Systems and methods for automated inoculation in seed cultivation and production processes

Abstract

A system and method for automatically inoculating a bioreactor during a seed culture process includes: an expansion chamber for expanding an initial cell stock to a viable cell density; a bioreactor for inoculating with the expanded cell stock; a fluid communication path between the expansion chamber and the bioreactor; a pump for controlling fluid flow through the fluid communication path; a Raman spectrometer for generating Raman spectral data; a multivariate model for providing predictions of process variables in the expansion chamber; and a computer system for controlling the pump to automatically inoculate the bioreactor from the expansion chamber via the fluid communication path when the computer system determines, based on the Raman spectral data, that one or more predefined triggering events have occurred.

Description

[0001] CROSS-REFERENCE TO RELATED APPLICATIONS

[0002] This application claims the benefit of priority to U.S. Provisional Patent Application No. 62 / 925,940, filed on October 25, 2019, which is hereby incorporated by reference in its entirety, where permitted. Technical Field

[0003] The invention encompassed herein includes a bioreactor system and methods for monitoring and controlling a seed cultivation process in a bioreactor system. Certain embodiments further include a bioreactor system comprising a Raman spectrometer and methods for monitoring and controlling a seed cultivation process using Raman spectroscopy. Background Art

[0004] Therapeutic antibodies, and in particular monoclonal antibodies (mAbs), have become important tools 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 disorders.

[0005] The target protein of interest is produced by a cell line that is expanded from an initial cryopreserved cell stock through a seed culture process through one or more stages until a predetermined viable cell density (VCD) is achieved. At this point, the expanded cell stock is then introduced into a production bioreactor for inoculation of the culture medium maintained therein. Following inoculation, the cell culture continues to grow in the bioreactor until the desired amount of target protein is expressed, at which point the cell culture fluid can be harvested and the target protein can be isolated and purified.

[0006] The traditional seed culture process involves multiple stages of cell growth and expansion using vessels of increasing size between an initial cryopreserved cell stock and a final production bioreactor. In earlier processes, an initial cryopreserved cell stock could be expanded through several stages, which could include, for example, one or more shake flasks, one or more spinner flasks, one or more woven bags, and one or more expansion chambers, before reaching a predetermined VCD for inoculation of a production bioreactor. In recent years, more efficient seed culture processes have been developed that achieve a predetermined VCD with fewer steps. However, modern processes still require cell expansion through at least one expansion chamber to reach a predetermined VCD before inoculating the culture medium in the final production bioreactor.

[0007] In general, the final target protein concentration can be increased, and batch-to-batch consistency can be reduced by using an inoculum with the same VCD in the production bioreactor. However, there is a range of inoculum VCD that produces target production bioreactor performance. For example, an inoculum VCD that is too low may result in undesirable cellular metabolism of lactate, while an inoculum VCD that is too high may result in slower cell growth in the production bioreactor due to cells exiting the exponential growth phase.

[0008] Undesirable lactate cell metabolism and reduced cell growth in the production bioreactor result in the production of lower amounts of target protein than would otherwise be produced from those cells, and thus lead to yield losses and an overall reduction in process efficiency. Thus, it is desirable to expand cell growth to a predetermined VCD in order to achieve desirable lactate cell metabolism and maintain exponential cell growth in the production bioreactor, and to inoculate the production bioreactor as soon as possible after reaching the predetermined VCD.

[0009] It will be appreciated that the target VCD range will vary between cell lines based on the properties of the individual cell lines. However, another difficulty is that cell expansion may also vary between individual production lines of a common cell line due to variations in culture medium and other operating conditions. Therefore, the timing of inoculating the production bioreactor after cells have expanded to a predetermined VCD in the upstream expansion chamber may be variable.

[0010] Despite the many advances currently in this technology, there is still a need for further improvements in the seed culture process to further advance the state of the art and improve yields overall. As a non-limiting example, the state of the art would benefit from improvements that facilitate inoculation of production bioreactors after cell expansion to a predetermined VCD. Summary of the Invention

[0011] 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 improve process consistency. In one aspect, the systems and methods according to the present invention reduce the reliance on manual operations to obtain and verify offline samples to confirm target cell density and initiate transfer of cell cultures between bioreactors, such as when inoculating a final production bioreactor. Raman spectroscopy, combined with PAT data management software, enables continuous monitoring of cell growth and automated transfer of cell cultures between two vessels upon detection of a predefined trigger event (e.g., upon detection of a target viable cell density).

[0012] The systems herein are used to monitor cell cultures in expansion chambers using Raman spectroscopy and control the inoculation of production bioreactors with inoculum from the expansion chambers based on the Raman spectral data. In some examples, the system control scheme includes automatically inoculating the production bioreactor using an inline pump based on determining that the cell culture in an upstream expansion chamber (e.g., a relatively small volume upstream bioreactor) has reached a predetermined viable cell density (VCD). These systems and methods can be used with cell cultures comprising mammalian cells, such as Chinese hamster ovary (CHO) cells, and the cell cultures can be cultured to produce proteins comprising antibodies, antigen-binding fragments thereof, or fusion proteins.

[0013] The system herein may further include one or more processors in communication with a computer-readable medium (e.g., a physical non-transitory memory), the computer-readable medium storing software code for execution by the one or more processors, for causing the system to receive data from a Raman spectrometer containing a VCD of a cell culture; and to inoculate a production bioreactor based on the Raman spectral data. The software code stored on the computer-readable medium may be further configured to interpret the Raman spectral data using one or more multivariate models, such as a partial least squares regression model. The software code may be further configured to control the system to perform one or more signal processing techniques, such as noise reduction techniques, on the spectral data.

[0014] The system disclosed herein is used to monitor and control a seed culture process and may include: an expansion chamber for receiving an initial cell stock for expansion into a living cell culture; a bioreactor in fluid communication with the expansion chamber for receiving the living cell culture; a pump for enabling transfer of the living cell culture from the expansion chamber to the bioreactor via a fluid communication path between the expansion chamber and the bioreactor; a multivariate model for correlating Raman spectral data with one or more process variables of a cell expansion process in the expansion chamber using Raman spectroscopy, the Raman spectrometer being adapted to generate the Raman spectral data; and a computer system in signal communication with the Raman spectrometer for receiving the Raman spectral data and in signal communication with the pump for controlling operation of the pump to enable transfer of the living cell culture from the expansion chamber to the bioreactor.

[0015] The Raman spectrometer may be adapted to generate Raman spectral data, the multivariate model may correlate the Raman spectral data with one or more process variables, and the computer system may be adapted to compare process variable measurements to one or more predefined process setpoints to determine whether the one or more process variable measurements have met a predefined trigger value. When the computer system determines that the process variable measurements in the Raman spectral data have met the predefined trigger value, the control system instructs the pump to automatically transfer the cell culture volume from the expansion chamber to the bioreactor, thereby automatically inoculating culture medium in the bioreactor with the cell culture from the expansion chamber.

[0016] The computer system processes the Raman spectral data from the Raman spectrometer to generate a multivariate model of the one or more process variables, which may include a partial least squares regression model. When comparing the process variable measurements from the Raman spectral data to one or more predefined process set points, the computer system may use the process variable measurements from a plurality of predefined isolation zones of the Raman spectral data, such as 800-850 cm -1 ; 1260-1470cm -1 ; 1650-1840cm -1 ; and / or 2825-3080cm -1 wavelength region.

[0017] The system herein can be used to automatically inoculate a bioreactor by: expanding a cell stock in an expansion chamber; generating Raman spectroscopy data using a multivariate model to predict one or more process variables of cell expansion in the expansion chamber; comparing, using a computer system, the process variable predictions from the Raman spectroscopy data to predefined process set points; and actuating a pump to automatically inoculate the bioreactor with a viable cell culture from the expansion chamber when the computer system determines that the one or more process variable predictions from the Raman spectroscopy data meet a predefined trigger value.

[0018] The system herein can process the Raman spectral data received from the Raman spectrometer to generate a multivariate model of the one or more process variables, and can then obtain a process variable prediction from the multivariate model for comparison with a stored predefined trigger value. After completing the seed cultivation process, the system can store the multivariate model of the completed seed cultivation process for use in monitoring and controlling subsequent seed cultivation processes. During the seed cultivation process, the system can use one or more multivariate models from one or more previous seed cultivation processes for comparison with one or more process variable measurements in a subsequent seed cultivation process. The system can use one or more multivariate models from one or more previous seed cultivation processes to monitor the processing conditions in the expansion chamber and / or bioreactor.

[0019] Both the foregoing general description and the following detailed description are exemplary and illustrative only and are intended to provide further explanation of the invention as claimed. The accompanying drawings are included to provide a further understanding of the invention; the accompanying drawings are incorporated in and constitute a part of this specification; illustrate embodiments of the invention; and together with the description, serve to explain the principles of the invention. BRIEF DESCRIPTION OF THE DRAWINGS

[0020] Additional features and advantages of the present invention may be identified from the following detailed description provided in conjunction with the drawings described below:

[0021] Figure 1 An example of a system according to the present invention is shown;

[0022] Figure 2 Showcase Figure 1 an example of a computer architecture for use with a computer system of systems;

[0023] Figure 3 Demonstration use Figure 1 An example of a method for automatically inoculating a bioreactor using a system;

[0024] Figure 4 a-4d shows the use of spectral data collection and processing Figure 1 The system generates a regression model based on the collected spectral data;

[0025] Figure 5 Demonstration use Figure 1 Examples of regression models generated by the system from Raman spectroscopy data;

[0026] Figure 6 Display for use Figure 1 The data range of the spectral model used in the process of systematically generating the regression model;

[0027] Figure 7 Display and utilization Figure 1 A comparative example of two regression models generated by the system using different regions of Raman spectroscopy data; and

[0028] Figure 8 Show by Figure 1 A weighted regression model of the predicted process value generated by the system. DETAILED DESCRIPTION

[0029] The following disclosure discusses the invention with reference to the examples shown in the drawings, but does not limit the invention to those examples.

[0030] As used herein, unless the context clearly indicates otherwise, the singular forms "a / an" and "the" include plural references. Unless otherwise required, the use of any and all examples or exemplary language (such as, for example, "for example") provided herein is intended only to better illustrate the present invention and does not impose limitations on the scope of the present invention. No language in this specification should be understood to indicate that any unclaimed element is necessary or critical for the practice of the present invention. Unless the context clearly indicates otherwise, terms such as "first," "second," "third," etc., when used to describe multiple devices or elements, are only used to convey the relative action, positioning, and / or function of the individual devices, and do not enforce a specific order of such devices or elements, or any specific number of such devices or elements.

[0031] As used herein with respect to any property or circumstance, the word "substantially" refers to a degree of deviation that is sufficiently small as not to significantly detract from the identified property or circumstance. The exact degree of deviation permissible in a given situation will depend on the specific context, as will be understood by one of ordinary skill in the art.

[0032] The use of the terms "about" or "approximately" is intended to describe values ​​above and / or below the stated value or range, as would be understood by one of ordinary skill in the art in the respective context. In some instances, this may encompass values ​​within approximately + / - 10%; in other instances, values ​​within approximately + / - 5%; in still other instances, values ​​within approximately + / - 2%; and in yet other instances, values ​​within approximately + / - 1%. The context will clearly make the applicable range for each instance clear, and no further limitation is implied.

[0033] It will be understood that the term “comprising” when used in this specification specifies the presence of stated features, integers, steps, operations, elements and / or components, but does not preclude the presence or addition of one or more other features, integers, steps, operations, elements, components and / or groups thereof unless otherwise indicated herein or clearly contradicted by context.

[0034] Recitation of ranges of values ​​herein serves as a shorthand method of referring individually to each separate value falling within the stated range, including the end points of each range, each separate value within each range, and all intermediate ranges encompassed by each range, unless otherwise indicated herein, each of which is incorporated into the specification as if it were individually recited herein.

[0035] All methods described herein can be performed with the individual steps performed in any suitable order. Unless otherwise indicated herein or clearly contradicted by context, the methods can be performed in the exact order disclosed without any intervening steps, with one or more additional steps interposed between the disclosed steps, with the disclosed steps performed in an order other than the exact order disclosed, with one or more steps performed simultaneously, or with one or more disclosed steps omitted.

[0036] The terms "cell culture" and "cell culture medium" are used interchangeably and encompass any solid, liquid, or semisolid designed to support the growth and maintenance of microorganisms, cells, or cell lines. Components such as polypeptides, sugars, salts, nucleic acids, cell debris, acids, bases, pH buffers, oxygen, nitrogen, agents for adjusting viscosity, amino acids, growth factors, cytokines, vitamins, cofactors, and nutrients may be present in a cell culture medium. Some examples may provide mammalian cell culture processes using mammalian cells or cell lines, such as Chinese hamster ovary (CHO) cell lines, grown in a chemically defined basal medium.

[0037] As used herein, the term "nutrient" may refer to any compound or substance that provides the nutrients necessary for the growth and survival of a cell culture. 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, vitamin B, and vitamin E.

[0038] As used herein, the term "signal communication" may refer to any means of transmitting signals between two or more devices, including but not limited to physical connections (e.g., hardwired signal paths) and non-physical connections (e.g., wireless signal paths). Unless otherwise stated, signal communication between two devices may be direct (e.g., a transmitter in a first device communicates directly with a receiver in a second device) or indirect (e.g., a transmitter in a first device and a receiver in a second device communicate with each other via an intermediate transceiver).

[0039] In one example, Figure 1As shown in FIG, a system 10 is provided that includes an expansion chamber 110, a spectrometer 120, a pump 130, a production bioreactor 140, and a computer system 150. The expansion chamber 110 and the bioreactor 140 are in fluid communication with each other via a feed line 135, wherein the flow of fluid through the feed line 135 is controlled by the pump 130. The spectrometer 120 has at least one detector 125 suitable for monitoring the cell culture within the expansion chamber 110 and is in signal communication with the computer system 150. The computer system 150 is in signal communication with at least the spectrometer 120 and the pump 130, but may also be in signal communication with one or more or each of the expansion chamber 110, the bioreactor 140, and the network. In some examples, two or more of the Raman spectrometer 120, the computer system 140, and the pump 130 may be provided as a single, integrated device.

[0040] The expansion chamber 110 and the bioreactor 140 can be operated as batch, fed-batch, and / or continuous units. The volumes of both the expansion chamber 110 and the bioreactor 140 can range from about 2 L to about 10,000 L. As an example, the expansion chamber 110 can be a 50 L stainless steel unit and the bioreactor 140 can be a 250 L unit. Both the expansion chamber 110 and the bioreactor 140 should maintain a flow rate of about 0.25×10 6 cells / ml to approximately 100×10 6 Cell counts were within the range of 10 cells / mL.

[0041] In one example, spectrometer 120 is a Raman spectrometer that can monitor and collect data about any component of a cell culture that has a detectable Raman spectrum. The systems and methods herein can be used to monitor any component of a cell culture medium, including components added to the cell culture, substances secreted from the cells, and cellular components present after cell death. Components of cell culture medium that can be monitored by the systems and methods include, but are not limited to: nutrients, such as amino acids and vitamins; lactate; cofactors; growth factors; cell growth rate; pH; oxygen; nitrogen; viable cell count; acids; bases; cytokines; antibodies; and metabolites.

[0042] The computer system 150 may be implemented using one or more specially programmed general-purpose computer systems, such as embedded processors, systems on a chip, personal computers, workstations, server systems, and microcomputers or mainframe computers, or in a distributed networked computing environment. The computer system 150 may include one or more processors (CPUs) 1502A-1502N, input / output circuitry 1504, a network adapter 1506, and memory 1508. The CPUs 1502A-1502N execute program instructions to implement the functions of the systems and methods of the present invention. Typically, the CPUs 1502A-1502N are one or more microprocessors, such as Intel processors. processor.

[0043] Input / output circuitry 1504 provides the ability to input data into or output data from computer system 150. For example, input / output circuitry 1504 may include input devices such as a keyboard, mouse, touchpad, trackball, scanner, analog-to-digital converter, output devices such as a video adapter, monitor, printer, and input / output devices such as a modem. Network adapter 1506 interfaces computer system 150 with a network 1510, which may be any public or private LAN or WAN, including but not limited to the Internet.

[0044] The memory 1508 stores program instructions executed by the CPUs 1502A-1502N and data used and processed by the CPUs 1502A-1502N to perform the functions of the computer system 150. The memory 1508 may include, for example, electronic memory 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, etc., and electromechanical memory such as disk drives, tape drives, optical drives, etc., which may use an integrated drive electronics (IDE) interface or variations or enhancements thereof (e.g., Enhanced IDE (EIDE) or Ultra Direct Memory Access (UDMA)), or an interface based on the Small Computer System Interface (SCSI) or variations or enhancements thereof (e.g., Fast SCSI, Wide SCSI, Fast and Wide SCSI, etc.), or Serial Advanced Technology Attachment (SATA) or variations or enhancements thereof, or a Fibre Channel Arbitrated Loop (FC-AL) interface.

[0045] Memory 1508 may include controller routines 1512, controller data 1514, and an operating system 1516. The controller routines may include software to perform processing to implement one or more controllers. The controller data may include data required for the controller routines to perform processing. In one embodiment, the controller routines may include multivariate software for performing multivariate analysis, such as PLS regression modeling. In this regard, the controller routines may include SIMCA (Sartorius Stedim Data Analytics AB, Umeå, Sweden) for performing PLS modeling. In another embodiment, the controller routines may also include software for performing noise reduction on a data set. In this regard, the controller routines may include MATLAB Runtime (Mathworks, Natick, Massachusetts) for executing noise reduction filter models. In addition, the controller routines may include software (e.g., MATLAB Runtime) for operating automated control units such as proportional-integral-derivative (PID) controllers. The software used to operate the system should also be able to calculate the difference between a predefined set point and a measured process variable (e.g., a measured nutrient concentration) and provide a prediction of when the predefined set point will be reached. When functionality is included for predicting when a predefined set point is reached, such as with a PID controller, the computer system 150 also communicates signals with the pump 130 so that the correct amount of inoculum can be pumped into the expansion chamber 110 and / or bioreactor 140, as predefined. The system 10 can monitor and control the expansion chamber 110 and bioreactor 140 (e.g., Figure 1 ) or multiple expansion chambers and / or multiple bioreactors.

[0046] Figure 3 A flow chart showing a method 200 for performing a seed culture process using the system 10. After a cryopreserved cell stock is introduced into the culture medium in the expansion chamber 110, the Raman spectrometer 120 collects Raman spectral data from the expanded cell culture in the expansion chamber 110 ( Figure 4 a) (step 201). Raman spectroscopy is a form of vibrational spectroscopy that provides information about molecular vibrations that can be used for sample identification and quantification. Raman spectrometer 120 collects Raman spectral data via detector 125, which can be a contact or non-contact detector. The non-contact detector 125 enables in situ Raman analysis of cell culture without the need for contacting or extracting from the cell culture. In situ Raman analysis is advantageous in that it is non-invasive and therefore reduces the risk of contamination of the cell culture, which may have undesirable effects on the cell culture and the resulting protein.

[0047] Raman spectral data is acquired at a regular frequency so that the spectral data is continuously updated. Spectral data may be collected approximately every 10 to 120 minutes, approximately every 15 to 60 minutes, or approximately every 20 to 30 minutes. An appropriate sampling frequency may be determined on a case-by-case basis, for example, based on a particular cell line and / or treatment conditions deemed appropriate to ensure that the spectral data adequately indicates the current state of a given cell culture. Any commercially available Raman spectrometer may be utilized, non-limiting examples of which may include the RamanRXN2 and RamanRXN4 spectrometers (Kaiser Optical Systems, Ann Arbor, Michigan).

[0048] After collection, in step 202, the raw spectral data is transmitted to the computer system 150 where it is pre-processed and the processed Raman data ( Figure 4 b) stored in memory 1508 at a dedicated location for subsequent use (step 202). In some examples, processing of the Raman spectral data includes applying one or more spectral filters to correct for any baseline shift. For example, the raw spectral data can be processed using a point smoothing technique or a normalization technique. Normalization may be required to correct for any laser power variations and exposure time of the Raman spectrometer. In some examples, point smoothing (e.g., with a 21 cm -1 The raw Raman spectroscopy data are processed by using the first derivative of point smoothing) and regularization (e.g., standard normalized variance (SNV) regularization).

[0049] In parallel with the collection of Raman spectral data (step 201), process variable data is also collected via an alternative "offline" method (step 203) and also stored in memory 1508 (step 204). This can be done via any suitable analytical method, such as via BioProfile Offline process variable data is collected by manually obtaining samples of cell cultures tested in a local analyzer, such as a Raman Spectroscopy Analyzer (Nova Biomedical, Inc., Massachusetts, USA). Offline process variable data is collected less frequently than Raman spectroscopy data—for example, approximately every 24 hours, approximately every 12 hours, or approximately every 6 hours—and serves as a baseline reference for the Raman spectroscopy data. When collected, the offline process variable data is also stored in a dedicated location in the computer system 150. When collecting offline process variable data, the computer system 150 may also store information from a PAT and / or data management system (e.g., a laboratory data management system and / or continuous online process data).

[0050] The computer system 150 uses the processed Raman data to generate a multivariate model reporting one or more process variables of the cell culture (step 205). When offline process variable data is available, the computer system 150 compares the Raman spectral data with the corresponding offline process variable data to correlate peak values ​​between the two data sets. The computer system uses stored PAT and / or data management data to correlate the offline process variable data with the corresponding Raman spectral data. Any type of multivariate software package, such as SIMCA 13 (Sartorius Stedim Data Analytics AB, Umeå, Sweden), can be used to correlate peak values ​​between the two spectral data sets.

[0051] The multivariate modeling performed by the computer system 150 may include, but is not limited to, partial least squares (PLS), principal component analysis (PCA), orthogonal partial least squares (OPLS), multivariate regression, canonical correlation, factor analysis, cluster analysis, graphical programs, etc. Figure 4 In the example shown in c-4d, the available measurements obtained from the Raman spectroscopy data and the offline process variable data ( Figure 4 c) to create a PLS regression model, and remove outliers to obtain a linear prediction model ( Figure 4 d) to optimize (step 206 ) the model. This PLS regression model can be used to provide predicted process values, such as predicted concentration values ​​of specific variables to be monitored by computer system 150 to effect control of system 10 .

[0052] Model optimization may include applying additional signal processing techniques to the multivariate model and the predicted process values ​​therein. In one example, noise reduction techniques may be applied to the predicted process values ​​to perform data smoothing and / or signal rejection. This noise reduction technique provides a filtered model. One noise reduction technique is to combine the original measurement value with a model-based estimate of what the measurement value should produce according to the model. The noise reduction technique may combine the current predicted process value with its uncertainty, which may be determined by the repeatability of the predicted process value and the current processing conditions. Once the next predicted process value is observed, the estimate of the predicted process value is updated using a weighted average, where a greater weight is given to the estimate with higher certainty. Using an iterative approach, the final process value may be updated based on the previous measurement value and the current processing conditions. In this regard, the algorithm should be recursive and able to run in real time to utilize the current predicted process value, previous values, and experimentally determined constants. The noise reduction technique improves the robustness of the measurements from Raman analysis and PLS predictions.

[0053] Computer system 150 includes an automation control unit (ACU) 155, which operates in step 207 to evaluate the modeled spectral data to determine whether pump 130 should be activated to transfer cell culture volume from expansion chamber 110 to bioreactor 140 to inoculate the bioreactor 140 with culture medium. ACU 155 stores one or more predefined setpoint values, each of which defines a triggering event for performing automatic inoculation. ACU 155 can be any type of automation controller capable of comparing filtered process values ​​to one or more predefined setpoint values ​​and automatically performing a predefined action upon determining that the one or more filtered process values ​​meet the conditions of the corresponding setpoint values ​​(e.g., at or above a maximum setpoint value; at or below a minimum setpoint value; etc.). If ACU 155 determines that the predefined conditions for inoculation have been met, ACU 155 activates pump 130 to achieve fluid flow through fluid line 135, thereby automatically inoculating bioreactor 140 (step 208). Otherwise, the process returns to data collection via an iterative loop (step 209).

[0054] In one example, the ACU 155 stores a predefined setpoint value (also referred to herein as a "trigger value") based on the target VCD of the cell culture that is the subject of the current seed culture process. This predefined setpoint value can be set to the target VCD so that desirable cell metabolism can occur in the production bioreactor while also maintaining the cells in the exponential growth phase. For example, the VCD-based trigger value can be set to a value equal to a predetermined target VCD; a value minus 2.5% of the target VCD; a value minus 5% of the target VCD; a value minus 10% of the target VCD; and so on. In this example, if the ACU 155 determines that the measured VCD value is equal to a value greater than the predefined VCD-based trigger value, the ACU 155 treats the condition as a trigger event for actuating the pump 130 to achieve fluid flow through the fluid line 135, so that the culture medium in the bioreactor 140 is automatically inoculated with the cell culture volume from the expansion chamber 110.

[0055] The ACU 155 can store any number of predefined trigger values, thereby establishing conditions for any number of trigger events. For example, a first trigger value can be set based on a target VCD, and a second trigger value can be set based on a minimum lactate value. The VCD-based trigger value can represent a target VCD for use when inoculating the bioreactor 140 (e.g., as described earlier), while the lactate-based trigger value can identify a minimum lactate level that has been predetermined to signal a change in cell growth state. This lactate-based trigger value can be set to a value equal to a predetermined minimum lactate level; a value of the minimum lactate level + 2.5%; a value of the minimum lactate level + 5%; a value of the minimum lactate level + 10%; and so on. In this example, the ACU 155 may be adapted to automatically inoculate the bioreactor 140 upon detection of any triggering event, such that the system 10 inoculates the bioreactor 140 once a predefined VCD trigger value is reached, but if a lactate level measurement equal to or less than a predefined lactate-based trigger value is detected, automatic inoculation may be triggered at a lower VCD, thereby ensuring that the bioreactor 140 is inoculated before the cell growth state changes.

[0056] As another example, the ACU 155 can operate based on a first trigger value of a target VCD, a second trigger value based on any process variable that has been previously predetermined to indicate a change in cell growth state, and a third trigger value based on a model-predicted VCD. The model-predicted VCD-based trigger value can be set to a value equal to the maximum model-predicted VCD deemed acceptable for a given seed culture process; a value of minus 2.5% of the maximum cell growth rate; a value of minus 5% of the maximum cell growth rate; a value of minus 10% of the maximum cell growth rate; and so on. In this example, the ACU 155 can be adapted to automatically inoculate the bioreactor 140 upon detection of any of the triggering events, such that the system 10 inoculates the bioreactor 140 once the predefined VCD trigger value is reached, but inoculation can be triggered at a lower VCD if a process variable value that satisfies a condition that has been previously determined to indicate a change in cell growth state is detected, with the added precaution of also triggering earlier automatic inoculation if a model-predicted VCD equal to or greater than the predefined model-predicted VCD trigger value is detected. In this way, if a cell culture begins to experience increased model-predicted VCD before a predefined VCD trigger value is detected, and no other process variables are detected that provide an alert of a change in cell growth state, the system can trigger automatic seeding before an unacceptable cell growth state is initiated.

[0057] The ACU 155 can operate at any number of predefined trigger values ​​based on any number of different process variables, which may include (but are not limited to) any one or combination of the following: one or more nutrients (e.g., amino acids and vitamins); lactate; cofactors; growth factors; cell growth rate; pH; oxygen; nitrogen; viable cell count; cell death count; acid; base; cytokines; antibodies; and metabolites.

[0058] The computer system 150 may also have controls that enable real-time changes to the system including the ACU 155 from a platform interface. For example, there may be an interface that allows the user to: select one or more trigger conditions based on several different process variables (e.g., a trigger condition based on VCD; a trigger condition based on lactate; a trigger condition based on cell growth rate, etc.); enter desired values ​​to be used as predefined setpoint values ​​in the trigger conditions (e.g., a trigger value based on target VCD; a minimum lactate level; a maximum cell growth rate, etc.); and adjust one or more pre-set trigger values. The ACU 155 should be able to adjust the conditions that trigger automatic inoculation in response to changes in one or more predefined trigger values.

[0059] During the first seed cultivation, Raman spectroscopy data was used to analyze the 450-1800 cm -1 and 2600-3100cm -1 The entire range of process variable measurement values ​​(excluding 1800 <x<2600cm -1 In parallel with the Raman spectroscopy measurements, BioProfile The analyzer performed off-line spectral measurements. Raman spectral measurements were taken every 15-60 minutes, and off-line process variable measurements were taken approximately 4 hours, 24 hours, 48 ​​hours, and 72 hours after the cryopreserved cell stock was introduced into the expansion chamber 110. Figure 5 Data from two spectral measurements are presented along with the target VCD (4.0 x 10 6 cells / ml).

[0060] like Figure 5 As seen in Figure 2, the two spectral data sets are relatively consistent with each other at the 24-hour mark corresponding to the second offline measurement, but begin to diverge at approximately the 32-hour mark. Although the two data sets converge again at approximately the 72-hour mark corresponding to the fourth offline measurement, a roughly 2.0x10 6 This shift is significant. For example, when the goal is to inoculate 4.0x10 6If the ACU 155 were to rely on Raman spectroscopy data, then automatic inoculation of the bioreactor 140 would occur at approximately 44 hours if the VCD of the bioreactor 140 were to be 0.5 x 10 cells / mL. However, based on the offline spectroscopy data, automatic inoculation at 44 hours would be premature because the VCD at that time would actually be approximately 2.5 x 10 6 cells / ml. In reality, assuming the offline spectral data is accurate, the target VCD for inoculation will not be achieved until approximately 60 hours. Thus, automated inoculation based on Raman spectral data will occur at a suboptimal VCD, which can result in a significant shortfall in the output of the production process.

[0061] For accurate and reliable inoculation of bioreactors, e.g. Figure 5 Drifts in the VCD can be problematic. For example, if there are errors in the Raman spectroscopy data, automatic seeding may be performed too early, before the optimal VCD has actually been achieved. On the other hand, when offline process variable measurements are taken only once every few hours, manual seeding may be performed too late, for example, by not performing manual seeding at the 48-hour mark where the VCD falls below the target (e.g., Figure 5 Instead, manual inoculation is performed at the 72-hour mark after the target VCD has been exceeded ( Figure 5 ).

[0062] To improve the accuracy and reliability of Raman spectroscopy measurements, cell injection studies were performed with CHO cell suspensions at six different densities, as noted in Table 1 below:

[0063]

[0064] Raman spectroscopy measurements were repeated three times at six different cell densities, duplicating the scan times used in the upstream Raman data collection, and using the Variable Impact Predicted (VIP) curve to identify those wavelength regions of the Raman spectroscopy data where the strongest values ​​were observed to correlate with the cell density measurements. -1 ; 1260-1470cm -1 ; 1650-1840cm -1 ; and 2825-3080cm -1 The wavelength region is annotated as most accurately reporting cell density ( Figure 6 ).

[0065] Figure 7 Results showing an example of a comparison between measurements obtained from two spectral datasets, where the first spectral dataset is based on Raman spectroscopy measurements performed according to conventional practice, and the second spectral dataset is based on Raman spectroscopy measurements performed according to novel practice, both datasets being plotted relative to offline spectral data. Conventional Raman spectroscopy measurements span 450-1800 cm -1and 2600-3100cm -1 The novel Raman spectroscopy measurements span the wavelength range of 800-850 cm -1 ; 1260-1470cm -1 ; 1650-1840cm -1 ; and 2825-3080cm -1 It can be seen that although both Raman data sets show some shift relative to the offline measurements, the measurements performed according to the novel practice in this paper show significantly less inter-sample variability.

[0066] In another aspect of the invention, in addition to the computer system 150 maintaining the predicted process variables from the multivariate model, the computer system 150 may also maintain a time series of the predicted process variables in the expansion chamber 110 or bioreactor 140. This time series may be subjected to noise reduction techniques that may be predictive or retrospective. When incorporating noise reduction techniques, the ACU 155 may be a locally weighted regression model such as Cleveland, WS, Robust Locally Weighted Regression and Smoothed Scatter Points. Journal of the American Statistical Association, Vol. 74, No. 368 (1979): 829-836, which is incorporated herein by reference in its entirety. For example, the weighting function may be a fifth order polynomial that de-emphasizes earlier points in the batch and emphasizes more recent points. Prior knowledge suggests that the cell growth in N-1 is S-shaped, which suggests that the intermediate growth region is approximately linear. The local regression model may estimate this linearity and extrapolate to update the predicted inoculation time, and also calculate the time between measurements where the estimated process value will equal the trigger value. This approach reduces Figure 8 Variations in predictions from a single Raman measurement are shown.

[0067] Although the present invention is described with reference to specific embodiments, those skilled in the art will understand that the foregoing disclosure merely sets forth exemplary embodiments; that the scope of the present invention is not limited to the disclosed embodiments; and that the scope of the present invention may encompass additional embodiments that include various changes and modifications relative to the examples disclosed herein without departing from the scope of the invention as defined in the appended claims and their equivalents.

[0068] To the extent necessary to understand or complete the present disclosure, all publications, patents, and patent applications mentioned herein are expressly incorporated herein by reference to the same extent as if each were individually so incorporated. No license, express or implied, shall be granted under any patent incorporated herein.

[0069] The present invention is not limited to the exemplary embodiments shown herein, but is characterized by the appended claims.

Claims

1. A system for controlling a seed cultivation process, comprising: an expansion chamber for receiving an initial stock of cells for expansion into a living cell culture; a bioreactor in fluid communication with the expansion chamber for receiving a living cell culture; a pump for effecting transfer of a living 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 detector for monitoring a cell expansion process within the expansion chamber using Raman spectrometry, the Raman spectrometer being adapted to generate Raman spectral data; a multivariate model that provides predictions of one or more process variables based on Raman spectroscopy data; as well as a computer system in signal communication with the Raman spectrometer for receiving Raman spectral data and in signal communication with the pump for controlling operation of the pump to effect transfer of the living cell culture from the expansion chamber to the bioreactor, wherein the Raman spectrometer is adapted to generate the Raman spectral data, and the computer system processes the Raman spectral data received from the Raman spectrometer to generate a multivariate model of the one or more process variables, and the computer system is adapted to compare a process variable prediction to one or more predefined process set points to determine whether the one or more process variable predictions have met a predefined trigger value, wherein the computer system is adapted to control the pump to perform the automatic transfer of the cell culture volume from the expansion chamber to the bioreactor upon determining that one or more process variables predicted in the Raman spectroscopy data have met the predefined trigger value, wherein the multivariate model is a partial least squares regression model, and wherein the computer system is adapted to use a process variable prediction based on the 800-850 cm -1 ; 1260-1470cm -1 ; 1650-1840cm -1 ; and 2825-3080cm -1 Raman spectroscopy data in the wavelength region of 100 nm provides predictions of process variables.

2. The system of claim 1, wherein the computer 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 process variable other than the VCD.

3. The system according to claim 2, wherein the second predefined trigger value is a lactate level value.

4. A method for automatically inoculating a bioreactor using the system according to claim 1, comprising: expanding a reserve of cells in the expansion chamber; generating Raman spectroscopy data using the Raman spectrometer to provide the data to a multivariate model predicting one or more process variables of cell expansion in the expansion chamber; the computer system comparing a process variable prediction from the multivariable model to a predefined process setpoint at the computer system; When the computer system determines that one or more process variable predictions from the multivariate model meet predefined trigger values, the computer system controls the pump to automatically inoculate the bioreactor with the living cell culture from the expansion chamber.

5. The method according to claim 4, wherein The predefined trigger value is a live cell density value.

6. The method according to claim 5, wherein The predefined trigger value is set to be equal to a predetermined target live cell density minus 10% or a live cell density value within this range.

7. The method according to claim 4, wherein The predefined trigger value is a lactate level value.

8. The method according to claim 7, wherein The predefined trigger value is set to be equal to a predetermined minimum lactate level + 10% or a lactate level value within this range.

9. The method according to claim 4, wherein The predefined trigger value is the VCD predicted by the model.

10. The method according to claim 9, wherein The predefined trigger value is set to be equal to a predetermined maximum cell growth rate minus 10% or a model-predicted VCD value within this range.

11. The method according to claim 4, wherein The computer system stores a first predefined trigger value based on a predetermined target viable cell density and stores a second predefined trigger value based on a predetermined process variable other than the viable cell density, and The computer system is adapted to control the pump to automatically inoculate the bioreactor with the viable cell culture from the expansion chamber when the computer system determines that a process variable prediction from the multivariate model meets either of the first or second predefined trigger values.

12. The method according to claim 11, wherein The second predefined trigger value is a lactate level value.

13. The method according to claim 11, wherein The first predefined trigger value is a VCD value predicted by the model.

14. The method according to claim 4, wherein The computer system processes Raman spectral data received from the Raman spectrometer to generate a multivariate model of the one or more process variables, and obtains process variable measurements from the multivariate model for comparison with the predefined trigger value.

15. The method according to claim 14, wherein The computer system generates a partial least squares regression model.

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