Second, referring to Fig. 1 IB and 11C, in some embodiments, a first substrate sensor sample event 122 is detected, at the maximum increase rate 1150 of the curve defined by the first signal data 112 (2)and (3). In some embodiments, the median value before (baseline signal 126 illustrated in Fig. 1 ID) and the median value 124 after the event 122 is measuredIn some embodiments, referring to Fig. 1 ID, the value before the event (baseline signal 126) is subtracted from the median value 124 to calculate the first substrate-based sensor baseline corrected event signal 128 @. In some alternative embodiments, the baseline estimator is performed with the minimum of the 10 percentile of the before-event signal. Also, in some embodiments the derivative of 112 is used to estimate the baseline corrected event signal 128, for instance in cases where the concentration of the first polypeptide type in the sample is high. In some embodiments, the second signal data is similarly evaluated to obtain a second single value, in the form of a second substrate-based sensor baseline corrected event signal.

[0370] Referring to block 390, in some embodiments the analysis of the first and second signal comprises inputting at least the first and second signal into a model comprising a plurality of parameters thereby obtaining the calculated concentration of the first polymer type in the samplethrough interaction of the first plurality of parameters with the first and second signal. In some such embodiments the first and second signal, in the form of the first substrate-based sensor baseline corrected event signal and the second substrate-based sensor baseline corrected event signal is inputted into the model 148 comprising the plurality of parameters { 150-1, 150-P} thereby obtaining the calculated concentration of the first polymer type 152 in the sample through interaction of the first plurality of parameters with the first substrate-based sensor baseline corrected event signal and the second substrate-based sensor baseline corrected event signal.

[0371] In some embodiments, a model used in the methods and systems described herein is trained using a plurality of parameters as independent variables and concentrations for at least the first polypeptide type are used as dependent variables for the training. That is, the model is trained to determine a concentration for at least the first polypeptide type as a function of the plurality of parameters observed for a test sample. Accordingly, when methods and systems using these trained models are integrated into a manufacturing process, as described herein, observed values for the same plurality of parameters are used as inputs to the model to obtain, as output from the model, at least a calculated concentration of the first polypeptide type.

[0372] In some embodiments, the plurality of parameters used as independent variables for training the model include signals from first and second analytical inline devices 482 when exposed to the sample containing the biologic. In some embodiments, raw signals from the analytical inline devices 482 are used for training the model. For instance, in some embodiments when the analytical inline devices 482 are Localized Surface Plasmon Resonance (LSPR) detectors, wavelength absorption measurements over time are used as independent variables for training the model. In other embodiments, processed signals from the analytical inline devices 482 are used for training the model. For instance, in some embodiments when the analytical inline devices 482 are Localized Surface Plasmon Resonance (LSPR) detectors, shifts in wavelength absorption over time (e.g., as illustrated in Fig. 13) are used as independent variables for training the model. In some embodiments, the processed signals from the analytical inline devices 482 are kinetic parameters determined from the raw signals measured by the analytical inline devices 482. For example, in some embodiments when the analytical inline devices 482are Localized Surface Plasmon Resonance (LSPR) detectors, protein binding constants (e.g., Kd, Kon, and / or Koff), determined from the raw signals measured by the analytical inline devices 482 are used as independent variables for training the model.

[0373] In some embodiments, the plurality of parameters used as independent variables for training the model includes one or more solution conditions. For example, in some embodiments, the plurality of parameters includes a pH of the sample. In some embodiments, the pH is measured directly from the sample. In some embodiments, the pH is measured from a larger volume from which the sample is obtained or from a second sample obtained therefrom. For example, referring to Figure 12, a sample of a manufacturing intermediate is redirected from the main product stream at valve 480 to analytical inline devices 482, and a pH of the main product stream is measured at pH meter 1204.

[0374] In some embodiments, the plurality of parameters used as independent variables for training the model includes a conductivity of the sample. In some embodiments, the conductivity is measured directly from the sample. In some embodiments, the conductivity is measured from a larger volume from which the sample is obtained or from a second sample obtained therefrom.

[0375] In some embodiments, the plurality of parameters used as independent variables for training the model includes a component of the sample containing the first polypeptide, e.g., a buffering component, a salt component, a solvent content, etc. For example, in some embodiments where the sample is a process intermediate of an alcohol precipitation process, the plurality of parameters includes an alcohol content of the sample.

[0376] In some embodiments, the plurality of parameters used as independent variables for training the model includes a temperature of the sample. In some embodiments, the temperature is measured directly from the sample. In some embodiments, the temperature is measured from a larger volume from which the sample is obtained or from a second sample obtained therefrom. In yet other embodiments, the temperature may be taken from a room or area in which the manufacturing step is being performed, e.g., in a temperature-controlled room or area.

[0377] In some embodiments, the plurality of parameters used as independent variables for training the model includes a flow rate of the sample. In some embodiments, the flow rate is a flow rate of the sample when flowed over the substrate-based sensor. In some embodiments, the flow rate is a flow rate used in a portion of the manufacturing process. For example, in some embodiments, the flow rate is a flow rate of the manufacturing intermediate when exposed to a chromatographic resin, e.g., an ion exchange resin, a hydrophobic interaction (HIC) resin, a mixed mode resin, a gel fdtration resin, an immuno-affinity resin, a protein A or protein G resin, etc. In some embodiments, the flow rate is a flow rate of the manufacturing intermediate during filtration, e.g., during tangential flow filtration (TFF).

[0378] Non-limiting examples of the types of information that can be used as features for a model according to the current disclosure include those listed in Table 2 below:Table 2 - Example features for models according to the present disclosure.

[0379] In some embodiments, the model uses at least 2 features from those listed in Table 2. In some embodiments, the model uses at least features 1 and 3 listed in Table 2. In some embodiments, the model uses at least features 1 and 4 listed in Table 2. In some embodiments, the model uses at least features 1 and 5 listed in Table 2. In some embodiments, the model uses at least features 1 and 6 listed in Table 2. In some embodiments, the model uses at least features 1 and 7 listed in Table 2. In some embodiments, the model uses at least features 1 and 8 listed in Table 2. In some embodiments, the model uses at least features 1 and 9 listed in Table 2. In some embodiments, the model uses at least features 2 and 3 listed in Table 2. In someembodiments, the model uses at least features 2 and 4 listed in Table 2. In some embodiments, the model uses at least features 2 and 5 listed in Table 2. In some embodiments, the model uses at least features 2 and 6 listed in Table 2. In some embodiments, the model uses at least features 2 and 7 listed in Table 2. In some embodiments, the model uses at least features 2 and 8 listed in Table 2. In some embodiments, the model uses at least features 2 and 9 listed in Table 2.

[0380] In some embodiments, the model uses at least 3 features from those listed in Table 2. In some embodiments, the model uses at least features 1, 3, and 4 listed in Table 2. In some embodiments, the model uses at least features 1, 3, and 5 listed in Table 2. In some embodiments, the model uses at least features 1, 3, and 6 listed in Table 2. In some embodiments, the model uses at least features 1, 3, and 7 listed in Table 2. In some embodiments, the model uses at least features 1, 3, and 8 listed in Table 2. In some embodiments, the model uses at least features 1, 3, and 9 listed in Table 2. In some embodiments, the model uses at least features 1, 4, and 5 listed in Table 2. In some embodiments, the model uses at least features 1, 4, and 6 listed in Table 2. In some embodiments, the model uses at least features 1, 4, and 7 listed in Table 2. In some embodiments, the model uses at least features 1, 4, and 8 listed in Table 2. In some embodiments, the model uses at least features 1, 4, and 9 listed in Table 2. In some embodiments, the model uses at least features 1, 5, and 6 listed in Table 2. In some embodiments, the model uses at least features 1, 5, and 7 listed in Table 2. In some embodiments, the model uses at least features 1, 5, and 8 listed in Table 2. In some embodiments, the model uses at least features 1, 5, and 9 listed in Table 2. In some embodiments, the model uses at least features 1, 6, and 7 listed in Table 2. In some embodiments, the model uses at least features 1, 6, and 8 listed in Table 2. In some embodiments, the model uses at least features 1, 6, and 9 listed in Table 2. In some embodiments, the model uses at least features 1, 7, and 8 listed in Table 2. In some embodiments, the model uses at least features 1, 7, and 9 listed in Table 2. In some embodiments, the model uses at least features 1, 8, and 9 listed in Table 2. In some embodiments, the model uses at least features 2, 3, and 4 listed in Table 2. In some embodiments, the model uses at least features 2, 3, and 5 listed in Table 2. In some embodiments, the model uses at least features 2, 3, and 6 listed in Table 2. In someembodiments, the model uses at least features 2, 3, and 7 listed in Table 2. In some embodiments, the model uses at least features 2, 3, and 8 listed in Table 2. In some embodiments, the model uses at least features 2, 3, and 9 listed in Table 2. In some embodiments, the model uses at least features 2, 4, and 5 listed in Table 2. In some embodiments, the model uses at least features 2, 4, and 6 listed in Table 2. In some embodiments, the model uses at least features 2, 4, and 7 listed in Table 2. In some embodiments, the model uses at least features 2, 4, and 8 listed in Table 2. In some embodiments, the model uses at least features 2, 4, and 9 listed in Table 2. In some embodiments, the model uses at least features 2, 5, and 6 listed in Table 2. In some embodiments, the model uses at least features 2, 5, and 7 listed in Table 2. In some embodiments, the model uses at least features 2, 5, and 8 listed in Table 2. In some embodiments, the model uses at least features 2, 5, and 9 listed in Table 2. In some embodiments, the model uses at least features 2, 6, and 7 listed in Table 2. In some embodiments, the model uses at least features 2, 6, and 8 listed in Table 2. In some embodiments, the model uses at least features 2, 6, and 9 listed in Table 2. In some embodiments, the model uses at least features 2, 7, and 8 listed in Table 2. In some embodiments, the model uses at least features 2, 7, and 9 listed in Table 2. In some embodiments, the model uses at least features 2, 8, and 9 listed in Table 2.

[0381] In some embodiments, the model uses at least 4 features from those listed in Table 2. In some embodiments, the model uses at least features 1, 3, 4, and 5 listed in Table 2. In some embodiments, the model uses at least features 1, 3, 4, and 6 listed in Table 2. In some embodiments, the model uses at least features 1, 3, 4, and 7 listed in Table 2. In some embodiments, the model uses at least features 1, 3, 4, and 8 listed in Table 2. In some embodiments, the model uses at least features 1, 3, 4, and 9 listed in Table 2. In some embodiments, the model uses at least features 1, 3, 5, and 6 listed in Table 2. In some embodiments, the model uses at least features 1, 3, 5, and 7 listed in Table 2. In some embodiments, the model uses at least features 1, 3, 5, and 8 listed in Table 2. In some embodiments, the model uses at least features 1, 3, 5, and 9 listed in Table 2. In some embodiments, the model uses at least features 1, 3, 6, and 7 listed in Table 2. In some embodiments, the model uses at least features 1, 3, 6, and 8 listed in Table 2. In someembodiments, the model uses at least features 1, 3, 6, and 9 listed in Table 2. In some embodiments, the model uses at least features 1, 3, 7, and 8 listed in Table 2. In some embodiments, the model uses at least features 1, 3, 7, and 9 listed in Table 2. In some embodiments, the model uses at least features 1, 3, 8, and 9 listed in Table 2. In some embodiments, the model uses at least features 1, 4, 5, and 6 listed in Table 2. In some embodiments, the model uses at least features 1, 4, 5, and 7 listed in Table 2. In some embodiments, the model uses at least features 1, 4, 5, and 8 listed in Table 2. In some embodiments, the model uses at least features 1 , 4, 5, and 9 listed in Table 2. In some embodiments, the model uses at least features 1, 4, 6, and 7 listed in Table 2. In some embodiments, the model uses at least features 1, 4, 6, and 8 listed in Table 2. In some embodiments, the model uses at least features 1, 4, 6, and 9 listed in Table 2. In some embodiments, the model uses at least features 1, 4, 7, and 8 listed in Table 2. In some embodiments, the model uses at least features 1, 4, 7, and 9 listed in Table 2. In some embodiments, the model uses at least features 1, 4, 8, and 9 listed in Table 2. In some embodiments, the model uses at least features 1, 5, 6, and 7 listed in Table 2. In some embodiments, the model uses at least features 1, 5, 6, and 8 listed in Table 2. In some embodiments, the model uses at least features 1, 5, 6, and 9 listed in Table 2. In some embodiments, the model uses at least features 1, 5, 7, and 8 listed in Table 2. In some embodiments, the model uses at least features 1, 5, 7, and 9 listed in Table 2. In some embodiments, the model uses at least features 1, 5, 8, and 9 listed in Table 2. In some embodiments, the model uses at least features 1, 6, 7, and 8 listed in Table 2. In some embodiments, the model uses at least features 1, 6, 7, and 9 listed in Table 2. In some embodiments, the model uses at least features 1, 6, 8, and 9 listed in Table 2. In some embodiments, the model uses at least features 1, 7, 8, and 9 listed in Table 2. In some embodiments, the model uses at least features 2, 3, 4, and 5 listed in Table 2. In some embodiments, the model uses at least features 2, 3, 4, and 6 listed in Table 2. In some embodiments, the model uses at least features 2, 3, 4, and 7 listed in Table 2. In some embodiments, the model uses at least features 2, 3, 4, and 8 listed in Table 2. In some embodiments, the model uses at least features 2, 3, 4, and 9 listed in Table 2. In some embodiments, the model uses at least features 2, 3, 5, and 6 listed in Table 2. In someembodiments, the model uses at least features 2, 3, 5, and 7 listed in Table 2. In some embodiments, the model uses at least features 2, 3, 5, and 8 listed in Table 2. In some embodiments, the model uses at least features 2, 3, 5, and 9 listed in Table 2. In some embodiments, the model uses at least features 2, 3, 6, and 7 listed in Table 2. In some embodiments, the model uses at least features 2, 3, 6, and 8 listed in Table 2. In some embodiments, the model uses at least features 2, 3, 6, and 9 listed in Table 2. In some embodiments, the model uses at least features 2, 3, 7, and 8 listed in Table 2. In some embodiments, the model uses at least features 2, 3, 7, and 9 listed in Table 2. In some embodiments, the model uses at least features 2, 3, 8, and 9 listed in Table 2. In some embodiments, the model uses at least features 2, 4, 5, and 6 listed in Table 2. In some embodiments, the model uses at least features 2, 4, 5, and 7 listed in Table 2. In some embodiments, the model uses at least features 2, 4, 5, and 8 listed in Table 2. In some embodiments, the model uses at least features 2, 4, 5, and 9 listed in Table 2. In some embodiments, the model uses at least features 2, 4, 6, and 7 listed in Table 2. In some embodiments, the model uses at least features 2, 4, 6, and 8 listed in Table 2. In some embodiments, the model uses at least features 2, 4, 6, and 9 listed in Table 2. In some embodiments, the model uses at least features 2, 4, 7, and 8 listed in Table 2. In some embodiments, the model uses at least features 2, 4, 7, and 9 listed in Table 2. In some embodiments, the model uses at least features 2, 4, 8, and 9 listed in Table 2. In some embodiments, the model uses at least features 2, 5, 6, and 7 listed in Table 2. In some embodiments, the model uses at least features 2, 5, 6, and 8 listed in Table 2. In some embodiments, the model uses at least features 2, 5, 6, and 9 listed in Table 2. In some embodiments, the model uses at least features 2, 5, 7, and 8 listed in Table 2. In some embodiments, the model uses at least features 2, 5, 7, and 9 listed in Table 2. In some embodiments, the model uses at least features 2, 5, 8, and 9 listed in Table 2. In some embodiments, the model uses at least features 2, 6, 7, and 8 listed in Table 2. In some embodiments, the model uses at least features 2, 6, 7, and 9 listed in Table 2. In some embodiments, the model uses at least features 2, 6, 8, and 9 listed in Table 2. In some embodiments, the model uses at least features 2, 7, 8, and 9 listed in Table 2.

[0382] In some embodiments, the model uses at least 5 features from those listed in Table 2. In some embodiments, the model uses at least features 1, 3, 4, 5, and 6 listed in Table 2. In some embodiments, the model uses at least features 1, 3, 4, 5, and 7 listed in Table 2. In some embodiments, the model uses at least features 1, 3, 4, 5, and 8 listed in Table 2. In some embodiments, the model uses at least features 1, 3, 4, 5, and 9 listed in Table 2. In some embodiments, the model uses at least features 1, 3, 4, 6, and 7 listed in Table 2. In some embodiments, the model uses at least features 1, 3, 4, 6, and 8 listed in Table 2. In some embodiments, the model uses at least features 1 , 3, 4, 6, and 9 listed in Table 2. In some embodiments, the model uses at least features 1, 3, 4, 7, and 8 listed in Table 2. In some embodiments, the model uses at least features 1, 3, 4, 7, and 9 listed in Table 2. In some embodiments, the model uses at least features 1, 3, 4, 8, and 9 listed in Table 2. In some embodiments, the model uses at least features 1, 3, 5, 6, and 7 listed in Table 2. In some embodiments, the model uses at least features 1, 3, 5, 6, and 8 listed in Table 2. In some embodiments, the model uses at least features 1, 3, 5, 6, and 9 listed in Table 2. In some embodiments, the model uses at least features 1, 3, 5, 7, and 8 listed in Table 2. In some embodiments, the model uses at least features 1, 3, 5, 7, and 9 listed in Table 2. In some embodiments, the model uses at least features 1, 3, 5, 8, and 9 listed in Table 2. In some embodiments, the model uses at least features 1, 3, 6, 7, and 8 listed in Table 2. In some embodiments, the model uses at least features 1, 3, 6, 7, and 9 listed in Table 2. In some embodiments, the model uses at least features 1, 3, 6, 8, and 9 listed in Table 2. In some embodiments, the model uses at least features 1, 3, 7, 8, and 9 listed in Table 2. In some embodiments, the model uses at least features 1, 4, 5, 6, and 7 listed in Table 2. In some embodiments, the model uses at least features 1, 4, 5, 6, and 8 listed in Table 2. In some embodiments, the model uses at least features 1, 4, 5, 6, and 9 listed in Table 2. In some embodiments, the model uses at least features 1, 4, 5, 7, and 8 listed in Table 2. In some embodiments, the model uses at least features 1, 4, 5, 7, and 9 listed in Table 2. In some embodiments, the model uses at least features 1, 4, 5, 8, and 9 listed in Table 2. In some embodiments, the model uses at least features 1, 4, 6, 7, and 8 listed in Table 2. In some embodiments, the model uses at least features 1, 4, 6, 7, and 9 listed in Table 2. In some embodiments, the model uses at least features 1, 4, 6, 8, and 9 listed in Table 2. In someembodiments, the model uses at least features 1, 4, 7, 8, and 9 listed in Table 2. In some embodiments, the model uses at least features 1, 5, 6, 7, and 8 listed in Table 2. In some embodiments, the model uses at least features 1, 5, 6, 7, and 9 listed in Table 2. In some embodiments, the model uses at least features 1, 5, 6, 8, and 9 listed in Table 2. In some embodiments, the model uses at least features 1, 5, 7, 8, and 9 listed in Table 2. In some embodiments, the model uses at least features 1, 6, 7, 8, and 9 listed in Table 2. In some embodiments, the model uses at least features 2, 3, 4, 5, and 6 listed in Table 2. In some embodiments, the model uses at least features 2, 3, 4, 5, and 7 listed in Table 2. In some embodiments, the model uses at least features 2, 3, 4, 5, and 8 listed in Table 2. In some embodiments, the model uses at least features 2, 3, 4, 5, and 9 listed in Table 2. In some embodiments, the model uses at least features 2, 3, 4, 6, and 7 listed in Table 2. In some embodiments, the model uses at least features 2, 3, 4, 6, and 8 listed in Table 2. In some embodiments, the model uses at least features 2, 3, 4, 6, and 9 listed in Table 2. In some embodiments, the model uses at least features 2, 3, 4, 7, and 8 listed in Table 2. In some embodiments, the model uses at least features 2, 3, 4, 7, and 9 listed in Table 2. In some embodiments, the model uses at least features 2, 3, 4, 8, and 9 listed in Table 2. In some embodiments, the model uses at least features 2, 3, 5, 6, and 7 listed in Table 2. In some embodiments, the model uses at least features 2, 3, 5, 6, and 8 listed in Table 2. In some embodiments, the model uses at least features 2, 3, 5, 6, and 9 listed in Table 2. In some embodiments, the model uses at least features 2, 3, 5, 7, and 8 listed in Table 2. In some embodiments, the model uses at least features 2, 3, 5, 7, and 9 listed in Table 2. In some embodiments, the model uses at least features 2, 3, 5, 8, and 9 listed in Table 2. In some embodiments, the model uses at least features 2, 3, 6, 7, and 8 listed in Table 2. In some embodiments, the model uses at least features 2, 3, 6, 7, and 9 listed in Table 2. In some embodiments, the model uses at least features 2, 3, 6, 8, and 9 listed in Table 2. In some embodiments, the model uses at least features 2, 3, 7, 8, and 9 listed in Table 2. In some embodiments, the model uses at least features 2, 4, 5, 6, and 7 listed in Table 2. In some embodiments, the model uses at least features 2, 4, 5, 6, and 8 listed in Table 2. In some embodiments, the model uses at least features 2, 4, 5, 6, and 9 listed in Table 2. In some embodiments, the model uses at least features 2, 4, 5, 7, and 8 listed in Table 2. In someembodiments, the model uses at least features 2, 4, 5, 7, and 9 listed in Table 2. In some embodiments, the model uses at least features 2, 4, 5, 8, and 9 listed in Table 2. In some embodiments, the model uses at least features 2, 4, 6, 7, and 8 listed in Table 2. In some embodiments, the model uses at least features 2, 4, 6, 7, and 9 listed in Table 2. In some embodiments, the model uses at least features 2, 4, 6, 8, and 9 listed in Table 2. In some embodiments, the model uses at least features 2, 4, 7, 8, and 9 listed in Table 2. In some embodiments, the model uses at least features 2, 5, 6, 7, and 8 listed in Table 2. In some embodiments, the model uses at least features 2, 5, 6, 7, and 9 listed in Table 2. In some embodiments, the model uses at least features 2, 5, 6, 8, and 9 listed in Table 2. In some embodiments, the model uses at least features 2, 5, 7, 8, and 9 listed in Table 2. In some embodiments, the model uses at least features 2, 6, 7, 8, and 9 listed in Table 2.

[0383] In some embodiments, the model uses at least 6 features from those listed in Table 2. In some embodiments, the model uses at least features 1, 3, 4, 5, 6, and 7 listed in Table 2. In some embodiments, the model uses at least features 1, 3, 4, 5, 6, and 8 listed in Table 2. In some embodiments, the model uses at least features 1, 3, 4, 5, 6, and 9 listed in Table 2. In some embodiments, the model uses at least features 1, 3, 4, 5, 7, and 8 listed in Table 2. In some embodiments, the model uses at least features 1, 3, 4, 5, 7, and 9 listed in Table 2. In some embodiments, the model uses at least features 1, 3, 4, 5, 8, and 9 listed in Table 2. In some embodiments, the model uses at least features 1, 3, 4, 6, 7, and 8 listed in Table 2. In some embodiments, the model uses at least features 1, 3, 4, 6, 7, and 9 listed in Table 2. In some embodiments, the model uses at least features 1, 3, 4, 6, 8, and 9 listed in Table 2. In some embodiments, the model uses at least features 1, 3, 4, 7, 8, and 9 listed in Table 2. In some embodiments, the model uses at least features 1, 3, 5, 6, 7, and 8 listed in Table 2. In some embodiments, the model uses at least features 1, 3, 5, 6, 7, and 9 listed in Table 2. In some embodiments, the model uses at least features 1, 3, 5, 6, 8, and 9 listed in Table 2. In some embodiments, the model uses at least features 1, 3, 5, 7, 8, and 9 listed in Table 2. In some embodiments, the model uses at least features 1, 3, 6, 7, 8, and 9 listed in Table 2. In some embodiments, the model uses at least features 1, 4, 5, 6, 7, and 8 listed in Table 2. In some embodiments, the model uses at least features 1, 4, 5, 6, 7, and 9 listed in Table 2. In some embodiments, the model uses at least features 1, 4, 5, 6, 8, and 9 listed in Table 2. In someembodiments, the model uses at least features 1, 4, 5, 7, 8, and 9 listed in Table 2. In some embodiments, the model uses at least features 1, 4, 6, 7, 8, and 9 listed in Table 2. In some embodiments, the model uses at least features 1, 5, 6, 7, 8, and 9 listed in Table 2. In some embodiments, the model uses at least features 2, 3, 4, 5, 6, and 7 listed in Table 2. In some embodiments, the model uses at least features 2, 3, 4, 5, 6, and 8 listed in Table 2. In some embodiments, the model uses at least features 2, 3, 4, 5, 6, and 9 listed in Table 2. In some embodiments, the model uses at least features 2, 3, 4, 5, 7, and 8 listed in Table 2. In some embodiments, the model uses at least features 2, 3, 4, 5, 7, and 9 listed in Table 2. In some embodiments, the model uses at least features 2, 3, 4, 5, 8, and 9 listed in Table 2. In some embodiments, the model uses at least features 2, 3, 4, 6, 7, and 8 listed in Table 2. In some embodiments, the model uses at least features 2, 3, 4, 6, 7, and 9 listed in Table 2. In some embodiments, the model uses at least features 2, 3, 4, 6, 8, and 9 listed in Table 2. In some embodiments, the model uses at least features 2, 3, 4, 7, 8, and 9 listed in Table 2. In some embodiments, the model uses at least features 2, 3, 5, 6, 7, and 8 listed in Table 2. In some embodiments, the model uses at least features 2, 3, 5, 6, 7, and 9 listed in Table 2. In some embodiments, the model uses at least features 2, 3, 5, 6, 8, and 9 listed in Table 2. In some embodiments, the model uses at least features 2, 3, 5, 7, 8, and 9 listed in Table 2. In some embodiments, the model uses at least features 2, 3, 6, 7, 8, and 9 listed in Table 2. In some embodiments, the model uses at least features 2, 4, 5, 6, 7, and 8 listed in Table 2. In some embodiments, the model uses at least features 2, 4, 5, 6, 7, and 9 listed in Table 2. In some embodiments, the model uses at least features 2, 4, 5, 6, 8, and 9 listed in Table 2. In some embodiments, the model uses at least features 2, 4, 5, 7, 8, and 9 listed in Table 2. In some embodiments, the model uses at least features 2, 4, 6, 7, 8, and 9 listed in Table 2. In some embodiments, the model uses at least features 2, 5, 6, 7, 8, and 9 listed in Table 2.

[0384] In some embodiments, the model uses at least 7 features from those listed in Table 2. In some embodiments, the model uses at least features 1, 3, 4, 5, 6, 7, and 8 listed in Table 2. In some embodiments, the model uses at least features 1, 3, 4, 5, 6, 7, and 9 listed in Table 2. In some embodiments, the model uses at least features 1, 3, 4, 5, 6, 8, and 9 listed in Table 2. In some embodiments, the model uses at least features 1, 3, 4, 5, 7, 8, and 9 listed in Table 2. In some embodiments, the model uses at least features 1, 3, 4, 6, 7, 8, and 9 listed in Table 2. Insome embodiments, the model uses at least features 1, 3, 5, 6, 7, 8, and 9 listed in Table 2. In some embodiments, the model uses at least features 1, 4, 5, 6, 7, 8, and 9 listed in Table 2. In some embodiments, the model uses at least features 1, 3, 4, 5, 6, 7, 8, and 9 listed in Table 2. In some embodiments, the model uses at least features 2, 3, 4, 5, 6, 7, and 8 listed in Table 2. In some embodiments, the model uses at least features 2, 3, 4, 5, 6, 7, and 9 listed in Table 2. In some embodiments, the model uses at least features 2, 3, 4, 5, 6, 8, and 9 listed in Table 2. In some embodiments, the model uses at least features 2, 3, 4, 5, 7, 8, and 9 listed in Table 2. In some embodiments, the model uses at least features 2, 3, 4, 6, 7, 8, and 9 listed in Table 2. In some embodiments, the model uses at least features 2, 3, 5, 6, 7, 8, and 9 listed in Table 2. In some embodiments, the model uses at least features 2, 4, 5, 6, 7, 8, and 9 listed in Table 2. In some embodiments, the model uses at least features 2, 3, 4, 5, 6, 7, 8, and 9 listed in Table 2.

[0385] In some embodiments, a model is trained to output a calculated concentration for a plurality of polypeptide types. For instance, in one embodiment, the model is trained to output a concentration of IgG3 and a concentration of total IgG in the sample. In some embodiments, the model is trained to output a concentration of IgG and a concentration of IgA in the sample. In some embodiments, the model is trained to output a concentration of IgG3, a concentration of total IgG, and a concentration of IgA in the sample.

[0386] In one embodiment, the model uses at least any 2 features listed in Table 2 to output at least a concentration of IgG3 and a concentration of total IgG in the sample. In one embodiment, the model uses at least any 2 features listed in Table 2 to output at least a concentration of IgG and a concentration of IgA in the sample. In one embodiment, the model uses at least any 2 features listed in Table 2 to output at least a concentration of IgG3, a concentration of total IgG, and a concentration of IgA in the sample.

[0001] In one embodiment, the model uses at least any 3 features listed in Table 3 to output at least a concentration of IgG3 and a concentration of total IgG in the sample. In one embodiment, the model uses at least any 3 features listed in Table 3 to output at least a concentration of IgG and a concentration of IgA in the sample. In one embodiment, the model uses at least any 3 features listed in Table 3 to output at least a concentration of IgG3, a concentration of total IgG, and a concentration of IgA in the sample.

[0002] In one embodiment, the model uses at least any 4 features listed in Table 4 to output at least a concentration of IgG3 and a concentration of total IgG in the sample. In one embodiment, the model uses at least any 4 features listed in Table 4 to output at least a concentration of IgG and a concentration of IgA in the sample. In one embodiment, the model uses at least any 4 features listed in Table 4 to output at least a concentration of IgG3, a concentration of total IgG, and a concentration of IgA in the sample.

[0003] In one embodiment, the model uses at least any 5 features listed in Table 5 to output at least a concentration of IgG3 and a concentration of total IgG in the sample. In one embodiment, the model uses at least any 5 features listed in Table 5 to output at least a concentration of IgG and a concentration of IgA in the sample. In one embodiment, the model uses at least any 5 features listed in Table 5 to output at least a concentration of IgG3, a concentration of total IgG, and a concentration of IgA in the sample.

[0004] In one embodiment, the model uses at least any 6 features listed in Table 6 to output at least a concentration of IgG3 and a concentration of total IgG in the sample. In one embodiment, the model uses at least any 6 features listed in Table 6 to output at least a concentration of IgG and a concentration of IgA in the sample. In one embodiment, the model uses at least any 6 features listed in Table 6 to output at least a concentration of IgG3, a concentration of total IgG, and a concentration of IgA in the sample.

[0005] In one embodiment, the model uses at least any 7 features listed in Table 7 to output at least a concentration of IgG3 and a concentration of total IgG in the sample. In one embodiment, the model uses at least any 7 features listed in Table 7 to output at least a concentration of IgG and a concentration of IgA in the sample. In one embodiment, the model uses at least any 7 features listed in Table 7 to output at least a concentration of IgG3, a concentration of total IgG, and a concentration of IgA in the sample.

[0387] In one embodiment, the model uses at least any 8 features listed in Table 8 to output at least a concentration of IgG3 and a concentration of total IgG in the sample. In one embodiment, the model uses at least any 8 features listed in Table 8 to output at least a concentration of IgG and a concentration of IgA in the sample. In one embodiment, the model uses at least any 8features listed in Table 8 to output at least a concentration of IgG3, a concentration of total IgG, and a concentration of IgA in the sampled

[0388] In some embodiments, the first and second signal, in the form of the plurality of first time-resolved measurements { 120-1, 120-Q} and the plurality of second time-resolved measurements { 138-1, 138-Q} is inputted into the model 148 comprising the plurality of parameters { 150-1, .. ., 150-P} thereby obtaining the calculated concentration of the first polymer type 152 in the sample through interaction of the first plurality of parameters with the first substrate-based sensor baseline corrected event signal and the second substrate-based sensor baseline corrected event signal.

[0389] Referring to block 392, in some embodiments, the model is a random forest model, a decision tree, a boosted tree algorithm, an ElasticNet model, or a light gradient boosting machine (LightGBM) model.

[0390] Decision trees suitable for use as machine learning models in the present disclosure for determining the concentration of the first polypeptide type are described generally by Duda, 2001, Pattern Classification, John Wiley & Sons, Inc., New York, pp. 395-396, which is hereby incorporated by reference. Tree-based methods partition the feature space into a set of rectangles, and then fit a model (like a constant) in each one. In some embodiments, the decision tree is random forest regression. One specific algorithm that can be used is a classification and regression tree (CART). Other specific decision tree algorithms include, but are not limited to, ID3, C4.5, MART, and Random Forests. CART, ID3, and C4.5 are described in Duda, 2001, Pattern Classification, John Wiley & Sons, Inc., New York, pp. 396-408 and pp. 411-412, which is hereby incorporated by reference. CART, MART, and C4.5 are described in Hastie et al., 2001, The Elements of Statistical Learning, Springer-Verlag, New York, Chapter 9, which is hereby incorporated by reference in its entirety. Random Forests are described in Breiman, 1999, “Random Forests— Random Features,” Technical Report 567, Statistics Department, U.C. Berkeley, September 1999, which is hereby incorporated by reference in its entirety. Another form of decision tree is the gradient boosting decision tree (GBDT). Nonlimiting examples of GBDT that can serve as a machine learning models in the present disclosure for determining the concentration of the first polypeptide type include, but are not limited to, XGBoost, pGBRT, andLightGBM. See Ke et al., 2017, “LightGBM: A Highly Efficient Gradient Boosting Decision Tree,” 31st Conference on Neural Information Processing Systems (NIPS 2017), Long Beach, CA, USA. In some embodiments, the decision tree model includes at least 10, at least 20, at least 50, at least 100, at least 1000, at least 100,000 or at least 1 x 106parameters (e.g., weights and / or decisions) and requires a computer to calculate because it cannot be mentally solved.

[0391] ElasticNet models suitable for use as a machine learning models in the present disclosure for determining the concentration of the first polypeptide type are described in further detail in block 394, in which it is explained that ElasticNet is a form of regularization of regression.

[0392] Referring to block 394, in some embodiments, the machine learning model for determining the concentration of the first polypeptide type is a regression model. A regression model can be any type of regression. For example, in some embodiments, the regression model is a logistic regression model. In some embodiments, the regression model is logistic regression with LASSO, L2 or ElasticNet regularization. In some embodiments, the regression model is a regression with Ridge, LASSO, L2 or ElasticNet regularization. See, for example, Balcan et al., “Provably tuning the ElasticNet across instances,” arXiv:2207.10199v2 [cs.Lg] 15 Jan 2024, which is hereby incorporated by reference. In some embodiments, those extracted features that have a corresponding regression coefficient that fails to satisfy a threshold value are pruned (removed from) consideration. In some embodiments, a generalization of the logistic regression model that handles multi category responses is used as the machine learning model for determining the concentration of the first polypeptide type. Logistic regression algorithms are disclosed in Agresti, An Introduction to Categorical Data Analysis, 1996, Chapter 5, pp. 103- 144, John Wiley & Son, New York, which is hereby incorporated by reference. In some embodiments, the machine learning model makes use of a regression model disclosed in Hastie et al., 2001, The Elements of Statistical Learning, Springer-Verlag, New York. In some embodiments, the regression model includes at least 10, at least 20, at least 50, at least 100, or at least 1000 parameters (e.g., weights) and requires a computer to calculate because it cannot be mentally solved.

[0393] Referring to block 396, in some embodiments, the model is a neural network model, a support vector machine model, a Naive Bayes model, a nearest neighbor model, a boosted treesmodel, a random forest model, a decision tree model, a multinomial logistic regression model, a linear model, or a linear regression model.

[0394] Neural network models suitable for use as a machine learning models in the present disclosure for determining the concentration of the first polypeptide type, also known as artificial neural networks (ANNs), include convolutional and / or residual neural network algorithms (deep learning algorithms). Neural networks models can be trained to map an input data set to an output data set, where the neural network comprises an interconnected group of nodes organized into multiple layers of nodes. For example, in some embodiments the neural network model comprises at least an input layer, one or more hidden layers, and an output layer. In some embodiments the neural network model comprises any total number of layers, and any number of hidden layers, where the hidden layers function as trainable feature extractors that allow mapping of a set of input data to an output value or set of output values. In some embodiments the neural network model is a deep learning neural network model (DNN). A DNN is a neural network comprising a plurality of hidden layers, e.g., two or more hidden layers. Each layer of the neural network comprises a number of neurons (interchangeably, “nodes”). A node receives input that comes either directly from the input data or the output of nodes in previous layers, and performs specific operation, e.g., a summation operation. In some embodiments, a connection from an input to a node is associated with a parameter (e.g., a weight and / or weighting factor). In some embodiments, the node sums up the products of all pairs of inputs, xi, and their associated parameters. In some embodiments, the weighted sum is offset with a bias, b. In some embodiments, the output of a node or neuron is gated using a threshold or activation function, f, that is a linear or non-linear function. In some embodiments the activation function is, for example, a rectified linear unit (ReLU) activation function, a Leaky ReLU activation function, or other function such as a saturating hyperbolic tangent, identity, binary step, logistic, arcTan, softsign, parametric rectified linear unit, exponential linear unit, softPlus, bent identity, softExponential, Sinusoid, Sine, Gaussian, or sigmoid function, or any combination thereof.

[0395] The weighting factors, bias values, and threshold values, or other computational parameters of the neural network, may be “taught” or “learned” in a training phase using one or more sets of training data. For example, the parameters may be trained using the input data froma training data set and a gradient descent or backward propagation method so that the output value(s) that the ANN computes are consistent with the examples included in the training data set. The parameters may be obtained from a back propagation neural network training process.

[0396] Any of a variety of neural network models are suitable for use in determining the concentration of the first polypeptide type. Examples include, but are not limited to, feedforward neural networks, radial basis function networks, recurrent neural networks, residual neural networks, convolutional neural networks, residual convolutional neural networks, and the like, or any combination thereof. In some embodiments, the machine learning makes use of a pretrained and / or transfer-learned ANN or deep learning architecture. Convolutional and / or residual neural networks can be used for the concentration of the first polypeptide type in accordance with the present disclosure.

[0397] In some embodiments a deep neural network model comprises an input layer, a plurality of individually parameterized (e.g., weighted) convolutional layers, and an output scorer. The parameters (e.g., weights) of each of the convolutional layers as well as the input layer contribute to the plurality of parameters (e.g., weights) associated with the deep neural network model. In some embodiments, at least 100 parameters, at least 1000 parameters, at least 2000 parameters, at least 5000 parameters, at least 10,000 parameters, at least 100,000 parameters, or at least 1 x 106parameters are associated with the deep neural network model. As such, deep neural network models require a computer to be used because they cannot be mentally solved. In other words, given an input to the model, the model output needs to be determined using a computer rather than mentally in such embodiments. See, for example, Krizhevsky el al., 2012, “Imagenet classification with deep convolutional neural networks,” in Advances in Neural Information Processing Systems 2, Pereira, Burges, Bottou, Weinberger, eds., pp. 1097-1105, Curran Associates, Inc.; Zeiler, 2012 “ADADELTA: an adaptive learning rate method,”' CoRR, vol. abs / 1212.5701; and Rumelhart et al., 1988, “Neurocomputing: Foundations of research,” ch.Learning Representations by Back-propagating Errors, pp. 696-699, Cambridge, MA, USA: MIT Press, each of which is hereby incorporated by reference.

[0398] Neural network models, including convolutional neural network models, suitable for use as a model for determining the concentration of the first polypeptide type are disclosed in, forexample, Vincent et al., 2010, “Stacked denoising autoencoders: Learning useful representations in a deep network with a local denoising criterion,” J Mach Learn Res 11, pp. 3371-3408; Larochelle et al., 2009, “Exploring strategies for training deep neural networks,” J Mach Learn Res 10, pp. 1-40; and Hassoun, 1995, Fundamentals of Artificial Neural ' Networks, Massachusetts Institute of Technology, each of which is hereby incorporated by reference.Additional example neural networks suitable for use as models for determining the concentration of the first polypeptide type are disclosed vciDuda et al., 2001, Pattern Classification, Second Edition, John Wiley & Sons, Inc., New York; and Hastie et al., 2001 , The Elements of Statistical Learning, Springer-Verlag, New York, each of which is hereby incorporated by reference in its entirety. Additional example neural networks suitable for use as models for determining the concentration of the first polypeptide type are also described in Draghici, 2003, Data Analysis Tools for DNA Microarrays, Chapman & Hall / CRC; and Mount, 2001, Bioinformatics: sequence and genome analysis, Cold Spring Harbor Laboratory Press, Cold Spring Harbor, New York, each of which is hereby incorporated by reference in its entirety.

[0399] Support vector machine models suitable for use as a machine learning models in the present disclosure for determining the concentration of the first polypeptide type are generally described in, for example, Cristianini and Shawe-Taylor, 2000, “An Introduction to Support Vector Machines,” Cambridge University Press, Cambridge; Boser et al., 1992, “A training algorithm for optimal margin classifiers,” in Proceedings of the 5th Annual ACM Workshop on Computational Learning Theory, ACM Press, Pittsburgh, Pa., pp. 142-152; Vapnik, 1998, Statistical Learning Theory, Wiley, New York; Mount, 2001, Bioinformatics: sequence and genome analysis, Cold Spring Harbor Laboratory Press, Cold Spring Harbor, N.Y.; Duda, Pattern Classification, Second Edition, 2001, John Wiley & Sons, Inc., pp. 259, 262-265; and Hastie, 2001, The Elements of Statistical Learning, Springer, New York; and Furey et al., 2000, Bioinformatics 16, 906-914, each of which is hereby incorporated by reference in its entirety. SVMs separate a given set of binary labeled data with a hyper-plane that is maximally distant from the labeled data. For cases in which no linear separation is possible, SVMs can work in combination with the technique of 'kernels', which automatically realizes a non-linear mapping to a feature space. The hyper-plane found by the SVM in feature space can correspond to a nonlinear decision boundary in the input space. In some embodiments, the plurality of parameters(e.g., weights) associated with the SVM define the hyper-plane. In some embodiments, the hyper-plane is defined by at least 100 parameters, at least 1000 parameters, at least 2000 parameters, at least 5000 parameters, at least 10,000 parameters, at least 100,000 parameters, or at least 1 x 106parameters and the SVM classifier requires a computer to calculate because it cannot be mentally solved.

[0400] Naive Bayes models suitable for use as a machine learning models in the present disclosure for determining the concentration of the first polypeptide type are generally disclosed, for example, in Ng et al., 2002, “On discriminative vs. generative classifiers: A comparison of logistic regression and naive Bayes,” Advances in Neural Information Processing Systems, 14, which is hereby incorporated by reference. A Naive Bayes model is any model in a family of “probabilistic models” based on applying Bayes' theorem with strong (naive) independence assumptions between the features. In some embodiments, they are coupled with Kernel density estimation. See, for example, Hastie et al., 2001, The elements of statistical learning : data mining, inference, and prediction, eds. Tibshirani and Friedman, Springer, New York, which is hereby incorporated by reference.

[0401] Nearest neighbor models suitable for use as a machine learning models in the present disclosure for determining the concentration of the first polypeptide type are described in Duda, Pattern Classification, Second Edition, 2001, John Wiley & Sons, Inc; and Hastie, 2001, The Elements of Statistical Learning, Springer, New York, each of which is hereby incorporated by reference. In some embodiments a nearest neighbor model suitable for use as a machine learning models in the present disclosure for determining the concentration of the first polypeptide type is a k-nearest neighbor model. A k-nearest neighbor model is a non-parametric machine learning model in which the input consists of the k closest training examples in feature space. The output is a class membership. An object is classified by a plurality vote of its neighbors, with the object being assigned to the class most common among its k nearest neighbors (k is a positive integer, typically small). If k = 1, then the object is simply assigned to the class of that single nearest neighbor. See, Duda et al., 2001, Pattern Classification, Second Edition, John Wiley & Sons, which is hereby incorporated by reference. In some embodiments, the number of distancecalculations needed to solve the ^-nearest neighbor classifier is such that a computer is used to solve the classifier for a given input because it cannot be mentally performed.

[0402] Boosted trees models and random forest models suitable for use as a machine learning models in the present disclosure for determining the concentration of the first polypeptide type are types of decision trees. Decision trees are discussed above in more detail in conjunction with block 392.

[0403] Multinomial logistic regression models suitable for use as a machine learning models in the present disclosure for determining the concentration of the first polypeptide type are a form of regression model. Regression models are discussed above in more detail in conjunction with block 394.

[0404] Linear models suitable for use as a machine learning models in the present disclosure for determining the concentration of the first polypeptide type. Linear models suitable for use as a machine learning models in the present disclosure for determining the concentration of the first polypeptide type are described in Dobson and Barnett, “An Introduction to Generalized Linear Models,” Chapman & Hall / CRC Texts in Statistical Science) 4th Edition.

[0405] Linear regression models suitable for use as a machine learning models in the present disclosure for determining the concentration of the first polypeptide type are a form of regression model. Regression models are discussed above in more detail in conjunction with block 394.

[0406] Referring to block 398, in some embodiments, the analysis of the first and second signal comprises a polynomial fitting of the first and second signal. Polynomial fitting is a type of regression analysis where the relationship between the independent variables (e.g., the first and second signal) and the dependent variable (the concentration of the first polypeptide type) is modeled as an n-degree polynomial. Unlike linear regression, which fits a straight line to the data, polynomial fitting can capture more complex, non-linear relationships by fitting a curve. See, for example, Fan and Gijbels, Local Polynomial Modelling and Its Applications, Chapman & Hall / CRC Monographs on Statistics and Applied Probability) 1st Edition.

[0407] Referring to block 400, in some embodiments, the analysis of the first and second signal comprises a finite or infinite impulse response evaluation of the first and second signal. Generalexamples of such analysis are disclosed in Finite or Infinite Dimensional Complex Analysis: proceedings of the Seventh International Colloquium, Lecture Notes in Pure and Applied Mathematics, 1st Edition, Kajiwara et al. eds., CRC Press.

[0408] Referring to block 402, in some embodiments, the analysis of the first and second signal comprises a Z-Transform analysis of the first and second signal. In such embodiments, the Z- transform analysis converts the plurality of first time-resolved measurements and the plurality of second time-resolved measurements into complex frequency domain representations. See “The z-Transform,” Chapager Sundararajan, 2023, “The z-Transform,” Signals and Systems, pp. 287- 329, Springer, Cham.

[0409] Referring to block 404, in some embodiments, the analysis of the first and second signal comprises mechanistic modeling of the first and second signal. Referring to block 406, in some such embodiments, the mechanistic modeling is a Scatchard model, a higher order Scatchard model, a steric mass action chromatography model, a colloidal particle adsorption chromatography model, manifold learning, or application of a convolutional neural network. Examples of mechanistic modeling are described in Shekhawat and Rathore, 2019, “An overview of mechanistic modeling of liquid chromatography,” Preparative Biochemistry & Biotechnology 49(6), 623-638; and Close, EJ; (2015) “The derivation of bioprocess understanding from mechanistic models of chromatography.” Doctoral thesis , UCL (University College London). Convolutional neural networks are a form of neural network. Neural networks are described in more detail above, in conjunction with block 396.

[0410] In some embodiments, data in addition to the first and second signal are inputted into the model in order to obtain a calculated concentration of the first polypeptide type in the sample. In some embodiments pH, temperature, and / or flow rate is also inputted into the model. In some embodiments any of the process parameters disclosed herein or that are disclosed in Rossi ’s Principles of Transfusion Medicine, eds. Simon et al. 2016, John Wiley & Sons Inc., Hoboken, NJ, are inputted into the model in addition to the first and second signal.

[0411] In some embodiments the first signal comprises an LSPR response signal indicative of protein concentration as well as a measured extinction signal from the first substrate-based sensor. In some such embodiments any of the process parameters disclosed herein or that aredisclosed in Rossi ’s Principles of Transfusion Medicine, eds. Simon e / al. 2016, John Wiley & Sons Inc., Hoboken, NJ, are inputted into the model in addition to the first signal.

[0412] In some embodiments the second signal comprises an LSPR response signal indicative of protein concentration as well as a measured extinction signal from the second substrate-based sensor. In some such embodiments any of the process parameters disclosed herein or that are disclosed in Rossi ’s Principles of Transfusion Medicine, eds. Simon et al. 2016, John Wiley & Sons Inc., Hoboken, NJ, are inputted into the model in addition to the second signal.

[0413] Referring to block 408 of Fig. 2P, in some embodiments, the first step is associated with a first process parameter and a corresponding first validated range, and the method further comprises adjusting the first process parameter from a first value to a second value in the corresponding first validated range responsive to the calculated concentration of the first polymer type in the sample. For example, in some embodiments, when the calculated concentration of the first polypeptide type is outside of a predetermined range, the first process parameter is adjusted from a first value to a second value. Referring to block 410, in some such embodiments, the first process parameter is a pH, a salt concentration, a loading velocity, a protein density, a temperature, or a flow rate of the first step.

[0414] In some embodiments, in accordance with block 408 the first step is a clearance step using a media, e.g., a filtration step or clarifying incubation with a media such as fumed silica or diatomaceous earth, and the first process parameter is an amount of media per input value, e.g., the amount of media used per unit of total protein exposed to the media, and the amount of media per input value is adjusted when the calculated concentration of the first polypeptide type is outside of the predetermined range.

[0415] For instance, in some embodiments, in a plasma-derived IgG manufacturing process where the first polypeptide type is IgG3 and the clearance step is being performed with a media that binds IgG3 more tightly than other IgG subclasses, in response to observing a concentration of IgG3 in the clarified product below a predetermined range, the amount of media per input value is decreased, resulting in less loss of IgG3 in the clarifying step to bring the amount of IgG3 in the process back into the predetermined range. Similarly, in some embodiments, in response to observing a concentration of IgG3 in the clarified product above a predeterminedrange, the amount of media per input value is increased, resulting in greater loss of IgG3 in the clarified product to bring the amount of IgG3 in the process back into the predetermined range.

[0416] In some embodiments, in accordance with block 408 the first step is a clearance step using a media, e.g., a filtration step or clarifying incubation with a media such as fumed silica or diatomaceous earth, and the first process parameter is a pH at which the clarifying step is performed and the pH of the process is adjusted when the calculated concentration of the first polypeptide type is outside of the predetermined range.

[0417] For instance, in some embodiments, in a plasma-derived IgG manufacturing process where the first polypeptide type is IgG3 and the clearance step is incubation with fumed silica at a first pH at which IgG3 binds the fumed silica more tightly than other IgG subclasses, in response to observing a concentration of IgG3 in the clarified product below a predetermined range, the pH of the step is adjusted to reduce IgG3 binding to the fumed silica to bring the amount of IgG3 in the process back into the predetermined range. Similarly, in some embodiments, in response to observing a concentration of IgG3 in the clarified product above a predetermined range, the pH of the step is adjusted to increase IgG3 binding to the fumed silica to bring the amount of IgG3 in the process back into the predetermined range.

[0418] In some embodiments, in accordance with block 408 the first step is a clearance step using a media, e.g., a filtration step or clarifying incubation with a media such as fumed silica or diatomaceous earth, and the first process parameter is a post wash condition for the clarifying step and the post wash condition of the process is adjusted when the calculated concentration of the first polypeptide type is outside of the predetermined range. In some embodiments, the post wash condition is a volume of the post wash. In some embodiments, the post wash condition is a pH of the post wash buffer. In some embodiments, the post wash condition is a conductivity of the post wash buffer.

[0419] For instance, in some embodiments, in a plasma-derived IgG manufacturing process where the first polypeptide type is IgG3 and the clearance step is a depth filtration step in which some content of IgG3 is present in the filter cake, in response to observing a concentration of IgG3 in the clarified product below a predetermined range, the post wash conditions are adjusted to extract more IgG3 from the filter cake to bring the amount of IgG3 in the process back into thepredetermined range. Similarly, in some embodiments, in response to observing a concentration of IgG3 in the clarified product above a predetermined range, the post wash conditions are adjusted to extract less IgG3 from the filter cake to bring the amount of IgG3 in the process back into the predetermined range.

[0420] In some embodiments, in accordance with block 408 the first step is a chromatographic step and the first process parameter is an amount of chromatographic media per input value, e.g., the amount of media used per unit of total protein applied to the column, and the amount of chromatographic media per input value is adjusted when the calculated concentration of the first polypeptide type is outside of the predetermined range. This can also be expressed as the loading density, the amount of protein loaded per unit resin.

[0421] For instance, in some embodiments, in a plasma-derived IgG manufacturing process where the first polypeptide type is IgG3 and the chromatography step is being performed in flow-through mode with a resin that binds IgG3 more tightly than other IgG subclasses, in response to observing a concentration of IgG3 in the flow-through below a predetermined range, the amount of chromatographic media per input value is decreased, resulting in less loss of IgG3 in the flow-through of the chromatographic step to bring the amount of IgG3 in the process back into the predetermined range. Similarly, in some embodiments, in response to observing a concentration of IgG3 in the flow-through above a predetermined range, the amount of chromatographic media per input value is increased, resulting in greater loss of IgG3 in the flow- through of the chromatographic step to bring the amount of IgG3 in the process back into the predetermined range.

[0422] In some embodiments, in a plasma-derived IgG manufacturing process where the first polypeptide type is IgG3 and the chromatography step is being performed in bind-elute mode with a resin that binds IgG3 less tightly than other IgG subclasses, in response to observing a concentration of IgG3 in the eluate below a predetermined range following the chromatographic step, the amount of chromatographic media per input value is increased, resulting in less loss of IgG3 in the elute of the chromatographic step to bring the amount of IgG3 in the process back into the predetermined range. Similarly, in some embodiments, in response to observing a concentration of IgG3 in the eluate above a predetermined range, the amount of chromatographicmedia per input value is decreased, resulting in greater loss of IgG3 in the eluate of the chromatographic step to bring the amount of IgG3 in the process back into the predetermined range.

[0423] In some embodiments, in accordance with block 408 the first step is a chromatographic step and the first process parameter is a flow rate at which the chromatography is performed, and the flow rate of the chromatographic step is adjusted when the calculated concentration of the first polypeptide type is outside of the predetermined range.

[0424] For instance, in some embodiments, in a plasma-derived IgG manufacturing process where the first polypeptide type is IgG3 and the chromatography step is being performed in flow-through mode with a resin that binds IgG3 more tightly than other IgG subclasses, in response to observing a concentration of IgG3 in the flow-through below a predetermined range, the flow rate of the process is increased, reducing the contact time of the solution with the resin resulting in less loss of IgG3 in the flow-through of the chromatographic step to bring the amount of IgG3 in the process back into the predetermined range. Similarly, in some embodiments, in response to observing a concentration of IgG3 in the flow-through above a predetermined range, the the flow rate of the process is decreased, increasing the contact time of the solution with the resin resulting in greater loss of IgG3 in the flow-through of the chromatographic step to bring the amount of IgG3 in the process back into the predetermined range.

[0425] In some embodiments, in a plasma-derived IgG manufacturing process where the first polypeptide type is IgG3 and the chromatography step is being performed in bind-elute mode with a resin that binds lgG3 less tightly than other IgG subclasses, in response to observing a concentration of IgG3 in the eluate below a predetermined range following the chromatographic step, the flow rate of the process is decreased, increasing the contact time of the solution with the resin resulting in less loss of IgG3 in the elute of the chromatographic step to bring the amount of IgG3 in the process back into the predetermined range. Similarly, in some embodiments, in response to observing a concentration of IgG3 in the eluate above a predetermined range, the flow rate of the process is increased, reducing the contact time of the solution with the resinresulting in greater loss of IgG3 in the eluate of the chromatographic step to bring the amount of IgG3 in the process back into the predetermined range.

[0426] Referring to block 412, in some embodiments, a second step precedes the first step in the multi-step production process. In some embodiments in accordance with block 412, the second step is associated with a second process parameter and a corresponding second validated range, and the method further comprises adjusting the second process parameter from a first value to a second value in the corresponding second validated range responsive to the calculated concentration of the first polymer type in the sample.

[0427] In some embodiments, in accordance with block 412 the first step (which occurs after the second step in accordance with the stated nomenclature) is a chromatographic step and the second step is a precipitation. In some such embodiments the second process parameter is a percentage, by volume or weight, of ethanol used in the precipitation, a pH used in the precipitation, a temperature used in the precipitation, or an amount of time the precipitation is conducted.

[0428] In some embodiments, in accordance with block 412 the first step (which occurs after the second step in accordance with the stated nomenclature) is filtration and the second step is a chromatographic step. In some such embodiments the second process parameter is a pH, a salt concentration, a column loading velocity, a protein density, a temperature, or a flow rate of the second step.

[0429] Referring to block 414, in some embodiments in accordance with block 412, the second process parameter is a pH, a salt concentration, a loading velocity, a protein density, a temperature, or a flow rate of the second step.

[0430] Referring to block 416, in some embodiments, a second step is after the first step in the multi-step production process, the second step is associated with a second process parameter and a corresponding second validated range, and the method further comprises adjusting the second process parameter from a first value to a second value in the corresponding second validated range responsive to the calculated concentration of the first polymer type in the sample.

[0431] In some embodiments, in accordance with block 416 the second step (which occurs after the first step in accordance with the stated nomenclature of block 416) is a chromatographic step and the first step is a precipitation. In some such embodiments the second process parameter is a pH, a salt concentration, a loading velocity, a protein density, a temperature, or a flow rate of the second step.

[0432] In some embodiments, in accordance with block 416 the second step (which occurs after the first step in accordance with the stated nomenclature of block 416) is filtration and the first step is a chromatographic step. In some such embodiments the second process parameter is a flow rate of the second step and / or a filter size used in the second step.

[0433] Referring to block 418 of Fig. 2R, in some embodiments, the analysis determines that the concentration of the first polymer type is in a validated range associated with the first step and the method further comprises releasing the biologic for use in treating a condition of a species.

[0434] Referring to block 420, in some embodiments, the biologic is a human immunoglobulin biologic and the condition is a human condition (e.g., the human condition is primary immunodeficiency, multifocal motor neuropathy, humoral immunodeficiency secondary to myeloma, chronic lymphocytic leukemia, chronic inflammatory demyelinating polyneuropathy, a bacterial infection, peritonitis, or sepsis).

[0435] In some embodiments the biologic is a human immunoglobulin delivered intravenously and is used as a replacement therapy in primary and secondary immunodeficiency, immunomodulation in autoimmune diseases including immune thrombocytopenia purpura, Kawasaki disease, Guillain-Barre syndrome, autoimmune polyneuropathy, and myasthenia gravis Treatment of hepatitis A, hepatitis B, cytomegalovirus, varicella-zoster, or tetanus infections.

[0436] In some embodiments the biologic is a human anti RH(D) immunoglobulin and is used in the prevention of Rh(D) isoimmunization due to fetus-maternal rhesus D incompatibility, or the treatment of immune thrombocytopenia purpura.

[0437] In some embodiments the biologic is factor VIII and is used as a replacement in hemophilia A-factor VIII deficiency, or the induction of immune tolerance for anti-factor VIII antibodies. In some such embodiments the first polypeptide type is factor VIII.

[0438] In some embodiments the biologic is Von Willebrand factor (vWF) and is used to treat vWF deficiency. In some such embodiments the first polypeptide type is vWF.

[0439] In some embodiments the biologic is activated prothrombin complex concentrate (aPCC) composed predominantly of prothrombin and factors VIII, VII, Vila, IX, X, Xa, and protein C and is used to treat patients with inhibitory antibodies to factor VIII or factor IX. In some such embodiments the first polypeptide type is prothrombin, factor VIII, factor VII, factor Vila, factor IX, factor X, factor Xa, or protein C.

[0440] In some embodiments the biologic is prothrombin complex concentrate (PCC) composed predominantly of factors II, VII, IX, and X, as well as regulatory proteins C, S and Z and is used to treat patients with rare conditions of factor VII and factor X deficiencies, reversal of the anticoagulant activity of warfarin and prevention of massive bleeding. In some such embodiments the first polypeptide type is factor II, factor VII, factor IX, factor X, regulatory protein C, regulatory protein S, or regulatory protein Z.

[0441] In some embodiments the biologic is fibrinogen and is used as a replacement in congenital fibrinogen deficiency and in acquired deficiency following massive bleeding. In some such embodiments the first polypeptide type is fibrinogen.

[0442] In some embodiments the biologic is factor XI and is used in the treatment of factor XI deficiency. In some such embodiments the first polypeptide type is factor XI.

[0443] In some embodiments the biologic is factor XIII and is used in the treatment of factor XIII deficiency. In some such embodiments the first polypeptide type is factor XIII.

[0444] In some embodiments the biologic is factor XIII and is used in the treatment of factor X deficiency. In some such embodiments the first polypeptide type is factor X.

[0445] In some embodiments the biologic is fibrin glue (fibrinogen and thrombin components) and is used in surgery as a sealant to achieve hemostatis, as a surgical glue or to promote wound healing. In some such embodiments the first polypeptide type is fibrinogen or thrombin.

[0446] In some embodiments the biologic is alphai-protease inhibitor and is used in the treatment of alphai-protease inhibitor deficiency. In some such embodiments the first polypeptide type is alphai-protease inhibitor.

[0447] In some embodiments the biologic is antithrombin III and is used in the treatment of acquired and hereditary deficiency to antithrombin III. In some such embodiments the first polypeptide type is antithrombin III.

[0448] In some embodiments the biologic is Cl-esterase inhibitor and is used to treat hereditary angioedema. In some such embodiments the first polypeptide type is Cl-esterase inhibitor.

[0449] Referring to block 422, in some embodiments, the first step comprises a fractionation of a plurality of plasma units from a plurality of donors, the sample is from a first fraction arising from the fractionation, and the method further comprises further purifying the biologic from the first fraction after the analysis determines that the concentration of the first polymer type is in a predetermined range. In some such embodiments the first fraction is Cohn cryoprecipitate, fraction I, fraction II + III, fraction II, fraction IV- 1, fraction IV-4, or fraction V.

[0450] Referring to block 424 of Fig. 2S, in some embodiments, the biologic is a human immunoglobulin biologic, the analysis determines that the concentration of the first polymer type is in a predetermined range and the method further comprises releasing the human immunoglobulin biologic for use in treating a human condition. When the analysis determines that the concentration of the first polymer type is outside the predetermined range the method further comprises adjusting a first process parameter, within a validated range, of the first step. In some such embodiments the first step is a precipitation step, a delipidation step, anion exchange chromatography, pasteurization, pH incubation, caprylic acid precipitation, affinity chromatography, viral filtration (e.g, 20 to 35 nm), polyethylene glycol precipitation, ethanol precipitation, pH treatment, cation exchange chromatography, and / or solvent / detergent treatment.

[0451] In the case where the biologic is IgG comprising IgG3 and IgG4 and the first polymer type is IgG3, adjustment of a process parameter can affect the relative concentration of IgG3 to other IgGs in the biologic such as IgG4. For instance, such adjustment, in accordance with block 424 may be needed when a determination is made that the concentration of IgG3 is outside a specified predetermined range. In some embodiments, the concentration of IgG3 is outside a specified predetermined range when the IgG3, as a percent of the total protein amount, is outside an allowed predetermined range. In some embodiments, the concentration of IgG3 is outside a specified predetermined range when a IgG3 to some other IgG isotope, such as IgG4 is outside an allowed ratio range. In such instance, a process parameter of a step in the multistep production process can be altered responsive to determining that IgG3 is out of range so that the IgG3 concentration in the final biologic is in range. There are numerous process parameters that can be modified so that the so that the IgG3 concentration in the final biologic is in range. Many of these process parameters make use of the differing physical properties of IgG3 relative to other isotypes, such as IgG4. For example, in the case where the first step is column chromatography, the behavior of IgG3 and IgG4 can differ due to their structural and physicochemical properties and thus adjustment of process parameters associated with chromatography can affect the relative concentration of IgG3 to IgG4 in the biologic. As another example, in the case of affinity in the chromatography stationary phase, IgG3 generally has a higher affinity for this phase of the chromatography compared to IgG4. This means that IgG3 may bind more strongly to the column matrix, resulting in slower elution or retention on the column. As another example, in the case of elution conditions, IgG3 often requires more stringent elution conditions, such as higher salt concentrations or changes in pH, to be effectively eluted from the column. By contrast, IgG4 may elute more readily under milder elution conditions. As still another example, in the case of binding capacity, IgG3 typically has a higher binding capacity for certain ligands or affinity resins used in column chromatography compared to IgG4. This can be advantageous when purifying IgG3 specifically or when using IgG3 as a capture antibody. As still another examples, IgG3 is generally more prone to aggregation and fragmentation compared to IgG4. This can impact the stability and integrity of IgG3 during column chromatography, potentially leading to reduced yield or compromised quality. The behavior of IgG3 and IgG4 during column chromatography can also be influenced by otherfactors such as the specific chromatography method, column matrix, ligands, and experimental conditions. See, for example, Zarrineh et al., 2020, “Mechanism of antibodies purification by protein A,” Analytical Biochemistry 609; Introduction to Biology, Chapter 1.14: Column Chromatography, Orange County Biotechnology Education Collaborative, ASCCC Open Educational Resources Initiative; and Ripens and Huijbers, 2023, “The unique properties of IgG4 and its roles in health and disease,” Nature Reviews Immunology 23, pp. 753-778. Moreover, the relative size differential between IgGl, IgG2, IgG4 (146 kDa) versus IgG3 (170 kDa) also provides basis for adjusting chromatographic process parameters (resin type, flow rate, salt concentrations, pH) in order to influence the relative concentration of IgG3 versus IgGl, IgG2, and IgG4 (146 kDa).

[0452] In some embodiments where the first polymer type is IgG3 and it is determined that the concentration of IgG3 is above optimal levels, the adjustment of the process parameter can comprise reducing the flow rate at loading to ANX Sepharose to increase contact time of IgG3 to the resin.

[0453] In some embodiments where the first polymer type is IgG3 and it is determined that the concentration of IgG3 is above optimal levels, the adjustment of the process parameter can comprise the loading pH and / or conductivity can be targeted to the lower validated limit (e.g., pH 6.3) to increase IgG3 binding.

[0454] In some embodiments where the first polymer type is IgG3 and it is determined that the concentration of IgG3 is below optimal levels, the adjustment of the process parameter can comprise increasing the flow rate at loading to ANX Sepharose to decrease contact time of IgG3 to the resin.

[0455] In some embodiments where the first polymer type is IgG3 and it is determined that the concentration of IgG3 is below optimal levels, the adjustment of the process parameter can comprise the loading pH and / or conductivity can be targeted to the upper validated limit (e.g., pH 6.5) to decrease IgG3 binding.

[0456] Referring to block 426, in some embodiments in accordance with block 424, the process parameter is a pH, a salt concentration, a loading velocity, a protein density, a temperature, or aflow rate associated with a purification process occurring upstream or downstream of the first step in the multi-step production process. In some embodiments the process parameter is pH and the adjusting comprises adjusting the pH by up to ±0.1 pH units, ±0.2 pH units, ±0.3 pH units, ±0.4 pH units, ±0.5 pH units, ±0.6 pH units, ±0.7 pH units, ±0.8 pH units, ±0.9 pH units, or ±1.0 pH units.

[0457] Referring to block 428, in some embodiments, the method further comprises performing a regeneration cycle on the first substrate-based sensor and the second substrate-based sensor.

[0458] Referring to block 430, in some embodiments, the regenerating cycle has a duration of between one minute and five minutes.

[0459] F. Examples

[0460] Example 1 - Real-time IgG3 measurement using nanoplasmonic sensing in upstream and downstream fractions in a human plasma fractionation process applying a multi-line design facilitating adaptive processing.

[0461] IgG3 is effective at engaging effector mechanisms but represents a relatively small percentage of circulating IgG in human serum. The dominant feature of IgG3 is the long hinge connecting the Fab domains to the Fc portion of the molecule. The hinge of IgG3 is 62 amino acids long, more than four times that of IgGl, and contains 11 disulfide bonds. IgG3 triggers effector functions including complement activation (by hexameric platforms), antibody (Ab)- mediated phagocytosis, or Ab-mediated cellular cytotoxicity (ADCC). Measurement of IgG3 in human plasma solutions is important since established limits must be met to avoid complement activation in a human when delivering an IgG product. Examples of such limits are found in Table 1.

[0462] Table 1 - Applicable limits for plasma fractions and the final product.

[0463] In conventional multi-step production processes, only limited offline data is available and information on the distribution of IgG3 (removal and concentration) during plasma fractionation is not available.

[0464] In this example, localized surface plasmon resonance (LSPR) was utilized for the estimation of IgG (all idiotypes) and IgG3 using sensors based on protein A and protein G.

[0465] Surface Plasmon Resonance (SPR) is the interaction of electromagnetic radiation (light) with the interface of a conductive material, usually a metal such as gold, and a permissive material such as water / aqueous solution (non-conductive but polarizable). The resonant vibration / oscillation of the electrons on the metal surface to a photon of a particular wavelength can absorb and / or hold its energy (plasmon). This photon absorption / retention can be detected by common means such as photodetectors.

[0466] In one form of this technology, the incident photons come at a critical angle (Total Internal Reflection angle) and the optical setting must be bound to the metal / dielectric surface. The application to biosensing comes from changing the dielectric interface properties, manifested also in the refractive index (RI), which displaces the preferred angle / wavelength of the plasmon. That is, the presence of molecules in the surface of the metal affects the signal response of the metal to the light, which can be correlated for concentration or refractive index of the solution.

[0467] Functionalization of the surface means to covalently attach a molecule (binder) that can bind the molecule of interest. The variation of the signal is weak, and the functionalized sensor has a very narrow margin (wavelength shift of picometers or 0.1° angle in the TIR approach). Other factors such as temperature and the influence of the background depth has also an impact, which requires SPR to have a tight temperature control of the sensor.

[0468] In the case of Localized SPR (LSPR), the metal is, instead of a flat metal surface, a nanometric volume (15 nm), normally a sphere as illustrated in Fig. 3. In this case, all theelectrons of the volume oscillate coordinately to restricted frequencies. This metal nanoparticles remain at a minimum distance between each other (>3 diameters) but at the same time densely packed. The greater advantage over conventional SPR is that they can have a higher surface density in depth than the planar conventional one. It is also not sensitive to temperature and the background noise of the solution. This implies, that no temperature control is required, and the sensor can have a very small footprint.

[0469] More conveniently, the signal shift (1 nm) is higher than for SPR, and the light absorption can be detected with reflection of a perpendicular beam instead the far more complicated Total Internal Reflection setup. Even better, the nanoparticle matrix does not have to be attached to the optical signal transductor, so that the optical path and the gold particle platform can be independent of each other. Therefore, once the cell with the optical path is fixed, the sensor chip is a simple holder with the functionalized matrix of gold nanoparticles on top, to be attached at the other side of the cell and can be frequently and easily replaced.

[0470] By using chips coated with gold nanoparticles treated with specific ligands that make them selective towards the target proteins, a signal proportional to the concentration of these proteins was generated using LSPR and transmitted to a detector caused by the light absorption spectral shift when the ligand was bound to the target protein. These chips have the advantage of no need for temperature control, very small footprint, sensor chip easily replaceable as a consumable (single use chips), polyvalent, as the replaceable chips can have different binders, simplicity and robustness, higher available surface, that is, higher sensitivity, and less background effects than SPR.

[0471] As illustrated in Fig. 3, protein G binds all human idiotypes of IgG, while Protein A does not bind IgG3 and partially binds IgA and with different kinetics. The difference between the measured concentrations using protein G and protein A, and of their binding kinetics, is utilized in this example so that an algorithm combining the two translated signals allows for the estimation of IgG3 concentration, as well as the concentration of all IgG combined.

[0472] To accomplish this, a pair of sensors (multi-line) of protein G and protein A was installed on process equipment illustrated in Fig. 4. In accordance with Fig. 4, the installation points were in a human plasma fractionation process, carried out with the Cohn process, its modifications, orothers. As illustrated in Fig. 3, one sensor in the pair of sensors comprises gold beads conjugated with Protein A while the other sensor in the pair of sensors comprised gold beads conjugated with Protein G. In each sensor the golds beads were adhered in a polymeric matrix to a flow cell. By means of this functionalization, the sensors bind passing IgG of a solution according to the affinity of the binder (protein A or protein B). The gold beads absorb light with a maximum at a given frequency due to their electronic resonance to a specific frequency, which shifts proportional to the presence of IgG bound to the bead (see Figure 5). This shift in the absorption maximum can be monitored in real-time, and not only concentration but also dynamic aspects of the biding can be estimated as well.

[0473] As illustrated in Fig. 3, the pair of sensors facilitate localized-surface plasmon resonance (LSPR) in which the illustrated gold particles absorb light of a particular frequency to which the electrons of the base resonate. As illustrated in Fig. 5A, binding of the protein of interest (first polypeptide type) to the bead changes the local refractive index to the wavelength denoted by line 502 in Fig. 4A, causing the plasmon response over time illustrated in Fig. 5B. That is, Fig. 5B is a measure of the wavelength denoted by line 502 of Fig. 5 A over time. It is seen in Fig. 1 when the beads are in the unbound state “1”, very little plasmon response is measured in Fig. 5B. As the bead bind to the protein of interest (first polypeptide type) in bound state “2”, the local refractive index of the beads shifts to that denoted by line 502 and so a plasmon response is measured in Fig. 5B.

[0474] As illustrated in Figs. 3 and 4, a multi-line system was created to facilitate testing of upstream and downstream (till inclusion final product) fractions of IgG purification process (see sketch below). When sensor chips are pH and component sensitive, a desalting column can be applied to assure a constant matrix. This additional device decreased data bias and increased accuracy of the measured samples. Thereby real-time IgG, IgG3 among others, evaluation of upstream and downstream IgG fractions was made feasible as using the multi-line system illustrated in Fig. 4.

[0475] The designed multi-line equipment is not limited to LSPR and can be applied for further analytical methods e.g. enzyme-linked immunosorbent assay (ELISA), capillary zone electrophoresis among others. The design of Fig. 4 consists of a modular mechatronic systemthat combines actuators (pumps and valves), sensors, and controllers to preprocess samples for condition-sensitive analytical sensors.

[0476] As illustrated in Fig. 4, the system has valves, pumps, and sensors, and optional desalting columns. These parts were connected with rigid or flexible tubing made of material suitable for the liquid passed through device. The liquid flow path in Fig. 4 was controlled with an independent computer, digital and analog IO, and power drivers, controlled with software. The system was designed to offer multiple defmed-volume injections to increase the detection range of the sensors (High Dynamic Range, HDR) by using two injection valves in parallel. The system provided an in-situ buffer exchange option using a desalting column to control buffer and other parameters considered relevant for the process.

[0477] The system is configured to optionally connect additional pre-processing steps including an inline filter, an inline mixing chamber, and temperature incubation with Peltier elements, or others able to achieve the same purpose.

[0478] The system illustrated in Fig. 4 can be operated in inline, online, and atline / offline mode and is therefore considered as multi-line equipment.

[0479] The system required only one calibration solution and self-calibrates with up to (n2) calibration points (including blank), for n-inj ection valve setup.

[0480] The designed multi-line equipment illustrated in Fig. 4 facilitates inline IgG3 measurement (among other attributes). Inline IgG3 was detectable and quantifiable. It can be applied for process knowledge increase, input evaluation and real-time release of the final IgG product.

[0481] Example 2 - Technical details of example multi-line equipment.

[0482] A multi-line device 460 was designed to allow measurement of upstream fractions under harsh conditions and downstream fraction included final container (IgG). Device 460 overcomes challenges of, for example, LSPR chip pH and salt component interference by application of desalting columns. In consequence, accuracy and precision were increased to an acceptablelevel. Through this example, the technology was made applicable to human plasma IgG purification (upstream and downstream) process, in the presence of previously mentioned disturbing factors.

[0483] Referring to Fig. 4, three flow circuits were interconnected. Circuit 1 and circuit 2 were connected via n injection valves 462-1, .. ., 462-«, where n is a positive integer (e.g., 1, 2, 3, 4, 5, or 6 or more), and where only injection values 462-1 and 462-2 are shown in Fig. 4. Circuit 1 and 3 were connected via circuit 3 valve 464. Circuit 1 had at least one pump 466 and one input valve 468, the serial or parallel connected injection valves 462, and m pre-processing valves {470-1, . . ., 470- M), where m is a positive integer, with one or more preprocessing elements {472-1, 472-m) per valve 470. Circuit 2 had at least one circuit pump 474 and one input circuit valve 476, with the injection valves 462 in between.

[0484] When the injection valves 462 were in load mode, circuit 2 ran through the injection loops 478 and circuit 1 ran independently through injection valves 462. The loops 478 filled with material from circuit 2 are thus integrated into circuit 1 when the injection valves 462 were in inject mode. Each valve 462 is independently controlled. Pre-processing elements 472 in circuit 1 included a desalting column for buffer exchange, heat incubation, filtering, mixing, and centrifugation. Circuit 3 included p analytical by-pass valves {480-1, . . ., 480-p{, where p is a positive integer. Each by-pass valve 480 contains y analytical inline devices {482-1, .. ., 482- { where is a positive integer. Examples of analytical inline devices include UV detectors, conductivity detectors, LSPR detectors, and chromatography detectors, among others. The aforementioned components are controlled with a controlling unit 484 that controls the different actuators, collects sensor data, generates outputs to connect to other computing systems, and represents data on a screen.

[0485] In one example in accordance with Fig. 4, the multi-line equipment 460 included four 3- way valves, six injection valves, two peristaltic pumps, one Runge UV sensor, one Runge Conductivity sensor, one LSPR sensor, one 5 ml desalting column, silicon tubing, and 0.75 i.d. rigid tubing connecting all these elements. The example multi-line equipment could take three conditioning solutions, one calibration solution, and one sample.I l l

[0486] The example multi-line equipment 460 incorporated two Runge sensors, each with multiple wavelength absorption photometer or conductometer, and an LSPR-based specific target protein sensor.

[0487] The example multi-line equipment 460 was controlled with a control unit 484 in the form of a Linux-based ARM independent computer that received data from sensors via USB or other communication channels. The example multi-line equipment 460 provided analog output (voltage-based) of the sensors and the control unit 484 could recognize digital or other kind of inputs. The control unit 484 was connected to an Ethernet network, and the software run on the control unit 484 could connect to an Open Platform Communications Unified Architecture (OPC UA) server or similar for real-time data broadcasting. OPC UA (Open Platform Communications Unified Architecture) server is a software application that implements the OPC UA specification to provide data exchange and communication capabilities in industrial automation and related domains. OPC UA is a standardized communication protocol designed for interoperability and seamless integration between different industrial devices, systems, and software applications. The software recorded metadata from the analyzed sample by interacting with a user via a graphical user interface or with a user or automated system via OPC UA communication or an equivalent.

[0488] The example multi-line equipment 460 had multiple measurement and calibration methods and was configured to support custom application methods. The software was configured to export calibrated data in different data formats, including CSV, JSON, or other formats.

[0489] The example multi-line equipment 460 reported, by way of example, using LSPR sensors but can be applied to all other analytical techniques. The example multi-line equipment 460 was designed to be connected and compatible with an autosampler and a fraction collector.

[0490] Example 3 - Real time online measurement.

[0491] Ethanol fractionation selectively precipitates proteins according to ethanol concentration, temperature, pH, protein concentration and conductivity as major variables under the principle ofsalting-out. II+III fractionation consists of adjustment of the pH to 6.9 and addition of ethanol up to 25% (v / v) final concentration starting at 0 °C and ending at -5 to - 6 °C).

[0492] An IgG-diluted surrogate plasma was prepared immediately before the experiment (4 % albumin, 0.3 % IgG, 0.9 % NaCl, pH 6.9 corrected with acetic acid). This was done by mixing 1 :2 the provided surrogate plasma containing 1 % IgG, with an ad-hoc prepared mix of 4 % albumin in 0.9 % sodium chloride, without IgG. The reason for this IgG dilution was to keep the maximum concentration of IgG inside the limit of quantification, that is, below 5 g / 1. The solution was set under constant stirring on a brine (ice) bath of -9 °C (2M NaCl ice on 2M NaCl solution). When the temperature of the solution reached 0 °C, the addition of ethanol took place in steps of 4 %, drop by drop, with the intent to prevent asymmetric ethanol concentration in the solution, to simulate production flow rates and to let the reaction’s exothermic heat to be dissipated. During the pauses from addition, a sample for online measurement was taken directly from the mix.

[0493] After adding the corresponding amount of ethanol, for centrifugation, a sample of 4 ml was taken at different time-points and centrifuged for 10 min at - 8 °C, 4500 rpm. The supernatant was collected and measured with ArgusEye ArgusOne (offline, 100 q). Before the injection of samples, the sensor system was calibrated using standards with IgG concentrations varying from 0 to 5 g / L (Figs. 6A and 6B - LSPR signal of IgG fractionation on surrogate plasma (4% Albumin, 0.3% IgG, 0.9% NaCl, pH 6.9); 5 g / 1 curve not shown). After about 10 injections of samples or after the waiting time, the sensor was recalibrated by again injecting one of the standards. IgG concentrations of samples were quantified using the binding response 115 seconds after injection. For raw samples, there was no significant change in the IgG concentrations during the fractionation process, except for a small reduction for sample R11.The abnormal change for sample R11 could be due to an error in the injection volume, especially when there was a lot of precipitate present in the sample. On the other hand, a fifty percent reduction of IgG concentrations, from 2.8 g / L to 1.4 g / L, was found for supernatant samples when EtOH reached 25%.

[0494] While no meaningful change was observed for the aggregate’s suspension samples (Fig. 7A), a decrease in binding responses, from 840 pm to 577 pm, was found for supernatant samples when the ethanol percentages increased from 0 to 25% (Fig. 7B).

[0495] The stable IgG concentration in the raw samples (suspension) can be explained by the binding of both monomers in solution and aggregates to the Protein A. In addition, the lag time between the sample being taken and the moment it reaches the sensor. In this time, the sample is in a tube flanked by PBS and at room temperature through a peristaltic pump. By the time the sample reaches the cell, the temperature, the concentration of ethanol and pH from diffusion of PBS will no longer meet the precipitation conditions, and the aggregates probably redissolved.

[0496] Example 4 - Correlation of concentration to signal using IgG titration.

[0497] The setup used for these experiments is illustrated in Fig. 8. If not indicated otherwise, PBS was the equilibration and loading buffer. Human serum IgG, IgA and IgM, purchased from Sigma-Aldrich, were serially diluted 1 :2 in PBS starting from 0.5 mg / ml (see concentrations in Fig. 9). The sensor was first equilibrated with PBS, then an injection of 100 pl of the sample took place and then the sensor was washed continuously with PBS. A regeneration step with 0.05 M NaOH strips the bound IgG in preparation for the next run.

[0498] The binding shows the expected hyperbolic profile, reaching a flat asymptote when the signal is saturated. The concentrations can be estimated either from the flat part of the signal or by the rate of the slope. In either case, the response correlates to the serial dilution. For the measurement at equilibrium (arbitrary timing after injection) the chip saturates and stops having a linear behavior at around 1 g / 1 (see Fig. 10). This gives a lx logio range of detection.However, for the atline measurements, this can be extended by simply reducing the volume of injection, without additional effort. To sum up, IgG titration showed satisfactory correlation of concentration to signal.

[0499] G. Conclusion

[0500] Although the present invention has been described in terms of specific exemplary embodiments and examples, it will be appreciated that the embodiments disclosed herein are for illustrative purposes only and various modifications and alterations might be made by those skilled in the art without departing from the spirit and scope of the invention as set forth in the following claims.

[0501] H. References

[0502] All references cited herein are hereby incorporated by reference herein in their entirety.

Claims

WHAT IS CLAIMED:

1. A method of monitoring a concentration of a first polypeptide type in a multi-step production process of a biologic, the method comprising: obtaining a first signal from a plurality of first time-resolved measurements, occurring over a time period, from a first optical sensor in optical communication with a first substratebased sensor exposed to a sample during the time period, wherein the sample includes the biologic, wherein the sample is associated with a first step in the multi-step production process, the first substrate-based sensor has a first plurality of binding affinities, each respective binding affinity in the first plurality of binding affinities is for a corresponding polypeptide type in a first plurality of polypeptide types that includes the first polypeptide type, the first polypeptide type is present in the sample, and the first signal includes a resonance contribution, for each respective first time- resolved measurement in the plurality of first time-resolved measurements, from each respective polypeptide type in the first plurality of polypeptide types as a function of (i) a concentration of the respective polypeptide type in the sample during the respective first time-resolved measurement and (ii) the respective binding affinity for the respective polypeptide type in the first plurality of binding affinities; obtaining a second signal from a plurality of second time-resolved measurements, occurring over the time period, from a second optical sensor in optical communication with a second substrate-based sensor exposed to the sample during the time period, wherein the second substrate-based sensor has a second plurality of binding affinities, each respective binding affinity in the second plurality of binding affinities is for a corresponding polypeptide type in a second plurality of polypeptide types that includes the first polypeptide type, the second signal includes a resonance contribution, for each respective second time-resolved measurement in the plurality of second time-resolved measurements, from each respective polypeptide type in the second plurality of polypeptide types as afunction of (i) a concentration of the respective polypeptide type in the sample during the respective second time-resolved measurement and (ii) the respective binding affinity for the respective polypeptide type in the second plurality of binding affinities, and the respective binding affinity in the first plurality of binding affinities for the first polypeptide type is other than the respective binding affinity in the second plurality of binding affinities for the first polypeptide type; and responsive to an analysis of the first and second signal, obtaining a calculated concentration of the first polypeptide type in the sample.

2. The method of claim 1, wherein the biologic is plasma-derived.

3. The method of claim 1 or 2, wherein the first step is associated with a first process parameter and a corresponding first validated range, and the method further comprises: adjusting the first process parameter from a first value to a second value in the corresponding first validated range responsive to the calculated concentration of the first polypeptide type in the sample.

4. The method of any one of claims 1-3, wherein a second step precedes the first step in the multi-step production process, the second step is associated with a second process parameter and a corresponding second validated range, and the method further comprises: adjusting the second process parameter from a first value to a second value in the corresponding second validated range responsive to the calculated concentration of the first polypeptide type in the sample.

5. The method of any one of claims 1-3, wherein a second step is after the first step in the multi- step production process, the second step is associated with a second process parameter and a corresponding second validated range, and the method further comprises: adjusting the second process parameter from a first value to a second value in the corresponding second validated range responsive to the calculated concentration of the first polypeptide type in the sample.

6. The method of any one of claims 1-5, wherein the first plurality of polypeptide types and the second plurality of polypeptide types differ by at least one polypeptide type.

7. The method of any one of claims 1-6, wherein the first plurality of polypeptide types is identical to the second plurality of polypeptide types.

8. The method of any one of claims 1-7, wherein the respective binding affinity in the first plurality of binding affinities for the first polypeptide type is negligible, and the respective binding affinity in the second plurality of binding affinities for the first polypeptide type is other than negligible.

9. The method of any one of claims 1-7, wherein the respective binding affinity in the first plurality of binding affinities for the first polypeptide type is less than half of the respective binding affinity in the second plurality of binding affinities for the first polypeptide type.

10. The method of any one of claims 1-9, wherein the first polypeptide type is immunoglobulin IgG.1.

11. The method of any one of claims 1-10, wherein at least one of IgGi, IgG2, and IgG4 is present in the sample, and the first plurality of polypeptide types and the second plurality of polypeptide types each comprise at least one of IgGi, IgG2, and IgG4 that contributes to the first and second signal.

12. The method of any one of claims 1-10, wherein at least two of IgGi, IgG2, and IgG4 is present in the sample, and the first plurality of polypeptide types and the second plurality of polypeptide types each comprise at least two of IgGi, IgG2, and IgG4 that each contribute to first and second signal.

13. The method of any one of claims 1-10, whereinIgGi, IgG2, and IgG4 are each present in the sample, and the first plurality of polypeptide types and the second plurality of polypeptide types each comprise IgGi, IgG2, and I G4 and each contribute to the first and second signal.

14. The method of any one of claims 1-13, wherein the first and second plurality of polypeptide types have at least two polypeptide types, present in the sample, in common and contributing to the first and second signal.

15. The method of any one of claims 1-13, wherein the first and second plurality of polypeptide types have at least three polypeptide types, present in the sample, in common and contributing to the first signal and the second signal.

16. The method of any one of claims 1-15, wherein at least one polypeptide type in the first plurality of polypeptide types, present in the sample and contributing to the first signal, is not represented in the second plurality of polypeptide types.

17. The method of any one of claims 1-16, wherein at least one polypeptide type in the first plurality of polypeptide types, present in the sample and contributing to the first signal, does not contribute to the second signal.

18. The method of any one of claims 1-16, wherein at least two polypeptide types in the first plurality of polypeptide types, present in the sample and contributing to the first signal, are not represented in the second plurality of polypeptide types and do not contribute to the second signal.

19. The method of any one of claims 1-18, wherein the first plurality of polypeptide types and the second plurality of polypeptide types each comprise at least one of, at least 2 of, or at least 3 of the group consisting of: immunoglobulin IgA, immunoglobulin IgM, immunoglobulin IgE, albumin, protein C, complement component Cl, protein S, anti -A hemagglutinin antibody, anti-B hemagglutinin antibody, anti-D hemagglutinin antibody, complement component 3, complement component C2a, complement component C3a, complement component C4a, complement component C5a, complement component C2b, complement component C3b, complement component C4b, complement component C5b, hemoglobin, hemopexin, parvo-19 antibody, an antibody to the polio virus, an antibody to the measles virus, a diphtheria antibody, alpha-2 macroglobulin, transferrin, fibrinogen, ceruloplasmin, plasmin, tissue thromboplastin (CD 142), an apolipoprotein, alpha- 1 -antitrypsin, anti-thrombin, factor Xia, factor Xlla, prothrombin (factor two), factor XII, factor VIII, factor IX, factor X, factor XI, Von Willebrand factor, antithrombin III, Cl -esterase inhibitor, and haptoglobin.

20. The method of any one of claims 1-19, wherein the first polypeptide type is immunoglobulin IgAl or immunoglobulin IgA2.

21. The method of any one of claims 1-20, wherein the first signal is modulated by first Kon, Kotr, or Kd from the first set of time-resolved measurements arising from the interaction of the first plurality of polypeptide types, in the sample, with a first functionalized solid surface of the first substrate-based sensor, and the second signal is modulated by second Kon, Koff, or Kd from the second set of time- resolved measurements arising from the interaction of the second plurality of polypeptide types with a second functionalized solid surface of the second substrate-based sensor.

22. The method of any one of claims 1-21, the method further comprises: injecting the sample into a flow cell in fluid communication with the first substrate-based sensor and the second substrate-based sensor, and wherein the first signal further comprises one or more first respective auxiliary measurements, accompanying each respective first time-resolved measurement in the plurality of first time- resolved measurements, that is a conductivity of the sample, a flow rate of the first step, a flow rate of the sample in the flow cell, a volume of the sample in the flow cell, a pressure of the sample in the flow cell, a pH of the sample, a temperature of the sample, an identity of a bufferin the sample, an ionic strength of the sample, or any combination thereof, representative of the sample during the respective first time-resolved measurement, and the second signal further comprises one or more respective auxiliary measurements, accompanying each respective second time-resolved measurement in the plurality of second time-resolved measurements, that is a conductivity of the sample, a flow rate of the first step, a flow rate of the sample in the flow cell, a volume of the sample, a pressure of the sample in the flow cell, a pH of the sample, a temperature of the sample, an identity of a buffer in the sample, an ionic strength of the sample, or any combination thereof, representative of the sample during the respective second time-resolved measurement.

23. The method of claim 22, wherein the flow cell has a void volume of 1 mb or less.

24. The method of claim 22, wherein the flow cell has a void volume of between 0.25 mL and 0.9 mL.

25. The method of any one of claims 1-24, the method further comprises: acquiring an injection time in which the sample is injected into a flow cell containing the first substrate-based sensor and the second substrate-based sensor, and wherein each respective first time-resolved measurement in the plurality of first time-resolved measurements is a baseline corrected median localized surface plasmon resonance signal or extinction signal, after the injection time, from a first functionalized solid surface of the first substrate-based sensor that is exposed to the sample, and each respective second time-resolved measurement in the plurality of second time- resolved measurements is a baseline corrected median localized surface plasmon resonance signal or extinction signal, after the injection time, from a second functionalized solid surface of the second substrate-based sensor that is exposed to the sample.

26. The method of claim 25, wherein the flow cell has a void volume of 1 mL or less.

27. The method of claim 25, wherein the flow cell has a void volume of between 0.25 mL and 0.9 mL.

28. The method of any one of claims 1-24, wherein the first step is associated with an injection time in which the sample is injected into a flow cell containing the first substrate-based sensor and the second substrate-based sensor; each respective first time-resolved measurement in the plurality of first time-resolved measurements is a baseline corrected median localized surface plasmon resonance signal or extinction signal, around the injection time, from a first functionalized solid surface of the first substrate-based sensor that is exposed to the sample, and each respective second time-resolved measurement in the plurality of second time- resolved measurements is a baseline corrected median localized surface plasmon resonance signal or extinction signal, around the injection time, from a second functionalized solid surface of the second substrate-based sensor that is exposed to the sample.

29. The method of claim 28, wherein the flow cell has a void volume of 1 mL or less.

30. The method of claim 28, wherein the flow cell has a void volume of between 0.25 mL and 0.9 mL.

31. The method of any one of claims 25-30, wherein the first polypeptide type is immunoglobulin IgGs, the first solid surface is functionalized with protein A, and the second solid surface is functionalized with protein G.

32. The method of any one of claims 25-30, wherein the first functionalized solid surface comprises a first plurality of metal nanoparticles coated with protein A that are fixed to a first substrate exposed to the sample, and the second functionalized solid surface comprises a second plurality of metal nanoparticles coated with protein G that are fixed to second substrate exposed to the sample.

33. The method of claim 32, wherein the first plurality of metal nanoparticles and the second plurality of metal nanoparticles are gold nanoparticles, silver nanoparticles, or copper nanoparticles.

34. The method of any one of claims 25-33, wherein the sample passes through the flow cell at a predetermined flow velocity during the time period.

35. The method of any one of claims 1-34, wherein the plurality of first time-resolved measurements are ultra-violet light measurements of the sample, and the plurality of second time-resolved measurements are ultra-violet light measurements of the sample.

36. The method of any one of claims 1-35, wherein the analysis of the first and second signal comprises inputting at least the first and second signal into a model comprising a plurality of parameters thereby obtaining the calculated concentration of the first polypeptide type in the sample through interaction of the first plurality of parameters with the first and second signal.

37. The method of claim 36, wherein the model is an ElasticNet model, a random forest model, or a light gradient boosting machine (LightGBM) model.

38. The method of claim 36, wherein the model is a regression model.

39. The method of claim 36, wherein the model is a neural network model, a support vector machine model, a Naive Bayes model, a nearest neighbor model, a boosted trees model, a random forest model, a decision tree model, a multinomial logistic regression model, a linear model, or a linear regression model.

40. The method of any one of claims 1-35, wherein the analysis of the first and second signal comprises a polynomial fitting of the first and second signal.

41. The method of any one of claims 1-35, wherein the analysis of the first and second signal comprises a finite or infinite impulse response evaluation of the first and second signal.

42. The method of any one of claims 1-35, wherein the analysis of the first and second signal comprises a Z-Transform analysis of the first and second signal.

43. The method of any one of claims 1-35, wherein the analysis of the first and second signal comprises a mechanistic modeling of the first and second signal.

44. The method of claim 43, wherein the mechanistic modeling is a Scatchard model, a higher order Scatchard model, a steric mass action chromatography model, a colloidal particle adsorption chromatography model, manifold learning, or application of a convolutional neural network.

45. The method of any one of claims 1-44, wherein the first step comprises a fractionation of a plurality of plasma units from a plurality of donors.

46. The method of claim 45, wherein the time period occurs during the fractionation.

47. The method of claim 45, wherein the time period occurs, at least in part, during the fractionation.

48. The method of claim 45, wherein the time period occurs upon completion of the fractionation.

49. The method of claim 48, wherein the plurality of first time-resolved measurements and the plurality of second time-resolved measurements are taken within an hour of completion of the fractionation.

50. The method of claim 48, wherein the plurality of first time-resolved measurements and the plurality of second time-resolved measurements are taken within 12 hours of completion of the fractionation.

51. The method of claim 48, wherein the plurality of first time-resolved measurements and the plurality of second time-resolved measurements are taken within a day of completion of the fractionation.

52. The method of any one of claims 1-44, wherein the first step comprises a precipitation of a plasma component.

53. The method of claim 52, wherein the precipitation is ethanol precipitation, ammonium sulfate precipitation, polyethylene glycol precipitation, citrate precipitation, or acid precipitation.

54. The method of claim 52 or 53, wherein the time period occurs during the precipitation.

55. The method of claim 52 or 53, wherein the time period occurs, at least in part, during the precipitation.

56. The method of claim 52 or 53, wherein the time period occurs upon completion of the precipitation.

57. The method of claim 56, wherein the plurality of first time-resolved measurements and the plurality of second time-resolved measurements are taken within an hour of completion of the precipitation.

58. The method of claim 56, wherein the first and second plurality of time-resolved measurements are taken within 12 hours of completion of the precipitation.

59. The method of claim 56, wherein the plurality of first time-resolved measurements and the plurality of second time-resolved measurements are taken within a day of completion of the precipitation.

60. The method of any one of claims 1-44, wherein the first step comprises application of a chromatographic step on the sample.

61. The method of claim 60, wherein the chromatographic step is protein A affinity chromatography, protein G affinity chromatography, ion exchange chromatography, size exclusion chromatography, or hydrophobic interaction chromatography.

62. The method of claim 60 or 61, wherein the time period occurs during the chromatographic step.

63. The method of claim 60 or 61, wherein the time period occurs, at least in part, during the chromatographic step.

64. The method of claim 60 or 61, wherein the time period occurs upon completion of the chromatographic step.

65. The method of claim 64, wherein the plurality of first time-resolved measurements and the plurality of second time-resolved measurements are taken within an hour of completion of the chromatographic step.

66. The method of claim 64, wherein the plurality of first time-resolved measurements and the plurality of second time-resolved measurements are taken within 12 hours of completion of the chromatographic step.

67. The method of claim 64, wherein the plurality of first time-resolved measurements and the plurality of second time-resolved measurements are taken within a day of completion of the chromatographic step.

68. The method of any one of claims 1-44, wherein the first step comprises a filtration of the sample.

69. The method of claim 68, wherein the filtration comprises microfiltration using a membrane with pores between 0.1 and 10 micrometers in size.

70. The method of claim 68, wherein the filtration comprises ultrafiltration using a membrane with pores between 1 nanometer and 100 nanometers in size.

71. The method of claim 68, wherein the filtration comprises nanofiltration with a membrane with pores between 1 and 10 nanometers in size.

72. The method of claim 68, wherein the filtration comprises a depth filtration.

73. The method of claim 68, wherein the filtration comprises a tangential flow filtration.

74. The method of any one of claims 68-73, wherein the time period occurs during the filtration.

75. The method of any one of claims 68-73, wherein the time period occurs, at least in part, during the filtration.

76. The method of any one of claims 68-73, wherein the time period occurs upon completion of the filtration.

77. The method of claim 76, wherein the first and second plurality of time-resolved resonancemeasurements are taken within an hour of completion of the filtration.

78. The method of claim 76, wherein the plurality of first time-resolved measurements and the plurality of second time-resolved measurements are taken within 12 hours of completion of the filtration.

79. The method of claim 76, wherein the plurality of first time-resolved measurements and the plurality of second time-resolved measurements are taken within a day of completion of the filtration.

80. The method of any one of claims 1-79, wherein the biologic is immunoglobulin G purified from human plasma from a plurality of donors.

81. The method of any one of claims 1-80, wherein the analysis determines that the concentration of the first polypeptide type is in a validated range associated with the first step and the method further comprises releasing the biologic for use in treating a condition of a species.

82. The method of claim 81, wherein the biologic is a human immunoglobulin biologic and the condition is a human condition.

83. The method of claim 82, wherein the human condition is primary immunodeficiency.

84. The method of claim 82, wherein the human condition is multifocal motor neuropathy, humoral immunodeficiency secondary to myeloma, chronic lymphocytic leukemia, chronic inflammatory demyelinating polyneuropathy, a bacterial infection, peritonitis, or sepsis.

85. The method of any one of claims 1-44, wherein the first step comprises a fractionation of a plurality of plasma units from a plurality of donors,the sample is from a first fraction arising from the fractionation, and the method further comprises further purifying the biologic from the first fraction after the analysis determines that the concentration of the first polypeptide type is in a predetermined range.

86. The method of any one of claims 1-44, wherein the biologic human immunoglobulin biologic, when the analysis determines that the concentration of the first polypeptide type is in a predetermined range the method further comprises releasing the human immunoglobulin biologic for use in treating a human condition, and when the analysis determines that the concentration of the first polypeptide type is outside the predetermined range the method further comprises adjusting a first process parameter, within a validated range, of the first step.

87. The method of claim 86, wherein the process parameter is a pH, a salt concentration, a loading velocity, a protein density, a temperature, or a flow rate associated with a purification process occurring upstream of the first step in the multi-step production process.

88. The method of claim 3, wherein the first process parameter is a pH, a salt concentration, a loading velocity, a protein density, a temperature, or a flow rate of the first step.

89. The method of claim 4 or 5, wherein the second process parameter is a pH, a salt concentration, a loading velocity, a protein density, a temperature, or a flow rate of the second step.

90. The method of any one of claims 1-89, wherein the first plurality of polypeptide types comprises two or more polypeptide types, and the second plurality of polypeptide types comprises two or more polypeptide types.

91. The method of any one of claims 1-89, whereinthe first plurality of polypeptide types comprises three or more polypeptide types, and the second plurality of polypeptide types comprises three or more polypeptide types.

92. The method of any one of claims 1-89, wherein the first plurality of polypeptide types comprises four or more polypeptide types, and the second plurality of polypeptide types comprises four or more polypeptide types.

93. The method of any one of claims 1-89, wherein the sample comprises IgGi (IgG ) and IgGi (IgGi) at a percent weight IgG.3 to IgGi ratio of between 0.056 to 0.16, and at least the first plurality of polypeptide types or the second plurality of polypeptide types comprises IgGi and IgG .

94. The method of any one of claims 1-89, wherein the sample comprises IgGi (IgGi) and IgG2 (IgG2) at a percent weight IgGi to IgG2 ratio of between 0.10 and 0.34, and at least the first plurality of polypeptide types or the second plurality of polypeptide types comprises IgG2 and IgGi.

95. The method of any one of claims 1-89, wherein the sample comprises IgGi and IgG4 at a percent weight IgGi to IgG4 ratio of between0.80 and 3.3, and at least the first plurality of polypeptide types or the second plurality of polypeptide types comprises IgGi and I G4.

96. The method of any one of claims 1-95, wherein the time period is between thirty seconds and ten minutes.

97. The method of any one of claims 1-95, wherein the time period is between one minute and six minutes.

98. The method of any one of claims 1-97, wherein the first substrate-based sensor and the second substrate-based sensor are atline with respect to the first step.

99. The method of any one of claims 1-97, wherein the first substrate-based sensor and the second substrate-based sensor are inline with respect to the first step.

100. The method of any one of claims 1-97, wherein the first substrate-based sensor and the second substrate-based sensor are off-line with respect to the first step.

101. The method of any one of claims 1-100, wherein each step of the multi-step production process is inline.

102. The method of any one of claims 1-101, the method further comprising: performing a regeneration cycle on the first substrate-based sensor and the second substrate-based sensor.

103. The method of claim 102, wherein the regenerating cycle has a duration of between one minute and five minutes.

104. The method of claim 102, wherein the regenerating cycle has a duration of between 30 seconds and 120 seconds.

105. The method of any one of claims 1-104, wherein the first signal and the second signal are obtained concurrently.

106. The method of any one of claims 1-105, wherein the sample further comprises ethanol.

107. The method of any one of claims 1-106, wherein the sample is at a pH of between 5 and 6.

108. The method of any one of claims 1-106, wherein the sample is at a pH of between 4 and 7.

109. The method of any one of claims 1-108, wherein the sample has a conductivity of between 0 mS / cm and 20 mS / cm.

110. The method of any one of claims 1-109, wherein the biologic comprises the first polypeptide type.

111. The method of any one of claims 1-110, wherein a first aliquot of the sample is used in the obtaining the first signal, and a second aliquot of the sample is used in the obtaining the second signal.

112. The method of any one of claims 1-110, wherein the same aliquot of the sample is used in the obtaining the first signal and the second signal.

113. The method of any one of claims 1-111, wherein the analysis of the first and second signal further comprises analysis of one or more auxiliary features of the sample.

114. The method of claim 113, wherein the one or more auxiliary features comprises a pH of the sample.

115. The method of claim 113 or 114, wherein the one or more auxiliary features comprises a conductivity of the sample.

116. The method of any one of claims 113-115, wherein the one or more auxiliary features comprises a solution component identity.

117. The method of any one of claims 113-116, wherein the one or more auxiliary features comprises a buffer identity.

118. The method of any one of claims 113-117, wherein the one or more auxiliary features comprises a salt identity.

119. The method of any one of claims 113-118, wherein the one or more auxiliary features comprises a salt concentration of the sample.

120. The method of any one of claims 113-119, wherein the one or more auxiliary features comprises a flow rate of the sample.

121. The method of any one of claims 1-120, wherein , when the calculated concentration of the first polypeptide type is outside of a predetermined range, a first process parameter of the multi- step production process is adjusted.

122. The method of claim 121, wherein the first process parameter is an amount of media used in a clarifying step of the multi-step production process.

123. The method of claim 121 or 122, wherein the first process parameter is a pH of a clarifying step of the multi-step production process.

124. The method of any one of claims 121-123, wherein the first process parameter is a post wash condition for a clarifying step of the multi-step production process.

125. The method of any one of claims 121-124, wherein the first process parameter is a flow rate for a chromatographic step of the multi-step production process.

126. The method of any one of claims 121-125, wherein the first process parameter is a loading pH for a chromatographic step of the multi-step production process.

127. The method of any one of claims 121-126, wherein the first process parameter is a loading condictivity for a chromatographic step of the multi-step production process.

128. The method of any one of claims 121-127, wherein the first process parameter is a loading density of a chromatographic step of the multi-step production process.

129. A computer system for monitoring a concentration of a first polypeptide type in a multi- step production process of a biologic, the computer system comprising: one or more processors; and memory addressable by the one or more processors, the memory storing at least one program for execution by the one or more processors, the at least one program comprising instructions for: obtaining a first signal from a plurality of first time-resolved measurements, occurring over a time period, from a first optical sensor in optical communication with a first substratebased sensor exposed to a sample, wherein the sample includes the biologic, during the time period, wherein the sample is associated with a first step in the multi-step production process, the first substrate-based sensor has a first plurality of binding affinities, each respective binding affinity in the first plurality of binding affinities is for a corresponding polypeptide type in a first plurality of polypeptide types that includes the first polypeptide type, the first polypeptide type is present in the sample, and the first signal includes a resonance contribution, for each respective first time- resolved measurement in the plurality of first time-resolved measurements, from each respective polypeptide type in the first plurality of polypeptide types as a function of (i) a concentration of the respective polypeptide type in the sample during the respective first time-resolved measurement and (ii) the respective binding affinity for the respective polypeptide type in the first plurality of binding affinities; obtaining a second signal from a second plurality of second time-resolved measurements, occurring over the time period, from a second optical sensor in optical communication with a second substrate-based sensor exposed to the sample during the time period, wherein the second substrate-based sensor has a second plurality of binding affinities,each respective binding affinity in the second plurality of binding affinities is for a corresponding polypeptide type in a second plurality of polypeptide types that includes the first polypeptide type, the second signal includes a resonance contribution, for each respective second time-resolved measurement in the plurality of second time-resolved measurements, from each respective polypeptide type in the second plurality of polypeptide types as a function of (i) a concentration of the respective polypeptide type in the sample during the respective second time-resolved measurement and (ii) the respective binding affinity for the respective polypeptide type in the second plurality of binding affinities, and the respective binding affinity in the first plurality of binding affinities for the first polypeptide type is other than the respective binding affinity in the second plurality of binding affinities for the first polypeptide type; and responsive to an analysis of the first and second signal, obtaining a calculated concentration of the first polypeptide type in the sample.

130. A computer system for monitoring a concentration of a first polypeptide type in a multi- step production process of a biologic, the computer system comprising: one or more processors; and memory addressable by the one or more processors, the memory storing at least one program for execution by the one or more processors, the at least one program comprising instructions for performing the method of any one of claims 1-128.

131. A non-transitory computer readable storage medium, wherein the non-transitory computer readable storage medium stores instructions, which when executed by a computer system, cause the computer system to perform a method of monitoring a concentration of the first polypeptide type in a multi-step production process of a biologic, the method comprising: obtaining a first signal from a plurality of first time-resolved measurements, occurring over a time period, from a first optical sensor in optical communication with a first substratebased sensor exposed to a sample, wherein the sample includes the biologic, during the time period, whereinthe sample is associated with a first step in the multi-step production process, the first substrate-based sensor has a first plurality of binding affinities, each respective binding affinity in the first plurality of binding affinities is for a corresponding polypeptide type in a first plurality of polypeptide types that includes the first polypeptide type, the first polypeptide type is present in the sample, and the first signal includes a resonance contribution, for each respective first time- resolved measurement in the plurality of first time-resolved measurements, from each respective polypeptide type in the first plurality of polypeptide types as a function of (i) a concentration of the respective polypeptide type in the sample during the respective first time-resolved measurement and (ii) the respective binding affinity for the respective polypeptide type in the first plurality of binding affinities; obtaining a second signal from a second plurality of second time-resolved measurements, occurring over the time period, from a second optical sensor in optical communication with a second substrate-based sensor exposed to the sample during the time period, wherein the second substrate-based sensor has a second plurality of binding affinities, each respective binding affinity in the second plurality of binding affinities is for a corresponding polypeptide type in a second plurality of polypeptide types that includes the first polypeptide type, the second signal includes a resonance contribution, for each respective second time-resolved measurement in the plurality of second time-resolved measurements, from each respective polypeptide type in the second plurality of polypeptide types as a function of (i) a concentration of the respective polypeptide type in the sample during the respective second time-resolved measurement and (ii) the respective binding affinity for the respective polypeptide type in the second plurality of binding affinities, and the respective binding affinity in the first plurality of binding affinities for the first polypeptide type is other than the respective binding affinity in the second plurality of binding affinities for the first polypeptide type; and responsive to an analysis of the first and second signal, obtaining a calculated concentration of the first polypeptide type in the sample.

132. A non-transitory computer readable storage medium, wherein the non-transitory computer readable storage medium stores instructions, which when executed by a computer system, cause the computer system to perform a method of monitoring a concentration of a first polypeptide type in a multi-step production process of a biologic, the method comprising the method of any one of claims 1-128.

133. An apparatus for a multi-step production process of a biologic, the apparatus comprising: a first substrate-based sensor, wherein the first substrate-based sensor has a first plurality of binding affinities, and each respective binding affinity in the first plurality of binding affinities is for a corresponding polypeptide type in a first plurality of polypeptide types that includes a first polypeptide type; a first optical sensor in optical communication with the first substrate-based sensor; a second substrate-based sensor, wherein the second substrate-based sensor has a second plurality of binding affinities, each respective binding affinity in the second plurality of binding affinities is for a corresponding polypeptide type in a second plurality of polypeptide types that includes the first polypeptide type, the respective binding affinity in the first plurality of binding affinities for the first polypeptide type is other than the respective binding affinity in the second plurality of binding affinities for the first polypeptide type; a second optical sensor in optical communication with the second substrate-based sensor; a flow through that houses the first substrate-based sensor and the second substrate-based sensor; a processing module comprising instructions for: acquiring a first signal that includes a resonance contribution, for each respective first time-resolved measurement in a plurality of first time-resolved measurements, from each respective polypeptide type in the first plurality of polypeptidetypes as a function of (i) a concentration of the respective polypeptide type in a sample exposed to the first substrate-based sensor during the respective first time-resolved measurement and (ii) the respective binding affinity for the respective polypeptide type in the first plurality of binding affinities, wherein the sample is associated with a first step in the multi-step production process; acquiring a second signal that includes a resonance contribution, for each respective second time-resolved measurement in a plurality of second time-resolved measurements, from each respective polypeptide type in the second plurality of polypeptide types as a function of (i) a concentration of the respective polypeptide type in the sample exposed to the second substrate-based sensor during the respective second time-resolved measurement and (ii) the respective binding affinity for the respective polypeptide type in the second plurality of binding affinities; and obtaining a calculated concentration of the first polypeptide type in the sample through an analysis of the first and second signal.

134. The apparatus of claims 132, wherein the first plurality of polypeptide types and the second plurality of polypeptide types differ by at least one polypeptide type.

135. The apparatus of claims 132 or 133, wherein the first plurality of polypeptide types is identical to the second plurality of polypeptide types.

136. The apparatus of any one of claims 132-135, wherein the respective binding affinity in the first plurality of binding affinities for the first polypeptide type is negligible, and the respective binding affinity in the second plurality of binding affinities for the first polypeptide type is other than negligible.

137. The apparatus of any one of claims 132-136, wherein the respective binding affinity in the first plurality of binding affinities for the first polypeptide type is less than half of the respective binding affinity in the second plurality of binding affinities for the first polypeptide type.

138. The apparatus of any one of claims 132-137, wherein the first polypeptide type is immunoglobulin IgGs.

139. The apparatus of any one of claims 132-137, wherein at least one of IgGi, IgG2, and IgG4 is present in the sample, and the first plurality of polypeptide types and the second plurality of polypeptide types each comprise at least one of IgGi, IgGs, and IgG4 that contributes to the first and second signal.

140. The apparatus of any one of claims 132-137, wherein at least two of IgGi, IgG2, and IgG4 is present in the sample, and the first plurality of polypeptide types and the second plurality of polypeptide types each comprise at least two of IgGi, IgG2, and I G4 that each contribute to first and second signal.

141. The apparatus of any one of claims 132-137, whereinIgGi, IgG2, and IgG4 are each present in the sample, and the first plurality of polypeptide types and the second plurality of polypeptide types each comprise IgGi, IgG2, and IgG4 and each contribute to the first and second signal.

142. The apparatus of any one of claims 132-141, wherein the first and second plurality of polypeptide types have at least two polypeptide types, present in the sample, in common and contributing to the first and second signal.

143. The apparatus of any one of claims 132-141, wherein the first and second plurality of polypeptide types have at least three polypeptide types, present in the sample, in common and contributing to the first signal and the second signal.

144. The apparatus of any one of claims 132-143, wherein at least one polypeptide type in the first plurality of polypeptide types, present in the sample and contributing to the first signal, is not represented in the first plurality of polypeptide types.

145. The apparatus of any one of claims 132-143, wherein at least one polypeptide type in the first plurality of polypeptide types, present in the sample and contributing to the first signal, is not represented in the second plurality of polypeptide types and does not contribute to the second signal.

146. The apparatus of any one of claims 132-143, wherein at least two polypeptide types in the first plurality of polypeptide types, present in the sample and contributing to the first signal, are not represented in the second plurality of polypeptide types and do not contribute to the second signal.

147. The apparatus of any one of claims 132-146, wherein the first plurality of polypeptide types and the second plurality of polypeptide types each comprise at least one of, at least 2 of, or at least 3 of the group consisting of: immunoglobulin IgA, immunoglobulin IgM, immunoglobulin IgE, albumin, protein C, complement component Cl, protein S, anti -A hemagglutinin antibody, anti-B hemagglutinin antibody, anti-D hemagglutinin antibody, complement component 3, complement component C2a, complement component C3a, complement component C4a, complement component C5a, complement component C2b, complement component C3b, complement component C4b, complement component C5b, hemoglobin, hemopexin, parvo-19 antibody, an antibody to the polio virus, an antibody to the measles virus, a diphtheria antibody, alpha-2 macroglobulin, transferrin, fibrinogen, ceruloplasmin, plasmin, tissue thromboplastin (CD142), an apolipoprotein, alpha- 1 -antitrypsin, anti-thrombin, factor Xia, factor Xlla, factor Xlla, prothrombin (factor two), factor XII, factor VIII, factor IX, factor X, factor XI, Von Willebrand factor, antithrombin III, Cl -esterase inhibitor, and haptoglobin.

148. The apparatus of any one of claims 132-147, wherein the first polypeptide type is immunoglobulin IgAl or immunoglobulin IgA2.

149. The apparatus of any one of claims 132-148, whereinthe first signal is modulated by first Kon, Koff, or Ka from the first set of time-resolved measurements arising from the interaction of the first plurality of polypeptide types, in the sample, with a first functionalized solid surface of the first substrate-based sensor, and the second signal is modulated by second Kon, Koff, or Ka from the second set of time- resolved measurements arising from the interaction of the second plurality of polypeptide types with a second functionalized solid surface of the second substrate-based sensor.

150. The apparatus of any one of claims 132-149, wherein the processing module further comprises: instructions for acquiring an injection time in which the sample is injected into the flow through, and wherein each respective first time-resolved measurement in the plurality of first time-resolved measurements is a baseline corrected median localized surface plasmon resonance signal or extinction signal, after the injection time, from a first functionalized solid surface of the first substrate-based sensor that is exposed to the sample, and each respective second time-resolved measurement in the plurality of second time- resolved measurements is a baseline corrected median localized surface plasmon resonance signal or extinction signal, after the injection time, from a second functionalized solid surface of the second substrate-based sensor that is exposed to the sample.

151. The apparatus of any one of claims 132-149, wherein the processing module further comprises: instructions for acquiring an injection time in which the sample is injected into the flow through, and wherein each respective first time-resolved measurement in the plurality of first time-resolved measurements is a baseline corrected median localized surface plasmon resonance signal or extinction signal, around the injection time, from a first functionalized solid surface of the first substrate-based sensor that is exposed to the sample, and each respective second time-resolved measurement in the plurality of second time- resolved measurements is a baseline corrected median localized surface plasmon resonancesignal or extinction signal, around the injection time, from a second functionalized solid surface of the second substrate-substrate sensor that is exposed to the sample.

152. The apparatus of claims 150 or 151, wherein the first polypeptide type is immunoglobulin IgGs, the first solid surface is functionalized with protein A, and the second solid surface is functionalized with protein G.

153. The apparatus of any one of claims 150-152, wherein the first functionalized solid surface comprises a first plurality of metal nanoparticles coated with protein A that are fixed to a first substrate exposed to the sample, and the second functionalized solid surface comprises a second plurality of metal nanoparticles coated with protein G that are fixed to second substrate exposed to the sample.

154. The apparatus of claim 153, wherein the first plurality of metal nanoparticles and the second plurality of metal nanoparticles are gold nanoparticles, silver nanoparticles, or copper nanoparticles.

155. The apparatus of any one of claims 132-1154, wherein the plurality of first time-resolved measurements are ultra-violet light measurements of the sample, and the plurality of second time-resolved measurements are ultra-violet light measurements of the sample.

156. The apparatus of any one of claims 132-155, wherein the analysis of the first and second signal comprises inputting at least the first and second signal into a model comprising a plurality of parameters thereby obtaining the calculated concentration of the first polypeptide type in the sample through interaction of the first plurality of parameters with the first and second signal.

157. The apparatus of claim 156, wherein the model is an ElasticNet model, a random forest model, or a light gradient boosting machine (LightGBM) model.

158. The apparatus of claim 156, wherein the model is a regression model.

159. The apparatus of claim 156, wherein the model is a neural network model, a support vector machine model, a Naive Bayes model, a nearest neighbor model, a boosted trees model, a random forest model, a decision tree model, a multinomial logistic regression model, a linear model, or a linear regression model.

160. The apparatus of any one of claims 132-156, wherein the analysis of the first and second signal comprises a polynomial fitting of the first and second signal.

161. The apparatus of any one of claims 132-156, wherein the analysis of the first and second signal comprises a finite or infinite impulse response evaluation of the first and second signal.

162. The apparatus of any one of claims 132-156, wherein the analysis of the first and second signal comprises a Z-Transform analysis of the first and second signal.

163. The apparatus of any one of claims 132-156, wherein the analysis of the first and second signal comprises a mechanistic modeling of the first and second signal.

164. The apparatus of claim 163, wherein the mechanistic modeling is a Scatchard model, a higher order Scatchard model, a steric mass action chromatography model, a colloidal particle adsorption chromatography model, manifold learning, or application of a convolutional neural network.

165. The apparatus of any one of claims 132-164, wherein the first plurality of polypeptide types comprises two or more polypeptide types, and the second plurality of polypeptide types comprises two or more polypeptide types.

166. The apparatus of any one of claims 132-164, wherein the first plurality of polypeptide types comprises three or more polypeptide types, and the second plurality of polypeptide types comprises three or more polypeptide types.

167. The apparatus of any one of claims 132-164, wherein the first plurality of polypeptide types comprises four or more polypeptide types, and the second plurality of polypeptide types comprises four or more polypeptide types.

168. The apparatus of any one of claims 132-164, wherein the sample comprises IgGi (IgG ) and IgGi (IgGi) at a percent weight IgGi to IgGi ratio of between 0.056 to 0.16, and at least the first plurality of polypeptide types or the second plurality of polypeptide types comprises IgGi and IgG2.

169. The apparatus of any one of claims 132-168, wherein the time period is between thirty seconds and ten minutes.

170. The apparatus of any one of claims 132-169, wherein the time period is between one minute and six minutes.

171. The apparatus of any one of claims 132-170, wherein the first substrate-based sensor and the second substrate-based sensor are atline with respect to the first step.

172. The apparatus of any one of claims 132-170, wherein the first substrate-based sensor and the second substrate-based sensor are inline with respect to the first step.

173. The apparatus of any one of claims 132-170, wherein the first substrate-based sensor and the second substrate-based sensor are off-line with respect to the first step.

174. The apparatus of any one of claims 132-173, wherein the first signal and the second signal are obtained concurrently by the processing module.

175. The apparatus of any one of claims 132-174, wherein the flow through has a void volume of 1 mL or less.

176. The apparatus of any one of claims 132-174, wherein the flow through has a void volume of between 0.25 mL and 0.9 mL.

177. The apparatus of any one of claims 132-174, wherein a first aliquot of the sample is used in acquiring the first signal, and a second aliquot of the sample is used in acquiring the second signal.

178. The apparatus of any one of claims 132-174, wherein the same aliquot of the sample is used in acquiring the first and second signal.

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