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BR-122026003140-A2 - SYSTEM FOR CONTROLLING A SEED TRAIN PROCESS, AND A METHOD FOR SELF-INOCULATION OF A BIOREACTOR

BR122026003140A2BR 122026003140 A2BR122026003140 A2BR 122026003140A2BR-122026003140-A2

Abstract

This is a system and method for self-inoculating a bioreactor in a seed train process that includes an expansion chamber to expand an initial cell stock to a viable cell density; a bioreactor for inoculation with the expanded cell stock; a fluid communication path between the expansion chamber and the bioreactor; a pump to control fluid flow through the fluid communication path; a Raman spectrometer to generate Raman spectral data; a multivariate model that provides predictions of processing variables in the expansion chamber; and a computer system to control the pump to effect self-inoculation of the bioreactor from the expansion chamber, through the fluid communication path, when the computer system determines from the Raman spectral data that one or more predefined trigger events have occurred.

Inventors

  • Mark Czeterko
  • Alessandra STARLING
  • Colin ORR
  • WILLIAM SETH PIERCE
  • Matthew Conway

Assignees

  • REGENERON PHARMACEUTICALS, INC.

Dates

Publication Date
20260317
Application Date
20201023
Priority Date
20191025

Claims (20)

  1. 1. A system for controlling a seed train process, characterized in that it comprises: an expansion chamber for receiving an initial cell stock for expansion into a viable cell culture; a bioreactor in fluid communication with the expansion chamber for receiving a viable cell culture; a pump for effecting the transfer of a viable cell culture from the expansion chamber to the bioreactor via a fluid communication path between the expansion chamber and the bioreactor; a Raman spectrometer having at least one probe for monitoring the cell expansion process within the expansion chamber using Raman spectrometry, wherein the Raman spectrometer is adapted to generate Raman spectral data; a multivariate model that provides predictions of process variables based on Raman spectral data; and a computer system in signal communication with the Raman spectrometer to receive Raman spectral data and in signal communication with the pump to control the pump's operation to effect the transfer of a viable cell culture from the expansion chamber to the bioreactor, wherein the Raman spectrometer is adapted to generate Raman spectral data and a multivariate model that provides predictions of one or more process variables, and the computer system is adapted to compare the measurements of process variables with one or more predefined process setpoints to determine if one or more measurements of process variables have satisfied a predefined trigger value, wherein the computer system is adapted, upon determining that a measurement of a process variable in the Raman spectral data has satisfied one or more predefined trigger values, to control the pump to perform an autotransfer of a cell culture volume from the expansion chamber to the bioreactor, such that the autotransfer of the cell culture volume from the expansion chamber to the bioreactor is initiated by the computational system when at least one first trigger value The predefined value is reached, or when a value of one or more process variables satisfies a condition that has been predetermined as indicative of a change in the cell growth state.
  2. 2. System according to claim 1, characterized in that the computational system processes Raman spectral data received from the Raman spectrometer to generate a multivariate model of one or more process variables.
  3. 3. System according to claim 2, characterized in that the computational system generates a partial least squares regression model.
  4. 4. System according to claim 3, characterized in that the computational system is adapted, when comparing the predictions of the process variables of the multivariate model with one or more predefined process adjustment points, to use measurements of process variables from a plurality of predefined isolated regions of Raman spectral data.
  5. 5. System according to claim 4, characterized in that the computational system uses process variable measurements of Raman spectral data in the wavelength regions of 800-850 cm-1; 1260-1470 cm-1; 1650-1840 cm-1; and 2825-3080 cm-1.
  6. 6. System according to claim 1, characterized in that the computational system stores a first predefined trigger value based on a predetermined viable cell density (VCD) and stores a second predefined trigger value based on a predetermined processing variable different from the VCD, and a third predefined trigger value based on a VCD predicted by the model.
  7. 7. System according to claim 6, characterized in that the second preset trigger value is a lactate level value.
  8. 8. System according to claim 7, characterized in that the lactate level value is set to a value equal to a predetermined minimum lactate level.
  9. 9. A method for self-inoculating a bioreactor using a system according to claim 1, characterized in that it comprises: expanding a cell stock in the expansion chamber; generating Raman spectral data, using a Raman spectrometer, to provide data to a multivariate model that predicts one or more process variables of cell expansion in the expansion chamber; a computer system that compares the process variable predictions of the multivariate model with predefined process setpoints in the computer system; a computer system that controls the pump to self-inoculate the bioreactor with a viable cell culture from the expansion chamber when the computer system determines that one or more process variable predictions of the multivariate model satisfy a predefined trigger value, wherein one or more process variable predictions include an indication of a change in the cell growth state.
  10. 10. Method according to claim 9, characterized in that the predefined trigger value is a viable cell density value.
  11. 11. Method according to claim 10 characterized in that the predefined trigger value is adjusted to a viable cell density value that is equal to or within a range of -10% of a predetermined target viable cell density.
  12. 12. Method according to claim 9, characterized in that the predefined trigger value is a lactate level value.
  13. 13. Method according to claim 12, characterized in that the predefined trigger value is set to a lactate level value that is equal to or within a range of +10% of a predetermined minimum lactate level.
  14. 14. Method according to claim 9, characterized in that the predefined trigger value is a model-predicted VCD.
  15. 15. Method according to claim 14, characterized in that the predefined trigger value is set to a model-predicted VCD value that is equal to or within a range of -10% of a predetermined maximum cell growth rate.
  16. 16. Method according to claim 9, characterized in that the computer system stores a first predefined trigger value based on a predetermined viable cell density, and stores a second predefined trigger value based on a predetermined processing variable other than viable cell density, and the computer system is adapted to control the pump to autoinoculate the bioreactor with a viable cell culture from the expansion chamber when the computer system determines that a process variable prediction from the multivariate model satisfies the first or second predefined trigger value.
  17. 17. Method according to claim 9, characterized in that the second preset trigger value is a lactate level value.
  18. 18. Method according to claim 9, characterized in that the predefined trigger value is a model-predicted VCD value.
  19. 19. Method according to claim 9 characterized in that the computer system processes Raman spectral data received from the Raman spectrometer to generate a multivariate model of one or more process variables, and obtains the process variable measurements from the multivariate model for comparison with predefined trigger values.
  20. 20. Method according to claim 19, characterized in that the computer system generates a least squares partial regression model.

Description

[001] This application claims the benefit and priority of U.S. Provisional Patent Application No. 62/925,940 filed October 25, 2019, and, where permitted, is incorporated by reference in its entirety. FIELD OF THE INVENTION [002] The inventions encompassed herein include bioreactor systems and methods for monitoring and controlling a seed train process in a bioreactor system. Particular embodiments further include bioreactor systems that include a Raman spectrometer and methods that use Raman spectroscopy to monitor and control a seed train process. BACKGROUND OF THE INVENTION [003] Therapeutic antibodies, and in particular monoclonal antibodies (mAbs), have become an important tool in modern medicine for the development of target proteins that can be used in the treatment of a wide range of diseases, including cancer and autoimmune diseases. [004] The target proteins of interest are produced by a cell line that is expanded from an initial stock of cryopreserved cells through a seed train process through one or more stages until a predetermined viable cell density (VCD) is achieved, at which point the expanded cell stock is then introduced into a production bioreactor to inoculate a culture medium contained therein. After inoculation, the cell culture continues to grow in the bioreactor until a target protein is expressed in a desired quantity, after which the cell culture fluid can be harvested and the target protein can be isolated and purified. [005] Traditional seed train processes involved multiple stages of cell growth and expansion, using vessels of increasing size, between the initial stock of cryopreserved cells and the final production bioreactor. In initial processes, the initial stock of cryopreserved cells might be expanded through several stages which could include, for example, one or more shake flasks, one or more centrifuges, one or more wave bags, and one or more expansion chambers before the predetermined VCD is reached for inoculation into a production bioreactor. More recently, more efficient seed train processes have been developed to achieve a predetermined VCD in fewer steps. However, modern processes still require cell expansion through at least one expansion chamber to reach a predetermined VCD before inoculation of a culture medium into the final production bioreactor. [006] Generally, the final concentration of target protein can be increased and batch-to-batch consistency can be reduced in a production bioreactor by using an inoculum with the same VCD. However, there is a range for inoculum VCD that leads to targeted production bioreactor performance. For example, a very low inoculum VCD can cause undesirable lactate cell metabolism, and a very high inoculum VCD can produce lower cell growth in the production bioreactor due to cells exiting the exponential growth phase. [007] Undesirable lactate cell metabolism and reduced cell growth in the production bioreactor result in lower quantities of target protein being produced than could have been produced from these cells and therefore equate to a loss of yield and an overall decrease in process efficiencies. As such, it is desirable that cell expansion be cultivated to a predetermined VCD that leads to desirable lactate cell metabolism in the production bioreactor and maintains exponential cell growth, and that the production bioreactor be inoculated as soon as possible after reaching the predetermined VCD. [008] It will be appreciated that a target VCD range will vary from one cell line to another, based on the properties of the different cell lines. However, an additional complication arises, in that cell expansion may also vary between individual production runs of a common cell line due to variations in culture medium and other operating conditions. As a result, the time to inoculate a production bioreactor, after cell expansion in an upstream expansion chamber to a predetermined VCD, may be variable. [009] Despite the many advances made to date in the technique, there remains a need for further improvements in seed train processes to further advance the state of the art and improve overall yield. As a non-limiting example, the state of the art would benefit from improvements that facilitate the inoculation of a production bioreactor after cell expansion to a predetermined VCD. SUMMARY OF THE INVENTION [0010] The present invention relates to systems and methods that use Process Analytical Technology (PAT) tools and a PAT Knowledge Manager to provide monitoring and control strategies to increase process consistency. In one aspect, the systems and methods according to the present invention decrease the reliance on manual operations to obtain and verify offline samples to confirm target cell densities and to initiate the transfer of a cell culture between bioreactors, such as when inoculating a final production bioreactor. Raman spectroscopy is used, in conjunction with PAT data management software, to enable continuous monitoring