Use of Raman spectroscopy in downstream purification

Abstract

In situ Raman spectroscopy methods and systems are provided for characterizing or quantifying protein purification intermediates and / or final concentrators during preparation or manufacture. In one embodiment, in-situ Raman spectroscopy is used to characterize or quantify key quality attributes of a protein purification intermediate during downstream processing (i.e., after harvesting the protein purification intermediate). For example, the disclosed in situ Raman spectroscopy methods and systems can be used to characterize and quantify protein purification intermediates when the protein purification intermediates are purified, concentrated, or otherwise formulated into final pharmaceutical products to be sold or administered.

Description

[0001] CROSS-REFERENCE TO RELATED APPLICATIONS

[0002] This application is a divisional application of the Chinese invention patent application with application number 201980036991.X (the application date of which is August 26, 2019, and the invention name is "Application of Raman Spectroscopy in Downstream Purification"). The original application is a Chinese national phase application with international application number PCT / US2019 / 048100, which claims the rights and priority of U.S. Provisional Patent Application No. 62 / 723,188 filed on August 27, 2018, and incorporates it in its entirety by reference where permitted. Technical Field

[0003] The present invention generally relates to systems and methods for monitoring and controlling one or more critical quality attributes (CQAs) or parameters in a downstream protein purification process. Background Art

[0004] Over the past decade, the number of monoclonal antibodies (mAbs) approved for therapeutic use has increased significantly. This is partly due to improvements in large-scale manufacturing processes, which facilitate the preparation of large quantities of mAbs. In addition, initiatives such as the Process Analytical Technology (PAT) framework of the U.S. Food and Drug Administration (FDA) have provided innovative solutions for process development, process analysis, and process control to better understand the process and control product quality. Efficient recovery and purification of monoclonal antibodies from cell culture media is a critical part of the manufacturing process. The purification process should reliably prepare monoclonal antibodies that can be safely used in humans. This includes monitoring critical quality attributes (CQAs), which include protein properties and impurities such as host cell proteins, DNA, viruses, endotoxins, aggregates, concentrations, excipients, and other substances that may affect patient safety, efficacy, or potency. Protein concentration is also often a CQA in the purified material, and the appropriate protein concentration in the process intermediate may be a key process parameter for unit operation performance. These CQAs need to be monitored throughout the manufacturing process and throughout the program life cycle.

[0005] To ensure that the final preparation of mAb does not contain impurities exceeding a certain level, the mAb product is tested at each stage of downstream processing. Quality control in the manufacture of biological products (such as mAb) is usually completed by using an off-line method to analyze purified intermediates and prepared bulk drug samples for each batch of preparations. The sample is removed from a processing device (such as a UF / DF skid) and subjected to an off-line test to measure product CQA, such as protein concentration (g / L), buffer excipients, and size variants. It is impossible to monitor and analyze in real time during the manufacturing process, thereby resulting in increased processing time and a higher risk of batch failure due to failure to meet CQA. Therefore, a fast in-line method for real-time quality control monitoring of mAb is needed.

[0006] It is therefore an object of the present invention to provide systems and methods for real-time monitoring of critical quality attributes during a downstream purification process. Summary of the invention

[0007] Provided are in situ Raman spectroscopy methods and systems for characterizing or quantifying protein purification intermediates during preparation or manufacture. In one embodiment, in situ Raman spectroscopy is used to characterize or quantify the key quality attributes of a protein drug during downstream processing (i.e., after harvesting the protein purification intermediate from a cell culture fluid). For example, the disclosed in situ Raman spectroscopy methods and systems can be used to characterize and quantify the key quality attributes of a protein purification intermediate when purifying a protein purification intermediate (before being formulated into a final drug product to be sold or administered). Key quality attributes include, but are not limited to, protein concentration, excipients, high molecular weight (HMW) substances, antibody titer, and drug / antibody ratio.

[0008] One embodiment provides a method for preparing a concentrated protein purification intermediate, the method being performed as follows: using in situ Raman spectroscopy to measure the concentration of the protein purification intermediate in real time while concentrating / diafiltrating the protein purification intermediate and adjusting the parameters of the concentration step in real time to obtain the predetermined concentration target and excipient level required for the formulation of the bulk drug. 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, ultrafiltration is used to concentrate the protein purification intermediate to the desired concentration target during primary or final concentration. Diafiltration is used as a means of buffer exchange during the treatment after primary concentration to achieve the desired final formulation components. Protein purification intermediates can be harvested from bioreactors, fed-batch cultures, or continuous cultures. In another embodiment, the concentration of the protein purification intermediate can be measured continuously or intermittently in real time. The protein concentration can be quantified at intervals of about 5 seconds to 10 minutes, once an hour, or once a day. The protein purification intermediate can be an antibody or an antigen-binding fragment thereof, a fusion protein, or a recombinant protein. Can be selected from 977-1027cm -1 、1408-1485cm -1 、1621-1711cm -1 、2823-3046cm -1 Spectral data are collected within one or more wavenumber ranges, as well as combinations thereof.

[0009] Another embodiment provides a method for preparing a protein purification intermediate, the method being performed as follows: Raman spectroscopy analysis is performed independently on a plurality of protein purification intermediates to generate a general model capable of quantifying any of the plurality of protein purification intermediates. The concentration of the protein purification intermediate can be determined using the general model using in situ Raman spectroscopy from the beginning to the end of concentrating the protein purification intermediate during the concentration of the protein purification intermediate. Another embodiment provides a method for preparing a protein purification intermediate, the method being used to generate a protein-specific model capable of quantifying protein concentration, which model will be used for commercially viable protein production.

[0010] The model can be generated using partial least squares regression analysis of the raw spectral data and using orthogonal methods for offline protein concentration data. Preprocessing techniques such as standard normal variate (SNV) and / or point smoothing techniques can be used to first-order derivatives of the Raman spectral data at 21 cm -1Smoothing is performed to mitigate model variability and prediction errors. Further model refinement can be performed to isolate spectral regions relevant to CQA predictions such as protein concentration. In one embodiment, the model has an error tolerance of ≤ 5%, preferably ≤ 3%.

[0011] Another embodiment provides a method for the excipient level in the cell culture fluid and / or protein purification intermediate of monitoring and controlling harvest during downstream purification, the method being carried out as follows: using in-situ Raman spectroscopy to measure the concentration of the excipient in real time while purifying the cell culture fluid or protein purification intermediate, and adjusting the parameters of the purification step in real time to obtain or maintain a predetermined amount of excipient in the cell culture fluid and / or protein purification intermediate of the harvest. Excipient can be acetate, citrate, histidine, succinate, phosphate, hydroxymethylaminomethane (Tris), proline, arginine, sucrose or a combination thereof. Excipient can be a surface excipient such as polysorbate 80, polysorbate 20 and poloxamer 188. BRIEF DESCRIPTION OF THE DRAWINGS

[0012] Figure 1 is a flow chart illustrating an exemplary protein purification process.

[0013] Figure 2 is a representative spectral graph showing the initial model development for in-line Raman spectroscopy. The X-axis represents the Raman shift. The Y-axis represents the intensity. The legend on the right represents the protein concentration. The spectral region used during the initial model development includes 977-1027 cm from left to right. -1 (ring structure), 1408-1485cm -1 (arginine), 1621-1711cm -1 (secondary structure) and 2823-3046cm -1 (CH stretch).

[0014] Figure 3 It is a bar graph showing the protein concentration (g / L) of mAb1 during the standard ultrafiltration / diafiltration unit operation. The blank bar is the concentration measured using the offline method based on UV-Vis using the SoloVPE system (C-technologies), wherein the error bar is ± 5%, and ± 5% is the target of the Raman prediction within the line. The bar with wide hatching represents the universal model that does not include mAb1 in the Raman prediction referred to herein, and the bar with narrow hatching represents the universal model with mAb1. The bar with cross hatching corresponds to the prediction results from the initial universal model with a range of 0-120g / L. The shaded bar corresponds to the prediction results from the initial universal model of> 120g / L.

[0015] Figure 4 is a bar graph showing the absolute Raman model errors for various mAbs from initial universal model development. The shaded bars represent the initial universal model 0-120 g / L (primary concentration and diafiltration), and the open bars represent the Raman model targets from the initial universal model >120 g / L. (final concentration). The horizontal lines represent the Raman model targets with errors ≤ 5%.

[0016] Figure 5 is a bar graph showing the absolute Raman model errors for various mAbs. The wide shaded bars represent 0-120 g / L (primary concentration and diafiltration), and the narrow shaded bars represent >120 g / L (final concentration). The horizontal line represents the Raman model target with an error of ≤5%. Two versions of the general Raman model are shown. The shaded bars represent the initial general model, and the cross-hatched bars represent the updated general model.

[0017] Figure 6 is a schematic diagram of the ultrafiltration / diafiltration system, including the location for in-line Raman probe placement.

[0018] Figure 7 is a bar graph showing the protein concentration of mAb10 when the general model or mAb10-specific model is used for the final concentrated pool (FCP) measurement. The protein concentrations of the inline real-time Raman prediction (bars with wide shaded lines), the updated model (bars with narrow shaded lines), and SoloVPE (open bars) are shown. The X-axis represents the experimental group and the Y-axis represents the protein concentration.

[0019] FIG. 8A to FIG. 8B is a bar graph showing the Raman model error for laboratory-scale DoE modeling of protein concentration. Fig. 8A Real-time predicted Raman model errors in various stages of the UF / DF process (primary concentration, diafiltration, and final concentration tank) are shown. Figure 8B The Raman model errors of the final DoE model at various stages of the UF / DF process (primary concentration, diafiltration and final concentration tank) are shown.

[0020] Fig. 9A is a bar graph showing the model predictive ability percentage error when the model is scaled up to the pilot scale processing equipment for mAb 11. The laboratory scale model data is compared to the laboratory scale data incorporated with the pilot scale data. The X-axis represents the experimental group and the Y-axis represents the model predictive ability percentage error (%). Fig. 9B : is a bar graph showing the model prediction ability of pilot scale processing of various monoclonal antibodies. The X-axis represents the experimental group and the Y-axis represents the model prediction ability percentage error (%).

[0021] Fig. 10A is a schematic diagram of an exemplary automated batch UF / DF with Raman feedback. Fig. 10B is a schematic diagram of an exemplary automated single-pass TFF with Raman feedback.

[0022] Fig.11 is a graph showing the concentration of mAb14 during UF / DF treatment. The X-axis represents the throughput (L / m 2 ), the Y axis represents the mAb 14 concentration (g / L).

[0023] Fig. 12A is a bar graph showing the percentage of high molecular weight (HMW) species in mAb2 during various processing steps (primary concentration, diafiltration, final concentration) as determined by SE-UPLC or Raman modeling. The X-axis represents the experimental group and the Y-axis represents the mAb2 HMW percentage (%). Fig. 12B is a bar graph showing the percentage of high molecular weight (HMW) species predicted using various scan times (10 sec, 20 sec, 30 sec) for mAb 15. The X-axis represents the experimental group and the Y-axis represents the HMW percentage (%).

[0024] Fig.13 is a dot plot showing the actual titer (g / L) versus the Raman predicted titer for monoclonal antibody samples from mAb 14. The X-axis represents the Raman predicted titer and the Y-axis represents the actual titer (g / L).

[0025] Fig.14A is a scatter plot showing Raman histidine predictions 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. Fig. 14B is a dot plot showing the actual histidine concentration of the monoclonal antibody sample versus the Raman predicted histidine concentration. Fig. 14C is a dot plot showing the actual arginine concentration of the monoclonal antibody sample versus the Raman predicted arginine concentration.

[0026] Fig.15A is a scatter plot showing the actual drug / antibody ratio (DAR) versus the Raman predicted DAR for monoclonal antibody samples from mAb 3. The X-axis represents the Raman predicted DAR and the Y-axis represents the actual DAR measured by UV spectroscopy. Fig. 15B is a scatter plot showing the actual drug / antibody ratio (DAR) versus the Raman predicted DAR for the monoclonal antibody samples from mAb 1. The X-axis represents the Raman predicted DAR and the Y-axis represents the actual DAR. DETAILED DESCRIPTION

[0027] I. Definitions

[0028] It should be understood that the present disclosure is not limited to the compositions and methods described herein and the experimental conditions described, as they may vary. It should also be understood that the terminology used herein is for the purpose of describing certain embodiments only, and is not intended to be limiting, as the scope of the present disclosure will be limited only by the appended claims.

[0029] Unless otherwise defined, the meaning of all scientific and technological terms used herein is consistent with that generally understood by those of ordinary skill in the art to which the present disclosure belongs. Although any compositions, methods and materials similar or equivalent to those described herein can be used in the practice or testing of the present invention. All publications mentioned are incorporated herein by reference in their entirety.

[0030] The use of the terms "a," "an," "the" and similar referents in the context of describing the presently claimed invention (especially in the context of the claims) are to be construed to cover both the singular and the plural, unless otherwise indicated herein or clearly contradicted by context.

[0031] Recitation of ranges of values ​​herein are merely intended to serve as a shorthand method of referring individually to each separate value falling within the range, unless otherwise indicated herein, and each separate value is incorporated into the specification as if it were individually recited herein.

[0032] The use of the term "about" is intended to describe values ​​that are within a range of approximately ±10% above or below the stated value; in other embodiments, the numerical range of values ​​may be within a range of approximately ±5% above or below the stated value; in other embodiments, the numerical range of values ​​may be within a range of approximately ±2% above or below the stated value; in other embodiments, the numerical range of values ​​may be within a range of approximately ±1% above or below the stated value. The preceding ranges are intended to be explained clearly by the context and no further limitation is implied. Unless otherwise stated herein or the context is clearly contradictory, all methods described herein may be performed in any suitable order. Unless otherwise stated, the use of any and all examples or exemplary language (such as "such as") provided herein is intended only to better illustrate the present invention, rather than to limit the scope of the present invention. No language in the specification should be interpreted as indicating that any unclaimed element is essential to the implementation of the present invention.

[0033] As used herein, "protein" refers to a molecule comprising two or more amino acid residues connected to each other by peptide bonds. Proteins include polypeptides and peptides, and may also include modifications such as glycosylation, lipid linkage, sulfation, gamma carboxylation, alkylation, hydroxylation and ADP-ribosylation of glutamic acid residues. Proteins may have scientific or commercial value, including protein-based drugs, and proteins particularly include enzymes, ligands, receptors, antibodies and chimeric proteins or fusion proteins. Proteins are produced by various types of recombinant cells using well-known cell culture methods, and are typically introduced into cells by transfection of genetic engineering nucleotide vectors (e.g., such as sequences encoding chimeric proteins, or codon-optimized sequences, intron-free sequences, etc.), wherein the vector may exist as an episome or be integrated into the cell genome.

[0034] "Antibody" refers to an immunoglobulin molecule composed of four polypeptide chains (two heavy chains (H) and two light chains (L)) 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, namely 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 one domain (CL). The VH region and VL region can be further subdivided into hypervariable regions, called complementarity determining regions (CDRs), interspersed with more conserved regions called framework regions (FRs). Each VH and VL consists of three CDRs and four FRs, arranged from amino terminus to carboxyl terminus in the following order: FR1, CDR1, FR2, CDR2, FR3, CDR3, FR4. The term "antibody" includes glycosylated and non-glycosylated immunoglobulins of any isotype or subtype. The term "antibody" includes antibody molecules prepared, expressed, produced or isolated by recombinant means, such as antibodies isolated from host cells transfected to express the antibody. The term antibody also includes bispecific antibodies, which include heterotetrameric immunoglobulins that can bind to more than one different epitope. Bispecific antibodies are generally described in U.S. Patent Application Publication No. 2010 / 0331527, which is incorporated herein by reference.

[0035] "Secondary structure" refers to the local folded structures formed within a polypeptide due to interactions between the backbone atoms. The most common types of secondary structure are alpha helices and beta pleated sheets. Both structures are held in shape by hydrogen bonds, which form between the carbonyl group O of one amino acid and the amino group H of another amino acid.

[0036] As used herein, "excipient" refers to a pharmacologically inactive substance that is used as a stabilizer for long-term storage of formulated bulk drugs. Typically, additional excipients are added to the final concentration pool to prepare formulated bulk drugs. However, during the UF / DF process, the level of the excipient is monitored to ensure that the excipient level will not affect the desired formulation strategy. Excipients provide bulk volume for drug formulations, promote drug absorption or dissolution, and provide stability and prevent denaturation. Common pharmaceutical excipients include but are not limited to amino acids, fillers, adhesives, disintegrants, coatings, adsorbents, buffers, chelating agents, lubricants, glidants, preservatives, antioxidants, flavorings, sweeteners, coloring agents, solvents and cosolvents and tackifiers. In one embodiment, the excipient is polyethylene glycol, including but not limited to PEG-3550.

[0037] "Polyethylene glycol" or "PEG" is a polyether polymer of ethylene oxide commonly used in food, medicine and cosmetics. It is a nonionic macromolecule that can be used as a molecule to reduce the solubility of biomolecules. PEG is commercially available in different molecular weights ranging from 300 g / mol to 10,000,000 g / mol. Exemplary types of PEG include, but are not limited to, PEG 20000, PEG 8000, and PEG 3350. PEG has different geometric shapes, including linear, branched (3 to 10 chains attached to a central core), star-shaped (10 to 100 chains attached to a central core), and comb-shaped (multiple chains attached to the polymer backbone).

[0038] "Raman spectroscopy" is a spectroscopic technique used to measure the wavelength and intensity of inelastically scattered light from molecules. It is based on the following principle: monochromatic incident radiation on a material will be reflected, absorbed or scattered in a specific way, depending on the specific molecule or protein receiving the radiation. Most of the energy is scattered at the same wavelength, called elastic scattering or Rayleigh scattering. A small amount (<0.001%) is scattered at a different wavelength, called inelastic scattering or Raman scattering. Raman scattering is related to rotational, vibrational and electronic energy level transitions. Raman spectroscopy can reveal the chemical and structural composition of a sample.

[0039] As used herein, "ultrafiltration" refers to a membrane process widely used for protein concentration in downstream processing of protein therapeutics during recombinant protein purification. Ultrafiltration is a size-based separation in which materials larger than the membrane pores are retained while smaller materials pass freely. During processing, a protein solution is pumped tangentially across the surface of a semipermeable parallel flat plate membrane. The membrane is permeable to buffers and buffer salts, but is generally impermeable to monoclonal antibodies. The driving force for osmosis is exerted by the transmembrane pressure (TMP) caused by flow restriction at the outlet of the membrane flow channel (TMP = ((P 进料 +P 渗余物 ) / 2-P 渗透物 ..).

[0040] As used herein, "primary concentration" refers to the initial step in which the transmembrane pressure drives water and salts across a permeable membrane, which can reduce the liquid volume and thereby increase protein concentration. The degree of concentration in primary concentration can be optimized to balance throughput, protein stability, processing time, and buffer consumption.

[0041] As used herein, "diafiltration" refers to a technique that uses a semipermeable membrane to exchange a product of interest from one liquid medium to another. Buffer exchange and desalting are often performed using a diafiltration mode, where small amounts of impurities and buffer components are efficiently washed away from the product by the continuous addition of buffer designed to condition the protein to a stable pH and excipient concentration that allows for high product concentrations. This can be performed in a continuous or discontinuous mode based on the process technology.

[0042] Diafiltration is often used in conjunction with ultrafiltration to achieve the desired volume reduction while also removing impurities and salts. UF / DF is the last unit operation in downstream purification, which conditions the mAb to achieve a pH, excipient content, and protein concentration that is favorable for long-term storage and the addition of stabilizing excipients to generate the formulated drug substance (FDS).

[0043] As used herein, "final concentration" refers to the final step in which the transmembrane pressure drives water and salts through the permeable membrane, which can reduce the liquid volume and thereby increase the protein concentration to the desired target for storage and / or formulation. The resulting pool from the final concentration step is the final concentrated pool (FCP). The final concentration step can be performed in a continuous or discontinuous processing mode.

[0044] The terms "biological product" and "protein purification intermediate" are used interchangeably to refer to any antibody, antibody fragment, modified antibody, protein, glycoprotein or fusion protein purified from a bioreactor process, as well as the final drug substance.

[0045] The terms "control" and "controlling" refer to adjusting the amount or concentration level of a critical quality attribute in a harvested cell culture fluid to a predetermined set point.

[0046] As used herein, the term "upstream processing" refers to the first step in which an antibody or therapeutic protein is produced in a bioreactor, typically by a bacterial or mammalian cell line. Upstream processing includes media preparation, cell culture, and cell separation and harvesting. When the cells reach the desired density, the cells are harvested and moved to the downstream section of the bioprocess. The term "downstream processing" refers to the separation and purification that occurs after the antibody or therapeutic protein is harvested from the bioreactor. Typically, this means recovering the product from the aqueous solution via several different forms. The harvested product is processed to meet the purity and critical quality attribute requirements during downstream processing.

[0047] As used herein, the term "protein purification intermediate" refers to protein that has been harvested from a bioreactor, as well as to any intermediates during downstream processing.

[0048] The term "concentrated protein purification intermediate" refers to a protein purification intermediate having a concentration greater than 5 mg / mL. More preferably, the concentration is between 50 mg / mL and 300 mg / mL.

[0049] The terms "monitoring" and "performing monitoring" refer to regularly checking the amount or concentration level of a critical quality attribute in a cell culture or harvested cell culture fluid.

[0050] The term "harvested cell culture fluid" refers to the fluid removed from a bioreactor containing cells engineered to secrete a protein of interest. "Harvested cell culture fluid" optimally contains the secreted protein of interest, such as a monoclonal antibody.

[0051] As used herein, "critical quality attributes (CQA)" refers to physical, chemical, biological or microbiological properties or characteristics that should be within appropriate limits, ranges or distributions to ensure the desired product quality of a biotherapeutic drug product. These attributes may affect safety, efficacy and / or potency. CQA include, but are not limited to, protein concentration, high molecular weight substances, buffer excipients and pH.

[0052] As used herein, "formulated drug substance" refers to the active ingredient intended to provide pharmacological activity, but does not include intermediates used to synthesize such ingredient.

[0053] As used herein, "generic model" refers to a mathematical correlation of spectral properties of different recombinant proteins for predicting critical quality attributes.

[0054] As used herein, "mAb-specific model" refers to a mathematical correlation of spectral characteristics of a specific protein used to predict critical quality attributes.

[0055] As used herein, "titer" refers to the amount of antibody or protein molecules in a solution.

[0056] II. Systems and Methods for Characterizing Downstream Protein Purification Products

[0057] Provided are systems and methods for monitoring and controlling protein concentration during protein manufacturing. Due to the high viscosity of the associated solution (>10cP), concentrated protein solutions are difficult to measure accurately. Accurate quantification requires dedicated offline equipment and generally requires dilution of the solution. In high concentration UF / DF, due to the Gibbs-Donnan effect, the final excipient level is a function of protein concentration. Real-time monitoring and analysis cannot be performed during the manufacturing process, which increases processing time and may result in batch failure due to failure to meet CQA. The systems and methods disclosed herein can be used for in-line monitoring of protein concentration and other key quality attributes.

[0058] In one embodiment, the Raman spectroscopy system is an in-line or in-situ Raman spectroscopy system used during the preparation of the final concentration pool, which can be highly concentrated (≥150 g / L). Typically, a Raman spectroscopy system is employed downstream of the preparation of a protein purification intermediate, such as during post-harvest processing of a protein purification intermediate from a bioreactor or fed-batch culture system and subsequent purification. Figure 1 An exemplary protein purification process is shown. Typically, a protein purification intermediate is harvested from a cell culture (100) and processed through multiple purification steps such as affinity capture (110), virus inactivation (120), polishing chromatography (130 and 140), virus retention filtration (150), and ultrafiltration / diafiltration (160) to produce a final concentrated pool that is then formulated into a bulk drug. In one embodiment, monitoring the protein concentration in the harvested cell culture fluid is performed by in situ Raman spectroscopy.

[0059] A. Raman spectroscopy

[0060] In one embodiment, monitoring and controlling the protein concentration in the cell culture fluid harvested is performed by Raman spectroscopy. Raman spectroscopy is a form of vibrational spectroscopy that provides information about molecular vibrations that can be used for sample identification and key quality attribute quantification. In-situ Raman analysis is a method of analyzing a sample in its original position without extracting a portion of the sample for analysis in a Raman spectrometer. The advantage of in-situ Raman analysis is that the Raman spectrometer is non-invasive and non-destructive, which can reduce the risk of contamination and protein quality loss. In-line Raman analysis can be implemented to achieve continuous processing while monitoring the protein concentration of the cell culture fluid harvested, protein purification intermediates and / or final concentration pools.

[0061] In-situ Raman analysis can provide real-time assessment of protein concentration in protein purification intermediates. For example, the raw spectral data provided by in-situ Raman spectroscopy can be used to obtain and monitor the current protein concentration in protein purification intermediates. In this regard, in order to ensure that the raw spectral data is continuously updated, spectral data should be obtained from Raman spectroscopy approximately every 5 seconds to 10 hours. In another embodiment, spectral data should be obtained approximately every 15 minutes to 1 hour. In another embodiment, spectral data should be obtained approximately every 20 minutes to 30 minutes.

[0062] Monitoring of protein concentration in protein purification intermediates can be analyzed by any commercially available Raman spectrometer that can perform in-situ Raman analysis. The in-situ Raman analyzer should be able to obtain raw spectral data in protein purification intermediates. For example, the Raman analyzer should be equipped with a probe that can be inserted into the line of the fluid loop. Suitable Raman analyzers include, but are not limited to, RamanRXN2 and RamanRXN4 analyzers (Kaiser Optical Systems, Inc., Ann Arbor, MI, USA).

[0063] The raw spectral data obtained by in-situ Raman spectroscopy can be compared with the offline protein concentration measurement results to associate the peaks in the spectral data with the protein concentration. The offline protein concentration measurement results can be used to determine which spectral regions exhibit protein signals. The offline measurement data can be collected by any appropriate analytical method. For example, with respect to protein concentration, SoloVPE (C Technology) can be used to collect offline measurements. In addition, any type of multivariate software package, such as SIMCA 13 (MKSData Analytic Solutions, Umea, Sweden) can be used to associate the peaks in the raw spectral data with the offline measurements of protein concentration. However, in some embodiments, it may be necessary to pre-process the raw spectral data with a spectral filter to remove any baseline changes. For example, the raw spectral data can be pre-processed with any type of point smoothing technique or normalization technique. Normalization may be required to correct any probe, optical device, laser power variation, and exposure time of the Raman analyzer. In one embodiment, point smoothing (such as combining 21 cm -1 The raw spectral data are processed by first derivative of point smoothing) and normalization (such as standard normal variate (SNV) normalization). These preprocessing techniques can be combined for certain spectral regions to improve model predictions.

[0064] Chemometric modeling can also be performed on the obtained spectral data. In this regard, one or more multivariate methods can be applied to the spectral data, including but not limited to partial least squares (PLS), principal component analysis (PCA), orthogonal partial least squares (OPLS), multiple regression, canonical correlation, factor analysis, cluster analysis, graphical programs, etc. In one embodiment, the obtained spectral data are used to create a PLS regression model. The PLS regression model can be created by projecting the predicted variables and the observed variables into a new space. In this regard, the PLS regression model can be created using the measured values ​​obtained from Raman analysis and the offline measured values. The PLS regression model provides a predicted process value, such as a predicted protein concentration value. In one embodiment, the model provides a predicted protein concentration value with an error of ≤5% compared to the offline protein concentration value. In a preferred embodiment, the model provides a predicted protein concentration value with an error of ≤3% compared to the offline protein concentration value.

[0065] After chemometric modeling, signal processing techniques can be applied to the predicted protein concentration value. In one embodiment, signal processing techniques will mitigate model variability and prediction errors. In this regard, one or more pre-processing techniques can be applied to the predicted protein concentration value. Any pre-processing techniques known to those skilled in the art can be utilized. For example, noise reduction techniques can include data smoothing and / or signal removal. Smoothing is achieved through a series of smoothing algorithms and filters, while signal removal uses signal features to identify data that should not be included in the analyzed spectral data. In one embodiment, the predicted protein concentration value is reduced in noise by a noise reduction filter. The noise reduction filter provides the final predicted protein concentration value. In this regard, noise reduction techniques combine the original measurement results with model-based estimates to derive what the measurement results should obtain according to the model. In one embodiment, noise reduction techniques combine the current predicted protein concentration value with its uncertainty. Uncertainty can be determined by the repeatability of the predicted protein concentration value and the current protein concentration value. Once the next predicted protein concentration value is observed, the estimated value of the predicted protein concentration value is updated using a weighted average, wherein the estimated value with higher certainty is given more weight. Using an iterative approach, the final protein concentration value can be updated based on previous measurements and current measurements. In this regard, the algorithm should be recursive and able to run in real time in order to utilize the current predicted protein concentration value, previous values, and experimentally determined constants. Noise reduction techniques can improve the robustness of the measurements received from Raman analysis and PLS predictions by reducing the noise on which the automatic feedback controller will act.

[0066] B. How to use

[0067] The disclosed method can be used to monitor and control the protein concentration in the cell culture and / or protein purification intermediate fluid of the harvest in the downstream protein purification process. Common downstream purification processes include but are not limited to centrifugation, direct deep filtration, protein A affinity purification, virus inactivation steps, ion exchange chromatography, hydrophobic interaction chromatography, size exclusion chromatography, ultrafiltration / diafiltration, virus retention filtration and their combinations. These unit operations are used in combination in a limited order to separate the protein of interest and ensure that impurities and / or key quality attributes are monitored before preparing the formulated bulk drug. In one embodiment, the disclosed method includes a Raman probe in a fluid return line. In another embodiment, the disclosed method can be used to prepare a concentrated protein purification intermediate or a final concentration pool.

[0068] 1. Antibody titer and protein concentration

[0069] Both antibody titer and protein concentration are important factors in the purification of bioproducts. Antibody titer measured after the initial harvest of cell culture fluids is important for determining column loading and ensuring a reliable purification process for the removal of impurities. Monitoring protein concentration throughout the purification steps is important to ensure the proper concentration of the final product and proper performance of the purification unit operations performed. Inappropriate protein concentration can result in an ineffective drug product or formulated drug substance preparation.

[0070] In one embodiment, the harvested cell culture fluid is subjected to Raman spectroscopy analysis immediately after harvesting but before any other purification begins. Raman spectroscopy data can be used to quantify antibody titers in the harvested cell culture fluid after harvesting. The disclosed method can be used to measure protein concentration at multiple steps during the protein purification process, such as during affinity capture, during polishing chromatography, during virus retention filtration, or during ultrafiltration / diafiltration. In-line Raman probes can detect Raman scattering in harvested cell culture fluids and / or protein purification intermediates within a fluid circuit without removing samples from the system, thereby providing analytical characterizations that are typically measured in an off-line manner.

[0071] In one embodiment, if the protein concentration is not within the predetermined concentration during the ultrafiltration / diafiltration process, the system is notified and the protein purification intermediate is changed accordingly. For example, if the protein concentration in the protein purification intermediate is lower than the predetermined protein concentration, the protein purification intermediate can be further concentrated by performing ultrafiltration / diafiltration.

[0072] In one embodiment, the concentration step is performed by Protein A affinity chromatography.

[0073] 2. Drug / antibody ratio

[0074] In another embodiment, the disclosed method can be used to monitor and control the drug / antibody ratio (DAR). DAR is a quality attribute that is monitored during the development of antibody-drug conjugates (ADCs), antibody-radionuclide conjugates (ARCs), and common protein conjugates (effective steroids, non-cytotoxic payloads, etc.) to ensure consistent product quality and facilitate subsequent use of payloads for labeling. DAR is the average number of drugs or other therapeutic molecules conjugated to an antibody and is an important quality attribute in the preparation of therapeutic conjugates. DAR values ​​can affect the efficacy of conjugated drugs because low drug loading reduces drug efficacy, while high drug loading negatively affects pharmacokinetics and safety.

[0075] In one embodiment, the antibody-radionuclide conjugate is subjected to Raman spectroscopy analysis immediately after conjugation but before any other purification is performed. The DAR can be determined using Raman spectroscopy data after conjugation.

[0076] In one embodiment, if the DAR is not within a predetermined concentration during processing, the system is notified and the ADC intermediate is modified accordingly. For example, if the DAR in the ADC intermediate is below a predetermined DAR, the conjugation reaction components can be modified, for example, the reactant concentrations can be optimized, the type of linker can be changed, the temperature or other manufacturing variables can be optimized.

[0077] 3. Buffering excipients

[0078] The disclosed method can be used to monitor and control the level of buffer excipients in the cell culture fluid and / or protein purification intermediates harvested during downstream purification. Commonly used buffer excipients in the preparation of monoclonal antibodies 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. Antioxidant excipients include but are not limited to ascorbic acid, methionine and ethylenediaminetetraacetic acid (EDTA). Two commonly used amino acid excipients are histidine and arginine. In a preferred embodiment, the excipients monitored and controlled are histidine and arginine.

[0079] Due to the combination of excluded volume and Donnan effect, the final concentration pool excipient concentration is different from the composition of diafiltration buffer. Donnan effect is due to the membrane retaining net positively charged proteins during UF / DF, plus the phenomenon caused by requiring electrical neutrality in both retentate and permeate. In order to balance positively charged proteins, relative to diafiltration buffer, negatively charged buffer components are enriched in the retentate, while positively charged buffer components are discharged. This effect can cause FCP pH and buffer excipient concentration to be significantly different from diafiltration buffer composition (Stoner et al., J Pharm Sci, 93:2332-2342 (2004)).

[0080] Size exclusion describes the behavior of highly concentrated samples where the protein occupies a large fraction of the solution volume. Buffer is expelled from the volume occupied by the protein, resulting in a decrease in the buffer solute concentration as the protein concentration increases, expressed as moles (or mass) of solute per solution volume. Buffer is expelled from the volume occupied by the protein, resulting in a decrease in the buffer solute concentration as the protein concentration increases, expressed as moles (or mass) of solute per solution volume.

[0081] Based on these principles and that buffer excipient levels are a critical quality attribute, an in-line Raman probe would minimize offline analytical characterization and provide further process understanding to ensure that excipient levels are adequate prior to formulation.

[0082] 4. High molecular weight impurities

[0083] In the preparation of monoclonal antibodies, low levels of product-related impurities are present even after extensive purification steps. High molecular weight (HMW) species, such as antibody dimer species, are product-related impurities that contribute to the size heterogeneity of mAb products. The formation of HMW species due to protein aggregation in therapeutic mAb drug products may potentially compromise drug efficacy and safety. HMW species are considered CQ A that are routinely monitored during drug development and are considered part of the release testing of purified protein drug products during manufacturing.

[0084] In one embodiment, the disclosed method can be used to identify protein drug products containing HMW species. HMW species can be detected by Raman spectroscopy at multiple steps during the purification process (including but not limited to during affinity capture, during viral extinguishing, during polishing chromatography, during viral retention filtration, during ultrafiltration / diafiltration, or a combination thereof).

[0085] In one embodiment, the disclosed method detects HMW species in a harvested cell culture fluid and further processes the fluid to remove HMW species. The method of removing HMW species in a cell culture fluid comprises additional polishing steps including but not limited to cation exchange chromatography and anion exchange chromatography.

[0086] C.UF / DF system

[0087] Figure 6 An ultrafiltration / diafiltration processing system is shown, including multiple locations for in-line Raman probes (circled numbers 1 to 4). The protein purification intermediate is pumped into a retentate container 205 by a diafiltration pump 200, which can be a peristaltic pump, a rotary vane pump, a pressure transfer pump, or a diaphragm pump. The fluid from the retentate container 205 flows to a feed pump 210 (which can be a rotary vane pump, a peristaltic pump, or a diaphragm pump), passes through a feed pressure valve 215 and enters a tangential flow filtration module (TFFM) 220. In the TFFM 220, the protein purification intermediate undergoes transmembrane ultrafiltration. The biological product of interest is retained in the fluid (retentate), while water and low molecular weight solutes (including buffer excipients) pass through the membrane in the permeate (filtrate), which leaves the system by crossing the permeate pressure valve 225 and entering a waste tank 230. The retentate leaves the TFFM 220 and passes through the retentate pressure valve 235, the transmembrane pressure control valve 240, and the retentate return channel 245, where the retentate flows back to the retentate vessel 205. This process can be repeated as needed to concentrate the bioproduct, remove impurities, and ensure that CQAs are within acceptable limits. During diafiltration, the same flow path described above is followed, where permeable solutes are replaced as new buffer is washed into the product stream. When new buffer is added at the same rate as permeate is removed from the system, the sum of the retentate tank and skid holdup volumes will define the system volume. One turnover volume (TOV) is defined as the amount of diafiltration buffer added to the UF / DF process that is equal to the system volume. Typically, replacing 8 system volumes (8TOV) ensures >99.9% buffer exchange (Schwarts, L., Scientific and Technical Report, PN 33289)

[0088] Furthermore, during the UF / DF process, it is necessary to mix the protein solution in the retentate vessel 205. The density differences between the diafiltration buffer, retentate return, and bulk retentate during diafiltration require that the agitation in the tank be sufficient to ensure adequate buffer exchange, but moderate enough to avoid shear, as this has been observed to cause protein aggregation and sub-visible particle (SVP) generation in some products. Additionally, it is important to ensure adequate mixing of the retentate return during the concentration phase to prevent polarization of the protein concentration in the retentate tank, thereby resulting in a higher protein concentration being delivered to the UF / DF membrane.

[0089] In one embodiment, the Raman probe is positioned at position 1 in the retentate container 205 downstream of the diafiltration pump 200. Alternatively, the Raman probe may be positioned at position 2 inline downstream of the retentate container 205 before the feed pump 210. In another embodiment, the Raman probe is positioned inline at position 3 between the feed pressure pump 215 and the tangential flow filtration module 220. In another embodiment, the Raman probe is positioned inline in the retentate return channel 245. The location of the Raman probe is critical to ensuring accurate inline measurements in complex systems with engineering and processing constraints.

[0090] D. Cell culture

[0091] The cell culture fluid harvested can be harvested from a bioreactor containing cells engineered to produce monoclonal antibodies. The term "cell" includes any cell suitable for expressing a recombinant nucleic acid sequence. The cell includes a prokaryotic and eukaryotic cell, such as a bacterial cell, a mammalian cell, a human cell, a non-human animal cell, a bird cell, an insect cell, a yeast cell, or a cell fusion, such as, for example, a hybridoma or a quadruple hybridoma. 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) (e.g., CHO K1, DXB-11CHO, Veggie-CHO), COS (e.g., COS-7), retinal cells, Vero, CV1, kidney cells (e.g., HEK293, 293EBNA, MSR 293, MDCK, HaK, BHK21), HeLa, HepG2, WI38, MRC 5, Colo25, HB8065, HL-60, lymphocytes such as Jurkat (T lymphocytes) or Daudi (B lymphocytes), A431 (epidermal cells), U937, 3T3, L cells, C127 cells, SP2 / 0, NS-0, MMT cells, stem cells, tumor cells, and cell lines derived from the above cells. In some embodiments, the cell comprises one or more viral genes, such as retinal cells expressing viral genes (e.g., PER. In some embodiments, the cell is a CHO cell. In other embodiments, the cell is a CHOK1 cell.

[0092] In protein production, "fed-batch cell culture" or "fed-batch culture" refers to a batch culture in which cells and culture medium are initially supplied to a culture vessel, and additional culture nutrients are slowly fed to the culture in discontinuous increments during the culture, with or without periodic cell and / or product harvesting before terminating the culture. Fed-batch culture includes "semi-continuous fed-batch culture", in which the entire culture (which may include cells and culture medium) is periodically removed and replaced with fresh culture medium. Fed-batch culture is different from simple "batch culture", and all components used for cell culture in batch culture (including animal cells and all culture nutrients) are provided to the culture vessel at the beginning of the culture process. Fed-batch culture may differ from "perfusion culture" in that in a standard fed-batch process, the supernatant is not removed from the culture vessel, while in a perfusion culture, the cells are confined in the culture by, for example, filtration, and the culture medium is continuously or intermittently introduced into and removed from the culture vessel. However, it is envisaged that samples are removed during fed-batch cell culture for testing purposes. The fed-batch process is continued until maximum working volume and / or protein yield is determined to be achieved, after which the protein is harvested.

[0093] The phrase "continuous cell culture" refers to a technique for culturing cells continuously, usually in a specific growth phase. For example, if a continuous supply of cells is required, or a specific protein of interest needs to be produced, the cell culture may need to be maintained in a specific growth phase. Therefore, conditions must be continuously monitored and adjusted accordingly to maintain the cells in that specific growth phase.

[0094] The terms "cell culture medium" and "culture medium" refer to a nutrient solution for culturing mammalian cells, which generally provides necessary nutrients to enhance cell growth, such as carbohydrate energy, essential amino acids (such as phenylalanine, valine, threonine, tryptophan, methionine, leucine, isoleucine, lysine and histidine) and non-essential amino acids (such as alanine, asparagine, aspartic acid, cysteine, glutamic acid, glutamine, glycine, proline, serine and tyrosine), trace elements, energy, lipids, vitamins, etc. The cell culture medium may contain extracts, such as serum or peptone (hydrolysate), which may provide raw materials to support cell growth. The culture medium may contain extracts derived from yeast or soy extracts, rather than extracts derived from animals. A chemically defined medium refers to a cell culture medium in which all chemical components are known (i.e., having a known chemical structure). A chemically defined medium is completely free of animal-derived components, such as serum-derived or animal-derived peptones. In one embodiment, the culture medium is a chemically defined medium.

[0095] The solution may also contain components that enhance growth and / or survival above a minimum rate, including hormones and growth factors.The solution may be formulated to have an optimal pH and salt concentration for the survival and proliferation of the particular cells being cultured.

[0096] E. Protein of interest

[0097] Any protein of interest suitable for expression in prokaryotic or eukaryotic cells can be monitored using the disclosed methods. For example, proteins of interest include, but are not limited to, antibodies or antigen-binding fragments thereof, chimeric antibodies or antigen-binding fragments thereof, ScFv or fragments thereof, Fc fusion proteins or fragments thereof, growth factors or fragments thereof, cytokines or fragments thereof, or the extracellular domain of a cell surface receptor or fragments thereof. The protein of interest can be a simple polypeptide consisting of a single subunit or a complex multi-subunit protein consisting of two or more subunits. The protein of interest can be a biopharmaceutical product, a food additive or preservative, or any protein product subject to purification and quality standards.

[0098] In some embodiments, the antibody is selected from anti-programmed cell death 1 antibody (e.g., an anti-PD1 antibody as described in U.S. Pat. No. 9,987,500), anti-programmed cell death ligand 1 (e.g., an anti-PD-L1 antibody as described in U.S. Pat. No. 9,938,345), anti-D114 antibody, anti-angiopoietin 2 antibody (e.g., an anti-ANG2 antibody as described in U.S. Pat. No. 9,402,898), anti-angiopoietin-like 3 antibody (e.g., an anti-AngPtl3 antibody as described in U.S. Pat. No. 9,018,356), anti-platelet derived growth factor receptor antibody (e.g., an anti-PDGFR antibody as described in U.S. Pat. No. 9,265,827), anti-Erb3 antibody, anti-prolactin receptor antibody (e.g., an anti-PRLR antibody as described in U.S. Pat. No. 9,302,015), anti-complement 5 antibody (e.g., an anti-PGR antibody as described in U.S. Pat. No. 9,402,898), anti-angiopoietin-like 3 antibody (e.g., an anti-AngPtl3 ...018,356), anti-platelet derived growth factor receptor antibody (e.g., an anti-PDGFR antibody as described in U.S. Pat. No. 9,265,827), anti-Erb3 antibody, anti-prolactin receptor antibody (e.g., an anti-PRLR antibody as described in U.S. Pat. No No. 9,795,121), anti-TNF antibodies, anti-epidermal growth factor receptor antibodies (e.g., anti-EGFR antibodies as described in U.S. Pat. No. 9,132,192 or anti-EGFRvIII antibodies as described in U.S. Pat. No. 9,475,875), anti-proprotein convertase subtilisin Kexin-9 antibodies (e.g., anti-PCSK9 antibodies as described in U.S. Pat. No. 8,062,640 or U.S. Pat. No. 9,540,449), anti-growth and differentiation factor 8 antibodies (e.g., anti-GDF8 antibodies, also known as anti-myostatin antibodies, as described in U.S. Pat. No. 8,871,209 or No. 9,260,515), anti-glucagon receptor (e.g., anti-GCGR antibodies as described in U.S. Pat. No. 9,587,029 or No. 9,657,099), anti-VEGF antibodies, anti-IL1R antibodies, interleukin 4 receptor antibodies (e.g., as described in U.S. Pat. No. 9,657,099), Application Publication No. US2014 / 0271681A1 or U.S. Patent No. 8,735,095 or No. 8,945,559), anti-interleukin 6 receptor antibody (e.g., anti-IL6R antibody as described in U.S. Patent No. 7,582,298, No. 8,043,617 or No. 9,173,880), anti-IL1 antibody, anti-IL2 antibody, anti-IL3 antibody, anti-IL4 antibody, anti-IL5 antibody Antibodies, anti-IL6 antibodies, anti-IL7 antibodies, anti-interleukin 33 (e.g., anti-IL33 antibodies as described in U.S. Patent No. 9,453,072 or No. 9,637,535), anti-respiratory syncytial virus antibodies (e.g., anti-RSV antibodies as described in U.S. Patent Application Publication No. 9,447,173), anti-cluster of differentiation 3 (e.g., as described in U.S. Patent Nos. 9,447,173 and 9,447,173 and in U.S. Patent Application No. 9,447,173).62 / 222,605), anti-differentiation cluster 20 (e.g., anti-CD20 as described in U.S. Pat. Nos. 9,657,102 and 62 / 222,605), anti-differentiation cluster 20 (e.g., anti-CD20 as described in U.S. Pat. Nos. 9,657,102 and 9,657,102 and 9,657,102 and 9,62,150266966A1), anti-CD19 antibodies, anti-CD28 antibodies, anti-differentiation cluster 48 (e.g., anti-CD48 antibodies as described in U.S. Pat. No. 9,228,014), anti-Fel d1 antibody (e.g., as described in U.S. Patent No. 9,079,948), anti-Middle East respiratory syndrome virus (e.g., anti-MERS antibody as described in U.S. Patent No. 9,718,872), anti-Ebola virus antibody (e.g., as described in U.S. Patent No. 9,771,414), anti-Zika virus antibody, anti-lymphocyte activation gene 3 antibody (e.g., anti-LAG3 antibody or anti-CD223 antibody), anti-nerve growth factor antibody (e.g., anti-NGF antibody as described in U.S. Patent Application Publication No. US2016 / 0017029 and U.S. Patent Nos. 8,309,088 and 9,353,176), and anti-activin A antibody. .

[0099] In some embodiments, the bispecific antibody is selected from an anti-CD3×anti-CD20 bispecific antibody (as described in U.S. Patent No. 9,657,102 and U.S. Patent Application Publication No. US20150266966A1), an anti-CD3×anti-Mucin 16 bispecific antibody (e.g., an anti-CD3×anti-Muc16 bispecific antibody), and an anti-CD3×anti-prostate specific membrane antigen bispecific antibody (e.g., an anti-CD3×anti-PSMA bispecific antibody). In some embodiments, the protein of interest is selected from abciximab, adalimumab, adalimumab-atto, ado-trastuzumab, alemtuzumab, alirocumab, atezolizumab, avelumab, basiliximab, belimumab, benralizumab, bevacizumab, bezlotoxumab, blinatumomab, brentuximab vedotin, brodalumab, canakinumab, capromab pendetin, Pendetide, PEGylated certolizumabpegol), cemiplimab, cetuximab, denosumab, dinutuximab, dupilumab, durvalumab, eculizumab, elotuzumab, emicizumab-kxwh, emtansinealirocumab, evinacumab, evoloc umab), fasinumab, golimumab, guselkumab, ibritumomab tiuxetan, idarucizumab, infliximab, infliximab-abda, infliximab-dyyb, ipilimumab, ixekizumab, mepolizumab, necitu mumab), nesvacumab, nivolumab, obiltoxaximab, obinutuzumab, ocrelizumab, ofatumumab, olaratumab, omalizumab, panitumumab, pembrolizumab, pertuzumab, ramucirumab, ranibizumab zumab, raxibacumab, reslizumab, rinucumab, rituximab, sarilumab, secukinumab, siltuximab, tocilizumab, tocilizumab, trastuzumab, trevogrumab, ustekinumab, and vedolizumab.

[0100] Example

[0101] Example 1: General In-Line Protein Concentration Model for UF / DF Applications

[0102] Materials and methods

[0103] Data collection for the model included spectral data from Raman Rxn2 and Rxn 4 analyzers (Kaiser Optical Systems, Inc., Ann Arbor, MI, USA) using an MR-Probe-785 and RamanRxn Probehead-758 (Kaiser Optical Systems, Inc., Ann Arbor, MI, USA). In addition, several different optical components were used throughout the development process depending on availability. The operating parameters of the Raman analyzer were set to a 10-second scan time, 6 accumulations, and 5 repeats. SIMCA 13 (MKSData Analytic Solutions, Umeå, Sweden) was used to correlate peaks within the spectral data with offline protein concentration measurements. In-line measurements were performed at all different points in the UF / DF unit operation, including primary concentration, diafiltration, and final concentration. Off-line protein concentration was determined using SoloVPE (C Technology Ltd.). SoloVPE measurements were performed in triplicate.

[0104] Figure 2 The spectral regions used to build the chemometric model are shown. These regions include the region 1-977-1027 cm -1 (Ring structure), Area 2-1408-1485cm -1 (arginine), area 3-1621-1711cm -1 (Secondary structure) and region 4-2823-3046cm -1 (CH stretching). The following spectral filtering was performed on the raw spectral data: combining 21 cm -1 First derivative of point smoothing to remove moving baseline

[0105] result

[0106] To determine the feasibility of a universal in-line protein concentration model for ultrafiltration / diafiltration (UF / DF) applications, mAb1 was analyzed using Raman spectroscopy. Protein concentrations were measured before diafiltration (primary concentration), during diafiltration (diafiltration), and after diafiltration (final concentration). Concentrations calculated by the model were compared to protein concentrations determined by SoloVPE ( Figure 3 When the training set data were included (the mAb 1 spectrum was incorporated into the PLS model), the model error was 3.1% for 0-120 g / L (primary concentration and diafiltration) and 1.8% for >120 g / L (final concentration).

[0107] Processing errors can be detected by Raman spectroscopy. Figure 3 Air entrainment in the system during final recirculation through the UF / DF system is shown as detected by Raman spectroscopy data. The bars enclosed by rectangles show that the SoloVPE predicted mAbl concentration is >200 g / L, while the Raman predicted value is approximately 65 g / L.

[0108] Figure 4 The absolute Raman model errors of ten representative mAbs are shown. The data show that models for the mAbs shown were successfully developed, including different mAb isotypes (IgG1 and IgG4) and bispecific molecules. 14 of the 17 model predictions met errors ≤ 5%. However, specific models (0-120 g / L and >120 g / L) were created for each probe, which was used as a probe during development to detect the variability of the errors predicted by the PLS model.

[0109] To optimize the model, different probes and lasers were tested with multiple mAbs. Model refinement was performed, where only one spectral region was the focus of the updated general model: 2823-3046 cm -1 (CH stretching), spectral filtering was performed using a standard normal variate (SNV) to correct for laser power variations and probe variability as a baseline correction. A comparison of the two model components developed is summarized in Table 1. A partial least squares (PLS) regression model was created using the corresponding offline SoloVPE measurements (performed in triplicate). Details of the PLS regression model are shown in Table 2. The updated dataset predicted with the optimized laser / probe universal model showed that 15 out of 17 model predictions met ≤5% error, compared to only 14 out of 17 model predictions previously ( Figure 5 ).

[0110] Table 1. Comparison of common model components

[0111]

[0112] Table 2. Details of the general partial least squares regression model for protein concentration (version 2)

[0113] Final Model 0-120g / L >120g / L Sample size 1412 879 <![CDATA[R 2 X]]> 0.993 0.987 <![CDATA[Q 2 ]]> 0.984 0.958 RMSECV 3.34 7.77

[0114] R 2 X - Percentage of variation explained by the model, Target: R 2 >0.9

[0115] Q 2 - The percentage of variation in the model predictions during cross validation, target: Q 2 >0.8

[0116] RMSECV: root mean square error of cross validation

[0117] Example 2: Scale-up performance of protein concentration model

[0118] Materials and methods

[0119] The optimized generic model (version 2) was tested experimentally with mAb10 using an enlarged 1 / 2 inch Single Use Tangential Flow Filtration System (Pall Corporation) (see Table 1). The mAb10 load material was a formulated drug substance rather than a typical processed load material. The FDS material was diluted to a representative UF / DF load source (including protein concentration and buffer excipients). However, due to the presence of additional excipients in the load source that were not tested during the generic model development, a mAb 10-specific model was created. See Example 1 for methods of Raman data collection and scan length information. The mAb-specific model for >120 g / L used the same spectral region and preprocessing techniques as the generic model (version 2). However, the 0-120 g / L mAb-specific model used Figure 2 Four spectral regions are shown, pre-processed using a standard normal variate. Differences in the 0-120 g / L model can be attributed to additional excipients in the loading source.

[0120] result

[0121] When the training set was included in the model predictions, 2 of the 4 updated models met the model goal of ≤5% error, as shown in Table 3. Based on significant differences during the initial experiments at IOPS such as loading source, laser, and scale (laboratory scale vs. scale-up), a second run was performed. Prior to the second experiment, changes were made to the 0-120 g / L mAb 10-specific model. All four regions were included using SNV preprocessing, but three regions: 977-1027 cm -1 、1408-1485cm -1 and 1621-1711cm -1 Also use additional combination of 21cm -1 The first derivative of the point smoothing is shown in Table 3. The aggregated experimental results are summarized in Table 3. In the second experiment, when the training set was included in the model prediction, 3 out of 4 updated models met the model objective of error ≤ 5%, as shown in Table 3. Another observation during the data analysis was that the oversampling source was the main contributor to the increased error. If the oversampling samples were removed, the general model error would be reduced from 8.6% to 5.7%. Figure 7In the figure, the inline prediction results of the final concentrated pool (real time, wide shaded bars) and the results of the updated model (bars with narrow shaded lines) are shown, and the protein concentration is compared with the offline measurement results of SoloVPE (blank bars). For the final concentrated pool, the general model inline and updated model have an error of 2.7%, while the mAb 10 model inline and updated model have an error of 12.0% and 4.2%, respectively. The increase in error observed in the mAb 10 model can be attributed to the limited data in the range of about 250g / L, while the general model has a larger data set. Another factor leading to the increase in error is that the model cannot be extrapolated outside the characterization range (i.e., >250g / L in the mAb 10 model).

[0122] Table 3. Average model error in protein concentration prediction in scale-up experiments

[0123]

[0124] Example 3: Scale-up of protein concentration model to pilot processing equipment.

[0125] Materials and methods

[0126] During process development for the commercial process, UF / DF was characterized by unit operations following “quality by design” principles to understand key process parameters as well as critical quality attributes. Raman and model development were included during mAb 11 development to enhance process understanding and provide a streamlined approach to model development. Method information for Raman data collection is as in Example 1, but the scan duration was adjusted from 10 seconds to 5 seconds. The developed mAb 11 model used SNV preprocessing for all four spectral regions, but three regions: 977-1027cm -1 、1408-1485cm -1 and 1621-1711cm -1 , additionally used in combination with 21cm -1 Point smoothed first derivative.

[0127] result :

[0128] A laboratory-scale model was generated using four DoE experiments and four spectral regions. Fig. 8A Real-time predicted Raman model errors from four DoE experiments are shown. Figure 8B Raman model errors for an additional 15 experiments using the lab-scale model are shown. mAb-specific protein concentration models were generated for 0-120 g / L and >120 g / L with errors ≤ 5%.

[0129] Using the laboratory-scale model (n=15), the prediction errors for mAb11 in pilot-scale runs (n=3) were 0.6%-10.6% ( Fig. 9A ). When the pilot-scale data were incorporated into the laboratory-scale model (n=18), the pilot-scale prediction error dropped to 0.6-2.2%. The increased laboratory-scale model error is likely due to the effect of temperature on the Raman spectral shift caused by heat dissipation associated with the scaled-up equipment. Temperature will be a consideration in future Raman development and model validation initiatives.

[0130] In seven pilot-scale experiments using three different monoclonal antibodies (mAb J, mAb K, and mAb L), the final model prediction error was 0.6–2.2%; well within the 5% target ( Fig. 9B ).

[0131] Example 4: Real-time concentration determination using Raman modeling to enable processing decisions.

[0132] Materials and methods :

[0133] See Example 3 for further information, as the same protocol for Raman spectral collection and modeling was used for Raman automation. An automated control strategy was developed to use Raman spectral data to achieve the final protein concentration target. Using the generated predictive model, the data was filtered and used to provide input to the instrument on the UF / DF to terminate the unit operation when the protein concentration target was achieved. The SoloVPE measurements were performed in triplicate.

[0134] result :

[0135] Fig. 10A and 10B An exemplary screen setup for automated monitoring of batch UF / DF and single-pass TFF protein concentration is shown. Fig.11 The predictive model was shown to be useful for monitoring the real-time concentration of mAb14 at various processing steps and triggering the concentration unit operation to stop when the desired concentration target was reached. Raman predicted the final concentrated pool to be 260 g / L, compared to the offline SoloVPE measurement of 262 g / L, resulting in an error of 0.8%, meeting the ≤5% error target. Raman is an appropriate application for making automated processing decisions to verify that the desired protein concentration target is being met.

[0136] Example 5: Proof of concept for modeling high molecular weight (HMW) species in UF / DF.

[0137] Materials and methods :

[0138] Data collection for the HMW material model included spectral data from a Raman Rxn2 analyzer (Kaiser Optical Systems, Inc., Ann Arbor, MI, USA) and a RamanRxn Probehead-758 (Kaiser Optical Systems, Inc., Ann Arbor, MI, USA). In addition, several different optical components were used throughout the development process, depending on availability. The operating parameters of the Raman analyzer were set to a 72 s scan time with 1 accumulation and 25 repeats. In-line measurements were performed at all different points in the UF / DF unit operation, including primary concentration, diafiltration, and final concentration. The spectral range was 110–3415 cm -1 The raw spectral data were preprocessed using SNV and combined with 21cm -1 The smoothed first derivative was subjected to additional filtering. Off-line HMW species measurements were determined using size exclusion ultra-performance liquid chromatography.

[0139] result :

[0140] HMW species is another attribute that is considered as a preliminary critical quality attribute in protein purification.Current technology is unable to monitor HMW species in real time during processing. Fig. 12A The disclosed Raman modeling method was shown to be useful for monitoring HMW species during protein purification. Throughout the purification process (primary concentration, diafiltration, and final concentration), the predictions of HMW species obtained by Raman modeling were compared with measurements collected using SE-UPLC. Raman modeling effectively predicted the percentage of high molecular weight species in real time during protein processing. A model with an average error of 3.4% was generated.

[0141] Example 6. Proof of concept for modeling high molecular weight (HMW) species in polishing chromatography.

[0142] Materials and methods :

[0143] Data acquisition for the HMW material model included spectral data obtained from a Raman Rxn2 analyzer (Kaiser Optical Systems, Inc., Ann Arbor, Michigan, USA) using an MR-Probe-785. The operating parameters of the Raman analyzer were set to a scan time of 10, 30, or 60 seconds, 1 accumulation, and 5 replicates. Off-line measurements were performed using an anion exchange chromatography (AEX) cell with a total HMW of 6.2%-76.2%. The spectral range used for modeling and the pre-processing techniques used are described in Table 4. Size exclusion ultra-high performance liquid chromatography was used to determine the off-line HMW material measurements.

[0144] result :

[0145] The disclosed Raman modeling method can be used to monitor HMW species during polishing chromatographic protein purification. The HMW species predictions obtained by Raman modeling were compared with the measurements collected from the generated AEX pool using SE-UPLC. As summarized in Table 4, the RMSEP of the evaluated methods ranged from 3.2-7.6%. Fig. 12B In the 350-3100 cm -1 The spectral region and the model generated by the SNV pre-processing technique. By reducing the HMW range, the RMSEP is reduced to 1.2%, 1.4% and 2.1% for 10 seconds, 30 seconds and 60 seconds, respectively. Based on these results, Raman can be used to determine the HMW content in the AEX cell.

[0146] Table 4. Errors (RMSEP) of HMW model predictions when HMW content is 6.2%-76.2%.

[0147]

[0148]

[0149] Example 7. Proof of concept for titer modeling.

[0150] Materials and methods :

[0151] The training set model was 35 protein A flow-through samples doped with FCP (265 g / L) to achieve titers of 0.36-9.8 g / L. The model was evaluated with diluted deep filter filtrate samples with titers of 1.3-8.8 g / L. Data acquisition for the titer model included spectral data acquired from Raman Rxn2 (Kaiser Optical Systems, Inc., Ann Arbor, MI, USA) using an MR-Probe-785. Spectral data were generated offline using an immersed probe with operating parameters set to 20 s scan time, 1 accumulation, and 5 repeats. The spectral ranges were 977-1027, 1408-1485, 1621-1711, and 2823-3046 cm -1 The raw spectral data were preprocessed using SNV and combined with 21cm -1 The smoothed first-order derivatives are additionally filtered. The model characteristics are described in Table 5.

[0152] Table 5. Antibody titer model characteristics.

[0153]

[0154] result :

[0155] Antibody titer is a process attribute in protein purification that is needed to inform subsequent downstream purification unit operations, including affinity column loading, manufacturing consistency, and in-process intermediate volume limitations. Inaccurate column loading can affect subsequent preliminary critical quality attributes, necessitating monitoring techniques such as Raman spectroscopy. Fig.13 Actual antibody titers for monoclonal antibodies versus Raman predicted antibody titers are shown. In this experiment, the model error was 26%, which is above the desired target of ≤5%. Increasing the scan length and developing the model using diluted and non-diluted depth filtration filtrates will reduce the model error.

[0156] Example 8: Raman model for buffer excipient measurements that meets current orthogonal assay errors of approximately 10%.

[0157] Materials and methods :

[0158] Data was collected from previous concentration model development runs for various antibodies. For method information on Raman data collection, see Example 1. Table 6 shows the model components for detecting histidine and arginine in the sample. The spectral region is based on the known histidine / arginine peak (Zhu et al., Spectrochim Acta A Mol Biomol Spectrosc, 78(3):1187-1195(2011)). After the initial model development of histidine and arginine, further model characterization was performed with mAb 14. Using a non-contact optical probe, the operating parameters of the Raman analyzer were set to a 20 second scan time and 5 accumulations were performed. The spectral range of histidine is 1200-1480cm -1 , arginine is 860-1470cm -1 For both buffer excipients, the raw spectral data were preprocessed using SNV and the 21 cm -1 The smoothed first derivative was subjected to additional filtering. Offline histidine and arginine species measurements were determined using an ultra-performance liquid chromatography (UPLC)-based amino acid quantification method.

[0159] result :

[0160] To determine whether the disclosed Raman modeling methods and systems could be used to measure buffer excipients in processed antibody samples, data collected from previous concentration model development runs were analyzed for histidine and arginine. The values ​​predicted using Raman modeling were compared to the values ​​calculated from the UPLC-based amino acid method, as shown in Figure 2. Fig.14A The predicted values ​​and average model errors of the primary histidine / arginine Raman modeling are given in Table 6. Fig. 14BIn Figure 1, a dot plot of the predicted histidine versus actual histidine model for mAb 14 is shown, with an average model error of 8.2%, meeting the target of ≤10% for buffer excipients. The target of ≤10% is based on the assay variability of the current UPLC orthogonal method. Fig. 14C In FIG, a dot plot of the predicted arginine versus actual arginine model for mAb 14 is shown, with an average model error of 2.9%, meeting the target of ≤10%.

[0161] This data demonstrates that Raman modeling can be used to predict the levels of buffer excipients in in-process UF / DF and FCP materials. Successful quantification of these excipients ensures that the UF / DF provides a final concentrated pool that will enable subsequent formulation.

[0162] Table 6. Model components and data collected from processing the general concentration model (Example 1) for histidine / arginine.

[0163] Histidine Arginine <![CDATA[Spectral region (cm -1 )]]> 1200-1480 970-1100,1300-1500 Pretreatment technology First-order derivatives and SNV First-order derivatives and SNV <![CDATA[R 2 And]]> 0.940 0.964 <![CDATA[Q 2 ]]> 0.938 0.963 RMSEP 1.20mM 6.19mM Average model error 10.4％ 7.39％

[0164] Histidine range: 0-25mM; Arginine range: 0-81mM

[0165] SNV - Standard Normal Variable - mean centered and normalized

[0166] R 2 - the percentage of variation in the training set explained by the model, R 2 >0.9

[0167] Q 2 - the percentage of variation in the training set that the model predicted during cross-validation,

[0168] Q 2 >0.8

[0169] (RMSEP) Root Mean Square Error of Prediction

[0170] Example 9: Raman model for drug / antibody ratio measurement.

[0171] Materials and methods :

[0172] DAR is a quality attribute that is monitored during the development of antibody-drug conjugates (ADCs), antibody-radionuclide conjugates (ARCs), and general protein conjugates (potent steroids, non-cytotoxic payloads, etc.) to ensure consistent product quality and facilitate subsequent labeling with the payload. Raman was evaluated as a technique to monitor DAR levels, which can then be used as a control strategy for the reaction. The feasibility of DAR determination was evaluated using Raman for two different mAbs under development (mAb 1 and mAb 3). Using a non-contact optical probe, the operating parameters of the Raman analyzer were set to a 10 second scan time with 10 accumulations. The 350-3100cm -1 The raw spectral data were preprocessed with SNV and the 21 cm -1 The smoothed second derivative was filtered. Table 7 shows the model components used to determine the DAR in the samples. Off-line DAR measurements were determined using a UV spectroscopy based method.

[0173] Table 7. Model components for drug / antibody ratio measurement.

[0174]

[0175] result :

[0176] Figures 15A to 15B Raman modeling was shown to be useful for measuring the drug / antibody ratio of the iPET drug conjugates of mAb 1 and mAb 3. For both models, the cross-validated root mean square error was 0.6 DAR. The current UV-based orthogonal assay has a variability associated with 0.3 DAR (one standard deviation). This initial Raman prediction is within two standard deviations and suggests that with some further refinement, DAR can be successfully predicted with Raman.

[0177] Although the present invention has been described in the foregoing specification with respect to certain embodiments of the invention and many details have been set forth for purposes of illustration, it will be apparent to those skilled in the art that the present invention is susceptible to additional embodiments and that certain details described herein may be modified considerably without departing from the basic principles of the invention.

[0178] All references cited herein are incorporated by reference in their entirety. The present invention may be embodied in other specific forms without departing from the spirit or essential attributes thereof, and therefore, reference should be made to the appended claims, rather than to the foregoing specification, to indicate the scope of the invention.

Claims

1. A method for preparing a concentrated protein purification intermediate, comprising: while concentrating the protein purification intermediate, using in situ Raman spectroscopy to measure the concentration of the protein purification intermediate in real time; as well as The parameters of the concentration step are adjusted in real time to obtain the concentrated protein purification intermediate and / or the final concentrated pool.

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

3. The method of any one of claims 1 or 2, wherein the concentration of the protein purification intermediate is at least 50 mg / mL.

4. The method of any one of claims 1 or 2, wherein the concentration of the protein purification intermediate is at least 150 mg / mL.

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

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

7. The method according to any one of claims 1 to 6, wherein determining the concentration of the protein purification intermediate is performed continuously or intermittently in real time.

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

9. The method according to any one 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 one of claims 1 to 9, wherein the -1 、1408-1485cm -1 、1621-1711cm -1 、2823-3046cm -1 Spectral data are collected within one or more wavenumber ranges, as well as combinations thereof.

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