In situ Raman spectroscopy system and method for controlling process variables in cell culture

Abstract

To improve product quality and consistency, we provide in situ Raman spectroscopy and systems for monitoring and controlling one or more process variables in bioreactor cell cultures. [Solution] This method and system utilizes in-situ Raman spectroscopy and chemometric modeling techniques for real-time evaluation of cell culture in combination with signal processing techniques for accurate continuous feedback and model predictive control of cell culture process variables. Using real-time data from Raman spectroscopy, process variables in the cell culture are continuously or intermittently monitored, and an automated feedback controller maintains the process variables at predetermined setpoints or maintains a specific feed protocol that delivers a variable amount of active ingredient to the bioreactor to maximize the quality of the bioproduct.

Description

Technical Field

[0001] Cross - reference to Related Applications This application claims the benefit and priority of U.S. Provisional Application No. 62 / 572,828, filed on October 16, 2018, and U.S. Provisional Application No. 62 / 662,322, filed on April 25, 2018, which are hereby incorporated by reference in their entirety if allowed.

[0002] Field of the Invention The present invention generally relates to bioreactor systems and methods including in - situ Raman spectroscopy and systems for monitoring and controlling one or more process variables in bioreactor cell culture.

Background Art

[0003] Background of the Invention The Food and Drug Administration (FDA)'s Process Analytical Technology (PAT) framework encourages the voluntary development and implementation of innovative solutions for process development, process analysis, and process control to better understand processes and manage product quality. Process parameters are monitored and controlled during the manufacturing process. For example, feeding nutrients to the cell culture in a bioreactor during the production of a bioproduct is an important process parameter. Current bioproduct manufacturing involves a daily bolus feed strategy. In the current method, with a daily bolus feed, the nutrient concentration in the cell culture increases by at least 5 - fold daily. To prevent nutrient depletion from the culture between feeds, the daily bolus feed maintains nutrients at a high concentration level. In fact, each feed is designed to have all the nutrients necessary to maintain the culture until the next feed. However, since the daily bolus feed contains a large amount of nutrients, the nutrient levels in the bioreactor can vary significantly, which may cause inconsistencies in the product quality output of the product culture.

[0004] Furthermore, the high concentration of nutrients in each daily bolus feed leads to increased post-translational modifications of the resulting bioproduct. For example, high concentrations of glucose in cell cultures can lead to increased glycation of the final bioproduct. Glycation is the non-enzymatic addition of reducing sugars to amino acid residues of proteins, typically occurring at the N-terminal amine and positively charged amine groups of proteins. The resulting glycation products may have yellow or brown optical properties, potentially resulting in the production of colored pharmaceuticals (Hodge JE (1953) J Agric Food Chem. 1:928-943 (Non-Patent Literature 1)). Glycation can also result in charge variants and binding inhibition within a single production batch of therapeutic monoclonal antibodies (mAbs) (Haberger M et al. (2014) MAbs. 6:327-339 (Non-Patent Literature 2)).

[0005] Therefore, in efforts to promote the PAT initiative, there remains a need for methods or systems that can optimize nutrient concentrations within cell cultures to produce higher quality products. [Prior art documents] [Non-patent literature]

[0006] [Non-Patent Document 1] Hodge JE (1953) J Agric Food Chem.1:928-943 [Non-Patent Document 2] Haberger M et al.(2014)MAbs.6:327-339 [Overview of the project]

[0007] This specification discloses in situ Raman spectroscopy and systems for monitoring and controlling one or more process variables in bioreactor cell cultures.

[0008] One embodiment of the present invention includes a method for controlling cell culture medium conditions, comprising: quantifying one or more analytes in a cell culture medium using in situ Raman spectroscopy; and adjusting the concentration of one or more analytes in the cell culture medium to match a predetermined analyte concentration that maintains post-translational modifications of proteins in the cell culture medium at 1.0 to 30 percent. In some embodiments, post-translational modifications include glycation. In other embodiments, proteins in the cell culture medium include antibodies, their antigen-binding fragments, or fusion proteins. In yet another embodiment, the cell culture medium includes mammalian cells, such as Chinese hamster ovary cells.

[0009] In some embodiments, the analyte is glucose. In this embodiment, the predetermined glucose concentration is 0.5 to 8.0 g / L. In another embodiment, the predetermined glucose concentration is 1.0 g / L to 3.0 g / L. In yet another embodiment, the glucose concentration is 2.0 g / L or 1.0 g / L. In yet another embodiment, the predetermined analyte concentration maintains the post-translational modification of proteins in the cell culture medium at 1.0 to 20 percent or 5.0 to 10 percent. In yet another embodiment, analyte quantification is performed continuously, intermittently, or at intervals. For example, analyte quantification is performed at 5-minute intervals, 10-minute intervals, or 15-minute intervals. In yet another embodiment, analyte quantification is performed hourly or at least daily. In some embodiments, adjustment of the analyte concentration is performed automatically. In yet another embodiment, at least two, at least three, or at least four different analytes are quantified.

[0010] Another embodiment of the present invention includes a method for reducing post-translational modification of secreted proteins, comprising: culturing protein-secreting cells in a cell culture medium containing 0.5 to 8.0 g / L of glucose; incrementally determining the glucose concentration in the cell culture medium during cell culture using in situ Raman spectroscopy; and adjusting the glucose concentration to maintain it at 0.5 to 8.0 g / L by automatically delivering glucose multiple times per hour to maintain the post-translational modification of the secreted protein at 1.0 to 30.0 percent. In one embodiment, the glucose concentration is 1.0 to 3.0 g / L.

[0011] A further embodiment of the present invention includes a system for controlling cell culture medium conditions, the system including one or more processors that communicate with a computer-readable medium storing software code for execution by one or more processors, for the system to receive data from an in situ Raman spectrometer, including the concentrations of one or more analytes in the cell culture medium; and to adjust the concentrations of one or more analytes in the cell culture medium to match predetermined analyte concentrations that maintain post-translational modifications of proteins in the cell culture medium at 1.0 to 30 percent. In one embodiment, the software code is further configured to cause the system to perform chemometric analysis, e.g., partial least-squares regression modeling, on the data. In another embodiment, the software code is further configured to cause the system to perform one or more signal processing techniques, e.g., noise reduction techniques, on the data.

[0012] Another embodiment of the present invention includes a system for reducing post-translational modification of secreted proteins, the system including one or more processors communicating with a computer-readable medium containing software code for execution by one or more processors, to cause the system to incrementally receive spectral data from an in-situ Raman analyzer, including the concentration of glucose in the cell culture medium during the culture of protein-secreting cells; and to adjust the glucose concentration to maintain it at 0.5 to 8.0 g / L, for example, 1.0 to 3.0 g / L, by automatically delivering glucose multiple times per hour to maintain the post-translational modification of the secreted protein at 1.0 to 30.0 percent. In one embodiment, the software code is further configured to cause the system to correlate peaks in the spectral data with glucose concentrations. In another embodiment, the software code is further configured to perform partial least-squares regression modeling on the spectral data. In yet another embodiment, the software code is further configured to perform noise reduction techniques on the spectral data. In yet another embodiment, the glucose concentration adjustment is performed by automated feedback control software. [Invention 1001] A method for controlling cell culture medium conditions, Quantifying one or more analytes in the cell culture medium using in situ Raman spectroscopy; and Adjust the concentration of one or more analytes in the cell culture medium to match a predetermined analyte concentration that maintains the post-translational modification of proteins in the cell culture medium at 1.0 to 30 percent. The method, including the method described above. [Invention 1002] The method of the present invention 1001, wherein the post-translational modification includes saccharification. [Invention 1003] The method of the present invention 1001, wherein the protein in the cell culture contains an antibody or an antigen-binding fragment thereof. [Invention 1004] The method of the present invention 1001, wherein the protein in the cell culture comprises a fusion protein. [The present invention 1005] The method of the present invention 1001, wherein the cell culture medium comprises mammalian cells. [The present invention 1006] The method of the present invention 1005, wherein the mammalian cells comprise Chinese hamster ovary cells. [The present invention 1007] The method of the present invention 1001, wherein the analyte is glucose. [The present invention 1008] The method of the present invention 1007, wherein the predetermined glucose concentration is 0.5 to 8.0 g / L. [The present invention 1009] The method of the present invention 1007, wherein the glucose concentration is 1.0 g / L to 3.0 g / L. [The present invention 1010] The method of the present invention 1007, wherein the glucose concentration is 2.0 g / L. [The present invention 1011] The method of the present invention 1007, wherein the glucose concentration is 1.0 g / L. [The present invention 1012] The method of the present invention 1001, wherein the predetermined analyte concentration maintains the post-translational modification of the protein in the cell culture medium at 1.0 to 20 percent. [The present invention 1013] The method of the present invention 1001, wherein the predetermined analyte concentration maintains the post-translational modification of the protein in the cell culture medium at 5.0 to 10 percent. [The present invention 1014] The method of the present invention 1001, wherein the quantification of the analyte is performed continuously. [The present invention 1015] The method of the present invention 1001, wherein the quantification of the analyte is performed intermittently. [The present invention 1016] The method of the present invention 1001, wherein the quantification of the analyte is performed at intervals. [The present invention 1017] The method of the present invention 1001, wherein the quantification of the analyte is performed at 5-minute intervals. [The present invention 1018] The method of the present invention 1001, in which quantification of the analyte is performed at 10 - minute intervals. [The present invention 1019] The method of the present invention 1001, in which quantification of the analyte is performed at 15 - minute intervals. [The present invention 1020] The method of the present invention 1001, in which quantification of the analyte is performed hourly. [The present invention 1021] The method of the present invention 1001, in which quantification of the analyte is performed at least daily. [The present invention 1022] The method of the present invention 1001, in which adjustment of the analyte concentration is automatically performed. [The present invention 1023] The method of the present invention 1001, in which at least two different analytes are quantified. [The present invention 1024] The method of the present invention 1001, in which at least three different analytes are quantified. [The present invention 1025] The method of the present invention 1001, in which at least four different analytes are quantified. [The present invention 1026] A method for reducing post - translational modification of a secreted protein, comprising: Culturing cells that secrete the protein in a cell culture medium containing 0.5 - 8.0 g / L glucose; Incrementally determining the glucose concentration in the cell culture medium during culturing of the cells using in - situ Raman spectroscopy; Adjusting the glucose concentration to maintain it at 0.5 - 8.0 g / L by automatically delivering multiple doses of glucose per hour to maintain the post - translational modification of the secreted protein at 1.0 - 30.0 percent. The method as described above. [The present invention 1027] The method of the present invention 1026, in which the concentration of glucose is 1.0 - 3.0 g / L. [The present invention 1028] A system for controlling cell culture medium conditions, the system comprising: To receive data from an in situ Raman spectrometer that includes the concentration of one or more analytes in the cell culture medium; and To adjust the concentration of one or more analytes in the cell culture medium to match a predetermined analyte concentration that maintains the post-translational modification of proteins in the cell culture medium at 1.0 to 30 percent. The system includes one or more processors that communicate with a computer-readable medium storing software code for execution by one or more processors. [Invention 1029] The system of the present invention 1028, wherein the software code is further configured to cause the system to perform chemometric analysis on the data. [Invention 1030] The system of the present invention 1029, wherein the chemometric analysis includes partial least squares regression modeling. [Invention 1031] The system of the present invention 1028, wherein the software code is further configured to cause the system to perform one or more signal processing techniques on the data. [Invention 1032] The system of the present invention 1031, wherein the signal processing technology includes noise reduction technology. [Invention 1033] A system for reducing post-translational modifications of secreted proteins, wherein the system includes To incrementally receive spectral data from an in-situ Raman analyzer, including the glucose concentration in the cell culture medium, during the culture of cells that secrete the aforementioned protein; and In order to maintain the post-translational modification of the secreted protein at 1.0-30.0 percent, glucose concentration is adjusted to maintain a glucose concentration of 0.5-8.0 g / L by automatically delivering glucose multiple times per hour. The system includes one or more processors that communicate with a computer-readable medium storing software code for execution by one or more processors. [Invention 1034] The system of the present invention 1033, wherein the software code is further configured in the system to correlate the peaks in the spectral data with glucose concentration. [Invention 1035] The system of the present invention 1033, wherein the software code is further configured to perform partial least squares regression modeling on the spectral data. [Invention 1036] The system of the present invention 1033, wherein the software code is further configured to perform noise reduction techniques on the spectral data. [Invention 1037] The system of the present invention 1033, wherein the adjustment of the glucose concentration is performed by automated feedback control software. [Invention 1038] The system of the present invention 1033, wherein the glucose concentration is 1.0 to 3.0 g / L. [Brief explanation of the drawing]

[0013] Further features and advantages of the present invention can be found in the following detailed description provided in conjunction with the drawings described below.

[0014] [Figure 1] This is a flowchart of a method for controlling process variables in cell culture according to one embodiment of the present invention. [Figure 2] This is a schematic diagram of a system for controlling process variables in cell culture related to Figure 1 according to the present invention. [Figure 3] This graph shows predicted nutrient process values ​​confirmed by offline nutrient samples. [Figure 4] This graph shows the filtered final nutrient process values ​​after the signal processing technique according to the present invention. [Figure 5] This graph shows the predicted nutrient process value and the filtered final nutrient process value after a shift in a predetermined set point of nutrient concentration. [Figure 6] This is a line graph showing the effect of glucose concentration on post-translational modifications of feedback-controlled continuous nutrient feeds and bolus nutrient feeds according to the present invention. [Figure 7] This graph shows the in-situ Raman predicted glucose concentration values ​​for feedback-controlled continuous nutrient feeds and bolus nutrient feeds according to the present invention. [Figure 8] This is a line graph showing the antibody titers of feedback-controlled continuous nutrient feeds and bolus nutrient feeds according to the present invention. [Figure 9] This is a bar graph showing the normalized percentage of post-translational modifications resulting from glucose concentration. [Figure 10] This graph shows the glucose concentrations of feedback-controlled continuous nutrient feeds and bolus nutrient feeds according to the present invention. [Figure 11] This graph shows that feedback-controlled cell culture can reduce PTM by as much as 50% compared to bolus-fed strategic cell culture. [Modes for carrying out the invention]

[0015] Detailed explanation I. Definition As used herein, unless otherwise clearly indicated by the context, the singular forms “a,” “an,” and “the” include multiple references.

[0016] Unless otherwise specified herein, descriptions of value ranges are intended solely as abbreviations to indicate each distinct value within that range individually, and each distinct value is incorporated herein as if it were individually stated herein.

[0017] The use of the term "approximately" means to describe a value either above or below the mentioned value, within a range of approximately + / - 10%; in other embodiments, this value may be within a range of approximately + / - 5% above or below the mentioned value; in other embodiments, this value may be within a range of approximately + / - 2% above or below the mentioned value; in other embodiments, this value may be within a range of approximately + / - 1% above or below the mentioned value. The above ranges are intended to be clear from the context and are not further limiting. All methods described herein may be performed in any suitable order, unless otherwise specified herein or unless it is clearly inconsistent from the context. Any and all examples provided herein, or the use of exemplary language (e.g., "etc."), are merely intended to better illustrate the invention and, unless otherwise claimed, do not impose any limitation on the scope of the invention. The terminology of the specification should not be construed as indicating any unclaimed elements essential to the practice of the invention.

[0018] The term "bio-product" refers to any antibody, antibody fragment, modified antibody, protein, glycoprotein, or fusion protein, and the raw materials for the final product manufactured in a bioreactor process.

[0019] The terms "control" and "to control" refer to adjusting the volume or concentration level of a process variable in cell culture to a predefined set point.

[0020] The terms "monitor" and "suppress" refer to the periodic checking of the quantity or concentration levels of process variables or process conditions in cell culture.

[0021] The term "steady state" refers to maintaining the nutrient concentration, process parameters, or quality attributes of a cell culture at an invariant, constant, or stable level. An invariant, constant, or stable level is understood to refer to a level within a predetermined set point. This set point, and therefore the steady state level, may be shifted during the period of cell culture production by the operator.

[0022] II. Methods for producing bio-products One embodiment provides a method for monitoring and controlling one or more process variables in bioreactor cell culture to improve product quality and consistency. Process variables include, but are not limited to, glucose, amino acids, vitamins, growth factors, proteins, live cell count, oxygen, nitrogen, pH, dead cell count, cytokines, lactates, glutamine, other sugars such as fructose and galactose, ammonium, osmotic pressure, and combinations thereof. The disclosed method and system utilize in situ Raman spectroscopy and chemometric modeling techniques for real-time evaluation of cell culture in combination with signal processing techniques for accurate continuous feedback and model predictive control of cell culture process variables. In situ Raman spectroscopy of bioreactor contents allows for the analysis of one or more process variables within the bioreactor without the need to physically remove samples of the bioreactor contents for testing. Through the use of real-time data from Raman spectroscopy, process variables within cell cultures are continuously or intermittently monitored, and an automated feedback controller maintains the process variables at predetermined setpoints or maintains a specific feed protocol that delivers variable amounts of active ingredients to the bioreactor to maximize the quality of the bioproduct.

[0023] The disclosed methods and systems control one or more process variables in a cell culture process. The terms “cell culture” and “cell culture medium” may be used interchangeably and may include any solid, liquid, or semi-solid material designed to support the growth and maintenance of microorganisms, cells, or cell lines. Components such as polypeptides, sugars, salts, nucleic acids, cell fragments, acids, bases, pH buffers, oxygen, nitrogen, viscosity modifiers, amino acids, growth factors, cytokines, vitamins, cofactors, and nutrients may be present in the cell culture medium. One embodiment provides a mammalian cell culture process, comprising mammalian cells or cell lines. For example, the mammalian cell culture process may utilize a Chinese hamster ovary (CHO) cell line grown in a chemically defined basal medium.

[0024] The cell culture process may be carried out in a bioreactor. Examples of bioreactors include seed trains, feed batches, and continuous bioreactors. The bioreactor may have a capacity ranging from about 2 L to about 10,000 L. In one embodiment, the bioreactor may be a 60 L stainless steel bioreactor. In another embodiment, the bioreactor may be a 250 L bioreactor. Each bioreactor also has a cell count of about 5 × 10⁶ 6 cells / mL ~ approx. 100×10 6 The cells / mL must be maintained within the range of cells / mL. For example, a bioreactor should be approximately 20 × 10 6 A cell count of approximately 80 cells / mL must be maintained.

[0025] The disclosed methods and systems can monitor and control any analytes present in cell cultures that have detectable Raman spectra. For example, the methods of the present invention can be used to monitor and control any components of a cell culture medium, including components added to cell cultures, substances secreted by cells, and cellular components present at the time of cell death. Components of a cell culture medium that can be monitored and / or controlled by the disclosed systems and methods include, but are not limited to, nutrients such as amino acids and vitamins, lactates, cofactors, growth factors, cell growth rate, pH, oxygen, nitrogen, viable cell number, acid, base, cytokines, antibodies, and metabolites.

[0026] One embodiment provides a method for monitoring and controlling nutrient concentrations in a cell culture. As used herein, the term “nutrient” may refer to any compound or substance that provides nutrients essential for growth and survival. Examples of nutrients include, but are not limited to, monosaccharides such as glucose, galactose, lactose, fructose, or maltose; amino acids; and vitamins, such as vitamin A, B vitamins, and vitamin E. In another embodiment, the method of the present invention may include monitoring and controlling glucose concentrations in a cell culture. It has been found that by controlling nutrient concentrations in a cell culture, such as glucose concentrations, bioproducts such as proteins can be produced at lower concentration ranges than previously possible using daily bolus nutrient feed strategies.

[0027] Furthermore, by controlling nutrient concentrations and other process variables in cell culture, the methods of the present invention further provide the ability to modulate one or more post-translational modifications of proteins. While not bound by any particular theory, it is believed that providing lower nutrient concentrations in cell culture can reduce post-translational modifications of proteins and antibodies. Examples of post-translational modifications that can be modulated by the present invention include, but are not limited to, glycation, glycosylation, acetylation, phosphorylation, amidation, derivatization with known protecting / protecting groups, proteolytic cleavage, and modification with amino acids that do not exist in nature. Another embodiment provides methods and systems for modulating protein glycation. For example, providing a low concentration range of glucose in the cell culture medium can reduce the glycation level of secreted proteins or antibodies in the final bioproduct.

[0028] Figure 1 is a flowchart of an exemplary method for controlling one or more process variables, such as nutrient concentration, in bioreactor cell culture. Predetermined setpoints for each process variable to be monitored and controlled can be programmed into the system. The defined setpoints represent the amount of the process variable in the cell culture that is maintained or adjusted throughout the process. Glucose concentration is an example of a nutrient that can be monitored and adjusted. As briefly described above, it has been found that bioproducts (e.g., proteins, antibodies, fusion proteins, and pharmaceutical raw materials) can be produced by cells in a culture medium containing low levels of glucose compared to the glucose concentration of the medium using a daily bolus nutrient feed strategy. In one embodiment, the predetermined setpoint for nutrient concentration is the minimum concentration of nutrients required to grow and proliferate the cell line. The disclosed method and system can deliver multiple small amounts of nutrients to the medium over a period of time, or provide a stable flow of nutrients to the culture medium. In some embodiments, the predetermined setpoints can be increased or decreased during the process depending on the conditions in the cell culture medium. For example, if a predefined amount of nutrient concentration results in cell death or suboptimal growth conditions in the cell culture medium, the predefined setpoint may be increased. However, the nutrient concentration must be maintained at a predefined setpoint of approximately 0.5 g / L to approximately 10 g / L. In another embodiment, the nutrient concentration should be maintained at a predetermined setpoint of approximately 0.5 g / L to approximately 8 g / L. In yet another embodiment, the nutrient concentration should be maintained at a predetermined setpoint of approximately 1 g / L to approximately 3 g / L. In yet another embodiment, the nutrient concentration should be maintained at a predetermined setpoint of approximately 2 g / L. These predefined setpoints essentially provide a baseline level at which the nutrient concentration should be maintained throughout the entire process.

[0029] In one embodiment, monitoring of one or more process variables in a cell culture, such as nutrient concentration, is performed by Raman spectroscopy (step 101). Raman spectroscopy is a type of vibrational spectroscopy that provides information about molecular vibrations that can be used for the identification and quantification of a sample. In some embodiments, monitoring of process variables is performed using in situ Raman spectroscopy. In situ Raman analysis is a method of analyzing a sample in its original location without the need to extract a portion of the sample for analysis with a Raman spectrometer. In situ Raman analysis is advantageous in that the Raman spectrometer is non-invasive, reducing the risk of contamination and being non-destructive without affecting the viability of the cell culture or the quality of the protein.

[0030] In situ Raman analysis can provide real-time assessment of one or more process variables in cell culture. For example, the current amount of nutrient concentration in a cell culture can be obtained and monitored using raw spectral data provided by in situ Raman spectroscopy. In this embodiment, spectral data from Raman spectroscopy should be acquired approximately every 10 minutes to 2 hours to ensure that the raw spectral data is always up-to-date. In another embodiment, spectral data should be acquired approximately every 15 minutes to 1 hour. In yet another embodiment, spectral data should be acquired approximately every 20 minutes to 30 minutes.

[0031] In this embodiment, monitoring of one or more process variables in cell culture can be analyzed by any commercially available Raman spectrometer that enables in situ Raman analysis. The in situ Raman analyzer needs to acquire raw spectral data within the cell culture (for example, the Raman analyzer needs to be equipped with a probe that can be inserted into the bioreactor). Suitable Raman analyzers include, but are not limited to, the RamanRXN2 and RamanRXN4 analyzers (Kaiser Optical Systems, Inc. Ann Arbor, MI).

[0032] In step 102, to correlate peaks in the spectral data to process variables, the raw spectral data obtained by in situ Raman spectroscopy may be compared with offline measurements of a specific process variable being monitored or controlled (e.g., offline nutrient concentration measurements). For example, if the process variable being monitored or controlled is glucose concentration, offline glucose concentration measurements may be used to determine which spectral regions indicate a glucose signal. Offline measurement data can be collected using any suitable analytical method. Furthermore, any type of multivariate software package, such as SIMCA 13 (MKS Data Analytic Solutions, Umea, Sweden), may be used to correlate peaks in the raw spectral data with offline measurements of a specific process variable being monitored or controlled. However, in some embodiments, it may be necessary to preprocess the raw spectral data with a spectral filter to remove various baselines. For example, the raw spectral data may be preprocessed with any type of point smoothing or normalization technique. Normalization may be necessary to compensate for variations in laser power and exposure time from the Raman analyzer. In one embodiment, the raw spectral data is 21 cm². -1 The data may also be processed using point smoothing, such as first derivative with point smoothing, and normalization, such as standard normalized variable (SNV) normalization.

[0033] Chemometric modeling may be performed on the obtained spectral data. In this embodiment, one or more multivariate methods, including but not limited to partial least squares (PLS), principal component analysis (PCA), orthogonal partial least squares (OPLS), multivariate regression, canonical correlation, factor analysis, cluster analysis, and graphical procedures, may be used on the spectral data. In one embodiment, the obtained spectral data is used to construct a PLS regression model. The PLS regression model may be constructed by projecting the predicted and observed variables into a new space. In this embodiment, the PLS regression model may be constructed using measurements obtained from Raman analysis and offline measurements. The PLS regression model provides predicted process values, such as predicted nutrient concentration values.

[0034] After chemometric modeling, signal processing techniques may be applied to the predicted process values ​​(e.g., predicted nutrient concentration values) (step 103). In one embodiment, the signal processing technique includes noise reduction techniques. In this embodiment, one or more noise reduction techniques may be applied to the predicted process values. Any noise reduction technique known to those skilled in the art may be used. For example, noise reduction techniques may include data smoothing and / or signal rejection. Smoothing is achieved by a series of smoothing algorithms and filters, while signal rejection uses signal characteristics to identify data that should not be included in the analyzed spectral data. In one embodiment, the predicted process values ​​are noise-reduced by a noise reduction filter. The noise reduction filter provides the final filtered process values ​​(e.g., final filtered nutrient concentration values). In this embodiment, the noise reduction technique combines the raw measurements with model-based estimates from which the measurement results should be obtained based on the model. In one embodiment, the noise reduction technique combines the current predicted process values ​​with their uncertainty. The uncertainty may be determined by the reproducibility of the predicted process values ​​and the current process conditions. Once the next predicted process value is observed, the estimated predicted process value (e.g., predicted nutrient concentration value) is updated using a weighted average that gives more weight to the estimate with greater certainty. Using an iterative approach, the final process value may be updated based on previous measurements and current process conditions. In this embodiment, the algorithm is recursive and must be able to run in real time to utilize the current predicted process value, previous values, and experimentally determined constants. Noise reduction techniques improve the robustness of measurements received from Raman analysis and PLS predictions by reducing noise on which an automated feedback controller acts.

[0035] Once the final filtered process value (e.g., the final filtered nutrient concentration value) is obtained, the final value may be sent to an automated feedback controller (step 104). The automated feedback controller can be used to control and maintain process variables (e.g., nutrient concentration) at predefined setpoints. The automated feedback controller may be any type of controller capable of calculating an error value as the difference between a desired setpoint (e.g., a predefined setpoint) and the measured process variable, and automatically applying accurate and responsive corrections. The automated feedback controller also requires controls that can be changed in real time from a platform interface. For example, the automated feedback controller requires a user interface that allows adjustment of predefined setpoints. The automated feedback controller must be able to accommodate changes to predefined setpoints.

[0036] In one embodiment, the automated feedback controller may be a proportional-integral-derivative (PID) controller. In this embodiment, the PID controller is operable to calculate the difference between a predetermined setpoint and a measured process variable (e.g., a measured nutrient concentration) and to automatically apply an accurate correction. For example, when controlling the nutrient concentration of a cell culture, the PID controller may be operable to calculate the difference between the filtered nutrient value and a predetermined setpoint and to provide a correction for the nutrient amount. In this embodiment, the PID controller may be operablely connected to a nutrient pump on a bioreactor so that the corrected nutrient amount can be delivered to the bioreactor (step 105).

[0037] By using Raman real-time analysis and feedback control, the method of the present invention can provide a continuous and low concentration of nutrients to a cell culture. That is, the method of the present invention can provide steady-state nutrient delivery to a cell culture. In one embodiment, nutrients may be continuously delivered to the cell culture via a nutrient pump over a period of time to maintain a predetermined nutrient concentration. In another embodiment, nutrients may be added to the cell culture via a nutrient pump in a duty cycle. For example, in this embodiment, nutrient delivery may be staggered or intermittent over a period of time.

[0038] The disclosed methods and systems also enable the production of bioproducts in culture media including nutrient concentration ranges lower than those in culture media using a daily bolus nutrient feed strategy, such as a glucose concentration range. In one embodiment, the nutrient concentration, e.g., glucose concentration, is at least 3 g / L lower than that of the bolus nutrient feed. In another embodiment, the nutrient concentration, e.g., glucose concentration, is at least 5 g / L lower than the nutrient concentration in culture media obtained using a bolus nutrient feed. In yet another embodiment, the nutrient concentration, e.g., glucose concentration, is at least 6 g / L lower than the nutrient concentration obtained using a bolus nutrient feed.

[0039] Furthermore, the lower nutrient concentrations in the culture medium and steady-state addition achieved by the disclosed systems and methods enable a reduction in post-translational modifications of proteins and monoclonal antibodies. In one embodiment, the disclosed methods and systems deliver nutrients at or near the rate at which they are taken up or consumed by cells in the culture. By adding small amounts of nutrients steadily over time, it becomes possible to produce bioproducts with lower levels of post-translational modifications, e.g., low levels of glycation, compared to standard bolus feed addition. Importantly, reducing the concentration of nutrients and adding them steadily does not affect antibody production. In one embodiment, the reduction in nutrient concentration results in a reduction of as much as 30% of post-translational modifications compared to those observed with standard bolus feed addition. In another embodiment, the reduced nutrient concentration results in a reduction of as much as 40% of post-translational modifications compared to those observed with standard bolus feed addition. In yet another embodiment, the reduced nutrient concentration results in a reduction of up to 50% of post-translational modifications compared to those observed with standard bolus feed addition.

[0040] III. Bioreactor Systems Another embodiment provides a system for monitoring and controlling one or more process variables in a bioreactor cell culture. Multiple components are integrated into a single system with a single user interface. Referring to Figure 2, a Raman analyzer 200 may be operably connected to a bioreactor 300. In this embodiment, a Raman probe may be inserted into the bioreactor 300 to acquire raw spectral data of one or more process variables in the cell culture, for example, nutrient concentration. The Raman analyzer 200 may also be operably connected to a computer system 500 so that it can receive and process the acquired raw spectral data.

[0041] Computer system 500 can typically be implemented using one or more programmed general-purpose computer systems, such as embedded processors, chip-on systems, personal computers, workstations, server systems, and minicomputers or mainframe computers, or in a distributed network computing environment. Computer system 500 may comprise one or more processors (CPUs) 502A-502N, input / output circuits 504, network adapters 506, and memory 508. CPUs 502A-502N execute program instructions to perform the functions of this system and method. Typically, CPUs 502A-502N are one or more microprocessors, such as INTEL CORE® processors.

[0042] The input / output circuit 504 provides the function of inputting or outputting data to the computer system 500. For example, the input / output circuit may include input devices such as a keyboard, mouse, touchpad, trackball, scanner, and analog-to-digital converter, output devices such as a video adapter, monitor, and printer, and input / output devices such as a modem. The network adapter 506 interfaces the device 500 with the network 510. The network 510 may be any public or private LAN or WAN, including but not limited to the Internet.

[0043] Memory 508 stores program instructions executed by CPU 502 to perform the functions of computer system 500, as well as data used and processed by CPU 502. Memory 508 may include, for example, electrical storage devices such as random access memory (RAM), read-only memory (ROM), programmable read-only memory (PROM), electrically erasable programmable read-only memory (EEPROM), flash memory, and electromechanical memory such as magnetic disk drives, tape drives, optical disk drives (which may use an integrated drive electronics (IDE) interface), or variations or extensions thereof, such as Extended IDE (EIDE) or Ultra Direct Memory Access (UDMA), or Small Computer System Interface (SCSI) based interfaces, or variations or extensions thereof, such as High Speed ​​SCSI, Wide SCSI, High Speed ​​and Wide SCSI, or Serial Advanced Technology Attachment (SATA), or variations or extensions thereof, or Fibre Channel Arbitrated Loop (FC-AL) interfaces.

[0044] Memory 508 may include a controller routine 512, controller data 514, and an operating system 520. The controller routine 512 may include software routines that perform processing to implement one or more controllers. The controller data 514 may include data that the controller routine 512 requires to perform processing. In one embodiment, the controller routine 512 may include multivariate software for performing multivariate analysis, such as PLS regression modeling. In this embodiment, the controller routine 512 may include SIMCA-QPp (MKS Data Analytic Solutions, Umea, Sweden) for performing chemometric PLS modeling. In another embodiment, the controller routine 512 may also include software that performs noise reduction on the dataset. In this embodiment, the controller routine 512 may include a MATLAB runtime (The Mathworks Inc., Natick, MA) for running a noise reduction filter model. Furthermore, the controller routine 512 may include software such as a MATLAB runtime for operating automated feedback controllers, such as a PID controller. The software operating the automatic feedback controller must be able to calculate the difference between a predefined setpoint and a measured process variable (e.g., measured nutrient concentration) and automatically apply accurate corrections. Therefore, the computer system 500 can also be operably connected to the nutrient pump 400 so that the corrected nutrient amounts can be delivered to the bioreactor 300.

[0045] The disclosed system can control and monitor process variables of a single bioreactor or multiple bioreactors. In one embodiment, the system can control and monitor process variables of at least two bioreactors. In another embodiment, the system can control and monitor process variables of at least three bioreactors or at least four bioreactors. For example, the system can monitor up to four bioreactors per hour. [Examples]

[0046] The following non-limiting examples demonstrate methods for controlling one or more process variables in bioreactor cell culture according to the present invention. These examples are merely illustrative of preferred embodiments of the present invention and should not be construed as limiting the invention, the scope of which is defined by the appended claims.

[0047] Example 1 Materials and methods The mammalian cell culture process utilized Chinese hamster ovary (CHO) cell lines grown in chemically defined basic media. Production was carried out in a 60L pilot-scale stainless steel bioreactor controlled by RSLogix 5000 software (Rockwell Automation, Inc., Milwaukee, WI).

[0048] The model's data acquisition included spectral data from Kaiser RamanRXN2 and RamanRXN4 analyzers (Kaiser Optical Systems, Inc. Ann Arbor, MI) using BIO-PRO Optic (Kaiser Optical Systems, Inc. Ann Arbor, MI). The operating parameters of the RamanRXN2 and RamanRXN4 analyzers were set to a scan time of 10 seconds for 75 accumulations. An OPC Reader / Writer to an RSLinx OPC server was used for data flow.

[0049] SIMCA 13 (MKS Data Analytic Solutions, Umeda, Sweden) was used to correlate peaks in spectral data with offline glucose measurements. The following spectral filtering was performed on the raw spectral data: 21 cm-1 Using the first derivative with point smoothing, various baselines were removed, and laser power variations and exposure time were corrected by standard normalized variable (SNV) normalization.

[0050] A partial least squares regression model was constructed using corresponding offline measurements taken with Nova Bioprofile Flex (Nova Biomedical, Waltham, MA). Table 1A below shows the details of the partial least squares regression model for nutrient chemometrics.

[0051] (Table 1A) Details of the nutrient chemometric partial least squares regression model TIFF2026063384000002.tif74128

[0052] Signal processing techniques, particularly noise reduction filtering, were also performed. The noise reduction technique combined raw measurements with model-based estimates of the expected model-based measurements. An iterative approach allowed for updating the filtered measurements based on previous measurements and current process conditions.

[0053] The system utilizes inverse proportional-integral-derivative (PID) control with individually programmed algorithms in MATLAB Runtime (The Mathworks Inc., Natick, MA). All variables of the PID controller, such as tuning constants, have the capability to be changed in real time via the platform interface.

[0054] result Figure 3 shows the predicted nutrient process values ​​confirmed by offline nutrient samples. As can be seen from Figure 3, the Raman analyzer and chemometric model predicted nutrient concentration values ​​within the variability of the offline analysis method. This demonstrates that in situ Raman spectroscopy and chemometric modeling according to the present invention provide accurate measurement of nutrient concentration values.

[0055] Figure 4 shows the filtered final nutrient process values ​​after signal processing. As can be seen from Figure 4, the signal processing reduces noise in the raw predicted nutrient process values. Noise reduction filtering of the predicted nutrient values ​​improves the robustness of the entire feedback control system.

[0056] Figure 5 shows the predicted nutrient process value and filtered final nutrient process value after a shift at a predetermined setpoint of nutrient concentration in a feedback-controlled continuous nutrient feed batch. As can be seen from the adjustment of the filtered nutrient process value, a successful response from the feedback controller is observed when a shift in the nutrient concentration setpoint occurs. In fact, the PID controller was able to respond quickly to changes in the setpoint that deviated from the noise-filtered nutrient process value.

[0057] Based on the results shown in Figures 3 to 5, the method of the present invention provides real-time data that enables automatic feedback control for continuous and stable nutrient addition.

[0058] Example 2 Materials and methods Production was carried out in a 250L single-use bioreactor. A partial least squares regression model was created. Table 1B below shows the details of the nutrient chemometric partial least squares regression model.

[0059] (Table 1B) Details of the nutrient chemometric partial least squares regression model TIFF2026063384000003.tif76128

[0060] In this embodiment, no noise filtering techniques were used.

[0061] result Figure 6 shows the effect of glucose concentration on post-translational modifications. As can be seen from Figure 6, the higher the glucose concentration, the higher the percentage of PTMs. The data points from Figure 6, along with the normalized percentage of post-translational modifications (PTMs) and glucose concentration throughout the batch day, are shown in Table 2 below.

[0062] (Table 2) Data points for normalized PTM% and glucose concentration related to Figure 6 TIFF2026063384000004.tif164144

[0063] Figure 7 shows the in situ Raman predicted glucose concentration values ​​for feedback-controlled continuous and bolus nutrient feeds according to the present invention. The thick black line in Figure 7 represents a predefined setpoint. The predefined setpoint (SP1) was initially set to 3 g / L (SP1) and then increased to 5 g / L (SP2). As can be seen from Figure 7, Raman predicted the glucose concentration that was accurately adjusted during the shift of the predefined setpoint. The data points in Figure 7 for Raman predicted glucose concentration values ​​throughout the batch day are shown in Table 3 below.

[0064] (Table 3) Raman predicted glucose concentration data points in Figure 7 TIFF2026063384000005.tif224154TIFF2026063384000006.tif230154TIFF202 6063384000007.tif230154TIFF2026063384000008.tif230154TIFF2026063384 000009.tif230154TIFF2026063384000010.tif230154TIFF2026063384000011. tif230154TIFF2026063384000012.tif230154TIFF2026063384000013.tif59154

[0065] Figure 8 shows the antibody titers for feedback-controlled continuous nutrient feed and bolus nutrient feed. As can be seen from Figure 8, antibody production is not affected by either method. Tables 4 and 5 below show the data points for the antibody titers of the bolus feed and feedback-controlled antibody titers, respectively, for Figure 8.

[0066] (Table 4) Figure 8 Bolus feed antibody titer data points TIFF2026063384000014.tif96128

[0067] (Table 5) Data points of feedback-controlled antibody titers in Figure 8 TIFF2026063384000015.tif92128

[0068] Figure 9 shows the normalized percentage of PTM as a result of glucose concentration. As can be seen from Figure 9, when the glucose concentration decreases from approximately 6 g / L to 8 g / L (setpoint for bolus feed products) to 5 g / L (setpoint 2) to 3 g / L (setpoint 1), PTM decreases. In other words, less exposure to nutrients leads to a decrease in PTM. The data points in Figure 9 and the normalized percentage of PTM are shown in Table 6 below.

[0069] (Table 6) Normalized % PTM data points in Figure 9 TIFF2026063384000016.tif45154

[0070] Figure 10 shows the glucose concentrations of feedback-controlled continuous and bolus nutrient feeds according to the present invention. As shown in Figure 10, a reduced steady-state glucose concentration can be obtained by the method of the present invention. The data points in Figure 10 for glucose concentration are shown in Table 7 below.

[0071] (Table 7) Glucose concentration data points in Figure 10 TIFF2026063384000017.tif210147TIFF2026063384000018.tif227147TIFF2026063384000019.tif227147TIFF2026063384000020.tif227147TIFF202 6063384000021.tif227147TIFF2026063384000022.tif227147TIFF2026063 384000023.tif227147TIFF2026063384000024.tif227147TIFF20260633840 00025.tif227147TIFF2026063384000026.tif227147TIFF2026063384000027.tif227147TIFF2026063384000028.tif227147TIFF2026063384000029.t if227147TIFF2026063384000030.tif227147TIFF2026063384000031.tif227147TIFF2026063384000032.tif227147TIFF2026063384000033.tif174147

Claims

1. A method for controlling cell culture medium conditions, Quantifying one or more analytes in the cell culture medium using in situ Raman spectroscopy; and Adjust the concentration of one or more analytes in the cell culture medium to match a predetermined analyte concentration that maintains the post-translational modification of proteins in the cell culture medium at 1.0 to 30 percent. The method, including the method described above.

2. The method according to claim 1, wherein the post-translational modification includes glycation.

3. The method according to claim 1, wherein the protein in the cell culture comprises an antibody or an antigen-binding fragment thereof.

4. The method according to claim 1, wherein the protein in the cell culture contains a fusion protein.

5. The method according to claim 1, wherein the cell culture medium contains mammalian cells.

6. The method according to claim 5, wherein the mammalian cells include Chinese hamster ovary cells.

7. The method according to claim 1, wherein the analyte is glucose.

8. The method according to claim 7, wherein the predetermined glucose concentration is 0.5 to 8.0 g / L.

9. The method according to claim 7, wherein the glucose concentration is 1.0 g / L to 3.0 g / L.

10. The method according to claim 7, wherein the glucose concentration is 2.0 g / L.

11. The method according to claim 7, wherein the glucose concentration is 1.0 g / L.

12. The method according to claim 1, wherein the predetermined analyte concentration maintains the post-translational modification of proteins in the cell culture medium at 1.0 to 20 percent.

13. The method according to claim 1, wherein the predetermined analyte concentration maintains the post-translational modification of proteins in the cell culture medium at 5.0 to 10 percent.

14. The method according to claim 1, wherein the quantification of the analyte is performed continuously.

15. The method according to claim 1, wherein the quantification of the analyte is performed intermittently.

16. The method according to claim 1, wherein the quantification of the analyte is performed at intervals.

17. The method according to claim 1, wherein the quantification of the analyte is performed at 5-minute intervals.

18. The method according to claim 1, wherein the quantification of the analyte is performed at 10-minute intervals.

19. The method according to claim 1, wherein the quantification of the analyte is performed at 15-minute intervals.

20. The method according to claim 1, wherein the quantification of the analyte is performed every hour.

21. The method according to claim 1, wherein the quantification of the analyte is performed at least daily.

22. The method according to claim 1, wherein the adjustment of the analyte concentration is performed automatically.

23. The method according to claim 1, wherein at least two different analytes are quantified.

24. The method according to claim 1, wherein at least three different analytes are quantified.

25. The method according to claim 1, wherein at least four different analytes are quantified.

26. A method for reducing post-translational modifications of secreted proteins, Culturing cells that secrete the protein in a cell culture medium containing 0.5 to 8.0 g / L of glucose; To incrementally determine the glucose concentration in the cell culture medium during cell culture using in situ Raman spectroscopy; To maintain the post-translational modification of the secreted protein at 1.0–30.0 percent, the glucose concentration is adjusted to maintain a glucose concentration of 0.5–8.0 g / L by automatically delivering glucose multiple times per hour. The method, including the method described above.

27. The method according to claim 26, wherein the glucose concentration is 1.0 to 3.0 g / L.

28. A system for controlling cell culture medium conditions, wherein the system includes To receive data from an in situ Raman spectrometer that includes the concentration of one or more analytes in the cell culture medium; and To adjust the concentration of one or more analytes in the cell culture medium to match a predetermined analyte concentration that maintains the post-translational modification of proteins in the cell culture medium at 1.0 to 30 percent. The system includes one or more processors that communicate with a computer-readable medium storing software code for execution by one or more processors.

29. The system according to claim 28, wherein the software code is further configured to cause the system to perform chemometric analysis on the data.

30. The system according to claim 29, wherein the chemometric analysis includes partial least squares regression modeling.

31. The system according to claim 28, wherein the software code is further configured to cause the system to perform one or more signal processing techniques on the data.

32. The system according to claim 31, wherein the signal processing technology includes noise reduction technology.

33. A system for reducing post-translational modifications of secreted proteins, wherein the system includes To incrementally receive spectral data from an in-situ Raman analyzer, including the glucose concentration in the cell culture medium, during the culture of cells that secrete the aforementioned protein; and In order to maintain the post-translational modification of the secreted protein at 1.0–30.0 percent, glucose concentration is adjusted to maintain it at 0.5–8.0 g / L by automatically delivering glucose multiple times per hour. The system includes one or more processors that communicate with a computer-readable medium storing software code for execution by one or more processors.

34. The system according to claim 33, wherein the software code is further configured in the system to correlate the peaks in the spectral data with glucose concentration.

35. The system according to claim 33, wherein the software code is further configured to perform partial least squares regression modeling on the spectral data.

36. The system according to claim 33, wherein the software code is further configured to perform noise reduction techniques on the spectral data.

37. The system according to claim 33, wherein the adjustment of the glucose concentration is performed by automated feedback control software.

38. The system according to claim 33, wherein the concentration of glucose is 1.0 to 3.0 g / L.

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