USE OF RAMAN SPECTROSCOPY IN DOWNSTREAM PURIFICATION

Abstract

In situ Raman spectroscopy methods and systems are provided for the characterization or quantification of a protein purification intermediate and / or the final concentrated fraction during production or preparation. In one embodiment, in situ Raman spectroscopy is used to characterize or quantify critical quality attributes of protein purification intermediates during downstream processing (i.e., after the protein purification intermediate has been obtained). For example, the described in situ Raman spectroscopy methods and systems can be used to characterize and quantify protein purification intermediates as the protein purification intermediates are purified, condensed, or otherwise formulated into the final pharmaceutical product to be marketed or administered.

Description

USE OF RAMAN SPECTROSCOPY IN DOWNSTREAM PURIFICATION Cross reference to related requests This application claims the benefit and priority of US provisional patent application no. 62 / 723 188 filed August 27, 2018 and, where possible, incorporated in its entirety by this reference. Technical field of the invention The invention relates, broadly, to systems and methods for testing and controlling one or more Critical Quality Attributes (CQA) or parameters in downstream protein purification processes. Background of the invention In the past decade, the number of monoclonal antibodies (mAbs) accepted for therapeutic use has greatly increased. This is due, in part, to improvements in larger-scale manufacturing processes that facilitate the production of large numbers of mAbs. In addition, initiatives such as the Food and Drug Administration's (FDA) process analytical technology (PAT) framework have led to innovative solutions for the development, analysis, and process control to better understand them and control the quality of products. Efficient recovery and purification of mAbs from cell culture media is an essential part of the production process. The purification process should produce mAbs that can be used safely in humans. This includes checking for critical quality attributes (CQA) including protein attributes and impurities such as host cell proteins, DNA, viruses, endotoxins, aggregates, concentrations, excipients and other species that may affect the patient safety, efficacy or potency. Protein concentration is also often a CQA in the purified material, and the appropriate protein concentration in process intermediates can be an essential process parameter for unit operation performance. These CQAs need to be checked during production and during the program cycle. The mAb products are evaluated at various stages of downstream processing to ensure that the final mAb formulations do not contain excessive levels of impurities. In general, quality control of the production of bioproducts such as mAbs is carried out by analyzing purification intermediates and formulated active ingredient samples with off-line methods for each production batch. Samples are withdrawn from processing equipment, for example, a UF / DF structure, and are subjected to off-line analysis to measure product CQAs, such as protein concentration (g / L), buffering excipients, and product variants. size. It is not possible to check and analyze in real time during preparation, which increases processing time and the risk of batch failures due to non-compliance with CQA. Therefore, they are necessary R / zznn / Lznz / B / Yi rapid, online methods for real-time QC check of mAbs. Therefore, one of the objectives of the invention is to provide systems and methods for real-time checking of critical quality attributes during the downstream purification process. Brief description of the invention In situ Raman spectroscopy methods and systems are provided for the characterization or quantification of a protein purification intermediate during production or manufacturing. In one embodiment, in situ Raman spectroscopy is used to characterize or quantify critical quality attributes of a protein drug during downstream processing (ie, after the protein purification intermediate is obtained from the cell culture fluid). For example, the described in situ Raman spectroscopy methods and systems can be used to characterize and quantify critical quality attributes of protein purification intermediates as the protein purification intermediates are being purified, prior to formulation of the protein purification intermediates. final drug to be marketed or administered. Critical quality attributes include, but are not limited to, protein concentration, excipients, high molecular weight (HMW) species, antibody titer, and drug to antibody ratio. One embodiment provides a method of producing a concentrated protein purification intermediate by determining concentrations of a protein purification intermediate in real time using in situ Raman spectroscopy while concentrating or diafiltering the protein purification intermediate. and the parameters of the concentration step are adjusted in real time to obtain the predetermined concentration targets and the amounts of excipients necessary for the formulation of the active ingredient. The protein purification intermediate may have a concentration of 5 mg / mL to 300 mg / mL, preferably 50 mg / mL to 300 mg / mL, for subsequent formulation steps. In one embodiment, the protein purification intermediate is concentrated to a desired concentration target with ultrafiltration during primary or final concentration. Diafiltration is used during processing after primary concentration as a means of buffer exchange to obtain the desired final formulation components. The protein purification intermediate can be obtained by bioreactor, batch culture, or continuous culture. In another embodiment, determination of the concentration of the protein purification intermediate can occur continuously or intermittently in real time. Quantification of protein concentration can be carried out at intervals of approximately 5 seconds to 10 minutes, hourly or daily. The protein purification intermediate can be an antibody or an antigen-binding fragment thereof, a fusion protein, or a recombinant protein. Spectral data may be collected in one or more wavenumber intervals selected from the group consisting of 977-1027 cm1, 1408-1485 cm1, 1621-1711 cm1, 2823-3046 cm1, and combinations thereof. Another embodiment provides a method of producing a protein purification intermediate by performing independent Raman spectroscopy analysis on a variety of protein purification intermediates to produce a universal model capable of R / zznn / Lznz / B / Yi quantify any of a variety of protein purification intermediates. The concentration of a protein purification intermediate can be determined with Raman spectroscopy in situ with the universal model during the concentration of the protein purification intermediate from the start to the end of the concentration of the purification intermediate. Another embodiment provides a method of producing a protein purification intermediate to produce a protein-specific model capable of quantifying protein concentrations that would be used in commercial production of the protein. The model can be produced by partial least squares regression analysis of the raw spectrum data using an orthogonal method for offline protein concentration data. Pre-processing techniques such as standard normal variable (SNV) and / or spot damping techniques can be a first derivative with damping of 21 cm·' which can be applied to Raman spectroscopy data. to mitigate model variability and prediction error. In addition, it is possible to further tune the model to isolate spectral regions that are related to CQA predictions, such as protein concentration. In one embodiment, the model has a margin of error <5%, preferably a margin of error <3%. Yet another embodiment provides a method for monitoring and controlling excipient levels in the obtained cell culture fluid and / or protein purification intermediate during downstream purification by determining excipient concentrations in real time with spectroscopy. Raman in situ while purifying the cell culture fluid or protein purification intermediate, and adjusting the purification step parameters in real time to obtain or maintain predetermined amounts of the excipients in the obtained cell culture fluid and / or the protein purification intermediate. The excipient can be acetate, citrate, histidine, succinate, phosphate, hydroxymethylaminomethane (tris), proline, arginine, sucrose, or combinations of these. The excipient can be a surface excipient, such as polysorbate 80, polysorbate 20, and poloxamer 188. Brief description of the drawings Figure 1 is a flowchart showing an example of a protein purification process. Figure 2 is a representative spectrograph showing the development of the initial model for online Raman spectroscopy. The x-axis represents the Raman shift. Intensity is represented on the y-axis. The legend to the right indicates the protein concentration. Spectral regions used during initial model development include, from left to right, 9771027 cnr1(ring structure), 1408-1485 cm1(arginine), 1621-1711 cm1(secondary structure), and 28233046 cnr1(stretch of C-H). Figure 3 is a bar graph showing protein concentration (g / L) for mAb1 during a standard ultrafiltration / diafiltration unit operation. Unfilled bars represent concentrations determined with the UV-Vis based off-line method with a system R / zznn / Lznz / B / Yi SoloVPE (C-technologies) with error bars of ±5%, which is the goal of online Raman predictions. The light shaded bars represent the model referred to herein as the universal model without mAb1 included in the Raman predictions and the dark shaded bars represent the universal model with mAb1. The cross-hatched bars correspond to the predictions of the initial universal model with a range of 0-120 g / L. The shaded bars correspond to the predictions of the initial universal model >120 g / L. Figure 4 is a bar graph showing the absolute Raman model error for various mAbs from the initial universal model development. The shaded bars represent the initial universal model of 0-120 g / L (primary concentration and diafiltration) and the unfilled bars represent the initial universal model >120 g / L. (final concentration). The horizontal line represents the Raman model target with <5% error. Figure 5 is a bar graph showing the absolute Raman model error for various mAbs. Light shaded bars represent 0-120 g / L (primary concentration and diafiltration) and darker shaded bars represent >120 g / L (final concentration). The horizontal line represents the Raman model target with error <5%. Two versions of the universal Raman model are illustrated. The shaded bars represent the initial universal model and the cross hatched bars represent the updated universal model. Figure 6 is a schematic illustration of an ultrafiltration / diafiltration system including locations for in-line Raman probe placement. Figure 7 is a bar graph showing protein concentration for mAb10 using either the universal model or the mAb10-specific model for final concentrated fraction (FCP) measurements. Protein concentrations for real-time Raman predictions are shown online (lighter shaded bars), updated models (darker shaded bars), and SoloVPE (unfilled bars). The x axis represents the experimental group and the y axis represents the protein concentration. Figures 8A through 8B are bar graphs depicting the Raman model error for laboratory DoE modeling of protein concentration. Illustrated in Figure 8A is the error of the Raman model for real-time predictions at various stages of UF / DF processing (primary concentration, diafiltration, and final concentrated fraction). Illustrated in Figure 8B is the Raman model error for the final DoE model at various stages of the UF / DF processing (primary concentration, diafiltration, and final concentrated fraction). Figure 9A is a bar graph depicting the percent error of model predictability for model scaling to the pilot processing kit for mAb11. Laboratory-scale model data is compared with laboratory-scale model data incorporating the pilot-scale data. The x-axis represents the experimental groups and the y-axis represents the percentage error of the model's predictive ability (%). Figure 9B is a bar graph illustrating model predictability for pilot scale processing of various monoclonal antibodies. The x-axis represents the experimental groups and the y-axis represents the error. R / zznn / Lznz / B / Yi percentage (%) of the model's predictive ability. Figure 10A is a schematic of an example of automatic batch UF / DF with Raman feedback. Figure 10B is a schematic of an example of automatic single-passage TFF with Raman feedback. Figure 11 is a graph illustrating mAb14 concentration during UF / DF processing. The x-axis represents yield (L / m2) and the y-axis represents mAb 14 concentration (g / L). Figure 12A is a bar graph illustrating the percentage of high molecular weight (HMW) species in mAb2 during various processing steps (primary concentration, diafiltration, final concentration) determined either by Raman modeling or SE-UPLC. The x-axis represents the experimental group and the y-axis represents the percentage (%) of HMW mAb2. Figure 12B is a bar graph showing the percentage of high molecular weight (HMW) species predicted with various analysis times (10 seconds, 20 seconds, 30 seconds) for mAb 15. The x-axis represents the group experimental and the y-axis represents the percentage (%) of HMW. Figure 13 is a dot plot showing actual titers (g / L) as a function of Raman predicted titers for mAb 14 monoclonal antibody samples. The x-axis represents the Raman predicted titer and the x-axis represents y represents the actual titer (g / L). Figure 14A is a scatterplot illustrating Raman histidine prediction in various monoclonal antibodies. The x-axis represents the histidine concentration predicted by Raman modeling and the y-axis represents the actual histidine concentration determined by amino acid analysis. Figure 14B is a dot plot illustrating the actual histidine concentration and the Raman predicted histidine concentration for a monoclonal antibody sample. Figure 14C is a dot plot illustrating the actual arginine concentration and the Raman predicted arginine concentration for a single monoclonal antibody sample. Figure 15A is a scatterplot showing the actual drug to antibody ratio (DAR) and the Raman predicted DAR for mAb 3 monoclonal antibody samples. The x-axis represents the DAR. predicted by Raman and the y-axis represents the actual DAR determined by UV spectroscopy. Figure 15B is a scatterplot showing the actual drug to antibody ratio (DAR) and the Raman predicted DAR for mAb 1 monoclonal antibody samples. The x-axis represents the Raman predicted DAR and the x-axis y represents the actual DAR. Detailed description of the invention I. Definitions It should be appreciated that this description is not limited to the compositions and methods described herein or the experimental conditions described, as these may vary. It will also be understood that the terminology used herein is for the purpose of describing only certain embodiments and is not intended to be exhaustive, since the scope of the present description will be limited only by the appended claims. R / zznn / Lznz / B / Yu Unless otherwise indicated, all technical and scientific terms used herein have the same meaning as is commonly understood by a person skilled in the art to which this description pertains. However, in the practice or evaluation of the present invention, any composition, method, and material similar or equivalent to those described herein may be used. All publications mentioned are incorporated herein in their entirety by this reference. The use of the terms "a", "an", "the", "the" and similar referents used in the context of the description of the present invention (especially in the context of the claims) should be interpreted to cover both the singular or plural, unless otherwise stated herein or the context clearly contradicts it. Reference to ranges of values ​​herein is intended merely to be a shorthand method of referring to each independent value within the range, unless otherwise indicated herein, and each separate value is incorporated into the specification. as if individually mentioned herein. The use of the term “around” is intended to describe values ​​that are above or below the stated value within a range of approximately ±10%; in other modes, the values ​​may vary in value above or below the indicated value in a range of approximately ±5%; in other modes, the values ​​may vary in value either above or below the indicated value in a range of approximately ±2%; in other modes, the values ​​may vary in value either above or below the indicated value in a range of approximately ±1%. Context is intended to clarify the foregoing ranges and no further limitation is implied. All of the methods described herein may be carried out in any suitable order, unless otherwise stated herein or the context clearly indicates otherwise. The use of any and all examples or example terms (for example, "such as") provided herein is merely intended to better describe the invention and is not intended to limit the scope of the invention unless otherwise noted. claim otherwise. No expression in the specification should be construed as indicating that anything not claimed is essential to the practice of the invention. As used herein, "protein" refers to a molecule that comprises two or more amino acid residues linked together by a peptide bond. Protein includes polypeptides and peptides and may also include modifications such as glycosylation, lipid binding, sulfation, gamma carboxylation of glutamic acid residues, alkylation, hydroxylation, and ADP ribosylation. Proteins may be of scientific or commercial interest, including protein-based drugs, and proteins include, but are not limited to, enzymes, ligands, receptors, antibodies, and chimeric or fusion proteins. The proteins are produced by various types of recombinant cells using known cell culture methods and are generally introduced into the cell by transfection of genetically modified nucleotide vectors (for example, such as a sequence encoding a chimeric protein, or a codon-optimized sequence, a sequence without introns, etc.), where the vectors may reside as an episome or may integrate into the cell's genome. R / zznn / Lznz / B / Yi "Antibody" refers to an immunoglobulin molecule consisting of four polypeptide chains, two heavy (H) chains and two light (L) chains interconnected by disulfide bonds. Each heavy chain has a heavy chain variable region (HCVR or VH) and a heavy chain constant region. The heavy chain constant region contains three domains: CH1, CH2, and CH3. Each light chain has a light chain variable region and a light chain constant region. The light chain constant region consists of a domain (CL). The VH and VL regions can also be subdivided into regions of hypervariability, called complementarity determining regions (CDRs), interspersed with regions that are more conserved, called framework regions (FRs). Each VH and VL is composed of three CDRs and four FRs placed from amino to carboxy terminus in the following order: FR1, CDR1, FR2, CDR2, FR3, CDR3, FR4. The term "antibody" includes reference to both glycosylated and unglycosylated immunoglobulins of any isotype or subclass. The term "antibody" includes antibody molecules prepared, expressed, created, or isolated by recombinant means, such as antibodies isolated from a host cell transfected to express the antibody. The term "antibody" also includes bispecific antibody, which includes a heterotetrameric immunoglobulin that can bind to more than one different epitope. Bispecific antibodies are generally described in US Patent Application Publication No. 2010 / 0331527, which is incorporated into the present application by this reference. "Secondary structure" refers to local folded structures that are formed in a polypeptide due to interactions between atoms in the backbone. The most common types of secondary structures are the a-helix and the β-folded sheet. Hydrogen bonds formed between the O carbonyl of one amino acid and the H amino of another maintain the shape of both structures. As used herein, "excipient" refers to a pharmacologically inactive substance that is used as a stabilizing agent for long-term storage of the formulated active ingredient. In general, additional excipients are added to the final concentrated fraction to produce the formulated active ingredient. However, during UF / DF processing, excipient levels are monitored to ensure they do not affect the desired formulation strategy. Excipients bulk up pharmaceutical formulations, facilitate drug absorption or solubility, and provide stability and prevent denaturation. Common pharmaceutical excipients include, but are not limited to, amino acids, fillers, binders, disintegrants, coatings, absorbents, buffering agents, chelating agents, lubricants, glidants, preservatives, antioxidants, flavoring agents, sweeteners, coloring agents, solvents and cosolvents, and Viscosifying agents. In one embodiment, the excipient is polyethylene glycol including, but not limited to, PEG-3550. "Polyethylene glycol" or "PEG" is an ethylene oxide polyether polymer commonly used in food, medicine and cosmetics. It is a nonionic macromolecule which is useful as a molecule that reduces the solubility of a biomolecule. PEG is commercially available in different molecular weights in the range of 300 g / mol to 10,000,000 g / mol. Examples of PEG types include, but are not limited to, PEG 20000, PEG 8000, and PEG 3350. PEG is available with R / zznn / Lznz / B / Yi different geometries, including linear, branched (3-10 strands attached to a central core), star (10-100 strands attached to a central core), and comb (multiple strands attached to a central core) forms. to a backbone of the polymer). "Raman spectroscopy" is a spectroscopic technique used to measure the wavelength and intensity of inelastically scattered light from molecules. It is based on the principle that monochromatic radiation incident on materials is reflected, absorbed, or scattered in a specific way, which depends on the particular molecule or protein receiving the radiation. Most of the energy is scattered at the same wavelength, known as Rayleigh or elastic scattering. A small amount (<0.001%) is scattered at different wavelengths, known as Raman or inelastic scattering. Raman scattering is associated with rotational, vibrational, and electronic level transitions. Raman spectroscopy can reveal the chemical and structural composition of samples. As used herein, "ultrafiltration" refers to a membrane process that is used extensively for protein concentration in the downstream processing of therapeutic compounds during purification of recombinant proteins. Ultrafiltration is a size-based separation, in which species larger than the membrane pores are retained and smaller species pass freely. During processing, the protein solution is pumped tangentially over the surface of a semipermeable parallel flat sheet membrane. The membrane is permeable to buffers and buffer salts, but is generally impermeable to monoclonal antibodies. The driving force of permeation is applied transmembrane pressure (TMP) induced by flow restriction at the outlet of the membrane flow channel (TMP = (Poured + Pretended) / 2 - Permeated). . As used herein, "primary concentration" refers to the initial step in which transmembrane pressure drives the passage of water and salts across the permeable membrane, reducing the volume of fluid and thereby increases the protein concentration. It is possible to optimize the degree of concentration in the primary concentration to compensate for yield, protein stability, processing time and buffer consumption. As used herein, "diafiltration" refers to a technique that uses a semi-permeable membrane to exchange a product of interest from one liquid medium to another. Usually, buffer exchange and desalting are performed in a diafiltration mode in which minor impurities and buffer components are washed out of the product by continuous addition of a buffer intended to bring the protein to a pH and concentration of stable excipients that allow a high product concentration. This can be carried out continuously or batchwise based on processing techniques. Diafiltration is often combined with ultrafiltration to achieve the desired volume reduction while removing impurities and salts. UF / DF is the final unit operation in downstream purification, which conditions the mAb to achieve the pH, excipient content, and protein concentration conducive to long-term storage and the addition of stabilizing excipients for R / zznn / Lznz / B / Yi generate Formulated Active Ingredient (FDS). As used herein, "final concentration" refers to the initial step where transmembrane pressure drives the passage of water and salts across the permeable membrane, reducing the volume of fluid and thereby increases the protein concentration to the desired target for storage and / or formulation. The fraction resulting from the final concentration step is the final concentrated fraction (FCP). This final concentration step can be carried out in a batch or continuous processing mode. The terms "bioproduct" and "protein purification intermediate" can be used interchangeably and refer to any antibody, antibody fragment, modified antibody, protein, glycoprotein or fusion protein as well as final active ingredients purified from a bioreactor process. The terms "control" and "control" refer to adjusting an amount or concentration level of a critical quality attribute in a obtained cell culture fluid to a predefined set point. As used herein, the term "upstream processing" refers to the first step in which antibodies or therapeutic proteins are produced, usually by bacterial or mammalian cell lines, in bioreactors. Current arriga processing includes media preparation, cell culture, and cell separation and harvesting. When the cells reach the desired density, they are removed and moved to the downstream section of the bioprocess. The term "downstream processing" refers to the isolation and purification that occurs after obtaining an antibody or therapeutic protein from a bioreactor. Usually this means the recovery of a product from an aqueous solution by several different modalities. The obtained product is processed to meet the requirements for purity and critical quality attributes during downstream processing. As used herein, the term "protein purification intermediate" refers to a protein that is obtained from a bioreactor and refers to any intermediate during downstream processing. The term "concentrated protein purification intermediate" refers to a protein purification intermediate with a concentration greater than 5 mg / mL. More preferably, the concentration is between 50 mg / mL and 300 mg / mL. The terms "monitoring" and "checking" refer to regularly checking an amount or concentration level of a critical quality attribute in a cell culture or obtained cell culture fluid. The term "obtained cell culture fluid" refers to fluid that is extracted from a bioreactor containing cells modified to secrete proteins of interest. The "obtained cell culture fluid" contains the secreted protein of interest, eg a monoclonal antibody. As used herein, “critical quality attribute (CQA)” refers to a physical, chemical, biological or microbiological characteristic or property that should be within a limit, R / zznn / Lznz / B / Yi given range or distribution to ensure the desired product quality of a biological therapeutic pharmaceutical. These attributes may affect safety, efficacy, and / or potency. Critical quality attributes include, but are not limited to, protein concentration, excipients, high molecular weight species, buffer excipients, and pH. As used herein, "formulated active ingredient" refers to an active ingredient that is intended to have pharmacological activity, but does not include intermediates that are used in the synthesis of that ingredient. As used herein, "universal model" refers to a mathematical correlation of spectral properties of different recombinant proteins that is used to predict a critical quality attribute. As used herein, "mAb-specific model" refers to a mathematical correlation of spectral properties of a particular protein that is used to predict a critical quality attribute. As used herein, "titer" refers to the amount of an antibody or protein molecules in a solution. II. Systems and methods to characterize products of downstream protein purification Systems and methods for monitoring and controlling protein concentration during protein production are provided. It is difficult to accurately measure concentrated protein solutions due to the also high viscosities of the solutions (>10 cP). Accurate quantification requires specialized offline equipment and usually dilution of the solution. At high concentration UF / DF, final excipient levels are a function of protein concentration due to the Gibbs-Donnan effect. Real-time forms of testing and analysis are not available during production, increasing turnaround time and the potential for batch failures due to non-compliance with CQAs. The systems and methods described herein can be used for on-line checking of protein concentration and other critical quality attributes. In one embodiment, the Raman spectroscopy system is an on-line or in situ Raman spectroscopy system that is used during the production of a final concentrated fraction, which would have a high concentration (>150 g / L). Usually, the Raman spectroscopy system is employed downstream in the production of the protein purification intermediate, for example, during the processing of the protein purification intermediate after it is obtained from a bioreactor or batch culture system and its subsequent purification. An example of a protein purification process is shown in Figure 1. Usually, the protein purification intermediate is obtained from cell culture (100) and processed by various purification steps such as affinity capture (110), viral inactivation (120), polish chromatography (130 and 140). , filtration with virus retention (150) and ultrafiltration / diafiltration (160) to produce the final concentrated fraction, which is formulated as active principle. In one embodiment, checking for protein concentration in an obtained cell culture fluid is performed by in situ Raman spectroscopy. R / zznn / Lznz / B / Yi A. Raman spectroscopy In one embodiment, checking and controlling the protein concentration in an obtained cell culture fluid is performed by Raman spectroscopy. Raman spectroscopy is a form of vibrational spectroscopy that provides information about molecular vibrations that can be used for the identification and quantification of critical quality attributes of samples. In situ Raman analysis is a method of analyzing a sample in its original location without having to remove a portion of the sample for analysis in a Raman spectrometer. In situ Raman analysis is advantageous because Raman spectroscopy analyzers are non-invasive and non-destructive, reducing the risk of contamination and loss of protein quality. On-line Raman analysis can be implemented to enable continuous processing while monitoring the protein concentration of the obtained cell culture fluid, protein purification intermediates, and / or the final concentrated fraction. In situ Raman analysis can provide real-time assessments of protein concentration in protein purification intermediates. For example, raw spectrum data provided by in situ Raman spectroscopy can be used to obtain and monitor actual protein concentration in protein purification intermediates. In this regard, to ensure that the raw spectrum data is continuously updated, Raman spectroscopy data should be acquired about every 5 seconds to 10 hours. In another mode, spectrum data should be acquired about every 15 minutes to 1 hour. In yet another embodiment, spectrum data must be acquired about every 20 to 30 minutes. The protein concentration check in the protein purification intermediate can be analyzed by any commercially available Raman spectroscopy analyzer that allows Raman analysis in situ. The in situ Raman analyzer should be able to obtain data on the raw spectrum in the protein purification intermediate. For example, the Raman analyzer should be equipped with a probe that can be inserted in-line into the liquid loop. Suitable Raman analyzers include, but are not limited to, RamanRXN2 and RamanRXN4 analyzers (Kaiser Optical Systems, Inc., Ann Arbor, MI). Raw spectral data obtained by in situ Raman spectroscopy can be checked against off-line protein concentration measurements in order to correlate peaks in the spectrum data with protein concentration. Off-line protein concentration measurements can be used to determine which regions of the spectrum exhibit protein signal. Offline measurement data can be collected through any appropriate analytical method. In the case of protein concentration, for example, the offline measurement can be collected with SoloVPE (C-technologies). In addition, any type of multivariate software package, eg SIMCA 13 (MKS Data Analytic Solutions, Umea, Sweden), can be used to correlate peaks within the raw spectrum data with off-line measurements of the protein concentration. However, in some embodiments, it may be necessary to pre-process the raw spectrum data with R / zznn / Lznz / B / Yi spectral filters to remove any variable reference values. For example, the raw spectrum data can be pre-processed with any type of spot attenuation technique or normalization technique. Normalization may be required to correct for any variations in probe, optics, laser power, and exposure time by the Raman analyzer. In one embodiment, the raw spectrum data may be treated with point smoothing, such as first derivative with 21 cm1 point smoothing, and normalization, such as standard normal variable (SNV) normalization. These preprocessing techniques can be combined for certain regions of the spectrum to improve model predictions. It is also possible to perform chemometric modeling of the obtained spectrum data. In this regard, it is possible to use one or more multivariate methods including, but not limited to, partial least squares (PLS), principal component analysis (PCA), partial least squares orthogonal (OPLS), multivariate regression, canonical correlation, factor analysis, cluster analysis, graphical procedures, and the like, on spectrum data. In one embodiment, the obtained spectrum data is used to create a PLS regression model. A PLS regression model can be created by projecting the predicted variables and the observed variables into a new space. In this regard, a PLS regression model can be created by using the measure values ​​obtained from the Raman analysis and the offline measure values. The PLS regression model provides predicted process values, for example, predicted protein concentration values. In one embodiment, the model provides predicted protein concentration values ​​with <5% error compared to off-line protein concentration values. In a preferred embodiment, the model provides predicted protein concentration values ​​with <3% error compared to off-line protein concentration values. After chemometric modeling, it is possible to apply a signal processing technique to the predicted protein concentration values. In one embodiment, the signal processing technique will alter the model variability and prediction error. In this regard, one or more of the pre-processing techniques can be applied to the predicted protein concentration values. It is possible to use any pre-processing technique known to those skilled in the art. For example, the noise reduction technique may include data attenuation and / or signal rejection. Attenuation is achieved through a series of attenuation algorithms and filters, while signal rejection uses signal characteristics to identify data that should not be included in the analyzed spectrum data. In one embodiment, noise from predicted protein concentration values ​​is mitigated by means of a noise reduction filter. The noise reduction filter provides final predicted protein concentration values. In this regard, the noise reduction technique combines raw measurements with a model-based estimate of what the measurement should produce according to the model. In one embodiment, the noise reduction technique combines a current predicted protein concentration value with its uncertainties. Uncertainties can be determined by the repeatability of the predicted protein concentration values ​​and the R / zznn / Lznz / B / Yi current protein concentration values. Once the next protein concentration value is observed, the estimate of the predicted protein concentration value is updated by using a weighted average where more weight is given to the estimates with greater certainty. By using an iterative approach, the final protein concentration values ​​of the process can be updated based on the previous measurement and the current measurement. In this regard, the algorithm should be recursive and be able to run in real time to use the current predicted protein concentration value, the previous value, and experimentally determined constants. The noise reduction technique improves the robustness of the measurements received from Raman analysis and PLS predictions by reducing the noise on which the automatic feedback controller will act. B. Methods of use The described methods can be used to monitor and control the concentration of protein in the obtained cell culture and / or protein purification intermediates during downstream protein purification processes. Common downstream purification processes include, but are not limited to, centrifugation, direct depth filtration, protein A affinity purification, viral inactivation steps, ion exchange chromatography, hydrophobic interaction chromatography, size exclusion chromatography, ultrafiltration / diafiltration, filtration with viral retention and combinations of these. These unit operations are used in a defined sequential combination to isolate the protein of interest and ensure testing for impurities and / or critical quality attributes prior to production of the formulated active ingredient. In one embodiment, the described methods include an in-line Raman probe in the liquid circuit. In another embodiment, the described methods can be used to produce a concentrated protein purification intermediate or a final concentrated fraction. 1. Antibody Titer and Protein Concentration Both the antibody titer and the protein concentration are important factors in the purification of bioproducts. The antibody titer measured after the initial collection of cell culture fluid is important in determining column loadings and ensuring a robust purification process to remove impurities. It is important to check the protein concentration during the purification steps to ensure both the proper concentration of final product and the proper yield of the unit purification operations that are performed. Inadequate protein concentration could lead to ineffective drugs or the production of a formulated active ingredient. In one embodiment, the obtained cell culture fluid is subjected to Raman spectral analysis immediately after it is obtained, but before any further purification is initiated. Raman spectrum data can be used after the initial collection to quantify the antibody titer in the obtained cell culture fluid. Protein concentration can be measured with the described methods at various steps during the protein purification process, for example, during affinity capture, during polish chromatography, during virus-retaining filtration, or during ultrafiltration / diafiltration . Online Raman probes can detect Raman scattering in R / zznn / Lznz / B / Yi the obtained cell culture fluid and / or protein purification intermediate in the fluid loop without removing the sample from the system, providing analytical characterizations that are often determined offsite line. In one embodiment, if the protein concentration is not within a predetermined concentration during ultrafiltration / diafiltration, the system is notified and the protein purification intermediate is altered accordingly. For example, if the protein concentration in the protein purification intermediate is less than the predetermined protein concentration, the protein purification intermediate can be further concentrated by ultrafiltration / diafiltration. In one embodiment, the concentration step is carried out by protein A affinity chromatography. 2. Relationship between drug and antibody In another embodiment, the described methods can be used to test and control the drug-antibody relationship (DAR). The DAR is a quality attribute that is checked during the development of Antibody Drug Conjugates (ADC), Antibody Radionuclide Conjugates (ARC) and protein conjugates in general (potent spheroids, non-cytotoxic loads, etc.) to ensure a consistent product quality and to facilitate subsequent labeling with loads. The DAR is the average amount of drug or other therapeutic molecules conjugated to antibodies and is an important quality attribute in the production of therapeutic conjugates. The DAR value affects the efficacy of conjugated drugs as low drug loading reduces drug potency and high loading can negatively affect pharmacokinetics and safety. In one embodiment, the antibody and radionuclide conjugates are subjected to Raman spectral analysis immediately after their conjugation, but before further purification occurs. Data on the Raman spectrum can be used after conjugation to determine the DAR. In one embodiment, if the DAR is not within the predetermined concentration during processing, the system is notified and the ADC intermediate is altered accordingly. For example, if the DAR and ADC intermediate are below the predetermined DAR, the components of the conjugation reaction can be altered, eg, reagent concentrations can be optimized, linker type can be altered , temperature or other preparation variables can be optimized. 3. Buffering excipients The described methods can be used to monitor and control the levels of buffer excipients in obtained cell culture fluid and / or protein purification intermediate during downstream purification. Buffering excipients commonly used in monoclonal antibody productions include, but are not limited to, acetate, citrate, histidine, succinate, phosphate, and hydroxymethylaminomethane (tris), proline, and arginine. Surfactant excipients include, but are not limited to, polysorbate 80 (Tween 80), polysorbate 20 (Tween 20), and poloxamer 188. Polyol / disaccharide / polysaccharide excipients include, but are not limited to, mannitol, sorbitol, sucrose, and dextran 40. R / zznn / Lznz / B / Yi Antioxidant excipients include, but are not limited to, ascorbic acid, methionine, and ethylenediaminetetraacetic acid (EDTA). Histidine and arginine are two commonly used amino acid excipients. In a preferred embodiment, the excipient that is monitored and controlled is histidine and arginine. Final concentrate excipient concentrations differ from diafiltration buffer composition due to the combination of volume excluded and Donnan effects. The Donnan effect is a phenomenon that occurs due to the retention of the net positively charged protein in the membrane during UF / DF combined with the requirement for neutral charge in both the retained and permeate components. Negatively charged buffer components are enriched in the retentate relative to the diafiltration buffer to equilibrate the positively charged protein, while positively charged buffer components are expelled. This effect can cause the concentrations of buffer excipients and the pH of FCP to differ greatly from the composition of the diafiltration buffer (Stoner et al., J Pharm Sci, 93:2332-2342 (2004)). Volume exclusion describes the behavior of highly concentrated samples in which the protein occupies a large fraction of the solution volume. Buffer is excluded from the volume occupied by protein, causing buffer solute concentrations to decrease as protein concentration increases and is expressed as moles (or mass) of solute per volume of solution. Buffer is excluded from the volume occupied by protein, causing buffer solute concentrations to decrease as protein concentration increases when expressed as moles (or mass) of solute per volume of solution. Based on both these principles and that buffer excipient levels are critical quality attributes, online Raman probes will minimize offline analytical characterization and provide greater process insight to ensure sufficient excipient levels prior to testing. formulation. 4. Impurities with high molecular weight When producing monoclonal antibodies, there are often low levels of impurities associated with the products even after extensive purification steps. High molecular weight species (HMW, eg, dimeric antibody species) are a product-related impurity that contributes to the size heterogeneity of mAb products. The formation of HMW species in a therapeutic mAb drug as a result of protein aggregation can compromise both the efficacy and safety of the drug. HMW species are considered to be a CQA that is routinely tested during drug development and as part of protein drug release assays during drug preparation. In one embodiment, it is possible to use the described methods to identify drugs that contain HMW species. HMW species can be detected by Raman spectroscopy at various steps during the purification process, including, but not limited to, during affinity capture, during viral inactivation, during polish chromatography, during filtration with R / zznn / Lznz / B / Yi virus retention, during ultrafiltration / diafiltration or combinations thereof. In one embodiment, the described methods detect HMW species in the obtained cell culture fluid and the fluid is further processed to remove the HMW species. Methods for removing HMW species from cell culture fluid include additional polishing steps including, but not limited to, cation exchange chromatography and anion exchange chromatography. C. UF / DF systems Illustrated in Figure 6 is an ultrafiltration / diafiltration processing system that includes several locations for in-line Raman probes (circled numbers 1-4). A filter pump 200, which may be a peristaltic, rotary lobe, pressure transfer, or diaphragm pump, pumps the protein purification intermediate into a retentate container 205. Fluid from the retentate container 205 flows to feed pump 210, which can be a rotary lobe, peristaltic, or diaphragm pump, past pressure valve 215 and into tangential flow filter module (TFFM) 220 In TFFM 220, the protein purification intermediate is subjected to ultrafiltration through a membrane. The bioproduct of interest is retained in the fluid (retentate), while water and low molecular weight solutes including buffering excipients pass through the membrane in the permeate (filtrate) component that leaves the system as it passes through the membrane. through the permeate pressure valve 225 to the waste tank 230. The retentate exits the TFFM 220 and passes through the retentate pressure valve 235, the retentate return valve 240, and the retentate return 245, where it flows back into retentate container 205. The process can be repeated as necessary to concentrate the byproduct, remove impurities, and ensure that CQAs are within acceptable limits. During diafiltration, the same flow path described above is followed, in which permeable solutes are replaced as new buffer is added to the product stream. When new buffer is added at the same rate that permeate is removed from the system, the sum of the retained component tank and the volume delayed in the structure defines the volume of the system. Turnover volume (TOV) is defined as an amount of diafiltration buffer added to the UF / DF process equal to the volume of the system. Usually, replacement of 8 times the system volume (8 TOV) guarantees > 99.9% buffer exchange (Schwarts, L., Scientific and Technical Report, PN 33289) In addition, during the UF / DF process, it is necessary to mix the protein solution in the retentate container 205. The density difference between the diafiltration buffer, retentate return, and batch retentate during diafiltration requires that agitation in the tank is sufficient to ensure adequate buffer exchange, but moderate enough to prevent drift, as this has been observed to lead to protein aggregations and subvisible particulates (SVPs) in certain products. Additionally, it is important to ensure the proper mix of retentate return during the stages of R / zznn / Lznz / B / Yi concentration to avoid polarization of the protein concentration in the retentate tank, causing higher protein concentrations to be delivered to the UF / DF membranes. In one embodiment, the Raman probe is placed at location 1, downstream of the diafiltration pump 200 in the retentate container 205. The Raman probe could alternatively be placed at location 2 downstream of the retentate container 205 inline before the feed pump 210. In another embodiment, the Raman probe is placed in-line at location 3 between the feed pump 215 and the tangential flow filter module 220. In yet another embodiment, the Raman probe is placed places online at retained component return 245. Raman probe placement is critical to ensuring accurate online measurements in a complicated system with engineering and processing constraints. D. Cell culture It is possible to obtain the cell culture fluid obtained from a bioreactor containing cells modified to produce monoclonal antibodies. The term "cell" includes any cell that is suitable for expressing a recombinant nucleic acid sequence. Cells include those prokaryotic and eukaryotic, such as bacterial cells, mammalian cells, human cells, non-human animal cells, avian cells, insect cells, yeast cells, or cell fusions such as, for example, hybridomas or quadromas. In certain embodiments, the cell is a human, monkey, ape, hamster, rat, or mouse cell. In other embodiments, the cell is selected from the following cells: Chinese hamster ovary (CHO) cells (eg, CHO K1, DXB-11 CHO, VeggieCHO), COS (eg, COS-7), cells of the retina, Vero, CV1, kidney cells (eg HEK293, 293 EBNA, MSR 293, MDCK, HaK, BHK21), HeLa, HepG2, WI38, MRC 5, Colo25, HB 8065, HL-60, lymphocytes, eg , Jurkat (T cells) or Daudi (B cells), A431 (epidermal), U937, 3T3, L cells, C127 cells, SP2 / 0, NS-0, MMT cells, stem cells, tumor cells, and a cell line derived from a cell mentioned above. In some embodiments, the cell comprises one or more viral genes, eg, a retinal cell that expresses a viral gene (eg, a PER.C6® cell). In some embodiments, the cell is a CHO cell. In other embodiments, the cell is a CHO K1 cell. In protein production, a “fed-batch cell culture” or “fed-batch culture” refers to a batch culture in which cells and culture medium are initially supplied to the culture vessel, and nutrients from Additional cultures are fed slowly, in discrete increments, to the culture during culture, with or without periodic harvesting of cells and / or products prior to termination of culture. Fed-batch culture includes “fed-batch culture” in which the entire culture (which may include cells and medium) is periodically removed and replaced with fresh medium. Fed-batch culture is distinguished from simple “batch culture” in that all components for cell culture (including animal cells and all culture nutrients) are supplied to the culture vessel at the start of the culture-growing process. batch. Fed-batch culture can be different from “perfusion culture” in that the surfactant is not removed from the culture vessel during a standard perfusion process. R / zznn / Lznz / B / Yi fed batches, whereas in perfusion culture, cells are retained in culture, for example, by filtration, and culture medium is introduced into and removed from the culture vessel of continuously or intermittently. However, removal of samples for assay purposes during fed-batch cell culture is contemplated. The fed batch process continues until it is determined that maximum working volume and / or protein production has been reached, and the protein is subsequently harvested. The term "continuous cell culture" refers to a technique used to cultivate cells continuously, usually in a particular growth phase. For example, if a constant supply of cells is required, or if production of a particular protein of interest is required, the cell culture may require maintenance at a particular growth phase. Therefore, conditions must be continually checked and adjusted as appropriate to maintain cells in that particular phase. The terms "cell culture medium" and "culture medium" refer to a nutrient solution used for the cultivation of mammalian cells that typically provide the necessary nutrients to enhance cell growth, such as a carbohydrate energy source. , essential (for example, phenylalanine, valine, threonine, tryptophan, methionine, leucine, isoleucine, lysine, and histidine) and non-essential (for example, alanine, asparagine, aspartic acid, cysteine, glutamic acid, glutamine, glycine, proline, serine and tyrosine), trace elements, energy sources, lipids, vitamins, etc. The cell culture medium may contain extracts, eg serum or peptones (hydrolysates) that supply raw materials that support cell growth. The media may contain extracts of soybean or yeast derivatives, instead of extracts of animal origin. Chemically defined medium refers to a cell culture medium in which all chemical components are known (ie, have a known chemical structure). The chemically defined medium is totally free of animal-derived components, such as animal-derived or serum-derived peptones. In one embodiment, the medium is a chemically defined medium. The solution may further contain components that enhance growth and / or survival above the minimum rate, including hormones and growth factors. The solution can be formulated at an optimal pH and salt concentration for the survival and proliferation of the particular cell being cultured. E. Proteins of interest Any protein of interest suitable for expression in prokaryotic or eukaryotic cells can be assayed with the methods described. For example, the protein of interest includes, but is not limited to, an antibody or antigen-binding fragment thereof, a chimeric antibody or antigen-binding fragment thereof, an ScFv or fragment thereof, a fusion protein of Fe or a fragment thereof, a growth factor or a fragment thereof, a cytokine or a fragment thereof, or an extracellular domain of a cell surface receptor or a fragment thereof. Proteins of interest can be simple polypeptides consisting of a single subunit, or multiple subunit complex proteins comprising two or more subunits. The protein of interest can be a biopharmaceutical product, food additive or preservative, or any protein product subject to quality standards. R / zznn / Lznz / B / Yi purification and quality. In some embodiments, the antibody is selected from the group consisting of an anti-programmed cell death 1 antibody (for example, an anti-PD1 antibody, such as described in US Pat. No. 9,987,500), an anti-cell death ligand programmed 1 antibody (eg, an anti-PD-L1 antibody, as described in US Pat. No. 9,938,345), an anti-DI14 antibody, an anti-angiopoietin 2 antibody (eg, an anti- ANG2 antibody, as described in US Patent No. 9,402,898), an antiangiopoietin 3-like antibody (for example, an anti-AngPt13 antibody, as described in US Patent No. 9,018,356), an anti -platelet growth factor receptor-derived (for example, an anti-PDGFR antibody, such as described in US Patent No. 9 265 827), an anti-Erb3 antibody, an anti-prolactin receptor antibody (for example, a anti-PRLR antibody, such as described in US Pat. 9 302 015), an anti-complement 5 antibody (for example, an anti-C5 antibody, as described in US Pat. No. 9 795 121), an anti-TNF antibody, a growth factor receptor antibody anti-epidermal (for example, an anti-EGFR antibody, such as described in US Pat. No. 9 132 192 or an anti-EGFRvIll antibody, such as described in US Pat. No. 9 475 875), an antibody anti-subtilisin kexin 9 proprotein convertase (eg, an anti-PCSK9 antibody, such as described in US Pat. No. 8,062,640 or US Pat. No. 9,540,449), an anti-growth and differentiation factor antibody 8 (eg, an anti-GDF8 antibody, also known as an anti-myostatin antibody, as described in US Pat. Nos. 8,871,209 or 9,260,515), an anti-glucagon receptor (eg, anti-GCGR antibody as described in described in US Pat. Nos. 9 587 029 or 9 657 099), an anti-VEGF antibody, an anti-IL1R antibody, an interleukin 4 receptor antibody (eg, an anti-IL4R antibody, as described in US patent application publication No. US2014 / 0271681A1 or US Pat. , 8 043 617 or 9 173 880), an anti-IL1 antibody, an anti-IL2 antibody, an anti-IL3 antibody, an anti-IL4 antibody, an anti-IL5 antibody, an anti-IL6 antibody, an anti- IL7, an anti-interleukin 33 (eg, anti-IL33 antibody, such as described in US Pat. Nos. 9,453,072 or 9,637,535), an anti-respiratory syncytial virus antibody (eg, anti-RSV antibody, such as described in US Patent Application Publication No. 9 447 173), an anti-cluster of differentiation 3 (eg, an anti-CD3 antibody, such as described in US Pat. 9 447 173 and 9 447 173 and in US application no. 62 / 222 605), an anti-differentiation 20 cluster (for example, an anti-CD20 antibody, as described in US Pat. Nos. 9 657 102 and US20150266966A1, and US Pat. No. 7 879 984), anti-CD19 antibody, an anti-CD28 antibody, an anti-cluster of differentiation 48 (eg, anti-CD48 antibody, as described in US Pat. No. 9,228,014), an anti-Fel d1 antibody (eg, as described in US Patent No. 9 079 948), an antivirus Middle East respiratory syndrome R / zznn / Lznz / B / Yi (for example, an anti-MERS antibody, as described in US Pat. No. 9,718,872), an Ebola virus antibody (for example, , as described in US Patent No. 9 771 414), an antibody against Zika virus, an antibody against lymphocyte activation gene 3 (for example, an anti-LAG3 antibody or an anti-CD223 antibody) , an anti-nerve growth factor antibody (for example, an anti-NGF antibody, as described in US Patent Application Publication No. US2016 / 0017029 and US Patent Nos. 8,309,088 and 9,353,176) and an anti-activin A antibody. In some embodiments, the bispecific antibody is selected from the group consisting of an anti-CD3 x anti-CD20 bispecific antibody (such as described in US Patent No. 9 657 102 and US Patent Application Publication No. US20150266966A1 ). , an anti-CD3 x anti-mucin 16 bispecific antibody (eg, an anti-CD3 x anti-Muc16 bispecific antibody) and an anti-CD3 x anti-prostate membrane antigen-specific bispecific antibody (eg, an anti-CD3 x anti-Muc16 bispecific antibody). CD3 x PSMA). In some embodiments, the protein of interest is selected from the group consisting of abciximab, adalimumab, adalimumab-atto, ado-trastuzumab, alemtuzumab, alirocumab, atezolizumab, avelumab, basiliximab, belimumab, benralizumab, bevacizumab, bezlotoxumab, blinatumomab, brentuximab vedotin, brodalumab, canakinumab, capromab pendetide, certolizumab pegol, cemiplimab, cetuximab, denosumab, dinutuximab, dupilumab, durvalumab, eculizumab, elotuzumab, emicizumab-kxwh, emtansine alirocumab, evinacumab, evolocumab, fasinumab, golimumab, guselkumab tizumab, ibritumomab, ibritumomab , Infilaximab-ABDA, Inflyximab-Dyyb, iPilimumab, Ixekizumab, Mopolizumab, necitumumab, nesvacumab, nivolumab, obiltoxaximab, obinutuzumab, ocrelizumab, opatumumab, olratumab, omalizumab, panitumumab, pEmbrolizumab, pertuzumab, pertuzumab, pertuzumab, pertuzumab, rax. umab, rituximab, sarilumab, secukinumab, siltuximab, tocilizumab, tocilizumab, trastuzumab, trevogrumab, ustekinumab, and vedolizumab. examples Example 1: Universal online protein concentration model for UF / DF applications Materials and methods Data collection for the model included spectrum data from Rxn2 and Rxn 4 Raman analyzers (Kaiser Optical Systems, Inc. Ann Arbor, MI) with MR-Probe-785 and RamanRxn Probehead758 (Kaiser Optical Systems, Inc. Ann Arbor, MI). MY). Additionally, several different perspectives were used during development based on availability. The operating parameters of the Raman analyzers were established at a scan time of 10 seconds for 6 accumulations, repeated 5 times. SIMCA 13 (MKS Data Analytic Solutions, Umea, Sweden) was used to correlate the peaks within the spectrum data with the off-line protein concentration measurements. On-line measurements were taken at different points in the UF / DF unit operation including primary concentration, diafiltration, and final concentration. Off-line protein concentrations were determined with SoloVPE (C Technologies, Inc.). SoloVPE measurements were performed in triplicate. R / zznn / Lznz / B / Yu Figure 2 illustrates the regions of the spectrum that were used to produce the chemometric models. Regions included region 1: 977-1027 cm1 (ring structure), region 2: 14081485 cnr1 (arginine), region 3: 1621-1711 cm1 (secondary structure), and region 4: 2823-3046 cm1 (C-H stretch). The following spectral filtering was performed on the raw spectral data: first derivative with 21 cm1 point attenuation to remove variable references Results mAb1 was analyzed using Raman spectroscopy to determine the feasibility of a universal online protein concentration model for ultrafiltration / diafiltration (UF / DF) applications. Protein concentration was measured before diafiltration (primary concentration), during diafiltration (diafiltration) and after diafiltration (final concentration). Concentrations calculated from the model were compared to the protein concentration determined by SoloVPE (Figure 3). The model error for 0-120 g / L (primary concentration and diafiltration) was 3.1% and the model error for >120 g / L (final concentration) was 1.8% when training data (spectra of mAb 1 incorporated into the PLS model). It is possible to detect processing errors using Raman spectroscopy. In Figure 3, it is shown that convective entrainment of air in the system during the final recirculation through the UF / DF system was detected using the Raman spectral data. The bars contained within the rectangle illustrate that the predicted concentration of mAb1 by SoloVPE was >200 g / L, while the Raman prediction was -65 g / L. Illustrated in Figure 4 is the absolute Raman model error for three representative mAbs. These data demonstrated the correct development of models for the mAbs, which included different mAb isotypes (lgG1 and IgG4), as well as bispecific molecules. Of the 17 model predictions, 14 met with <5% error. However, specific models (0-120 g / L and >120 g / L) were created for each probe that was used during development as a probe to demonstrate that variability increased the errors predicted by the PLS models. Different probes and lasers were evaluated online with multiple mAbs to optimize the model. The model was refined and only one spectral region was the focus of the updated universal model: 28233046 cnr1(C-H stretch) with a standard normal variable (SNV) to correct for laser power variance and probe variability as a baseline correction. A comparison of the components of the two models developed is summarized in Table 1. Partial least squares (PLS) regression models were created with SoloVPE measurements taken offline in triplicate. Table 2 shows the details of the partial least squares regression model. Using the updated dataset predicted with an optimized universal laser and probe model, 15 of 17 model predictions were shown to be within <5% error compared to 14 of 17 previously (Figure 5). R / zznn / Lznz / B / Yi Table 1. Comparison of universal model components. Component Description Universal Model (v. 1) Universal Model (v. 2) Laser 1 3 Optical 2 6 Pre-processing filter First derivative with 21 cm1 point attenuation Standard Normal Variable (SNV) Spectrum Regions 977-1027 cm1 . 1408-1485 cm1. 1621-1711 cm1. 2823-3046 cnr1. 2823-3046 cm1. R / zznn / Lznz / B / Yi Table 2. Details of universal partial least squares regression model of protein concentration (v. 2). Final models 0-120 g / L >120 g / L Sample size 1412 879 R2X 0.993 0.987 Q2 0.984 0.958 RMSECV 3.34 7.77 R2X - Percentage of variation explained by the model, Target: R2>0.9 Q2- Percentage of variation predicted by the model for cross-validation, Objective: Q2>0.8 RMSECV: Root mean square error of cross-validation Example 2: Performance of scaling up protein concentration models Materials and methods Optimized universal models (v.2) (see Table 1) were evaluated with a 1 / 2” scale-up single-use tangential flow filter system experiment (Pall Corporation) with mAb10. Instead of the usual processing filler, the mAb10 filler was formulated active ingredient. The SDS material was diluted to a representative UF / DF loading source including protein concentration and buffering excipients. However, due to the presence of additional excipients in the loading source that were not evaluated during the development of the universal model, specific models for mAb 10 were created. Methods for Raman data collection are referenced in Example 1 and sweep extent information. The model specific for mAb >120 g / L used the same spectral regions and pre-processing techniques as the universal model (v. 2). However, the mAb-specific model for 0-120 g / L used all four spectral regions as illustrated in Figure 2 with standard normal variable pre-processing. The differences in the 0-120 g / L model can be attributed to the additional excipients in the loading source. Results The model error target <5% was met in 2 / 4 of the updated models when the training set was included in the model predictions as illustrated in Table 3. A second sweep was performed with Based on noticeable differences such as load source, laser, and scaling (duty scaling vs. scaling) during preliminary experimentation on IOPS. Before the second experiment, it was switched to the specific model for mAb 10 from 0-120 g / L. All four regions preprocessed with SNV were included, but in three of the regions, 977-1027 cm1, 1408-1485 cm1, and 1621-1711 cm', first derivative with 21 cm1 dot attenuation was also used. Compiled experimental results are summarized in Table 3. During the second experiment, the model error target <5% was met in 3 / 4 of the updated models when the training set was included in the model predictions as illustrated in Table 3. It was also observed during the data analysis that the charging source was the main contributing factor to the increased error. If the loading sample were removed, the universal model error would be reduced from 8.6% to 5.7%. Figure 7 shows the results of the online predictions (real time, light shaded bars) and updated models (dark shaded bars) for the final concentrated fraction comparing the protein concentration to the offline measurement. of SoloVPE (unfilled bars). The universal online model and the updated model had an error of 2.7%, while the mAb 10 online model and the updated model had an error of 12.0% and 4.2% for the final concentrated fraction. The larger error observed in the mAb 10 model can be attributed to limited data in the ~250 g / L range, while the universal model has a larger data set. The inability of the models to extrapolate outside of the characterized range (ie, >250 g / L in the mAb 10 model) is a further contributing factor to the increased error. Table 3. Average model error for protein concentration predictions in scaling R / zznn / Lznz / E / Yi IOPS Experiment 1 IOPS Experiment 2 Online Updated Online Updated1 Universal 0-120 g / L 13.8% 8.9% 9.9% 8.6% Universal >120 g / L 8.8% 2.8% 5.1% 5.0% mAb 11 0-120 g / L 6.0 % 5.5% 4.1% 3.5% mAb 11 >120 g / L 12.0% 1.5% 8.8% 3.6% 1: Extraction of the loading sample reduces the error; Universal (0-120 g / L): 8.6% to 5.7% and mAb11 (0-120 g / L): 3.5% to 2.7% Example 3: Scaling of protein concentration model for pilot processing equipment Materials and methods During the development of commercially permitted processes, the UF / DF is characterized as a unitary operation in accordance with quality principles from design to understand the critical parameters of the process, as well as the critical quality attributes. Modeling and Raman development were included during the development of mAb 11 to improve understanding of the process and to propose a simplified approach to model development. In Example 1, information about the Raman data collection method is referenced, except for the sweep time stretch which was adjusted from 10 seconds to 5 seconds. The mAb 11 model developed used SNV pre-processing for all four regions of the spectrum, except for the three regions: 977-1027 cm1, 1408-1485 cm1, and 16211711 cm1, where first derivative with 21-point attenuation was also used. cm1. Results: The laboratory scale model was generated with four DoE experiments and four spectrum regions. The Raman model error for real-time predictions from four DoE experiments is shown in Figure 8A. The Raman model error for 15 additional experiments with the laboratory scale model is shown in Figure 8B. mAb-specific protein concentration models were generated for 0-120 g / L and >120 g / L with <5% error. Developments at pilot scale (n=3) with the laboratory scale model (n=15) have a prediction error of 0.6% to 10.6% for mAb11 (Figure 9A). The pilot scale prediction error was reduced to 0.6-2.2% when the pilot scale data was incorporated into the laboratory scale model (n=18). It is likely that the largest error in the laboratory-scale model is due to the impact of temperature on the spread of Raman spectra due to heat dissipation associated with the scaling equipment. Temperature will be a factor to consider during future Raman development and model verification claims. In seven pilot-scale experiments with three different monoclonal antibodies (mAb J, mAb K, and mAb L), the final model prediction error was 0.6-2.2%, well acceptable for the 5% target (Figure 9B). Example 4: Use of Raman models for real-time concentration determination to enable processing decisions Materials and methods: Additional information can be found in Example 3, as the same protocol was used for Raman spectral collection and modeling. An automated control strategy was developed to use Raman spectrum data to achieve final protein concentration targets. Data was filtered with the general predictive models and used to provide information for the UF / DF instrumentation to terminate the unit operation upon achieving the target protein concentration. SoloVPE measurements were performed in triplicate. Results: Examples of screens for automatic checking of batch UF / DF and protein concentration in single-passage TFF are illustrated in Figures 10A and 10B. Figure 11 shows that predictive modeling can be used to check the real-time concentration of mAb14 in various R / zznn / Lznz / B / Yi processing steps and trigger the concentration unit operation to stop once a desired concentration target is achieved. The final concentrated fraction was predicted by Raman to be 260 g / L compared to the off-line SoloVPE measurement of 262 g / L, implying an error of 0.8% which meets the <5% error target. Raman is a suitable application that can be used to make automated processing decisions that check that the desired protein concentration target is met. Example 5: Proof of Concept for High Molecular Weight (HMW) Species Modeling in UF / DF Materials and methods: Data collection for the HMW species model included spectrum data from Raman Rxn2 analyzers and (Kaiser Optical Systems, Inc. Ann Arbor, MI) with RamanRxn Probehead-758 (Kaiser OpticaI Systems, Inc. Ann Arbor, MI) . Additionally, several different perspectives were used during development based on availability. The operating parameters of the Raman analyzers were established at a scan time of 72 seconds for 1 accumulation, repeated 25 times. On-line measurements were taken at different points in the UF / DF unit operation including primary concentration, diafiltration, and final concentration. The spectral range was 110-3415 cm·1. Raw spectral data were pre-processed with SNV and also first derivative filtered with 21 cm'1 attenuation. Measurements of offline HMW species were determined with size exclusion high performance liquid chromatography. Results: HMW species are another attribute that is considered a critical preliminary quality attribute in protein purification. Current technologies cannot check HMW species in real time during processing. It is shown in Figure 12A that it is possible to use the Raman modeling method to check for HMW species during protein purification. Predictions of HMW species by Raman modeling were compared with measurements collected with SE-UPLC during purification (primary concentration, diafiltration, and final concentration). Raman modeling effectively predicted the percentage of high molecular weight species in real time during protein processing. The model was generated with an average error of 3.4%. Example 6. Proof of concept for modeling of high molecular weight (HMW) species in polish chromatography Materials and methods: Data collection for the HMW species model included spectrum data from Rxn2 and Raman analyzers (Kaiser Optical Systems, Inc. Ann Arbor, MI) with MR-Probe-785. The operating parameters of the Raman analyzer were established at scan times of 10, 30, or 60 seconds for 1 accumulation with 5 replicate measurements. Off-line measurements were carried out with anion exchange chromatography (AEX) fractions with 6.2%-76.2% total HMW. The spectral ranges used for the modeling and preprocessing techniques are described in Table 4. Measurements of offline HMW species were determined with liquid chromatography. R / zznn / Lznz / B / Yi high-yield size exclusion. Results: It is possible to use the described Raman modeling method to check for HMW species during protein purification by polish chromatography. Predictions of HMW species by Raman modeling were compared with measurements collected with SE-UPLC of the generated AEX fractions. As summarized in Table 4, RMSEP of the evaluated methods ranged from 3.2 to 7.6%. In Figure 12B, a condensed 6.2%-19.7% HMW data set was used to evaluate a model generated with a spectral region of 350-3100 cm1 and SNV pre-processing techniques. By reducing the HMW interval, RMSEP was reduced to 1.2%, 1.4%, and 2.1% for 10, 30, and 60 seconds, respectively. The HMW content can be determined with Raman in AEX fractions based on these results. Table 4. HMW model predicted error (RMSEP) for HMW content of 6.2%-76.2% R / zznn / Lznz / B / Yi 10s 30 s 60 s 1550-1725 cm 1 7.62% 5.31% 3.21% First derivative 350-3100 cm4 3.48% 3.37% 4.63% SNV 990-1020, 1550-1725 cm1 7.12% 7.48% 4.98% First derivative Example 7. Proof of concept of title modeling Materials and methods: The training set model was 35 protein A flow samples with FCP (265 g / L) to achieve titres ranging from 0.36 to 9.8 g / L. The model was tested on diluted deep-filtrate samples with titres in the range of 1.3-8.8 g / L. Data collected for the titration model included Raman Rxn2 spectrum data (Kaiser Optical Systems, Inc. Ann Arbor, MI) with MRProbe-785. An offline dip probe was used to generate spectrum data with 20 seconds scan time for 1 accumulation, repeated 5 times, as operating parameters. The spectrum intervals were 977-1027, 1408-1485, 1621-1711 and 2823-3046 cm'1. Data on the raw spectrum were preprocessed with SNV and also first derivative filtered with 21 cnr1 attenuation. The characteristics of the model are described in Table 5. R? zznn / Lznz / E / YiAi Table 5. Characteristics of the antibody titer pattern. Spectrum Regions (cm1) 3046-2823, 1711-1621, 1485-1408, 1027-977 Preprocessing Techniques First Derivative and SNV Accumulation and Length 5 x20 seconds Average Model Error 25.7% Results: Antibody titer is a process attribute in protein purification that is necessary for subsequent downstream unit purification operations including affinity column loading, consistency in production, as well as intermediate product volume constraints in the process. Inaccurate column loading can affect later preliminary critical quality attributes, thus a verification technique such as Raman spectroscopy is desired. An actual antibody titer versus the Raman predicted antibody titer for a monoclonal antibody is illustrated in Figure 13 . In this experiment, the model error was 26%, which is greater than the desired target of <5%. Increasing the sweep extents, as well as developing a model with diluted and undiluted deep filtrates, will reduce model error. Example 8: Raman models for buffer excipient measurements that meet a current orthogonal assay error of about 10% Materials and methods: Previous concentration model development data was collected for several antibodies. Information on methods for collecting Raman data is referenced in Example 1. Table 6 shows the components of the model to detect histidine and arginine in the samples. Spectral regions were based on known histidine and arginine peaks (Zhu et al., Spectrochim Acta A Mol Biomol Spectrosc, 78(3):1187-1195 (2011)). After development of the initial histidine and arginine pattern, the pattern was further characterized with mAb 14. Raman analyzer performance parameters were set at a scan time of 20 seconds for 5 pools with a non-contact optical probe. The spectral range used for histidine was 1200-1480 cm1 and that of arginine was 860-1470 cm1. Raw spectral data for both buffering excipients were preprocessed with SNV and also first derivative filtered with 21 cm L attenuation Histidine and arginine species measurements were determined off-line with an amino acid quantification-based method by ultra-high performance liquid chromatography (UPLC). Results: Data collected from previous concentration model developments for histidine and arginine were analyzed to determine if the described Raman modeling system and method could be used to measure buffering excipients in a processed antibody sample. As can be seen in Figure 14A, values ​​predicted with Raman modeling were compared with values ​​calculated based on the UPLC-based amino acid method. The average pattern error and predicted values ​​for preliminary histidine and arginine Raman modeling are presented in Table 6. In Figure 14B, a dot plot of a pattern of predicted versus actual histidine for mAb 14 is shown with an average model error of 8.2% which meets the target of <10% for buffer excipients. The <10% goal is based on the assay variability of the current UPLC orthogonal method. A dot plot of a predicted versus actual arginine model for mAb 14 is shown in Figure 14C with an average model error of 2.9% meeting the target of <10%. The data demonstrates that Raman modeling can be used to predict levels of buffering excipients from FCP and UF / DF material in process. The correct quantification of said excipients guarantees that UF / DF provides a final concentrated fraction that will allow the subsequent formulation. Table 6. Model components and data collected for histidine and arginine from universal concentration model run (Example 1) R / zznn / Lznz / B / Yi Histidine Arginine Spectral region (cm1) 1200- 1480 970-1100, 1300-1500 Pre-processing technique First derivative and SNV First derivative and SNV R2Y 0.940 0.964 Q2 0.938 0.963 RMSEP 1.20 mM 6.19 mM Average model error 10.4% 7.39% Histidine range: 0-25 mM; Arginine range: 0-81 mM SNV - Standard Normal Variable - average centered and normalized R2- Percentage of variation in the training set explained by the model, R2 > 0.9 Q2- Percentage of variation in the training set predicted by the model during cross-validation, Q2 > 0.8 (RMSEP) Root Mean Square Error Prediction Example 9: Raman Models for Drug-Antibody Relationship Measurements Materials and methods: The DAR is a quality attribute that is checked during the development of Antibody Drug Conjugates (ADC), Antibody Radionuclide Conjugates (ARC) and protein conjugates in general (potent spheroids, non-cytotoxic loads, etc.) to ensure a consistent product quality and to facilitate subsequent labeling with loads. Raman was evaluated as a technology to check DAR levels that could be used in a control strategy for the reaction. The feasibility of Raman DAR determination of two different mAbs under development (mAb 1 and mAb 3) was evaluated. The Raman analyzer's operating parameters were set at a scan time of 10 seconds for 10 pools with a non-contact optical probe. The spectral range 350-3100 cm1 was used with the raw spectral data previously processed with SNV and additionally second derivative filtered with 21 cm1 attenuation. Table 7 shows the components of the model to determine DAR in the samples. Off-line DAR measurements were determined with a method based on UV spectroscopy. Table 7. Model components for drug-antibody relationship measures R / zznn / Lznz / B / Yi Model name REGN2810 UV-DAR REGN910 UV-DAR Interval Y 0.81 -4.39 0.79-3.57 Best model Second derivative R2Y=0.788 Q2=0.548 RMSECV=0.64 Second derivative R2Y=0.994 Q2=0.776 RMSECV=0.58 Results: Figures 15A through 15B show that it is possible to use Raman modeling to measure the drug-antibody ratio for iPET drug conjugates for mAb 1 and mAb 3. In both models, the cross-validation mean square error was 0.6 DAR. . The current orthogonal UV-based assay has an associated variability of 0.3 DAR (one standard deviation). The initial Raman prediction is up to two standard deviations and suggests that the DAR could be correctly predicted with Raman if the model were further refined. While this invention has been described in the foregoing specification with respect to certain embodiments thereof, and many details have been included for illustrative purposes, it will be apparent to those skilled in the art that the invention is amenable to additional embodiments and that some of the details described herein may vary considerably without departing from the basic principles of the invention. All references mentioned herein are incorporated by this reference in their entirety. The present invention may take other specific forms without departing from its spirit or essential attributes, and reference should therefore be made to the appended claims, rather than the foregoing specification, indicating the scope of the invention.

Claims

1. A method for producing a concentrated protein purification intermediate comprising: determining the concentrations of a protein purification intermediate in real time using in situ Raman spectroscopy, while concentrating the protein purification intermediate, and adjusting the parameters of the real-time concentration step to obtain the concentrated protein purification intermediate and / or the final concentrated fraction.

2. The method according to claim 1, wherein the concentrated protein product has a concentration of 5 mg / mL to 300 mg / mL.

3. The method according to any of claim 1 or 2, wherein the concentration of the intermediate protein purification product is at least 50 mg / mL.

4. The method according to any of claim 1 or 2, wherein the concentration of the intermediate protein purification product is at least 150 mg / mL.

5. The method according to any of claims 1 to 4, wherein the intermediate protein purification product is concentrated using ultrafiltration, buffer exchange, or both.

6. The method according to any of claims 1 to 5, wherein the intermediate protein purification product is recovered from a bioreactor, batch culture, or continuous culture.

7. The method according to any of claims 1 to 6, wherein the determination of the concentration of the intermediate purification product occurs continuously or intermittently in real time.

8. The method according to any of claims 1 to 6, wherein the quantification of the protein concentration is carried out at intervals of 30 seconds to 10 minutes, per hour or per day.

9. The method according to any of claims 1 to 8, wherein the protein purification intermediate is an antibody or an antigen-binding fragment thereof, a fusion protein, or a recombinant protein.

10. The method according to any of claims 1 to 9, wherein data on the spectrum are collected in one or more wavenumber intervals selected from the group consisting of 977-1027 cm1, 1408-1485 cm1, 1621-1711 cm1, 2823-3046 cm1 and combinations thereof.

11. A method for producing a protein purification intermediate comprising: performing independent Raman spectroscopy analysis on a variety of protein purification intermediates to produce a universal model capable of quantifying any of the various protein purification intermediates; determining the concentrations of a protein purification intermediate using in situ Raman spectroscopy with the universal model during the concentration of the protein purification intermediate; and producing the concentrated protein purification intermediate when the concentrated protein purification intermediate reaches a predetermined concentration or the final concentrated fraction target.

12. The method according to claim 11, wherein the model is produced by partial least squares regression analysis of raw spectrum data and offline protein concentration data.

13. The method according to claim 12, which also comprises carrying out a point normalization or attenuation technique on Raman spectroscopy data.

14. The method according to claim 13, wherein the normalization technique comprises the standard normal variable method.

15. The method according to claim 13, wherein the point attenuation comprises first derivative with attenuation of 21 cm·1.

16. The method according to any of claims 11 to 15, wherein the model provides predicted concentration values ​​with error <5% compared to offline protein concentration values.

17. The method according to any of claims 11 to 15, wherein the model provides predicted concentration values ​​with error <3% compared to offline protein concentration values.

18. The method according to claim 11, wherein the concentrated protein purification intermediate product has a concentration of 5 mg / mL to 300 mg / mL.

19. The method according to claim 11, wherein the concentration of the intermediate protein purification product is at least 100 mg / mL.

20. The method according to claim 11, wherein the concentration of the intermediate protein purification product is at least 150 mg / mL.

21. The method according to claim 11, wherein the concentrated protein purification intermediate product is concentrated by ultrafiltration, diafiltration, or both.

22. The method according to claim 11, wherein the intermediate protein purification product is recovered from a concentrated bioreactor, batch culture, or continuous culture.

23. The method according to any of claims 1 to 22, wherein the determination of the concentration of the intermediate purification product occurs continuously or intermittently in real time.

24. The method according to any of claims 1 to 23, wherein the quantification of the protein concentration is carried out at intervals of 30 seconds to 10 minutes, per hour or per day. R / zznn / Lznz / B / Yi 25. The method according to any of claims 11 to 24, wherein the protein purification intermediate is an antibody or an antigen-binding fragment thereof, a fusion protein, or a recombinant protein.

26. The intermediate protein purification product produced according to any of claims 1 to 25.

27. A method for checking and controlling critical quality attributes in a protein purification intermediate during the downstream protein purification process comprising: quantifying one or more critical quality attributes in the protein purification intermediate using in situ Raman spectroscopy and adjusting the critical quality attribute(s) in the protein purification intermediate to correspond to predetermined critical quality attribute levels.

28. The method according to claim 27, wherein the critical quality attribute is selected from the group consisting of antibody concentration, protein concentration, high molecular weight species, drug-antibody ratio, and buffering excipients.

29. The method according to any of claims 27 or 28, wherein the downstream protein purification processing is ultrafiltration / diafiltration.

30. A method for checking and controlling the levels of excipients in the obtained cell culture fluid and / or protein purification intermediate during downstream purification comprising: determining the concentrations of the excipients in real time using in situ Raman spectroscopy, while the cell culture fluid or protein purification intermediate is being purified, and adjusting the parameters of the real-time purification step to obtain or maintain predetermined amounts of the excipients in the obtained cell culture fluid and / or protein purification intermediate.

31. The method according to claim 30, wherein the excipient comprises a buffering excipient.

32. The method according to any of claims 30 or 31, wherein the excipient is selected from the group consisting of acetate, citrate, histidine, succinate, phosphate, hydroxymethylaminomethane (Tris), proline, arginine, sucrose, or combinations thereof.

33. The method according to claim 30, wherein the excipient comprises a surfactant excipient.

34. The method according to any of claims 30 or 33, wherein the surfactant excipient is selected from the group consisting of polysorbate 80, polysorbate 20 and poloxamer 188.

35. The method according to claim 11, wherein the excipient comprises R / zznn / Lznz / B / Yi polyethylene glycol.