Dynamic Nutrition Control Process

Abstract

Materials and methods are provided for controlling nutrient supply in a cell culture process. A sample is received from a bioreactor containing a cell culture. A nutrient residual amount is determined from the received sample. A cell growth rate between the current culture day and the previous day is calculated. An integrated viable cell density for the next day is predicted. A nutrient target for the next day is calculated. Nutrients are supplied to the bioreactor according to the calculated nutrient target.

Description

[Technical field]

[0001] (CROSS REFERENCE TO RELATED APPLICATIONS) This application is a Patent Cooperation Treaty (PCT) application of U.S. Provisional Application No. 63 / 174,143, filed April 13, 2021, which claims priority under Article 8 of the PCT, the entirety of which is incorporated herein as if fully set forth below. [Background technology]

[0002] The productivity of cell culture depends on optimizing the culture medium management to allow high cell densities. Nutrient supply is a key parameter for process optimization. High cell density processes can require significant amounts of nutrients, with daily requirements varying with cell type or density.

[0003] Glucose feeding is a key factor for bioreactor process optimization. High cell density and productivity often means the need to feed significant amounts of glucose (usually more than 10 g / L per day at peak cell density). Existing glucose feeding algorithms rely on historical databases, which consume computational power and resources.

[0004] The daily glucose requirement for a cell line can vary widely between cell lines. Changes in process parameters such as production bioreactor target seeding density and basal medium glucose concentration can affect the daily glucose requirement. Summary of the Invention [Problem to be solved by the invention]

[0005] There is a need for a cell culture solution that automates a nutrient feeding process, such as a glucose feeding process, without relying on a historical database. There is also a need for a predictive cell-based glucose algorithm that takes into account changes in process parameters. [Means for solving the problem]

[0006] There is a need for improved materials and methods for predicting daily nutrient target requirements for a given cell line, which are addressed by the present invention. One aspect of the disclosed technology relates to a method for controlling nutrient supply in a cell culture process. A sample may be received from a bioreactor containing a cell culture. A residual amount of a nutrient may be measured from the received sample. A variable cell density for the current day may be determined based on the residual amount of the measured nutrient. A cell growth rate between the current culture day and the previous day may be calculated based at least on the determined live cell density for the current day. A live cell density for the next day may be predicted based at least on the calculated cell growth rate. An integrated live cell density for the next day may be predicted based at least on the predicted live cell density for the next day. A nutrient target for the next day may be calculated based at least on the predicted integrated live cell density for the next day. Nutrients may be supplied to the bioreactor according to the calculated nutrient target for the next day.

[0007] In one embodiment, the cell proliferation rate may be calculated between the current culture day and the previous day based on the determined viable cell density on the current day and the viable cell density measured on the previous day.

[0008] In one embodiment, the viable cell density for the next day may be predicted based on the calculated cell proliferation rate and the determined viable cell density for the current day.

[0009] In one embodiment, the integrated viable cell density for the next day may be predicted based on the predicted viable cell density for the next day, the determined viable cell density for the current day, and the integrated viable cell density for the current day.

[0010] In one embodiment, a specific nutrient consumption rate per day for the previous day may be calculated based at least on the calculated cell growth rate. An amount of a nutrient to be consumed between the current day and the next day may be predicted based on the specific nutrient consumption rate per day. A nutritional goal may be calculated based at least on the predicted amount of a nutrient to be consumed.

[0011] In one embodiment, a specific nutrient consumption rate per day for the previous day may be calculated based on the calculated cell growth rate, the integrated viable cell density for the current day, and the integrated viable cell density for the previous day.

[0012] In one embodiment, the amount of a nutrient consumed between the current day and the next day may be predicted based on a particular nutrient consumption rate per day, the predicted integrated viable cell density for the next day, and the integrated viable cell density for the current day.

[0013] In one embodiment, nutritional goals may be calculated based on predicted nutrients consumed and empirically determined nutrients to maintain value.

[0014] In one embodiment, one or more of the viable cell density measured the previous day, the integrated viable cell density for the current day, the integrated viable cell density for the previous day, and the empirically determined nutrients to maintain the values ​​may be read from a non-transitory computer readable medium.

[0015] In one embodiment, the nutrients may be selected from glucose, glutamate, galactose, lactate, and glutamine.

[0016] In one embodiment, the nutrient may include one or more simple sugars.

[0017] In one embodiment, the residual nutrient measurements may include analyzing nutrient concentrations within the bioreactor.

[0018] In one embodiment, the residual nutritional measurements may include taking one or more of an offline nutritional measurement and an in-line nutritional measurement.

[0019] In one embodiment, residual nutrient measurements may be performed by one or more of a NovaFlex instrument and a Raman probe.

[0020] In one embodiment, the bioreactor may be one or more of a Chinese hamster ovary (CHO) cell bioreactor and a 5L bioreactor. Other mammalian cell types that may be used in the manufacture of biologics other than CHO include recombinant cells and the like. Non-limiting examples of such mammalian cell types include HEK, 293 and PerC6. The process may also be used with other non-mammalian cell types such as yeast and bacteria.

[0021] In one embodiment, the cells in the bioreactor may be mammalian cells.

[0022] In one embodiment, the cell may be a CHO cell.

[0023] Another aspect of the disclosed technology relates to a method of controlling glucose supply in a cell culture process. A sample may be received from a bioreactor containing a cell culture. A residual amount of glucose may be measured from the received sample. A variable cell density for a current day may be determined based on the measured residual amount of glucose. A cell growth rate between a current culture day and a previous day may be calculated based at least on the determined live cell density for the current day. A live cell density for the next day may be predicted based at least on the calculated cell growth rate. An integrated live cell density for the next day may be predicted based at least on the predicted live cell density. A glucose target for the next day may be calculated based at least on the predicted integrated live cell density for the next day. Glucose may be supplied to the bioreactor according to the calculated glucose target for the next day.

[0024] Further features of the present disclosure and advantages offered thereby will be described in more detail below with reference to specific embodiments illustrated in the accompanying drawings, in which like elements are designated by like reference numerals. [Brief description of the drawings]

[0025] Reference is now made to the accompanying drawings, which are not necessarily drawn to scale, which are incorporated in and constitute a part of this disclosure, and which illustrate various implementations and aspects of the disclosed technology and, together with the description, serve to explain the principles of the disclosed technology. [Figure 1] FIG. 1 is a schematic diagram of an exemplary environment that can be used to implement one or more embodiments of the present disclosure. [Diagram 2] FIG. 1 is a schematic diagram of an exemplary environment that can be used to implement one or more embodiments of the present disclosure. [Diagram 3] 1 is a flow chart of a glucose algorithm in accordance with one aspect of the disclosed technology. [Figure 4] 1 is an example of an automated process for delivering glucose, according to one aspect of the disclosed technology. [Diagram 5] FIG. 1 is a block diagram of a nutritional delivery control system in accordance with one aspect of the disclosed technology. [Figure 6] 1 is a chart showing predicted-actual glucose consumed and average versus culture day. [Figure 7] 1 is a chart showing measured glucose and average versus culture day. [Figure 8] 1 is a chart showing glucose measurements versus algorithm. [Figure 9] 1 is an exemplary flow chart illustrating a process for controlling nutrient supply in a cell culture process. [Figure 10] 1 is another exemplary flow chart showing a process for controlling glucose supply in a cell culture process. [Figure 11] FIG. 1 is a chart showing glucose measurements versus algorithm in a development ("Ambr250") scale bioreactor, a pilot scale bioreactor, and a good manufacturing process ("GMP") scale bioreactor for growing monoclonal antibody 1 ("MAB1"). [Figure 12] 1 is a chart showing glucose measured for MAB1 and the average versus culture day. [Figure 13]FIG. 1 is a chart showing glucose measurements versus algorithm in a development scale (Ambr250), pilot scale, and GMP scale bioreactor for growing bispecific antibody 1 ("BsAb1"). [Figure 14] 1 is a chart showing glucose measured for BsAB1 and the average versus culture day. DETAILED DESCRIPTION OF THE PREFERRED EMBODIMENTS

[0026] Some implementations of the disclosed technology are more fully described with reference to the accompanying drawings. However, the disclosed technology may be embodied in many different forms and should not be construed as being limited to the embodiments described herein. The components described herein as constituting various elements of the disclosed technology are intended to be exemplary and not limiting. Many suitable components that perform the same or similar functions as the components described herein are intended to be included within the scope of the disclosed electronic device and method. Other components not described herein may include, for example, but are not limited to, components developed after the development of the disclosed technology.

[0027] It should also be understood that a reference to one or more method steps does not exclude the presence of additional or intervening method steps between those steps explicitly identified.

[0028] It should be noted that unless otherwise clear from the context, the term "a" or "an" entity refers to one or more of that entity. For example, "amino acid" is understood to represent one or more proteins. Thus, the terms "a" (or "an"), "one or more," and "at least one" can be used interchangeably herein.

[0029] The term "nutrient" can refer to any compound, molecule, or substance that an organism uses to survive, grow, or otherwise add to biomass. Examples of nutrients can include carbohydrate sources (e.g., simple sugars such as glucose, galactose, maltose, or fructose, or more complex sugars), amino acids, vitamins (e.g., B vitamins (e.g., B12), vitamin A, vitamin E, riboflavin, thiamine, and biotin). In the present invention, one or more nutrients can be utilized as surrogate molecules to determine the amount of total nutrient medium to be added to the bioreactor. In some embodiments, the term "nutrient" can refer to simple sugars, vitamins, and amino acids.

[0030] The term "amino acid" may refer to any of the 20 standard amino acids, namely glycine, alanine, valine, leucine, isoleucine, methionine, proline, phenylalanine, tryptophan, serine, threonine, asparagine, glutamine, tyrosine, cysteine, lysine, arginine, histidine, aspartic acid, and glutamic acid, their single stereoisomers, and racemic mixtures thereof. The term "amino acid" may also refer to known non-standard amino acids, such as 4-hydroxyproline, hydroxyproline, s-phosphocysteine, phosphotyrosine, ε-N,N,N-trimethyllysine, 3-methylhistidine, 5-hydroxylysine, O-phosphoserine, γ-carboxyglutamate, ε-N-acetyllysine, ω-N-methylarginine, N-acetylserine, N,N,N-trimethylalanine, N-formylmethionine, γ-aminobutyric acid, histamine, dopamine, thyroxine, citrulline, ornithine, β-cyanoalanine, homocysteine, azaserine, and S-adenosylmethionine. In some embodiments, the amino acid is glutamate, glutamine, lysine, tyrosine, or valine. In some embodiments, the amino acid is glutamate or glutamine.

[0031] The terms "nutrient medium", "feed medium", "feed", "total feed", and "total nutrient medium" may be used interchangeably and may include a "complete" medium used to propagate and grow a cell line and add biomass to the cell line. Nutrient media may be distinguished from simple media or substances that are not sufficient in themselves to propagate and grow a cell line. Thus, for example, glucose or simple sugars are not in themselves nutrient media, since they are not sufficient to grow and grow a cell line in the absence of other necessary nutrients.

[0032] In one embodiment, the disclosed system includes an automated process with feedback control during the cell culture process.

[0033] In one embodiment, both lactate and glucose are measured and step changes (0.5 g / L at a time) are made to the glucose target.

[0034] In one embodiment, only glucose is measured. Using only glucose streamlines the process in the lab and makes it easier for bioreactor operators to use algorithms to properly dose glucose.

[0035] In one embodiment, the glucose goal is calculated once a day. The algorithm may be updated to allow for multiple measurements, and the goal is for the next 24 hours.

[0036] The concept of the algorithm comes from taking the thought process used by a human operator and translating it into a flow diagram that uses measured glucose and lactate to make step changes to increase or decrease glucose targets.

[0037] The glucose algorithm of the present disclosure tends to be more accurate than a human operator, as a human operator tends to either underestimate glucose (leading to a depletion event) or overestimate the glucose target to ensure that glucose is not depleted. The algorithm of the present disclosure is better at controlling glucose to a desired concentration than a human operator could possibly control.

[0038] The methods of the present disclosure may be used for other monosaccharides, nutrients, etc. Non-limiting examples of such monosaccharides or nutrients include glutamate, galactose, lactate, and glutamine.

[0039] According to the technology of the present disclosure, glucose can be minimized to avoid glycation.

[0040] In some embodiments, additional nutrient medium may be added to the bioreactor cell culture in an amount sufficient to maintain a substantially stable concentration of amino acids throughout the bioreactor process.

[0041] In some embodiments, the bioreactor cell culture can include Chinese Hamster Ovary (CHO) cells, HEK-293 cells, or VERO cells. In some embodiments, the biologic can be an antibody or antibody-like polypeptide.

[0042] In one embodiment, the method of the present invention may be carried out in the presence of any cell culture medium. For example, the bioreactor process may be carried out in the presence of serum-free medium, protein-free medium (including but not limited to protein-free medium containing protein hydrolysates), or chemically defined medium.

[0043] A variety of analytical devices may be used in the present invention. Analytical devices may include any instrument or process capable of detecting and / or quantifying surrogate molecules or markers, such as amino acids or other substituents of cell culture media (e.g., vitamins, minerals, ions, sugars, etc.). Analytical devices may be devices for performing gas chromatography, HPLC, cation exchange chromatography, anion exchange chromatography, size exclusion chromatography, enzyme catalysis assays, and / or chemical reaction assays.

[0044] As shown in FIG. 1, the production reactor 102 may include a mammalian cell culture. The production reactor 102 may be a bioreactor, a cell culture reactor, or a sample bioreactor. The production reactor 102 may be at least one of a well plate, a shake flask, a benchtop vessel, and a commercial-scale (e.g., 15 kL) stainless steel reactor. A reaction sample may be removed from the production reactor 102 and sent to a nutrient supply control system 110. The nutrient supply control system 110 may include a glucose measurement system 104 that performs glucose measurements. The glucose measurements may be performed either offline or online. The nutrient supply control system 110 may also include a glucose target prediction system 106 that receives glucose measurements from the glucose measurement system 104 and performs a prediction of a glucose target. The nutrient supply control system 110 may include a glucose calculation system 108 that may then use the predicted glucose target to calculate the amount of glucose to add and send instructions to the nutrient supply system 120. In one embodiment, the processes performed by the glucose measuring system 104, the glucose goal prediction system 106, and the glucose calculation system 108 may be accomplished by one or more processors.

[0045] The nutrient supply system 120 may include a pump 111 that supplies the correct amount of glucose from a glucose supply 112 to the production reactor 102 .

[0046] 2 is a schematic diagram of an exemplary environment that may be used to implement one or more embodiments of the present disclosure. A nutrient supply control system 110 may communicate with the production reactor 102 and the nutrient supply system 120 via a network 180. The nutrient supply control system 110 may instruct the nutrient supply system 120 to supply one or more nutrients to the production reactor 102.

[0047] FIG. 3 shows a flow diagram of the glucose algorithm. At 302, the production reactor 102 can feed a sample. At 304, the glucose measurement system 104 can receive the sample and perform a glucose measurement. At 306, the glucose target prediction system 106 can predict the amount of glucose to add to the production reactor 102. At 308, the glucose calculation system 108 can calculate and output the correct amount of glucose to add. At 310, the correct amount of glucose can be fed to the production reactor 102. This algorithm can be applicable to pre-cultures. For example, the algorithm can be used to feed glucose to an enhanced seed train. The algorithm can also be applicable to N-1 perfusion processes and production perfusion processes.

[0048] 4 shows an example of an automated process. At 402, the production reactor 102 may feed a sample. At 404, a glucose measurement may be performed, for example, an in-line glucose measurement (e.g., NovaFlex) at 404a or a Raman probe at 404b. An instrument may be used to measure offline pH as well as glucose and lactate. At 406, the nutrient feed control system 110 may predict a glucose feed target. At 408, the reactor control station may process the predicted glucose feed target. At 410, the controller may calculate a glucose feed rate. At 412, the controller may feed glucose to the production reactor 102.

[0049] The methods disclosed herein may increase production in a subsequent bioreactor cell culture. In some embodiments, the methods may increase the amount of antibody (or other biologic) produced or reduce the production time of the antibody (or other biologic) in an antibody (or other biologic) producing bioreactor cell culture. The methods may include analyzing a culture sample (which may or may not involve extracting a sample from the bioreactor) by an automated sampling device (e.g., offline, online, in-line, or at-line sample analysis, etc.). The methods may include analyzing a culture sample (e.g., concentration of residual glucose) by an automated analysis device to generate data representative of the amount of a nutrient (or other surrogate marker). The methods may include processing the generated data (e.g., obtained from the analysis of residual glucose from the sample) with an algorithm or computer-based processing program, where the processed data is used to determine an amount of additional nutrient medium to add to the bioreactor. The methods may include adding the determined amount of nutrient medium determined by the automated feeding device to the bioreactor. The methods may include recording the time and amount of each nutrient medium addition.

[0050] Mammalian cells may include any mammalian cell that can be grown in culture. Exemplary mammalian cells include, for example, CHO cells (CHO-K1, CHOK1SV®, CHO DUKX-B11, CHO DG44, etc.), VERO, BHK, HeLa, CV1 (Cos, Cos-7, etc.), MDCK, 293, 3T3, C127, myeloma cell lines (especially murine), PC12, HEK-293 cells (HEK-293T and HEK-293E, etc.), PERC6, Sp2 / 0, NS0, and W138 cells. Mammalian cells derived from any of the aforementioned cells may also be used. In some embodiments, the bioreactor cell culture may include Chinese Hamster Ovary (CHO) cells, HEK-293 cells, or VERO cells.

[0051] The steps of the disclosed methods may be repeated and may occur at various intervals. In some embodiments, the steps disclosed herein may be repeated more than 10 times throughout the bioreactor process, or 10 to 1000 times, 20 to 500 times, or 30 to 100 times throughout the bioreactor process. In some embodiments, the steps may be repeated about every 4 minutes, 10 minutes, 30 minutes, 60 minutes, 2 hours, 3 hours, 6 hours, 8 hours, 12 hours, 16 hours, 18 hours, or 24 hours throughout the bioreactor process, or about every 4 to 18 hours, or about every 10 minutes to about 6 hours throughout the bioreactor process. In certain embodiments, the method includes measuring the amount of residual nutrients (e.g., residual glucose) once a day, with a target concentration of nutrient (glucose) produced over a period of about one day or after about 24 hours. In certain embodiments, the method includes measuring the amount of remaining nutrient (e.g., remaining glucose) multiple times per day, for example two, three or four times per day, with a target concentration of the nutrient (e.g., glucose) produced over a measurement period of about 24 hours.

[0052] The steps of the methods disclosed herein can be performed in a relatively short time, i.e., sampling, analysis, and addition of additional nutrient medium can be performed relatively quickly, in some embodiments, the steps of the methods disclosed herein are performed within about 1 minute to about 2 hours.

[0053] In some embodiments, the steps of the methods of the present disclosure are performed by one or more automated devices. The terms "automatic," "automatically," or "automated" refer to one or more mechanical devices that perform one or more tasks without human intervention or action, except for that which may be necessary to initially prepare one or more devices for task execution or to maintain the automatic operation of one or more devices. A "mechanical device" that automatically performs one or more tasks may optionally include a computer and necessary instructions (code) for processing collected data that may be used therein to make decisions that control and direct the execution of one or more devices, for example, in controlling the timing, duration, frequency, type, and / or characteristics of the tasks performed.

[0054] In various embodiments, "offline" analysis refers to permanently removing a sample from the production process and analyzing the sample at a later time such that the data analysis does not convey real-time or near real-time information about in-process conditions. In some embodiments, one or more analytical devices are used offline.

[0055] In one embodiment, the analytical device (or a sensor portion connected thereto) may be directly incorporated into the bioreactor or purification unit, or the device or sensor portion may be separated from the bioreactor or purification unit by a suitable barrier or membrane.

[0056] In some embodiments, the analytical device may be a kit, e.g., a test strip, that can be placed in contact with a sample to allow for rapid determination of cell concentration. In some embodiments, the kit may include a surrogate marker or a substrate that generates a chemical and / or enzyme-linked reaction that generates a detectable signal in the presence of a particular concentration of the surrogate marker. The detectable signal may include, for example, a colorimetric change or other visual signal. In some embodiments, the analytical device may be a disposable analytical device, e.g., a disposable test strip. Such kits may lend themselves to ease of operation and reduced cost compared to other larger, more complex analytical devices. Such kits may also be useful in small-scale cell culture growth to determine optimal culture health and productivity.

[0057] In various embodiments, the glucose algorithms and methods described herein may be effective to achieve a post-prandial day 1 residual glucose concentration of 0-3 g / L, 0.5-2 g / L, 2-5 g / L, less than 1 g / L, or less than 2 g / L.

[0058] A variety of biologics may be contemplated in the present invention. In some embodiments, the biologic may be an antibody, a recombinant protein, a glycoprotein, or a fusion protein. In some embodiments, the biologic may be a soluble protein. In some embodiments, the biologic may be an antibody, an antibody fragment, or a modified antibody (e.g., a multivalent antibody, a domain deleted antibody, a multimeric antibody, a hinge modified antibody, a stabilized antibody, a multispecific antibody, a linear antibody, an scFv, a linked ScFv antibody, a multivalent linear antibody, a multivalent antibody without Fc, a Fab, a multivalent Fab, etc.).

[0059] Reference will now be made in detail to exemplary embodiments of the disclosed technology, examples of which are illustrated in the accompanying drawings and disclosed herein. For convenience, the same reference numerals will be used throughout the drawings to refer to the same or like parts.

[0060] 5 is a block diagram of a nutritional delivery control system 110 according to one aspect of the disclosed technology. The nutritional delivery control system 110 may include one or more processors 510. The processes performed by the glucose measurement system 104, the glucose goal prediction system 106, and the glucose calculation system 108 may be effectuated by the one or more processors 510.

[0061] With reference to FIG. 5, the processor 510 may include one or more of a microprocessor, a microcontroller, a digital signal processor, a co-processor, etc., or combinations thereof, capable of executing stored instructions and operating on stored data. The processor 510 may be one or more known processing devices, such as the Pentium™ family of microprocessors manufactured by Intel™ or the Turion™ family of microprocessors manufactured by AMD™. The processor 510 may be a single core or a multi-core processor that simultaneously performs parallel processing. For example, the processor 510 may be a single core processor configured with virtual processing technology. In certain embodiments, the processor 510 may use a logical processor to simultaneously execute and control multiple processes. The processor 510 may implement virtual machine technology, or other similar known technology, to achieve the ability to execute, control, launch, manipulate, store, etc., multiple software processes, applications, programs, etc. One skilled in the art will appreciate that other types of processor configurations may be implemented that provide the capabilities disclosed herein.

[0062] The non-transitory computer readable medium 520 may, in some implementations, include one or more suitable types of memory (e.g., volatile or non-volatile memory, random access memory (RAM), read only memory (ROM), programmable read only memory (PROM), erasable programmable read only memory (EPROM), electrically erasable programmable read only memory (EEPROM), magnetic disk, optical disk, floppy disk, hard disk, removable cartridge, flash memory, redundant array of independent disks (RAID), etc.) for storing an operating system 522, application programs (e.g., a web browser application, a widget or gadget engine, and / or other applications as appropriate), files containing executable instructions and data. In one embodiment, the processing techniques described herein are implemented as a combination of executable instructions and data in the non-transitory computer readable medium 520. The non-transitory computer readable medium 520 may include one or more memory devices that store data and instructions used to execute one or more features of the embodiments of the present disclosure. The non-transitory computer readable medium 520 may also include any combination of one or more databases controlled by a memory controller device (e.g., a server, etc.) or software, such as a document management system, a Microsoft™ SQL database, a SharePoint™ database, an Oracle™ database, a Sybase™ database, or other relational or non-relational databases. The non-transitory computer readable medium 520 may include software components that, when executed by the processor 510, perform one or more processes consistent with embodiments of the present disclosure. In some embodiments, the non-transitory computer readable medium 520 may include a database 524 for performing one or more of the processes and functions associated with embodiments of the present disclosure. The non-transitory computer readable medium 520 may include one or more programs 526 for performing one or more functions of embodiments of the present disclosure.Additionally, the processor 510 may execute one or more programs 526 that are located remotely from the system 110. For example, the system 110 may access one or more remote programs 526 that, when executed, perform functions related to the disclosed embodiments.

[0063] System 110 may also include one or more I / O devices 560, which may comprise one or more interfaces for receiving signals or input from devices and providing signals or output to one or more devices, allowing data to be received and / or transmitted by system 110. For example, system 110 may include an interface component that may provide an interface to one or more input devices, such as one or more keyboards, mouse devices, touch screens, track pads, track balls, scroll wheels, digital cameras, microphones, sensors, etc., that allow system 110 to receive data from one or more users. System 110 may include a display, screen, touch pad, etc., for displaying images, videos, data, or other information. I / O devices 560 may include a graphical user interface 562.

[0064] In an exemplary embodiment of the techniques of this disclosure, system 110 may include any number of hardware and / or software applications executing to facilitate any operation. One or more I / O interfaces 560 may be utilized to receive or collect data and / or user instructions from a wide variety of input devices. Received data may be processed by one or more computer processors and / or stored in one or more memory devices as desired in various implementations of the techniques of this disclosure.

[0065] Network 180 may include a network of interconnected computing devices, more commonly referred to as the Internet. Network 180 may be of any suitable type, including individual connections over the Internet, such as cellular or WiFi networks. In some embodiments, network 180 may connect terminals, services, and mobile devices using radio frequency identification (RFID), near field communication (NFC), Bluetooth™, low energy Bluetooth™ (BLE), WiFi™, ZigBee™, ambient backscatter communication (ABC) protocol, USB, direct connections, such as WAN, or LAN. Because the information transmitted may be personal or sensitive, security concerns may dictate that one or more of these types of connections be encrypted or otherwise protected. However, in some embodiments, the information transmitted may not be personal, and thus a network connection may be selected for convenience over security. Network 180 may comprise any type of computer network configuration used to exchange data. For example, network 180 may be the Internet, a private data network, a virtual private network using a public network, and / or other suitable connections that allow components in the system environment to send and receive information between components of system 100. Network 180 may also include a public switched telephone network (PSTN) and / or a wireless network. Network 180 may also include local networks including any type of computer networking configuration used to exchange data within a localized area, such as WiFi, Bluetooth™, Ethernet, and other suitable network connections that allow components of the system environment to interact with one another.

[0066] The processor 510 can implement a predictive cell-based glucose algorithm to estimate the daily glucose target requirement. The processor 510 may calculate the daily glucose target for each bioreactor for each culture day. To predict the glucose requirements needed for the next day, the algorithm can make key assumptions: the cell growth rate and glucose consumption rate of the previous day remain the same for the next day. In making these assumptions, the viable cell density (VCD) of the next day can be predicted based on the VCD measurement of the current day and the growth rate from the previous day. The IVCD of the next day can be predicted, and the expected total daily glucose consumption can be calculated accordingly.

[0067] The presently disclosed algorithms do not have to be based on a historical database. By using the disclosed algorithms described herein, glucose levels can be maintained between 1.5 and 3.5 g / L.

[0068] 1. Calculation of Cell Proliferation Equations 1-3 show how data from the current and previous days can be utilized to predict the culture growth for the next day. The assumption underlying this predicted IVCD is that the growth rate does not change appreciably over the course of just one day. The IVCD can be calculated using a mathematical average approach.

[0069] For culture day 0, the growth rate, predicted VCD, and predicted IVCD do not need to be calculated. Equation 1 can be used to determine the growth rate for all other culture days. Equations 2 and 3 show how the growth rate from the current day can be used to predict the VCD and IVCD for the next day.

[0070]

number

[0071] 2. Calculating Glucose Consumption Once calculated, the predicted IVCD can be applied to a glucose consumption calculation to estimate the amount of glucose needed for the next day. Equation 4 can be used to calculate the daily specific glucose consumption rate over the previous day using the IVCD to specific metabolic rate method. The predicted daily glucose to be consumed can then be calculated as the product of the specific glucose consumption rate from the current day and the difference between the predicted IVCD for the next day and the current IVCD, as shown in Equation 5. Finally, the glucose goal (GT n ) is the predicted daily glucose consumed (GC n+1 ) and an empirically determined glucose (GM n+1 ) may be considered as the sum of

[0072]

number

[0073] In standard practice, GM may be set at 2 g / L for each culture day, but the project team may adjust this value if deemed necessary in subsequent bioreactor runs to prevent glucose depletion or accumulation during culture.

[0074] 3. Verification The glucose algorithm disclosed herein has been evaluated in two different recombinant cell lines, a first host cell line and a second host cell line. These groups are selected to ensure accuracy of the algorithm for different host cell lines and process parameters (e.g., target seeding density, glucose concentration in basal medium). For example, the first cell line process bioreactor is seeded with 0.5×10 glucose in a basal medium containing 4.2 g / L glucose. 6 The second cell line bioreactor was seeded with 1.5 × 10 vc / mL in basal medium containing 12 g / L glucose. 6The cells are inoculated at a target seeding density of 100 vc / mL (unless otherwise indicated). Table 1 below lists the relevant details for the bioreactor runs in which the algorithm of the present disclosure is implemented and compared to existing glucose algorithms. Six bioreactor runs using recombinant cell lines are fed using the algorithm of the present disclosure, however, four of these reactors used a basal medium containing 12 g / L glucose and the other two reactors used a basal medium containing 4.2 g / L glucose. This distinction is highlighted in Figures 6-8 described below. Each of these six reactors is fed using the glucose algorithm disclosed herein.

[0075] [Table 1]

[0076] For each bioreactor run, a glucose target is calculated each day using either an existing glucose algorithm or the currently disclosed glucose algorithm. The VCD data and glucose measurements from the bioreactor runs are input into the glucose algorithm each day, and a predicted glucose consumption and glucose target are calculated for each bioreactor and culture day. After the run is completed, the actual amount of glucose consumed is calculated for each bioreactor and culture day. The actual amount of glucose consumed is subtracted from the predicted amount of glucose consumed to calculate the error in the predicted glucose consumption value for each bioreactor and culture day. Below, the individual data and summary statistics (mean and standard deviation) are reported in FIG. 6 for each culture day. FIG. 6 shows that the actual glucose consumed is subtracted from the predicted glucose consumed for each culture day to evaluate the performance of the glucose algorithm to predict the glucose consumption for the next day. The arrow indicates the day when the glucose feed was first added to the culture. The average difference for the currently disclosed algorithms is between the median residual glucose values ​​for the predictive cell-based glucose algorithms is -0.86 to 1.67 g / L compared to -1.35 to 1.94 g / L for the existing glucose algorithms. The averages for all culture days across each cell line and glucose algorithm are tabulated below in Table 1.

[0077] [Table 2]

[0078] For both cell lines, the present algorithm provides a more accurate prediction of glucose consumption. In addition, the present algorithm shows a higher predicted-actual glucose consumption compared to existing glucose algorithms. As a result, the present glucose algorithm is less likely to underestimate the glucose required for culture compared to existing glucose algorithms.

[0079] In-process glucose measurements are also reported below in Figures 7-8, which demonstrate that the glucose algorithm of the present disclosure does not result in extreme depletion or accumulation during the fed-batch process.

[0080] Referring to FIG. 7, glucose is measured per day to evaluate the ability of the glucose algorithm of the present disclosure to control glucose within the desired range of 1-3 g / L. The currently disclosed glucose algorithm appears to adequately control glucose as do existing glucose algorithms throughout the run. Day 11 and day 12 glucose values ​​for M20K069 are not considered due to an unintentional overfeed on day 10 by the operator. Additionally, reactors M20K196-M20K206 had inaccurate cell counts that likely affected the glucose levels on day 5. The arrows in FIG. 7 indicate the days when glucose feed is first required and added in the process.

[0081] 8, total glucose measurements over the number of culture days for which the algorithm determined the previous day's glucose feed show that most glucose daily measurements are maintained within the desired 1-3 g / L range. As a result, the glucose algorithm of the present disclosure can ensure that glucose levels are maintained at the desired levels throughout the entire process.

[0082] The desired range of glucose concentration after 24 hours of sampling and feeding is 1-3 g / L. The presently disclosed glucose algorithm provides good control of glucose levels with most of the data falling within the range of 1-3 g / L. The middle 50% of the data also falls within this range for all conditions, except for the present glucose algorithm with the first recombinant cell line where the middle 50% of the data was 1.33-3.21 g / L. In addition, the presently disclosed glucose algorithm maintains tighter control (narrower range) of glucose levels, except for the inconsistency with the second reactor M20K069 mentioned in FIG. 7.

[0083] Slightly higher glucose values ​​are maintained using the first recombinant cell line with the glucose algorithm of the present disclosure, meaning that the risk of glucose depletion is reduced. This is because the database used to build the history algorithm is 1.5×10 6 Compared to a target seeding density of 10 vc / mL, the existing algorithm achieved a seeding density of 0.5 × 10 6 This is likely because the model is based on running the bioreactor at a target seeding density of vc / mL. This highlights an important advantage of utilizing the glucose algorithm of the present disclosure. Specifically, the predictive nature of the model avoids the need to accumulate a large database of daily glucose consumption prior to running the algorithm.

[0084] Considering the above, the glucose algorithm of the present disclosure can reliably predict total glucose consumption per day and maintain glucose levels in the desired range of about 1-3 g / L. As the glucose algorithm of the present disclosure is implemented, more data can be aggregated with this database to validate these trends. Once glucose targets are generated and tested using the algorithm, the project team can choose to adjust the targets by increasing or decreasing residual glucose to maintain the value. If deemed necessary, the targets may be adjusted based on the measured daily glucose consumption in the project goal process.

[0085] FIG. 9 is an exemplary flow chart illustrating a process of controlling nutrient supply in a cell culture process. At 902, a sample may be received from a bioreactor containing a cell culture. At 904, a residual amount of a nutrient may be measured from the received sample. At 906, the processor 510 may determine a variable cell density for the current day based on the measured residual amount of the nutrient. At 908, the processor 510 may calculate a cell growth rate between the current culture day and the previous day based at least on the determined live cell density for the current day. At 910, the processor 510 may predict a live cell density for the next day based at least on the calculated cell growth rate. At 912, the processor 510 may predict an integrated live cell density for the next day based at least on the predicted live cell density for the next day. At 914, the processor 510 may calculate a nutrient target for the next day based at least on the predicted integrated live cell density for the next day. At 916, nutrients may be supplied to the bioreactor according to the calculated nutrient target for the next day.

[0086] In one embodiment, the processor 510 may calculate the cell proliferation rate between the current culture day and the previous day based on the determined viable cell density for the current day and the viable cell density measured on the previous day.

[0087] In one embodiment, the processor 510 may predict the viable cell density for the next day based on the calculated cell proliferation rate and the determined viable cell density for the current day.

[0088] In one embodiment, the processor 510 may predict the integrated viable cell density for the next day based on the predicted viable cell density for the next day, the determined viable cell density for the current day, and the integrated viable cell density for the current day.

[0089] In one embodiment, the processor 510 may calculate a specific nutrient consumption rate per day for the previous day based at least on the calculated cell growth rate. The processor 510 may predict an amount of a nutrient to be consumed between the current day and the next day based on the specific nutrient consumption rate per day. The processor 510 may calculate a nutritional goal based at least on the predicted amount of a nutrient to be consumed.

[0090] In one embodiment, the processor 510 may calculate a specific nutrient consumption rate per day for the previous day based on the calculated cell growth rate, the integrated viable cell density for the current day, and the integrated viable cell density for the previous day.

[0091] In one embodiment, the processor 510 may predict the amount of a nutrient to be consumed between the current day and the next day based on a particular nutrient consumption rate per day, the predicted integrated viable cell density for the next day, and the integrated viable cell density for the current day.

[0092] In one embodiment, the processor 510 may calculate nutritional goals based on predicted nutrients to be consumed and empirically determined nutrients to maintain value.

[0093] In one embodiment, the non-transitory computer readable medium 520 may store one or more of the viable cell density measured the previous day, the accumulated viable cell density for the current day, the accumulated viable cell density for the previous day, and empirically determined nutrients to maintain the values, which may be read from the non-transitory computer readable medium 520.

[0094] In one embodiment, the nutrients may be selected from glucose, glutamate, galactose, lactate, and glutamine.

[0095] In one embodiment, the nutrient may include one or more simple sugars.

[0096] In one embodiment, the residual nutrient measurements may include analyzing nutrient concentrations within the bioreactor.

[0097] In one embodiment, the residual nutritional measurements may include taking one or more of an offline nutritional measurement and an in-line nutritional measurement.

[0098] In one embodiment, residual nutrient measurements may be performed by one or more of a NovaFlex instrument and a Raman probe.

[0099] In one embodiment, the bioreactor may be one or more of a Chinese Hamster Ovary (CHO) cell bioreactor and a 5L bioreactor. Other mammalian cell types that may be used in the manufacture of biologics other than CHO include recombinant cells and the like. Non-limiting examples of such mammalian cell types include HEK, 293 and PerC6. The process may also be used with other non-mammalian cell types, such as yeast and bacteria.

[0100] In one embodiment, the cells in the bioreactor may be mammalian cells.

[0101] In one embodiment, the cell may be a CHO cell.

[0102] FIG. 10 is another exemplary flow chart showing a process of controlling glucose supply in a cell culture process. At 1002, a sample may be received from a bioreactor containing a cell culture. At 1004, a residual amount of glucose may be measured from the received sample. At 1006, the processor 510 may determine a variable cell density for the current day based on the measured residual amount of glucose. At 1008, the processor 510 may calculate a cell growth rate between the current culture day and the previous day based at least on the determined live cell density for the current day. At 1010, the processor 510 may predict a live cell density for the next day based at least on the calculated cell growth rate. At 1012, the processor 510 may predict an integrated live cell density for the next day based at least on the predicted live cell density for the next day. At 1014, the processor 510 may calculate a glucose target for the next day based at least on the predicted integrated live cell density for the next day. At 1016, glucose may be delivered to the bioreactor according to the calculated glucose target for the next day.

[0103] In another example, FIG. 11 is a chart showing glucose measurements versus the algorithm in a development scale bioreactor, a pilot scale bioreactor, and a GMP scale bioreactor for growing MAB1. The daily glucose targets developed for the GMP bioreactor process for MAB1 were predicted during the pilot (Ambr250) experiment using the algorithm. The algorithm provided a daily glucose feed target such that the residual glucose levels measured on subsequent culture days were 2 g / L. FIG. 11 shows the median difference between the control condition in the experimental design and the residual glucose concentration of 2 g / L as 0.75 g / L for the pilot (Ambr250) experiment. The predicted glucose targets were then used to control glucose between 1 g / L and 3 g / L for the pilot scale bioreactor experiment (250 L scale) and the GMP scale bioreactor (1000 L scale).

[0104] As shown in FIG. 12, both developmental and GMP scale processes can use the same targets identified in the pilot (Ambr250) experiment for growing MAB1. In some examples, the targets for days 7 and 8 can be reduced by 1.5 g / L for developmental and GMP scale bioreactor processes, respectively, compared to the pilot experiment to bring the glucose concentration within the control range of 1-3 g / L for each bioreactor process. FIG. 12 shows that by using the present algorithm, glucose was well controlled within the desired range for each bioreactor process. In FIG. 12, the dashed reference line represents the target control region of glucose (e.g., 1-3 g / L) to minimize product saccharification. The lines in FIG. 12 represent the average of multiple bioreactor runs.

[0105] In another example, the algorithm was used to develop a daily glucose target for the production bioreactor process of BsAb1, as shown in FIG. 13. A pilot (Ambr250) experiment was used to predict the daily glucose target. The algorithm provided a daily glucose feed target such that the residual glucose level measured on the subsequent culture day was 2 g / L. FIG. 13 shows the median difference between the control condition in the experimental design and the residual glucose concentration of 2 g / L as 0.62 g / L for the pilot (Ambr250) experiment. The predicted glucose target was then used to control glucose between 1 g / L and 3 g / L for the pilot scale bioreactor experiment (250 L scale) and the GMP scale bioreactor (1000 L scale).

[0106] As shown in Figure 14, both developmental and GMP-scale processes can use the same targets identified in the pilot (Ambr250) experiments for growing BsAb1. In some instances, small modifications (e.g., less than 1 g / L) were made to the daily glucose targets predicted by the algorithm to fine-tune the results of the developmental and GMP-scale processes to keep the glucose concentration within a control range of 1-3 g / L. Figure 14 shows that by using the algorithm, glucose was well controlled within the desired range for each bioreactor process. In Figure 14, the dashed reference line represents the target control region of glucose (e.g., 1-3 g / L) to minimize product saccharification. The lines in Figure 14 represent the average of multiple bioreactor runs.

[0107] Results utilizing the algorithm to control glucose feeding of MAB1 and BsAb1, shown in Figures 11-14, indicate that the algorithm can be effective to provide glucose targets that are robust and transferable across a variety of bioreactor scales, including pilot (Ambr250), development (250 L), and GMP (1000 L) scales.

[0108] Below is a list of abbreviations and definitions.

[0109] BioTD API may refer to biotherapeutic development active pharmaceutical ingredient.

[0110] BioTD CDS may refer to Biotherapeutic Development Cell and Developability Sciences.

[0111] ELN may refer to Electronic Laboratory Notebook.

[0112] GC n may refer to the glucose (g / L) consumed between the current day and the previous day.

[0113] GC n+1may refer to the glucose (g / L) consumed between the current day and the next day.

[0114] GM n+1 can refer to next day maintenance glucose (g / L).

[0115] GR n is the calculated cell proliferation rate (days) between the current day and the previous day. -1 )

[0116] GT n may refer to the glucose goal (g / L) for the current day.

[0117] HT can mean high titer.

[0118] The IQR may refer to the interquartile range.

[0119] IVC n is the cumulative viable cell density (cells / mL) calculated on the current day * It can refer to the day.

[0120] IVC n-1 is the cumulative viable cell density (cells / mL) calculated the previous day. * It can refer to the day.

[0121] IVC n+1 is the cumulative viable cell density (cells / mL) calculated the next day. * It can refer to the day.

[0122] q glc,n is the specific glucose consumption rate (mg / (cells)) on the current day * It can also refer to the day.

[0123] VCD n may refer to the viable cell density (cells / mL) measured on the current culture day.

[0124] VCD n-1 may refer to the viable cell density (cells / mL) measured on the previous culture day.

[0125] VCD n+1 may refer to the viable cell density (cells / mL) for the next culture day.

[0126] MAB1 may refer to monoclonal antibody 1.

[0127] BsAB1 may refer to bispecific antibody 1.

[0128] GMP can refer to good manufacturing processes.

[0129] This specification uses examples to disclose particular implementations of the disclosed technology, including the best mode, and to enable those skilled in the art to practice particular implementations of the disclosed technology, including making and using any device or system and performing any incorporated methods. The patentable scope of certain implementations of the disclosed technology is set forth in the claims, and may include other examples that occur to those skilled in the art. Such other examples are intended to be within the scope of the claims if they have structural elements that do not differ from the literal language of the claims, or if they include equivalent structural elements that differ only insubstantially from the literal language of the claims.

[0130] While certain embodiments of the present disclosure have been described above in connection with what are presently considered to be the most practical and various embodiments, it is to be understood that the present disclosure is not limited to the disclosed embodiments, but rather is intended to cover various modifications and equivalent arrangements that fall within the scope of the appended claims. Although specific terms are employed herein, they are used in a generic and descriptive sense only and not for purposes of limitation.

[0131] Some implementations of the disclosed technology are described above with reference to block diagrams and flow diagrams of systems and methods and / or computer program products according to example implementations of the disclosed technology. It will be understood that one or more blocks of the block diagrams and flow diagrams, and combinations of blocks in the block diagrams and flow diagrams, respectively, can be implemented by computer-executable program instructions. Similarly, some blocks of the block diagrams and flow diagrams may not necessarily be executed in the order presented, or may not necessarily be executed at all, according to some embodiments of the disclosed technology.

[0132] These computer program instructions may also be stored in a computer readable memory that can instruct a computer or other programmable data processing apparatus to function in a particular manner, such that the instructions stored in the computer readable memory produce an article of manufacture that includes instruction means for implementing one or more functions specified in one or more of the flow diagram blocks.

[0133] Implementations of the disclosed technology may provide a computer program product comprising a computer usable medium having computer readable program code or program instructions embodied therein, the computer readable program code being adapted to be executed to implement one or more functions specified in one or more blocks of the flow diagrams. The computer program instructions may also be loaded into a computer or other programmable data processing apparatus and a series of operational elements or steps executed on the computer or other programmable data processing apparatus to create a computer implemented process, the instructions executing on the computer or other programmable apparatus providing the elements or steps for implementing the functions specified in the blocks of the flow diagrams.

[0134] Thus, the blocks in the block diagrams and flow diagrams correspond to combinations of means for performing the specified functions, combinations of elements or steps for performing the specified functions, and program instruction means for performing the specified functions. It will also be understood that each block of the block diagrams and flow diagrams, and combinations of blocks in the block diagrams and flow diagrams, can be implemented by a dedicated hardware-based computer system that performs the specified functions, elements or steps, or a combination of dedicated hardware and computer instructions.

Claims

1. 1. A method for controlling nutrient supply in a cell culture process, comprising the steps of: receiving a sample from a bioreactor containing a cell culture; determining the residual amount of nutrients from the received sample; determining a viable cell density for the current day based on the measured residual amount of the nutrient; calculating a cell proliferation rate between a current culture day and a previous day based at least on the determined viable cell density for the current day; predicting a predicted viable cell density for the next day based at least on the calculated cell proliferation rate; predicting a predicted integrated viable cell density for the next day based at least on the predicted viable cell density for the next day; calculating a nutritional goal for the next day based at least on the predicted integrated viable cell density for the next day; supplying nutrients to the bioreactor according to the calculated nutrient target for the next day; A method comprising:

2. 2. The method of claim 1, wherein the cell proliferation rate is calculated between the current culture day and a previous day based on the determined viable cell density for the current day and the viable cell density measured on the previous day.

3. The method of claim 2 , wherein the viable cell density measured the previous day is retrieved from a non-transitory storage medium.

4. The method of any one of claims 1 to 3, wherein the viable cell density for the next day is predicted based on the calculated cell proliferation rate and the determined viable cell density for the current day.

5. The method of any one of claims 1 to 3, wherein the predicted integrated viable cell density for the next day is predicted based on the predicted viable cell density for the next day, the determined viable cell density for the current day, and the integrated viable cell density for the current day.

6. 6. The method of claim 5, wherein the integrated viable cell density for the current day is retrieved from a non-transitory storage medium.

7. Calculating a specific daily nutrient consumption rate for the previous day based at least on the calculated cell proliferation rate; predicting an amount of a nutrient to be consumed between the current day and the next day based on the specific nutrient consumption rate per day; The method of any one of claims 1 to 3, further comprising: calculating the nutritional goal based at least on the amount of the nutrient predicted to be consumed.

8. 8. The method of claim 7, wherein the daily specific nutrient consumption rate for the previous day is calculated based on the calculated cell proliferation rate, the integrated viable cell density for the current day, and the integrated viable cell density for the previous day.

9. 9. The method of claim 8, wherein the integrated viable cell density for the current day and the integrated viable cell density for the previous day are retrieved from a non-transitory storage medium.

10. 8. The method of claim 7, wherein the amount of a nutrient consumed between the current day and the next day is predicted based on a particular nutrient consumption rate per day, the predicted integrated viable cell density for the next day, and an integrated viable cell density for the current day.

11. 8. The method of claim 7, wherein the nutritional goals are calculated based on empirically determined nutrients to maintain the amounts and values ​​of the nutrients predicted to be consumed.

12. 12. The method of claim 11, wherein the empirically determined nutrient to maintain values ​​are retrieved from a non-transitory storage medium.

13. The method according to any one of claims 1 to 3, wherein the nutrients are selected from glucose, glutamate, galactose, lactate and glutamine.

14. The method of any one of claims 1 to 3, wherein the nutrient comprises one or more monosaccharides.

15. The method of any one of claims 1 to 3, wherein measuring the residual amount of the nutrient comprises analyzing a nutrient concentration in the bioreactor.

16. The method of any one of claims 1 to 3, wherein measuring the residual amount of the nutrient comprises performing one or more of an offline nutrient measurement and an in-line nutrient measurement.

17. The method according to any one of claims 1 to 3, wherein measuring the residual amount of the nutrient is performed by one or more of a NovaFlex instrument and a Raman probe.

18. 4. The method of any one of claims 1 to 3, wherein the bioreactor is one or more of a Chinese Hamster Ovary (CHO) cell bioreactor and a 5 L bioreactor.

19. The method of any one of claims 1 to 3, wherein the cells in the bioreactor are mammalian cells.

20. 20. The method of claim 19, wherein the cell is a CHO cell.

21. 1. A method for controlling glucose supply in a cell culture process, comprising the steps of: receiving a sample from a bioreactor containing a cell culture; determining a residual amount of glucose from the received sample; determining a viable cell density for the current day based on the measured residual amount of glucose; calculating a cell proliferation rate between a current culture day and a previous day based at least on the determined viable cell density for the current day; predicting a predicted viable cell density for the next day based at least on the calculated cell proliferation rate; predicting a predicted integrated viable cell density for the next day based at least on the predicted viable cell density for the next day; calculating a glucose goal for the next day based at least on the predicted integrated viable cell density for the next day; supplying glucose to the bioreactor according to the calculated glucose target for the next day; A method comprising:

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