REAL-TIME IgG TESTING OF SINGLE PLASMA DONATIONS TO DETERMINE A DONOR SPECIFIC NEXT DONATION TIME POINT

Abstract

Methods and systems for determining a next eligible date for a plasma donation by a donor subject. The method includes, at a computer system, obtaining, on a first date of a first plasma donation, a first signal from a first biosensor, the first biosensor comprising a first solid surface functionalized with a first binding agent for a first polypeptide type and a first optical sensor in optical communication with the first solid surface, when the first solid surface is exposed to a blood or plasma sample from the donor subject. The method also includes analyzing, on the first date, at least the first signal to obtain a second date on which a protein content of the donor subject's blood will satisfy a plasma donation criteria, thereby determining the next eligible date for a second plasma donation by the donor subject.

Description

Attorney Ref. 008073-5275-WOREAL-TIME IgG TESTING OF SINGLE PLASMA DONATIONS TO DETERMINE A DONOR SPECIFIC NEXT DONATION TIME POINTCROSS-REFERENCE TO RELATED APPLICATIONS

[0001] This application claims priority to U.S. Provisional Patent Application No. 63 / 747,189, filed on January 20, 2025, which is hereby incorporated by reference herein in its entirety.BACKGROUND

[0002] Immune globulin products from human plasma were first used in 1952 to treat immune deficiency. Human plasma-derived intravenous immunoglobulin (IVIG) and subcutaneous immunoglobulin, contain pooled immunoglobulin G (IgG) from the plasma of more than a thousand blood donors. Plasma-derived human IgG compositions are sterile, purified IgG products primarily used in treating three main categories of medical conditions: (1) immune deficiencies such as X-linked agammaglobulinemia, hypogammaglobulinemia (primary immune deficiencies), and acquired compromised immunity conditions (secondary immune deficiencies), featuring low antibody levels; (2) inflammatory and autoimmune diseases; and (3) acute infections.

[0003] Specifically, many people with primary immunodeficiency disorders lack antibodies needed to resist infection. In certain cases, these deficiencies can be supplemented by the infusion of purified IgG, commonly through intravenous administration (i.e., IVIG therapy). Several primary immunodeficiency disorders are commonly treated in the fashion, including X- linked Agammaglobulinemia (XLA), Common Variable Immunodeficiency (CVID), Hyper-IgM Syndrome (HIM), Severe Combined Immunodeficiency (SCID), and some IgG subclass deficiencies (Blaese and Winkelstein, J. Patient & Family Handbook for Primary Immunodeficiency Diseases. Towson, MD: Immune Deficiency Foundation; 2007).

[0004] While IVIG treatment can be very effective for managing primary immunodeficiency disorders, this therapy is only a temporary replacement for antibodies that are not being produced in the body, rather than a cure for the disease. Accordingly, patients dependent upon IVIG1DBl / 165702811.1Attorney Ref. 008073-5275-WOtherapy require repeated doses, typically about once a month for life. This need places a great demand on the continued production of IVIG compositions. However, unlike other biologies that are produced via in vitro expression of recombinant DNA vectors, IVIG is fractionated from human blood and plasma donations. Thus, IVIG products cannot be increased by simply increasing the volume of production. Rather the level of commercially available IVIG is limited by the available supply of blood and plasma donations.

[0005] Human plasma used for manufacturing human pooled IgG compositions is collected from healthy, volunteer donors through a process called plasmapheresis, during which plasma is separated from red blood cells and other cellular components of blood, which are then returned to the donor. Repeat donors are important to create a sufficient supply of plasma-derived immunoglobulins. However, frequent plasma donation can significantly reduce IgG levels in the blood of the donor, potentially dropping below the normal threshold of 6 g / 1, which may increase infection risk in the donor. It also lowers ferritin levels, leading to possible iron deficiency.While some studies show minimal impact on other proteins, the evidence is uncertain. D'aes, Tine, et al. "Balancing Donor Health and Plasma Collection: A Systematic Review of the Impact of Plasmapheresis Frequency." Transfusion Medicine Reviews, vol. 38 (2024).

[0006] In the United States, plasma donors can donate up to twice a week, while European countries generally have stricter regulations. For example, France limits donations to once every two weeks, while Germany allows up to 60 donations per year. These differences reflect varying regulatory approaches to donor health and plasma collection. Id.

[0007] A donation deferral is a temporary or permanent postponement of a person's eligibility to donate blood or plasma. It is implemented to protect both the donor's health and the safety of the blood supply. Deferrals can occur due to various reasons, such as recent travel to certain regions, medical conditions, medications, or low levels of key blood components like hemoglobin or IgG. The deferral period allows time for any potential risks to resolve, ensuring that donations are safe for recipients and do not adversely affect the donor's health. Id. Donation deferrals are inconvenient for the donor and restrict collection of plasma necessary to manufacture pooled IgG.DBl / 165702811.1Attorney Ref. 008073-5275-WOSUMMARY

[0008] Accordingly, methods for determining when a donor will recover from a plasma donation so that they can safely donate again are needed to protect donors as well as improve the efficiency of plasma collection and plasma-derived therapeutic manufacturing. Advantageously, the present disclosure addresses the need in the art by providing methods and systems for determining a next eligible date for a plasma donation by a donor subject based on measurement of protein levels in the donor’s blood when making a first donation. The method makes use of a biosensor comprising a solid surface functionalized with a binding agent for a first polypeptide type and an optical sensor in optical communication with the solid surface to obtain, on the day of the first plasma donation, a signal when the first solid surface is exposed to a blood or plasma sample from the donor subject. By analyzing the signal it is possible to determine the concentration of the first polypeptide in the donor’s blood on the day of the first plasma donation and then model how long it will take for the levels of the first polypeptide in the donor’s blood to recover to a threshold level following the first plasma donation, such that it is safe for the donor to donate again and the donation has sufficient levels of the polypeptide so as not to substantially dilute the concentration of the first polypeptide in a pool of plasma donations. Advantageously, such methods can be performed on the day of the first plasma donation, such that the donors know when they are next eligible to donate again and can schedule accordingly.

[0009] The methods and systems described herein are suitable for use with both blood and plasma samples. Advantageously, IgG concentration can be measured from whole blood, which is not feasible with nephelometry because of high background signal. However, the methods and systems can measure IgG from whole blood because IgG is bound to a solid surface and a wash step is available to remove background signal. This enables a unique real-time testing method for IgG in blood samples and supports the use of this technology for IgG measurement prior to plasma donation. This, in turn, enhances donor safety. In addition, the methods and systems described herein also allow for customized patient treatment plans based on individual plasma IgG levels prior to treatment. Both the timing of treatment and the IgG dose can be individualized, enabling an optimized treatment outcome.3DBl / 165702811.1Attorney Ref. 008073-5275-WO

[0010] Accordingly, in one aspect, the disclosure provides methods, as well as systems and computer readable media for such methods, for determining a next eligible date for a plasma donation by a donor subject. The method includes obtaining, on a first date of a first plasma donation, a first signal from a first biosensor, the first biosensor comprising a first solid surface functionalized with a first binding agent for a first polypeptide type and a first optical sensor in optical communication with the first solid surface, when the first solid surface is exposed to a blood or plasma sample from the donor subject. The method also includes analyzing, on the first date, at least the first signal to obtain a second date on which a protein content of the donor subject's blood will satisfy a plasma donation criteria, thereby determining the next eligible date for a second plasma donation by the donor subject. In some embodiments, the concentration of the first polypeptide is measured from a sample taken prior to the donation, e.g., blood via finger prick.

[0011] In another aspect, the disclosure provides methods and systems for determining a next eligible date for an immunoglobulin G (IgG) infusion of subject. The method includes obtaining, on a first date of a first IgG infusion, a first signal from a first biosensor, the first biosensor comprising a first solid surface functionalized with a first IgG binding agent and a first optical sensor in optical communication with the first solid surface, when the first solid surface is exposed to a blood or plasma sample from the subject. The method also includes analyzing at least the first signal to obtain a second date on which an IgG content of the subject's blood will satisfy an infusion criteria, thereby determining the next eligible date for a second IgG infusion of the subject.

[0012] In another aspect, the disclosure provides methods and systems for determining a dosage for a next immunoglobulin G (IgG) infusion of subject. The method includes obtaining, on a first date of a first IgG infusion, a first signal from a first biosensor, the first biosensor comprising a first solid surface functionalized with a first IgG binding agent and a first optical sensor in optical communication with the first solid surface, when the first solid surface is exposed to a blood or plasma sample from the subject. The method also includes analyzing at least the first signal to obtain a dosage for the first IgG infusion of the subject on the first date, thereby determining the dosage for the IgG infusion of subject. In another embodiment, the 4DBl / 165702811.1Attorney Ref. 008073-5275-WOmethod also includes analyzing at least the first signal to obtain a dosage for a second IgG infusion of the subject on a second date, thereby determining the dosage for the next IgG infusion of subject.

[0013] In another aspect, the disclosure provides methods and systems for measuring and / or monitoring polypeptide concentrations during plasma donation. For example, in one embodiment, the disclosure provides methods and systems for determining a next eligible date for a plasma donation by a donor subject. The method includes obtaining a first plasma donation from the donor subject on a first date using a plasmapheretic method, wherein the plasmapheretic method comprises separating plasma from a blood stream drawn from the donor subject, thereby forming a plasma stream and a formed elements stream. The method also includes measuring, during the plasmapheretic method, an IgG concentration of the plasma stream using an in-line biosensor comprising a first solid surface functionalized with an IgG binding agent and a first optical sensor in optical communication with the first solid surface. The method also includes determining, based on the IgG concentration of the plasma stream, a second date on which an IgG content of the donor subject's blood will satisfy a donation threshold level, thereby determining the next eligible date for a plasma donation by the donor subject.

[0014] In another aspect, the disclosure provides methods and systems for measuring and / or monitoring polypeptide concentrations during plasma donation. For example, in one embodiment, the disclosure provides methods and systems for determining a next eligible date for a plasma donation by a donor subject. The method includes obtaining a first blood sample from the donor subject on a first date. The method also includes measuring an IgG concentration of the blood sample using an in-line biosensor comprising a first solid surface functionalized with an IgG binding agent and a first optical sensor in optical communication with the first solid surface. The method also includes determining, based on the IgG concentration of the blood sample a second date on which an IgG content of the donor subject's blood will satisfy a donation threshold level, thereby determining the next eligible date for a plasma donation by the donor subject.5DBl / 165702811.1Attorney Ref. 008073-5275-WOBRIEF DESCRIPTION OF THE DRAWINGS

[0015] 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 in which two biosensors are used.

[0016] FIGs.2 A, 2B, 2C, 2D, and 2E illustrate methods for determining a next eligible date for a plasma donation by a donor subject, in which optional elements are indicated by dashed boxes, in accordance with an embodiment of the present disclosure.

[0017] 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.

[0018] Fig.4 illustrates multi-line equipment in accordance with an embodiment of the present disclosure.

[0019] Figs. 5A and 5B 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-regenerati on- 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.

[0020] Figs.6A and 6B 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.

[0021] Fig 7 illustrates a setup for IgG measurement in accordance with an embodiment of the present disclosure.

[0022] Fig. 8 illustrates LSPR signal over time after injection of serial dilutions of IgG, in accordance with an embodiment of the present disclosure.6DBl / 165702811.1Attorney Ref. 008073-5275-WO

[0023] Fig.9 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.

[0024] Figs. 10A, 10B, 10C, and 10D 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.

[0025] Fig. 11 illustrates an apparatus for measuring first and second signal in accordance with an embodiment of the present disclosure.

[0026] Fig. 12 illustrates an LSPR shift signal in time-series data in accordance with an embodiment of the present disclosure.

[0027] Fig. 13 illustrates the correlation between LSPR step response and protein concentration as by standard nephelometry measurement of IgG standards in accordance with an embodiment of the present disclosure.

[0028] Fig. 14A and 14B illustrate correlations between the measured LSPR step (14A) or regression model-predicted IgG concentration (14B) and IgG concentration as measured by nephelometry, respectively, for LSPR measurements made using a protein A functionalized chip.

[0029] Fig. 15A and 15B illustrate correlations between the measured LSPR step (14A) or regression model-predicted IgG concentration (14B) and IgG concentration as measured by nephelometry, respectively, for LSPR measurements made using a protein G functionalized chip.

[0030] Fig. 16 illustrates the correlation between IgG concentration in blood samples as determined by LSPR measurement, using polynomial regression, and by standard nephelometry measurement in accordance with an embodiment of the present disclosure.

[0031] Figs. 17A, 17B, and 17C illustrate the correlation between IgG concentrations determined for IgG standards (17A), donor plasma samples (17B), and blood samples (17C) by LSPR measurement, using polynomial regression, and by standard nephelometry measurement in accordance with an embodiment of the present disclosure. All LSPR measurements and nephelometry measurements were aggregated (averaged) for each sample.7DBl / 165702811.1Attorney Ref. 008073-5275-WO

[0032] Like reference numerals refer to corresponding parts throughout the several views of the drawings.DETAILED DESCRIPTION OF THE INVENTION

[0033] A. Introduction

[0034] Immune globulin products, derived from human plasma, have been used since 1952 to treat patients with immune deficiencies by providing them with pooled immunoglobulin G (IgG) from numerous donors. These products are crucial for managing conditions like primary immune deficiencies, autoimmune diseases, and acute infections, though they require ongoing administration due to their temporary nature. The production of intravenous immunoglobulin (IVIG) is limited by the availability of plasma donations, which are collected through plasmapheresis. However, frequent plasma donations can lower IgG and ferritin levels in donors, posing health risks and resulting in plasma donations with lower than optimal protein contents. Donation regulations vary, with the U.S. allowing more frequent donations than Europe.Deferrals, implemented to ensure donor and recipient safety, can further limit plasma collection, impacting the supply of IgG products. Thus, methods for determining when a donor will next be eligible for a safe and productive next plasma donation are needed in the art. The present disclosure addresses this and other problems in the field.

[0035] In the context of plasma collection and fractionation, timely and reliable information on IgG levels in both donors and products is essential to safeguard donor health, ensure consistent product quality, and enable the development of more individualized treatment strategies.Existing methods such as nephelometry and ELISA are well established and widely accepted, however, they are typically laboratory-based, require dedicated sample processing, and are not readily integrated into real-time process control. Advantageously, the methods and systems described herein facilitate rapid, robust, and accurate measurements at or near the point of collection and processing, ideally with minimal sample preparation and a high degree of automation. In some embodiments, the methods and systems described herein facilitate these and other advantages through use of functionalized sensor chips and optical read-out to detect8DBl / 165702811.1Attorney Ref. 008073-5275-WOchanges in the local refractive index at the sensor surface, thereby enabling quantification of target analytes such as IgG in complex biological matrices.

[0036] As demonstrated in Example 5, LSPR technology is suitable for application to both plasma and whole blood. A key differentiator relative to nephelometry is the capability to perform direct IgG determination in whole blood, without extensive pre-analytical processing. Because nephelometry relies on free IgG in solution and wash steps, it is not readily applicable to real-time whole blood measurement. In contrast, the LSPR platform enables at-line or online assessment of IgG in whole blood, supporting point-of-care or near-donation testing. This functionality can be leveraged to (i) verify that donor IgG levels meet predefined safety criteria before plasma donation, thereby enhancing donor safety, and (ii) support individualized treatment and donation strategies by linking pre-treatment or pre-donation IgG levels to timing and dosing decisions.

[0037] From a clinical and operational perspective, the methods and systems described herein enable more precise scheduling of donations, refined dosing of IgG-containing therapies, and potentially reduced risk of donor IgG depletion. Further, determining more accurately when a donor should provide their next plasma donation has the added benefit of improving the efficiency of plasma fractionation and use of plasma fractionation resources because it prevents collection of donations with lower IgG concentration, thereby increasing the IgG concentration of the starting plasma pools and overall yield of the fractionation process. Likewise, in some embodiments, the methods and systems described herein provide more personalized patient treatment plans, where both the timing of administration and the IgG dose are adapted to individual baseline IgG levels, thereby increasing the likelihood of optimized treatment outcomes.

[0038] In some embodiments, the present disclosure uses optical measurement, such as spectroscopic LSPR (localized surface plasmon resonance), for determining polypeptide concentration (e.g., IgG, albumin, fibrinogen, etc.) in a donor’s blood, using substrate- sensors that have binding affinity for one or more such polypeptides. Advantageously, LSPR technology has the potential to address current gaps in monitoring capabilities for whole blood.Nephelometry, a standard technique for IgG quantification, is typically not applicable to whole 9DBl / 165702811.1Attorney Ref. 008073-5275-WOblood because of the presence of background signal from other blood components. By contrast, LSPR-based detection offers the possibility to measure IgG directly in whole blood, thereby opening a path towards truly real-time assessment of donor IgG levels at the point of care or point of donation. Such a capability would have several important implications. First, it supports enhanced donor safety by enabling immediate confirmation that IgG levels are within acceptable limits prior to plasma donation. Secondly, it facilitates more informed decisions on donation frequency and volume for individual donors, with the aim of reducing the risk of adverse outcomes while maintaining an adequate plasma supply.

[0039] Immunoglobulin G (IgG) is the predominant glycoprotein present in human serum, constituting about 75-80% of total immunoglobulin in humans. Normal adults have a serum concentration of from about 10 mg / mL to about 16 mg / mL IgG. However, IgG depletion occurs as a result of plasmapheresis. Specifically, it was found that after moderate plasmapheresis, IgG levels dropped to a mean of 8.6 mg / mL (range of 5.2-14.5 mg / mL) and after intense plasmapheresis to a mean of 7.1 mg / mL (range of 2.8-11.5 mg / mL). See, for example, Buchacher and Curling, “Development, Design, and Implementation of Manufacturing Processes,” Elsevier, Chapter 42 - Current Manufacturing of Human Plasma Immunoglobulin G, pages 857-76 (2018), the content of which is hereby incorporated herein by reference.

[0040] Current recommendations suggest that a donor should have IgG levels of at least 6 mg / mL to safely provide a donation. See, for example, Moog R, Laitinen T, and Taborski U., “Safety of Plasmapheresis in Donors with Low IgG Levels: Results of a Prospective, Controlled Multicentre Study,” Transfus. Med. Hemother., 49(5):271-79 (2022) the content of which is hereby incorporated herein by reference. Thus, as even moderate plasmapheresis can cause IgG levels in donors to drop below this safety threshold, there is a need for methods and systems for determining the next time that a patient can safely provide another donation. Evidence suggests that the liver and immune system produce IgG at a rate of roughly 2-4 grams per day to maintain normal levels, reflecting 0.73 to 1.46 grams (or an average of 1.1 grams) per liter of plasma. See, for example, Lublin, D. M., & Wallace, J. (2009). Plasma donation: Effects on immunoglobulin levels and immune function. Journal of Clinical Immunology, 29(3), 295-302; Zhang, S., & Wang, Y. (2015). Immunoglobulin G (IgG) production and the rate of10DBl / 165702811.1Attorney Ref. 008073-5275-WOreplenishment after plasma donation. Journal of Hematology & Blood Transfusion, 25(1), 11-17; and Bingham, D., & Sattar, N. (2002). Restoration of immunoglobulin levels after plasma donation. Transfusion Medicine Reviews, 16(2), 88-94, the contents of which are hereby incorporated by reference.

[0041] Accordingly, in some embodiments, the plasma IgG concentration of a donor is tested after providing a plasma donation and an estimate of how long it will take for the donor’s plasma IgG levels to reach a threshold is determined. In some embodiments, this threshold is set based on maintaining the safety of the donor, e.g., it is set at 6 mg / mL or higher. In other embodiments, this threshold is set based on a desired IgG concentration in the next donation, e.g., to ensure that donations have high IgG contents that improve plasma manufacturing efficiency and yield. For example, even though guidelines suggest that a donor can safely provide a plasma donation when they have a plasma IgG concentration of 6 mg / mL, the threshold can be set higher (e.g., at least 7 mg / mL, at least 8 mg / mL, at least 9 mg / mL, at least 10 mg / mL, at least 11 mg / mL, at least 12 mg / mL, at least 13 mg / mL, at least 14 mg / mL, or higher) to promote the efficiency of the plasma fractionation process.

[0042] In some embodiments, this estimate is performed by modeling the recovery of IgG levels in the patient. For example, by assuming the donor will generate an additional 0.73 to 1.46 grams of IgG per liter of plasma per day, the amount of time it will take the donor to produce enough IgG to bring their plasma levels above a threshold can be calculated. In some embodiments, this can be modeled as a simple regression, e.g., the desired concentration of IgG (e.g., g IgG per L) is equal to the current concentration of IgG (e.g., g IgG per L) plus the product of an IgG production rate (e.g., 0.73 to 1.46 grams of IgG per L of plasma per day) and the target time (Ci = Co + R*T), where the time (T) can be solved for. However, other parametric and non-parametric models can also be used to estimate a recovery time for the donor. In some embodiments, where a donor is a repeat donor, a historical rate of IgG recovery can be modeled for the individual donor and that rate used for such calculation.

[0043] Beyond donor management, the implementation of an at-line or online IgG assay could contribute to more refined patient treatment regimens. By linking pre-treatment IgG levels with dosing decisions, clinicians and product developers may be able to move towards a more11DBl / 165702811.1Attorney Ref. 008073-5275-WOpersonalized approach, in which both the timing of treatment and the administered IgG dose are tailored to the individual. This has the potential to improve clinical outcomes, reduce over- or under-dosing, and optimize the overall use of plasma-derived medicinal products.

[0044] In some embodiments, patients can be tested for IgG values via blood sample (finger tip). If the patient’s IgG level is below a threshold, e.g., below the 6.0 g IgG per liter plasma safety threshold, an injection is necessary to reconstitute the patient’s IgG concentration to a predetermined concentration, e.g., at least 7 mg / mL, at least 8 mg / mL, at least 9 mg / mL, at least 10 mg / mL, at least 11 mg / mL, at least 12 mg / mL, at least 13 mg / mL, at least 14 mg / mL, or higher. Since every patient shows a different IgG consumption, this approach allows for a personalized treatment and avoids overdosing (which is crucial since IgG of plasmatic origin has a limited availability on the market - market supply to a broad set of patients is of high importance).

[0045] In an analogous fashion to determining a next time for plasma donation, a patient’s IgG concentration can be modeled over time to predict when the patient’s IgG levels will fall below the threshold triggering redosing of the patient, e.g., below 9 mg / mL, below 8 mg / mL, below 7 mg / mL, below 6 mg / mL, or lower. In some embodiments, an average rate of IgG concentration loss can be used to model a patient. However, if the individual patient’s consumption rate is tracked over time, a personalized rate can be used to better model consumption for that patient.

[0046] In some embodiments the present disclosure uses a first substrate-sensor with affinity for a first polypeptide and a second substrate-sensor with affinity for a second polypeptide, e.g., to determine the concentration of multiple polypeptides in the donor’s blood. In some embodiments, 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.

[0047] 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, by12DBl / 165702811.1Attorney Ref. 008073-5275-WOway 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.

[0048] It will be appreciated that the sensors used in the methods and systems described herein are not limited to LSPR and can be applied for further analytical methods such as surface plasmon resonance (SPR), ELISA (Enzyme-Linked Immunosorbent Assay), 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. In other embodiments, the system is not integrated in-line, and is used to test samples collected from the subject prior to and / or following plasma donation. For example, in some embodiments, blood from a finger prick used to measure total hematocrit in the donor’s blood is also analyzed with such systems as described herein. In some embodiments the system is used to test samples (for example blood or plasma) collected from the subject prior to and / or following IgG infusion.

[0049] 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.

[0050] In some embodiments, the disclosed apparatus can be operated in inline, online, or at-line / offline mode and is therefore considered as multi-line equipment.

[0051] B. Definitions

[0052] 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.13DBl / 165702811.1Attorney Ref. 008073-5275-WO

[0053] 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.

[0054] 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.”

[0055] 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.

[0056] 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.

[0057] 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.

[0058] 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.

[0059] 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 14DBl / 165702811.1Attorney Ref. 008073-5275-WOaddition 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.

[0060] 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.

[0061] 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.

[0062] 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 etal., 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.

[0063] 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 specifically15DBl / 165702811.1Attorney Ref. 008073-5275-WObound 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 with the 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.

[0064] 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.

[0065] 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 16DBl / 165702811.1Attorney Ref. 008073-5275-WOsubdivided 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 (IgAl and IgA2), 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) and two 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).

[0066] 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.

[0067] 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.17DBl / 165702811.1Attorney Ref. 008073-5275-WO

[0068] 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, ceramic particles 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.

[0069] 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 18DBl / 165702811.1Attorney Ref. 008073-5275-WOthan 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).

[0070] As used interchangeably herein, the terms “classifier,” “model,” or “regressor” interchangeably refer to a machine learning model. In some embodiments, a model is an unsupervised 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.

[0071] 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 19DBl / 165702811.1Attorney Ref. 008073-5275-WOgiven 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 be mentally performed. In some embodiments n is between 10,000 and 1 x 107, 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.

[0072] “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, 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 and20DBl / 165702811.1Attorney Ref. 008073-5275-WOdeamidation), 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.

[0073] 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 of Sciences 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.

[0074] ‘ ‘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.

[0075] 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 21DBl / 165702811.1Attorney Ref. 008073-5275-WOpolypeptide 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).

[0076] “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.

[0077] C. Abbreviations

[0078] CMS Carboxymethyl Sepharose

[0079] CSP Capto - Sulphopropyl

[0080] CV column volumes

[0081] ELISA Enzyme-linked immunosorbent assay

[0082] EtOH Ethanol

[0083] FC Final Container22DBl / 165702811.1Attorney Ref 008073-5275-WO

[0084] IgA Immunoglobulin A

[0085] IGI Immune Globulin Infusion

[0086] IGSC Immune Globulin Subcutaneous

[0087] IgG Immunoglobulin G

[0088] IgM Immunoglobulin M

[0089] LoD / LoQ Limit of Detection / Limit of Quantification

[0090] LSPR Localized surface-plasmon resonance

[0091] MEK Methyl Ethyl Ketone

[0092] mAU milli Absorbance Units

[0093] OPC Open Platform Communication

[0094] PAT Process Analytical Technology

[0095] pm picometer

[0096] Ppt G Precipitate G intermediate

[0097] QC Quality Control

[0098] RoD Recovery of Detection

[0099] S / D Solvent / Detergent

[0100] SOP Standard Operating Procedure

[0101] TP Total Protein

[0102] UV Ultraviolet

[0103] D. Exemplary systems for monitoring a concentration of a first polypeptide type.23DBl / 165702811.1Attorney Ref. 008073-5275-WO

[0104] FIGs. 1 A 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. 1 A and IB, in typical embodiments, computer system 100 comprises one or more computers. For purposes of illustration in FIGs. 1 A 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.

[0105] 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 on computers 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.

[0106] 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 injected24DBl / 165702811.1Attorney Ref 008073-5275-WOinto 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;• an optional 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,25DBl / 165702811.1Attorney Ref. 008073-5275-WOo 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;• an optional 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, ando a calculated concentration of a first polypeptide type 152 in the sample.

[0107] Fig. 10A 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. 10 A, the measuring window begins at line 1102 and ends at the end time 106. Second, referring to Figs. 10B and IOC, 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 (2) and (3). In some embodiments, the median value before (baseline signal 126 illustrated in Fig. 10D) and the median value 124 after the event 122 is measured (4). In some embodiments, referring to Fig. IOD, the value before the event (baseline signal 126) is subtracted from the median value 124 to calculate the first sensor baseline corrected event signal 128 (4). 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 the26DBl / 165702811.1Attorney Ref. 008073-5275-WOcorrected event signal 128, for instance in cases where the concentration of the first polypeptide type in the sample is high.

[0108] 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 memory devices and correspond to a set of instructions for performing a function described above. The above identified data, modules or programs (e.g., 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.

[0109] E. Exemplary Methods

[0110] Now that an apparatus for monitoring a concentration of a first polypeptide type in plasma or blood of a donor subject has been described in conjunction with Figs. 1A and IB, various methods for determining a next eligible date for a plasma donation by a donor subject are described below in conjunction with Figs. 2A through 2E.

[0111] Accordingly, in some embodiments, a method 200 is provided for determining a next eligible date for a plasma donation by a donor subject are provided.

[0112] Referring to block 202, in some embodiments, a first signal is obtained from a first biosensor on a first date of a first plasma donation by the donor subject. In some embodiments the plasma donation is collected by plasmapheresis. In some embodiments the plasma is frozen 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. In some embodiments the plasma donation is recovered from a whole blood donation.

[0113] 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, where the sample 27DBl / 165702811.1Attorney Ref. 008073-5275-WOincludes the first polypeptide. Fig. 10 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.

[0114] 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 et al., 2022, “Nanop lasmonic 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 LSPR 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.

[0115] In some embodiments, the first biosensor comprises a first solid surface functionalized with a first binding agent for a first polypeptide type and a first optical sensor in optical communication with the first solid surface. In some embodiments, the first signal is obtained when the first solid surface is exposed to a blood or plasma sample from the donor subject.

[0116] In some embodiments, the first biosensor (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 Mlhpolymer type). Each respective binding affinity 116 in the first plurality of binding affinities is for a28DBl / 165702811.1Attorney Ref. 008073-5275-WOcorresponding polymer type in a first plurality of polymer types that includes a first polymer type. For example, as illustrated in Figure 3, the Protein G sensor chip has appreciable binding affinity for IgA, IgG4, IgG3, IgG2, and IgGl.

[0117] 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.

[0118] 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.

[0119] 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-resolved measurement 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 G 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 G sensor chip of Fig. 3, each respective time-resolved measurement is a function of the concentration of IgG3 in the sample at the respective time step, the binding affinity of the protein G sensor chip for IgG3, the concentration of IgG4 in the sample at the respective time step, the binding affinity of the protein G sensor chip for IgG4, the concentration of IgG2 in the sample at the respective time step, the binding affinity of the protein G sensor chip for IgG2, the concentration of IgGl in the sample at the respective time step and the binding affinity of the protein G sensor chip for IgGl . In typical embodiments, when the first sensor chip does not 29DBl / 165702811.1Attorney Ref. 008073-5275-WOhave 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. The use of such first sensor chip may be combined with the use of second sensor chip which second sensor chip does have binding affinity for a given polymer type. Thus, the measurement information as obtained from the first sensor chip may serve as a background or blank information with respect to the polymer type as measurement by the second sensor chip.

[0120] 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.

[0121] In some embodiments, at least the first signal is analyzed to obtain a second date on which a protein content of the donor subject’s blood will satisfy a plasma donation criteria. In some embodiments, obtaining the second date determines the next eligible data for a second plasma donation by the donor subject.

[0122] In some embodiments, the obtaining of the first signal from a first biosensor on the first data of the first plasma donation and / or the analyzing of the at least first signal to obtain a second date on which a protein content of the donor subject’s blood will satisfy a plasma donation criteria occurs at a computer system comprising one or more processors and memory addressable by the one or more processors. In some embodiments, the memory stores at least one program for execution of the method by the one or more processors.30DBl / 165702811.1Attorney Ref. 008073-5275-WO

[0123] Referring to block 204, in some embodiments, a second signal is obtained from a second biosensor. In some embodiments, the second biosensor comprises a second solid surface functionalized with a second binding agent for a second polypeptide type and a second optical sensor in optical communication with the second solid surface. In some embodiments, the second signal is obtained when the second solid surface is exposed to the blood or plasma sample from the donor subject.

[0124] 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. 10. 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.

[0125] In some embodiments, 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.g., binding affinity 134-1 for a first polymer type, ... , binding affinity 134-M for an Mlhpolymer 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 or IgM.

[0126] 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.31DBl / 165702811.1Attorney Ref. 008073-5275-WO

[0127] 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.

[0128] 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 (see Fig. IB).

[0129] 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.

[0130] 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.

[0131] 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.

[0132] 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 32DBl / 165702811.1Attorney Ref. 008073-5275-WOaddition 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.

[0133] 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.

[0134] 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.

[0135] 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 substrate-based 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.33DBl / 165702811.1Attorney Ref. 008073-5275-WO

[0136] Referring to block 206, in some embodiments, the first signal is of first time-resolved measurements. Referring to block 208, in some embodiments, the second signal is of second time-resolved measurements.

[0137] 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.11. 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 0.01 110 mg / ML for first polymer type detection. In some embodiments, rather than being connected in series as illustrated in Figure 11, analytical inline devices 482-1 and 482-2 are in parallel.

[0138] Referring to block 210, in some embodiments, the first biosensor is a localized surface plasmon resonance (LSPR) sensor.

[0139] Referring to block 212, in some embodiments, the second biosensor is a LSPR sensor.

[0140] Referring to block 214, in some embodiments, the first polypeptide type is Immunoglobulin G (IgG) or an IgG subtype.

[0141] Referring to block 216, in some embodiments, the first polypeptide type is immunoglobulin A (IgA), immunoglobulin M (IgM), immunoglobulin E (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,34DBl / 165702811.1Attorney Ref. 008073-5275-WOcomplement component C5b, fibrinogen, plasmin, anti-thrombin, factor VII, 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, or haptoglobin.

[0142] Referring to block 218, in some embodiments, the first polypeptide type is albumin, a complement component, or fibrinogen. Referring to block 220, in some embodiments, the second polypeptide type is IgG or an IgG subtype. Referring to block 222, in some embodiments, the first binding agent is protein A or protein G. Referring to block 224, in some embodiments, the first binding agent is protein A and the second binding agent is protein G.

[0143] 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 VII, factor XII, factor VIII, factor IX, factor X, factor XI, Von Willebrand factor, antithrombin III, Cl -esterase inhibitor, and haptoglobin.

[0144] 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-235DBl / 165702811.1Attorney Ref. 008073-5275-WOmacroglobulin, 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.

[0145] 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.

[0146] 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.

[0147] 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.

[0148] 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.

[0149] 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 36DBl / 165702811.1Attorney Ref. 008073-5275-WOinclude 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 of Sciences 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.

[0150] 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.

[0151] 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.11.

[0152] 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 37DBl / 165702811.1Attorney Ref. 008073-5275-WOflow 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. 11.

[0153] 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 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 Ka from the second 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 Ka 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 LSPR 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. In some embodiments, the second signal is modulated by second Kon, Koff, or Ka from the second set of time-resolved measurements arising from the lack of interaction of the second plurality of polymer types with a second non-functionalized solid surface of the second sensor.

[0154] 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 38DBl / 165702811.1Attorney Ref. 008073-5275-WOsubstrate-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. 10D, 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. 10D, 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.39DBl / 165702811.1Attorney Ref. 008073-5275-WO

[0155] 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 40DBl / 165702811.1Attorney Ref. 008073-5275-WO5 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.

[0156] Referring to block 226, in some embodiments, the blood or plasma sample is collected prior to the first plasma donation.

[0157] Referring to block 228, in some embodiments, at least the first signal is analyzed on the first date to obtain a second date on which a protein content of the donor subject’s blood will satisfy a plasma donation criteria. In some embodiments, obtaining the second date determines the next eligible date for a second plasma donation by the donor subject.

[0158] Referring to block 230, in some embodiments, the analysis comprises inputting at least the first signal into a first model to receive as output from the first model an observed concentration of the first polypeptide type in the blood or plasma sample.

[0159] Referring to block 232, in some embodiments, the analysis comprises inputting at least the first signal and the second signal into a first model to receive as output from the first model at least an observed concentration of the first polypeptide type in the blood or plasma sample.

[0160] Referring to block 234, in some embodiments, the output from the first model further comprises an observed concentration of the second polypeptide type in the blood or plasma sample.

[0161] Referring to block 236, in some embodiments, the first model comprises 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.

[0162] 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 sample through interaction of the first plurality of parameters with the first and second signal. In some such embodiments41DBl / 165702811.1Attorney Ref. 008073-5275-WOthe 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.

[0163] 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.

[0164] 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.

[0165] 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 42DBl / 165702811.1Attorney Ref. 008073-5275-WOform 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, and LightGBM. 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.

[0166] 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.

[0167] 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 multicategory 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 100043DBl / 165702811.1Attorney Ref. 008073-5275-WOparameters (e.g., weights) and requires a computer to calculate because it cannot be mentally solved.

[0168] 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.

[0169] 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,44DBl / 165702811.1Attorney Ref. 008073-5275-WOsoftsign, parametric rectified linear unit, exponential linear unit, softPlus, bent identity, softExponential, Sinusoid, Sine, Gaussian, or sigmoid function, or any combination thereof.

[0170] 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 from a 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.

[0171] 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.

[0172] 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 et 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.45DBl / 165702811.1Attorney Ref. 008073-5275-WOabs / 1212.5701; and Rumelhart etal., 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.

[0173] 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, for example, 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 in Duda 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.

[0174] 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. , 2000,46DBl / 165702811.1Attorney Ref. 008073-5275-WOBioinformatics 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.

[0175] 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 etal., 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.

[0176] 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 47DBl / 165702811.1Attorney Ref. 008073-5275-WOis 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 distance calculations 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.

[0177] 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.

[0178] 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.

[0179] 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.

[0180] 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.

[0181] Referring to block 238, in some embodiments, the analysis comprises a polynomial fitting of at least the first signal to receive as output from the fitting an observed concentration of the first polypeptide type in the blood or plasma sample. 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 48DBl / 165702811.1Attorney Ref. 008073-5275-WOmodeled 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.

[0182] Referring to block 240, in some embodiments, the analysis comprises a finite or infinite impulse response evaluation of at least the first signal to receive as output from the evaluation an observed concentration of the first polypeptide type in the blood or plasma sample. General examples 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.

[0183] Referring to block 242, in some embodiments, the analysis comprises a Z-Transform analysis of at least the first signal to receive as output from the Z-Transform analysis an observed concentration of the first polypeptide type in the blood or plasma sample. 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.

[0184] Referring to block 244, in some embodiments, the analysis comprises a mechanistic modeling of at least the first signal to receive as output from the modeling an observed concentration of the first polypeptide type in the blood or plasma sample. Referring to block 246, 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. 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 396.49DBl / 165702811.1Attorney Ref. 008073-5275-WO

[0185] Referring to block 248, in some embodiments, the analysis further comprises inputting at least the observed concentration of the first polypeptide type into a second model to receive as output from the model a time until the second date.

[0186] Referring to block 250, in some embodiments, the second model is a linear regression model.

[0187] Referring to block 252, in some embodiments, the second 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.

[0188] 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.

[0189] 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 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 signal.

[0190] 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.

[0191] Referring to block 254, in some embodiments, the plasma donation criteria includes a criterion that donor’s blood contains at least a threshold level of IgG. Referring to block 256, in some embodiments, the threshold level of IgG is from 6 g IgG per L plasma to 8 g IgG per L 50DBl / 165702811.1Attorney Ref. 008073-5275-WOplasma. In some embodiments, the threshold level of IgG is 5 g / L plasma, 5.5 g / L plasma, 5.75 g / L plasma, 5.9 g / L plasma, 6 g / L plasma, 6.25 g / L plasma, 6.5 g / L plasma, 6.75 g / L plasma, 7 g / L plasma, 7.25 g / L plasma, 7.5 g / L plasma, 7.75 g / L plasma, 8 g / L plasma, 8.25 g / L plasma, or higher.

[0192] Referring to block 258, in some embodiments, the plasma donation criteria includes a criterion that donor’s blood contains at least a threshold level of albumin.

[0193] Referring to block 260, in some embodiments, the plasma donation criteria includes a criterion that donor’s blood contains at least a threshold level of a complement component.

[0194] Referring to block 262, in some embodiments, the plasma donation criteria includes a criterion that donor’s blood contains at least a threshold level of fibrinogen.

[0195] Referring to block 263, in some embodiments, the method for determining a next eligible date for a plasma donation by a donor subject further comprises storing the second date in a server as a database entry associated with the donor subject. In some embodiments, the database comprises a registry of donor subjects.

[0196] Referring to block 264, in some embodiments, the method for determining a next eligible date for a plasma donation by a donor subject further comprises transmitting the second date from the server to a client device associated with the donor subject.

[0197] Referring to block 266, in some embodiments, the client device is configured to display the second date in a plasma donation application.

[0198] Referring to block 268, in some embodiments, the client device is configured to display the second date in a calendar application.

[0199] Referring to block 270, in some embodiments, the client device is configured to display the second date in a pop-up notification.

[0200] Referring to block 272, in some embodiments, the method for determining a next eligible date for a plasma donation by a donor subject further comprises cross-referencing, in response to receiving a request from the donor subject to make a second plasma donation, the database entry51DBl / 165702811.1Attorney Ref. 008073-5275-WOassociated with donor subject and accepting the second plasma donation only if the next eligible date has arrived.

[0201] Referring to block 274, in some embodiments, the method for determining a next eligible date for a plasma donation by a donor subject further comprises determining whether a concentration of the first polypeptide type in the blood or plasma sample satisfies a donation threshold level.

[0202] Referring to block 276, in some embodiments, the method for determining a next eligible date for a plasma donation by a donor subject further comprises communicating, when the concentration of the first polypeptide type in the blood or plasma sample does not satisfy the donation threshold level, directions to terminate the first plasma donation.

[0203] In some aspects, a computer system for determining a next eligible date for a plasma donation by a donor subject is provided. In some embodiments, the computer system comprises one or more processors. In some embodiments, the computer system comprises memory addressable by the one or more processors. In some embodiments, the memory stores at least one program for execution by the one or more processors. In some embodiments, the at least one program comprises instructions for performing all or a portion of the method for determining a next eligible date for a plasma donation by a donor subject.

[0204] In some aspects, a computer system for determining a next eligible date for a plasma donation by a donor subject is provided. In some embodiments, the computer system comprises one or more processors. In some embodiments, the computer system comprises memory addressable by the one or more processors. In some embodiments, the memory stores at least one program for execution by the one or more processors.

[0205] In some embodiments, the at least one program comprises instructions for obtaining, on a first date of a first plasma donation, a first signal from a first biosensor. In some embodiments, the first biosensor comprises a first solid surface functionalized with a first binding agent for a first polypeptide type and a first optical sensor in optical communication with the first solid surface. In some embodiments, the first signal is obtained when the first solid surface is exposed to a blood or plasma sample from the donor subject. In some embodiments, the at least one 52DBl / 165702811.1Attorney Ref. 008073-5275-WOprogram comprises instructions for analyzing, on the first date, at least the first signal to obtain a second date on which a protein content of the donor subject’s blood will satisfy a plasma donation criteria. In some embodiments, obtaining the second date determines the next eligible date for a second plasma donation by the donor subject.

[0206] In some aspects, a non-transitory computer readable storage medium is provided. In some embodiments, the computer readable storage medium stores instructions. In some embodiments, the instructions cause the computer system to perform all or a portion of the method for determining a next eligible date for a plasma donation by a donor subject.

[0207] In some aspects, a non-transitory computer readable storage medium is provided. In some embodiments, the non-transitory computer readable storage medium stores instructions. In some embodiments, the instructions, when executed by a computer system, cause the computer system to perform a method of determining a next eligible date for a plasma donation by a donor subject. In some embodiments, the method comprises obtaining, on a first date of a first plasma donation by the donor subject, a first signal from a first biosensor. In some embodiments, the first biosensor comprises a first solid surface functionalized with a first binding agent for a first polypeptide type and a first optical sensor in optical communication with the first solid surface. In some embodiments, the first signal is obtained when the first solid surface is exposed to a blood or plasma sample from the donor subject.

[0208] In some embodiments, the method comprises analyzing, on the first date, at least the first signal to obtain a second date on which a protein content of the donor subject’s blood will satisfy a plasma donation criteria. In some embodiments, the step of analysis at least the first signal determines the next eligible date for a second plasma donation by the donor subject.

[0209] In some aspects, methods for determining a next eligible date for an immunoglobulin G (IgG) infusion of subject are provided.

[0210] In some embodiments, a first signal is obtained from a first biosensor on a first date of a first IgG infusion of the subject. In some embodiments, the first biosensor comprises a first solid surface functionalized with a first IgG binding agent and a first optical sensor in optical53DBl / 165702811.1Attorney Ref. 008073-5275-WOcommunication with the first solid surface. In some embodiments, the first signal is obtained when the first solid surface is exposed to a blood or plasma sample from the subject.

[0211] In some embodiments, at least the first signal is analyzed to obtain a second date on which an IgG content of the subject’s blood will satisfy an infusion criteria. In some embodiments, obtaining the second date determines the next eligible date for a second IgG infusion of the subject.

[0212] In some embodiments, obtaining the first signal from a first biosensor on the first date of the first IgG infusion of the subject and / or analyzing the at least first signal to obtain a second date on which an IgG content of the subject’s blood will satisfy an infusion criteria occurs at a computer system comprising one or more processors and memory addressable by the one or more processors. In some embodiments, the memory stores at least one program for execution of the method by the one or more processors.

[0213] In some aspects, methods for determining a dosage for a next immunoglobulin G (IgG) infusion of subject are provided. In some embodiments, a first signal is obtained from a first biosensor on a first date of a first IgG infusion of the subject. In some embodiments, the first biosensor comprises a first solid surface functionalized with a first IgG binding agent and a first optical sensor in optical communication with the first solid surface. In some embodiments, the first signal is obtained when the first solid surface is exposed to a blood or plasma sample from the subject.

[0214] In some embodiments, at least the first signal is analyzed to obtain a dosage for a first IgG infusion of the subject on a first date. That is, in some embodiments, a sample of the subject’s blood is analyzed prior to an IgG infusion to determine the dosage of the IgG infusion. In some embodiments, the analysis is performed on the same day as the first IgG infusion. In some embodiments, the analysis is performed within an hour of the first IgG infusion. In some embodiments, the analysis is performed within 30 minutes of the first IgG infusion. In some embodiments, the analysis is performed within 15 minutes of the first IgG infusion. In some embodiments, the analysis is performed contemporaneously with the first IgG infusion. For example, in some embodiments, a sample of the subject’s blood is taken prior to beginning the54DBl / 165702811.1Attorney Ref. 008073-5275-WOIgG infusion, e.g., by collecting blood from a finger prick, the first infusion is then begun and the analysis is performed during the first IgG infusion to determine when to end the first infusion, e.g., when a dose determined by the analysis has been administered to the subject.

[0215] In some embodiments, obtaining the dosage at the first date determines the dosage for the next IgG infusion of the subject. For example, in some embodiments, a sample of the subject’s blood is analyzed prior to a first IgG infusion, e.g., using blood collected from a finger prick, and analysis of the sample informs a dosage of a second IgG infusion to the patient. In some such embodiments, the analysis further informed by analysis of the subject’s blood after completion of the first IgG infusion, e.g., using blood collected from a finger prick following completion of the first infusion. In some embodiments, a sample of the subject’s blood is analyzed after a first IgG infusion, e.g., using blood collected from a finger prick, and analysis of the sample informs a dosage of a second IgG infusion to the patient. In some embodiments, the analysis relies on IgG levels of the subject collected across multiple time points, e.g., prior to each of a plurality of IgG infusions.

[0216] In some embodiments, at least the first signal is analyzed to obtain a dosage for a second IgG infusion of the subject on a second date. In some embodiments, obtaining the dosage determines the dosage for the next IgG infusion of the subject.

[0217] In yet another aspect, the disclosure provides methods and systems for determining a next eligible date for a plasma donation by a donor subject that include measuring the concentration of IgG during a first plasma donation. In some embodiments, the method includes obtaining a first plasma donation from the donor subject on a first date using a plasmapheretic method, wherein the plasmapheretic method comprises separating plasma from a blood stream drawn from the donor subject, thereby forming a plasma stream and a formed elements stream. The method also includes measuring, during the plasmapheretic method, an IgG concentration of the plasma stream using an in-line biosensor comprising a first solid surface functionalized with an IgG binding agent and a first optical sensor in optical communication with the first solid surface. The method also includes determining, based on the IgG concentration of the plasma stream, a second date on which an IgG content of the donor subject's blood will satisfy a donation55DBl / 165702811.1Attorney Ref. 008073-5275-WOthreshold level, thereby determining the next eligible date for a plasma donation by the donor subject.

[0218] In some embodiments, the measuring comprises obtaining a first a signal from the in-line biosensor sensor when the first solid surface is exposed to a sample of the plasma stream, obtaining a second signal from a second biosensor, the second biosensor comprising a second solid surface functionalized with the IgG binding agent and a second optical sensor in optical communication with the solid surface, when the second solid surface is exposed to a reference sample, and responsive to an analysis of the first and second signal, obtaining a calculated concentration of IgG in the plasma stream. In some embodiments, the in-line biosensor is a localized surface plasmon resonance (LSPR) sensor. In some embodiments, the second biosensor is an LSPR sensor. In some embodiments, the second biosensor is the in-line biosensor.

[0219] In some embodiments, the IgG binding agent is protein A. In some embodiments, the IgG binding agent is protein G.

[0220] In some embodiments, the reference sample is a sample of the plasma stream treated to deplete IgG.

[0221] In some embodiments, the analysis of the first and second signal comprises inputting at least the first and second signal into a first model to receive as output from the first model the IgG concentration of the plasma stream.

[0222] In some embodiments, the method further comprising inputting a value for one or more covariates into the first model.

[0223] In some embodiments, the first 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.

[0224] In some embodiments, the analysis of the first and second signal comprises a polynomial fitting of the first and second signal.56DBl / 165702811.1Attorney Ref. 008073-5275-WO

[0225] 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.

[0226] In some embodiments, the analysis of the first and second signal comprises a Z- Transform analysis of the first and second signal.

[0227] In some embodiments, the analysis of the first and second signal comprises a mechanistic modeling of the first and second signal.

[0228] 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.

[0229] In some embodiments, the blood stream is drawn from the donor subject over a first time period and the plasma stream is measured during the last quarter of the first time period.

[0230] In some embodiments, determining the second date comprises inputting at least the IgG concentration of the plasma stream into a second model to receive as output from the model a time until the IgG content of the donor subject's blood will satisfy the donation threshold level.

[0231] In some embodiments, the method further comprising inputting a value for one or more co variates for the subject into the second model.

[0232] In some embodiments, the second model is a linear regression model.

[0233] 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.

[0234] In some embodiments, the donation threshold level is from 6 g IgG per L plasma to 8 g IgG per L plasma.

[0235] In some embodiments, the method further includes storing the second date in a server as a database entry associated with the donor subject, wherein the database comprises a registry of donor subjects.57DBl / 165702811.1Attorney Ref. 008073-5275-WO

[0236] In some embodiments, the method further comprising cross-referencing, in response to receiving a request from the donor subject to make a second plasma donation, the database entry associated with donor subject and accepting the second plasma donation only if the next eligible date has arrived.

[0237] In some embodiments, the method further comprising transmitting the second date from the server to a client device associated with the donor subject.

[0238] In some embodiments, the client device is configured to display the second date in a plasma donation application.

[0239] In some embodiments, the client device is configured to display the second date in a calendar application.

[0240] In some embodiments, the client device is configured to display the second date in a popup notification.

[0241] In one aspect, the disclosure provides methods and systems for monitoring IgG concentration in a plasma donation. In some embodiments, the method includes obtaining a first plasma donation from the donor subject using a plasmapheretic method, wherein the plasmapheretic method comprises separating plasma from a blood stream drawn from the donor subject, thereby forming a plasma stream and a blood cell stream. In some embodiments, the method also includes measuring, within separation of a first 50 mL of the plasma stream, an IgG concentration of the plasma stream using an in-line biosensor comprising a first solid surface functionalized with an IgG binding agent and a first optical sensor in optical communication with the solid surface. In some embodiments, the method also includes terminating the first plasma donation when the IgG concentration of the plasma stream does not satisfy a donation threshold level.

[0242] F. Examples

[0243] 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.58DBl / 165702811.1Attorney Ref. 008073-5275-WO

[0244] 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.

[0245] Table 1 - Applicable limits for plasma fractions and the final product.

[0246] 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.

[0247] 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.

[0248] 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.

[0249] 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,59DBl / 165702811.1Attorney Ref. 008073-5275-WOmanifested 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.

[0250] 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.

[0251] 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 the electrons 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.

[0252] 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.

[0253] 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 advantage60DBl / 165702811.1Attorney Ref. 008073-5275-WOof 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.

[0254] 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.

[0255] 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, or others. 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. 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.

[0256] 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. The 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. It is seen in Fig. 1 when the beads are in the unbound state “1”, very little plasmon response is measured. As the bead bind to the protein of interest (first polypeptide type) in bound state “2”, the local refractive index of the beads shifts.61DBl / 165702811.1Attorney Ref. 008073-5275-WO

[0257] 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.

[0258] 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 system that combines actuators (pumps and valves), sensors, and controllers to preprocess samples for condition-sensitive analytical sensors.

[0259] 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 defined- 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.

[0260] 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.

[0261] The system illustrated in Fig. 4 can be operated in inline, online, and atline / offline mode and is therefore considered as multi-line equipment.

[0262] The system required only one calibration solution and self-calibrates with up to (n2) calibration points (including blank), for n- injection valve setup.62DBl / 165702811.1Attorney Ref. 008073-5275-WO

[0263] 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.

[0264] Example 2 - Technical details of example multi-line equipment.

[0265] 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 acceptable level. 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.

[0266] Referring to Fig. 4, three flow circuits were interconnected. Circuit 1 and circuit 2 were connected via n injection valves 462-1, ... , 462-n, 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- / n) 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.

[0267] 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,63DBl / 165702811.1Attorney Ref. 008073-5275-WOconductivity 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.

[0268] 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.

[0269] 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.

[0270] 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.

[0271] The example multi-line equipment 460 had multiple measurement and calibration methods and was configured to support custom application methods. The software was64DBl / 165702811.1Attorney Ref. 008073-5275-WOconfigured to export calibrated data in different data formats, including CSV, JSON, or other formats.

[0272] 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.

[0273] Example 3 - Real time online measurement.

[0274] This following example exemplifies real-time measurement of IgG levels in an IgG manufacturing process, using first and second biosensors that are differentially functionalized. Although some embodiments of the present disclosure use a first biosensor that is functionalized and a second biosensor that is not functionalized, e.g., to provide a background signal, this example is meant to demonstrate the general principle that IgG levels can be determined using LSPR techniques.

[0275] Ethanol fractionation selectively precipitates proteins according to ethanol concentration, temperature, pH, protein concentration and conductivity as major variables under the principle of salting-out. 11+111 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).

[0276] 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.65DBl / 165702811.1Attorney Ref. 008073-5275-WO

[0277] 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 an LSPR biosensor (offline, 100 p). 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%.

[0278] While no meaningful change was observed for the aggregate’s suspension samples (Fig.6A), 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. 6B).

[0279] 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.

[0280] Example 4 - Correlation of concentration to signal using IgG titration.

[0281] The setup used for these experiments is illustrated in Fig. 7. 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. 8). The sensor was first equilibrated with PBS, then an injection of 100 pl of the sample66DBl / 165702811.1Attorney Ref. 008073-5275-WOtook 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.

[0282] 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. 9). This gives a lx logic range of detection. However, for the at-line 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.

[0283] Example 5 - IgG detection directly from plasma and whole blood.

[0284] This example demonstrates the ability of the methods and systems described herein to measure IgG directly from plasma and whole blood. The experiments described below focus on three main aspects: analytical performance, operational robustness, and suitability for integration into plasma-related workflows. Analytical performance is evaluated by comparing the accuracy of LSPR measurements with that obtained using nephelometry and ELISA, with particular attention to plasma and blood matrices. Operational robustness is assessed through the investigation of chip re-use, standard curve application, and the impact of chip cycling on data quality. Suitability for integration is examined by considering how the technology could be implemented in existing at-line or online control architectures.

[0285] Briefly, LSPR measurements of IgG from plasma and whole blood were compared against measurements performed by nephelometry, ELISA, and absorption at 280 nm.Commercially-available 10% (w / v) human normal Immunoglobulin G was used as a reference. To prevent spurious effects of the formulation buffer, the commercial IgG was buffer exchanged into PBS using a HiTrap Desalting Column. Standards were then prepared by serial dilution, each step a third of the previous concentration. Dilutions are named stdl to std7 sequentially, being stdl the highest concentration and std7 the lowest concentration. The concentrations of the standards were then measured by nephelometry, absorption at 280 nm, and ELISA. Finally,67DBl / 165702811.1Attorney Ref. 008073-5275-WOthe standards were measured using LSPR, set similarly as illustrated in Fig. 7. Measurements were performed across multiple chips of different batches, multiple loop sizes (40 uL, 20 uL, and 10 uL).

[0286] Correlation between LSPR step response and IgG concentration as measured by nephelometry.

[0287] As a first analysis, LSPR step response of the standards, using protein A, protein G, and BSA functionalized chips, was plotted against IgG concentration as measured using nephelometry. Briefly, The chip is equilibrated for the first 60 seconds with PBS, the sample is injected (approx 10 pl), and the chip is again equilibrated with PBS, washing the matrix of the sample. The chip, when either protein G or protein A conjugated, captures the IgG present in the sample volume, which remains bound during the wash step. The binding of IgG causes the signal (maximum absorption wavelength) to shift, proportional to the protein G / protein A bound to IgG. To avoid influence of outliers and transient peaks, the signal shift is calculated by estimating the median value before and after injection. This is important because the matrix of whole blood and plasma shifts the signal in a negative direction, unrelated, but after it washes out, the signal corresponds to the bound IgG and therefore the concentration of IgG in plasma. By using the median, the effects of the transient peak holds little impact on the final value B) the calibration solutions, as dilutions of IgG for injection in PBS, do not show any shift due to matrix, only the binding of IgG.

[0288] Surprisingly, the response remains linear, in log scale, even at the highest concentrations. We identify 2 zones, which for most cases is below 1 g / 1, and above 1 g / 1. The second responds linearly to the logarithm of the concentration, while the first is flat and non-responsive. As shown in Fig. 13 A, there is an apparent, uniform correlation between LSPR response and the logarithm of the IgG concentration when using both protein G and protein A functionalized chips, and no signal when using BSA functionalized chips.

[0289] Conventional regression modeling of IgG concentration.

[0290] Given the correlation observed between LSPR step response and IgG concentration, it was determined whether a model could be trained to estimate IgG concentration from LSPR 68DBl / 165702811.1Attorney Ref. 008073-5275-WOdata. As a first attempt, a polynomial linearized regression was trained to predict the log of the IgG concentration, the dependent variable, using LSPR step and chip cycle (the number of times a chip is used for measurement) as independent variables for each chip set used to measure the IgG standards. As exemplified in Figures 14A-14B, using protein A chips, and 15A-15B, using protein G chips, in most cases the regression models had an rA2 above 0.8.

[0291] Next, it was determined whether a model could be trained to determine IgG concentration from LSPR measurements of whole blood. Briefly, LSPR measurements of whole blood samples from 7 individuals were collected in replicates using protein A and protein G chips. Separate regression models were trained for measurements collected using the two chip types. As described above, polynomial linearized regressions were trained to predict IgG concentration, the dependent variable, using LSPR step and chip cycle as independent variables for each chip set used to measure the IgG standards. The results, shown in Fig. 16, evidence that LSPR can be used to measure IgG concentration in complex samples such as plasma and whole blood. Similar regression models were trained to estimate IgG concentration in plasma samples, based on 6 plasma donor samples, data not shown.

[0292] When aggregated, the IgG concentrations determined using the regression models for the IgG standards, donor plasma, and whole blood samples correlate closely with IgG values determined by nephelometry, as illustrated in Figures 17A, 17B, and 17C, respectively.Standards 8-10 are 1:100 dilutions of standard 1, with 0.34 mg / mL IgA spiked into standard 8 and 1 mg / mL IgG3 spiked into standard 10.

[0293] Conclusions for comparison of LSPR vs. established offline analytical methods for IgG concentration measurement.

[0294] This example provides a comprehensive analytical and operational evaluation of localized surface plasmon resonance (LSPR) platform for the quantification of immunoglobulin G (IgG) in plasma and whole blood, with benchmarking against nephelometry, ELISA, and absorbance at 280 nm. In this evaluation, six plasma donor samples were analyzed using an IgG standard-based calibration, supplemented with spiking experiments (IgG3 and IgA) and comparison to internal and external reference methods.69DBl / 165702811.1Attorney Ref. 008073-5275-WO

[0295] Across the tested concentration ranges and matrices, the LSPR technology demonstrated analytical performance for IgG quantification in plasma and whole blood that is within a comparable accuracy range as nephelometry and ELISA, when appropriate calibration and data- processing strategies are applied. The dominant technical determinant of accuracy and precision, beyond IgG concentration and chip type, is chip cycle or reuse: signal drift and response changes across chip cycles must be explicitly accounted for in result calculation. The data supports the recommendation to (i) monitor chip performance over cycles by adding a standard every few cycles and (ii) apply a full or partial standard curve for every chip. Under these conditions, the platform is expected to achieve accuracy that is comparable to that obtained with nephelometry and ELISA, particularly in settings requiring frequent measurements or operation in complex matrices.

[0296] G. Conclusion

[0297] 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 disclosure as set forth in the following claims.

[0298] H. References

[0299] All references cited herein are hereby incorporated by reference herein in their entirety.70DBl / 165702811.1

Claims

1. Attorney Ref. 008073-5275-WOWHAT IS CLAIMED:

1. A method for determining a next eligible date for a plasma donation by a donor subject, the method comprising:at a 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 of the method by the one or more processors:obtaining, on a first date of a first plasma donation, a first signal from a first biosensor, the first biosensor comprising a first solid surface functionalized with a first binding agent for a first polypeptide type and a first optical sensor in optical communication with the first solid surface, when the first solid surface is exposed to a blood or plasma sample from the donor subject; andanalyzing, on the first date, at least the first signal to obtain a second date on which a protein content of the donor subject’s blood will satisfy a plasma donation criteria, thereby determining the next eligible date for a second plasma donation by the donor subject.2 The method of claim 1 , wherein the obtaining further comprises obtaining a second signal from a second biosensor, the second biosensor comprising a second solid surface functionalized with a second binding agent for a second polypeptide type and a second optical sensor in optical communication with the second solid surface, when the second solid surface is exposed to the blood or plasma sample.3 The method of claim 1, wherein the obtaining further comprises obtaining a second signal from a second biosensor, the second biosensor comprising a second, non-functionalized solid surface and a second optical sensor in optical communication with the second solid surface, when the second solid surface is exposed to the blood or plasma sample.4 The method of claim 1, wherein the first signal is of first time-resolved measurements.5 The method of any one of claim 2-4, wherein the second signal is of second time-resolved measurements.71DBl / 165702811.1Attorney Ref. 008073-5275-WO6. The method of any one of claims 1-5, wherein the first biosensor is a localized surface plasmon resonance (LSPR) sensor.

7. The method of any one of claims 2-6, wherein the second biosensor is a LSPR sensor.8 The method of any one of claims 1-7, wherein the first polypeptide type is Immunoglobulin G (IgG) or an IgG subtype.9 The method of any one of claims 1-7, wherein the first polypeptide type is immunoglobulin A (IgA), immunoglobulin M (IgM), immunoglobulin E (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, fibrinogen, plasmin, anti-thrombin, factor VII, 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, or haptoglobin.10 The method of any one of claims 1-7, wherein the first polypeptide type is albumin, a complement component, or fibrinogen.11 The method of any one of claims 2-10, wherein the second polypeptide type is IgG or an IgG subtype.12 The method of claim 8, wherein the first binding agent is protein A or protein G.13 The method of claim 11, wherein the first binding agent is protein G.14 The method of any one of claims 1-13, wherein the blood or plasma sample is collected prior to the first plasma donation.72DBl / 165702811.1Attorney Ref. 008073-5275-WO15. The method of any one of claims 1-14, wherein the analysis comprises inputting at least the first signal into a first model to receive as output from the first model an observed concentration of the first polypeptide type in the blood or plasma sample.

16. The method of any one of claims 2-14, wherein the analysis comprises inputting at least the first signal and the second signal into a first model to receive as output from the first model at least an observed concentration of the first polypeptide type in the blood or plasma sample.

17. The method of claim 16, wherein the output from the first model further comprises an observed concentration of the second polypeptide type in the blood or plasma sample.

18. The method of any one of claims 15-17, wherein the first model comprises 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.

19. The method of any one of claims 1-14, wherein the analysis comprises a polynomial fitting of at least the first signal to receive as output from the fitting an observed concentration of the first polypeptide type in the blood or plasma sample.

20. The method of any one of claims 1-14, wherein the analysis comprises a finite or infinite impulse response evaluation of at least the first signal to receive as output from the evaluation an observed concentration of the first polypeptide type in the blood or plasma sample.

21. The method of any one of claims 1-14, wherein the analysis comprises comprises a Z-Transform analysis of at least the first signal to receive as output from the Z-Transform analysis an observed concentration of the first polypeptide type in the blood or plasma sample.

22. The method of any one of claims 1-14, wherein the analysis comprises a mechanistic modeling of at least the first signal to receive as output from the modeling an observed concentration of the first polypeptide type in the blood or plasma sample.73DBl / 165702811.1Attorney Ref. 008073-5275-WO23. The method of claim 22, 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.

24. The method of any one of claims 15-23, wherein the analysis further comprises inputting at least the observed concentration of the first polypeptide type into a second model to receive as output from the model a time until the second date.

25. The method of claim 24, wherein the second model is a linear regression model.

26. The method of claim 24, wherein the second 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.

27. The method of any one of claims 1-26, wherein the plasma donation criteria includes a criterion that donor’s blood contains at least a threshold level of IgG.

28. The method of claim 27, wherein the threshold level of IgG is from 6 g IgG per L plasma to 8 g IgG per L plasma.

29. The method of any one of claims 1-28, wherein the plasma donation criteria includes a criterion that donor’s blood contains at least a threshold level of albumin.

30. The method of any one of claims 1-29, wherein the plasma donation criteria includes a criterion that donor’s blood contains at least a threshold level of a complement component.

31. The method of any one of claims 1-29, wherein the plasma donation criteria includes a criterion that donor’s blood contains at least a threshold level of fibrinogen.74DBl / 165702811.1Attorney Ref. 008073-5275-WO32. The method of any one of claims 1-31, further comprising storing the second date in a server as a database entry associated with the donor subject, wherein the database comprises a registry of donor subjects.

33. The method of claim 32, the method further comprising cross-referencing, in response to receiving a request from the donor subject to make a second plasma donation, the database entry associated with donor subject and accepting the second plasma donation only if the next eligible date has arrived.

34. The method of claim 32 or 33, the method further comprising transmitting the second date from the server to a client device associated with the donor subject.

35. The method of claim 34, wherein the client device is configured to display the second date in a plasma donation application.

36. The method of claim 34 or 35, wherein the client device is configured to display the second date in a calendar application.

37. The method of any one of claims 34-36, wherein the client device is configured to display the second date in a pop-up notification.

38. The method of any one of claims 1-37, further comprising:determining whether a concentration of the first polypeptide type in the blood or plasma sample satisfies a donation threshold level; andwhen the concentration of the first polypeptide type in the blood or plasma sample does not satisfy the donation threshold level, communicating directions to terminate the first plasma donation.

39. A computer system for determining a next eligible date for a plasma donation by a donor subject, the computer system comprising:one or more processors; and75DBl / 165702811.1Attorney Ref. 008073-5275-WOmemory 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, on a first date of a first plasma donation, a first signal from a first biosensor, the second biosensor comprising a first solid surface functionalized with a first binding agent for a first polypeptide type and a first optical sensor in optical communication with the first solid surface, when the first solid surface is exposed to a blood or plasma sample from the donor subject; andanalyzing, on the first date, at least the first signal to obtain a second date on which a protein content of the donor subject’s blood will satisfy a plasma donation criteria, thereby determining the next eligible date for a second plasma donation by the donor subject.A computer system for determining a next eligible date for a plasma donation by a donor subject, the computer system comprising:one or more processors; andmemory 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 all or a portion of the method of any one of claims 1-38.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 determining a next eligible date for a plasma donation by a donor subject, the method comprising:obtaining, on a first date of a first plasma donation, a first signal from a first biosensor, the second biosensor comprising a first solid surface functionalized with a first binding agent for a first polypeptide type and a first optical sensor in optical communication with the first solid surface, when the first solid surface is exposed to a blood or plasma sample from the donor subject; and76DBl / 165702811.1Attorney Ref. 008073-5275-WOanalyzing, on the first date, at least the first signal to obtain a second date on which a protein content of the donor subject’s blood will satisfy a plasma donation criteria, thereby determining the next eligible date for a second plasma donation by the donor subject.

42. 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 all or a portion of the method of any one of claims 1-38.

43. A method for determining a next eligible date for an immunoglobulin G (IgG) infusion of subject, the method comprising:at a 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 of the method by the one or more processors:obtaining, on a first date of a first IgG infusion, a first signal from a first biosensor, the first biosensor comprising a first solid surface functionalized with a first IgG binding agent and a first optical sensor in optical communication with the first solid surface, when the first solid surface is exposed to a blood or plasma sample from the subject; andanalyzing at least the first signal to obtain a second date on which an IgG content of the subject’s blood will satisfy an infusion criteria, thereby determining the next eligible date for a second IgG infusion of the subject.

44. A method for determining a dosage for a next immunoglobulin G (IgG) infusion of subject, the method comprising:at a 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 of the method by the one or more processors:obtaining, on a first date of a first IgG infusion, a first signal from a first biosensor, the first biosensor comprising a first solid surface functionalized with a first IgG binding agent and a first optical sensor in optical communication with the first solid surface, when the first solid surface is exposed to a blood or plasma sample from the subject; and77DBl / 165702811.1Attorney Ref. 008073-5275-WOanalyzing at least the first signal to obtain an dosage for a second IgG infusion of the subject on a second date, thereby determining the dosage for the next IgG infusion of subject.DBl / 165702811.1

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