Dynamic monosaccharide control method

Abstract

Materials and methods for controlling nutrient feed in cell culture processes are provided. A sample is received from a bioreactor comprising a cell culture. A viable cell density and a residual nutrient measurement are determined from the received sample. A daily nutrient feed target is calculated based on the viable cell density and the residual nutrient measurement. The nutrient is fed to the bioreactor according to the calculated daily nutrient feed target.

Description

[0001] Cross-reference to related applications

[0002] This application claims the benefit of U.S. Provisional Patent Application Nos. 62 / 923,185, 62 / 923,204, 62 / 923,217, 63 / 000,361, 63 / 000,366, and 63 / 000,371, all filed on October 18, 2019, and March 26, 2020, respectively. The entire contents of these patent applications are incorporated herein by reference. Background Technology

[0003] Cell culture production capacity depends on optimized culture medium management to achieve high cell densities. Nutrient feeding is a crucial parameter for process optimization. High cell density processes may require large amounts of nutrients, and daily requirements vary depending on cell type and density. Summary of the Invention

[0004] There is a need for improved materials and methods for predicting daily nutrient target requirements for a given cell line, and this invention addresses that need. One aspect of the disclosed technology relates to a method for controlling nutrient feed during cell culture. Samples can be received from a bioreactor containing cell cultures. Viable cell density and residual nutrient measurements can be determined from the received samples. A daily nutrient feed target can be calculated based on the viable cell density and residual nutrient measurements. Nutrients can be fed into the bioreactor according to the calculated daily nutrient feed target.

[0005] In one implementation, the daily residual nutrient concentration in the bioreactor can be maintained within a predetermined range.

[0006] In one implementation, the daily nutrient feed target can be recalculated based on daily measurements of live cell density and residual nutrients.

[0007] In one implementation, the nutrient may be selected from glucose, glutamic acid, galactose, lactic acid, and glutamine.

[0008] In one implementation, the nutrient may include one or more monosaccharides.

[0009] In one embodiment, residual nutrient measurement may include determining the concentration of nutrients in the bioreactor.

[0010] In one implementation, residual nutrient measurement may include performing one or more of offline and online nutrient measurements.

[0011] In one embodiment, the bioreactor can be any bioreactor known in the art. In some embodiments, the bioreactor capacity ranges from about 15 ml to about 15,000 L. In one embodiment, the bioreactor can be one or more of the following: a Chinese hamster ovary (CHO) cell bioreactor and a 5 L bioreactor. Other mammalian cell types besides CHO can be used to produce biopharmaceuticals. Non-limiting examples of such mammalian cell types include HEK, 293, and PerC6. The process can also be used with other non-mammalian cell types, such as yeast and bacteria.

[0012] In some embodiments, the cells in the bioreactor are any type of cell known in the art. In one embodiment, the cells in the bioreactor may be mammalian cells. In another embodiment, the cells in the bioreactor may be bacterial, yeast, or insect cells.

[0013] In one implementation, the cells may be CHO cells, recombinant CHO cells, or a mixture thereof.

[0014] In one implementation, the daily nutrient feed target can be calculated at least in part based on the global average consumption values ​​of at least six cell lines and growth curves predetermined from multiple runs of the bioreactor.

[0015] Another aspect of the disclosed technology relates to a method for controlling nutrient feeding during cell culture. A sample can be received from a container containing cell culture. Viable cell density and residual nutrient measurements can be determined from the received sample. A daily nutrient feeding target can be calculated based on the viable cell density and residual nutrient measurements. Nutrients can be fed into the container according to the calculated daily nutrient feeding target.

[0016] In one implementation, the container may be a culture flask.

[0017] In one embodiment, the nutrient may be selected from glucose, glutamic acid, galactose, lactic acid, and glutamine. In another embodiment, the nutrient may be selected from amino acids or vitamins.

[0018] Another aspect of the disclosed technology relates to a method for balancing glucose feed during cell growth. Viable cell density can be periodically determined during cell growth. Glucose concentration can be periodically measured during cell growth. The glucose feed target of the nutrient can be periodically adjusted based on the viable cell density and glucose concentration. Glucose can be periodically fed into the cell growth process according to the glucose feed target.

[0019] One aspect of the disclosed technology relates to a system for controlling nutrient feed during cell culture. A processor can communicate with a bioreactor and a nutrient feed system. The cell culture process can take place within the bioreactor. The nutrient feed system can feed nutrients into the bioreactor. The processor can determine viable cell density and residual nutrient measurements from samples taken from the bioreactor. A daily nutrient feed target can be calculated based on the viable cell density and residual nutrient measurements. The processor can then guide the nutrient feed system to feed nutrients into the bioreactor based on the calculated daily nutrient feed target.

[0020] In one implementation, the nutrient feeding system can provide continuous or discontinuous feeding of nutrients during the cell culture process.

[0021] In one implementation, the nutrient may be selected from glucose, glutamic acid, galactose, lactic acid, and glutamine.

[0022] In one implementation, the nutrient may include one or more monosaccharides.

[0023] Another aspect of the disclosed technology relates to a system for balancing glucose feed during cell growth. A processor can communicate with a glucose feeding system that feeds glucose during cell growth. The processor can periodically determine the viable cell density and glucose concentration measured during cell growth. The processor can periodically adjust glucose feed targets based on the viable cell density and glucose concentration. The processor can periodically guide the glucose feeding system to feed glucose into the cell growth process according to the glucose feed targets.

[0024] Another aspect of the disclosed technology relates to a system for preventing glycation during cell culture. The processor can communicate with a bioreactor and a nutrient feeding system. The cell culture process can be carried out within the bioreactor. The nutrient feeding system can feed nutrients into the bioreactor. The processor can determine the residual amount of nutrients in a sample taken from the bioreactor. The processor can determine the amount of nutrients consumed since the last feed based on the residual amount of nutrients. The processor can determine the viable cell density within the sample. The processor can calculate the predicted amount of nutrients to be consumed before the next feed based on the nutrient consumption and viable cell density. The processor can calculate the target amount of nutrients for the current feed based on the predicted nutrient consumption before the next feed and a predetermined residual nutrient target. The processor can guide the nutrient feeding system to feed nutrients into the bioreactor based on the calculated target amount of nutrients.

[0025] One aspect of the disclosed technology relates to a method for adjusting the amount of saccharification of reagents during cell culture. The cell culture process can be carried out in a bioreactor. Samples can be received from the bioreactor. The residual amount of nutrients can be measured from the received samples. The amount of nutrients consumed since the last feed can be determined based on the residual amount of nutrients. The viable cell density can be determined from the received samples. The predicted amount of nutrients to be consumed before the next feed can be calculated based on the amount of nutrients consumed and the viable cell density. The target amount of nutrients for the current feed can be calculated based on the predicted amount of nutrients consumed before the next feed and a predetermined residual nutrient target. Nutrients can be fed into the bioreactor according to the calculated target amount of nutrients.

[0026] In one implementation, the predicted live cell density between the current feed and the next feed can be determined at least in part based on the determined live cell density. The nutrient consumption rate can be determined at least in part based on the amount of nutrient consumed. The predicted consumption rate can be calculated based on the predicted live cell density and the nutrient consumption rate.

[0027] In one implementation scheme, feeding can be carried out daily.

[0028] Another aspect of the disclosed technology relates to a method for controlling glucose feed during cell culture. A sample is received from a bioreactor containing cell cultures. The residual amount of glucose is measured from the received sample. The sampling time when the sample is received from the bioreactor is determined. A processor compares the residual amount of glucose with a predetermined glucose target. The processor calculates the amount of glucose consumed. When the residual amount of glucose is greater than the predetermined glucose target, the processor determines the amount of glucose consumed by determining the amount of glucose consumed between the previous day and the current day during the cell culture process. When the residual amount of glucose is not greater than the predetermined glucose target, the processor determines the amount of glucose consumed based on the difference between the predetermined glucose target and the residual amount of glucose. The processor calculates the integral viable cell density. The processor calculates the predetermined viable cell density for the next day based on the integral viable cell density. The processor calculates the specific glucose consumption rate based on the amount of glucose consumed and the integral viable cell density. The processor calculates the predicted glucose consumption by multiplying the specific glucose consumption rate by the predetermined viable cell density for the next day. The processor calculates the glucose target by adding the predetermined glucose consumption and the predetermined minimum amount of glucose. Glucose is fed into the bioreactor according to the glucose target.

[0029] In one implementation scheme, feeding can be carried out daily.

[0030] Other features of this disclosure and the advantages therefrom are explained in more detail below with reference to the specific embodiments shown in the accompanying drawings, wherein similar elements are indicated by similar reference numerals. Attached Figure Description

[0031] Referring now to the accompanying drawings, which are not necessarily drawn to scale and are incorporated in and constitute a part of this disclosure, various embodiments and aspects of the disclosed technology are illustrated, and the principles of the disclosed technology are explained together with the description. In the drawings:

[0032] Figure 1 This is a schematic diagram of an exemplary environment that can be used to implement one or more embodiments of this disclosure.

[0033] Figure 2 This is a schematic diagram of an exemplary environment that can be used to implement one or more embodiments of this disclosure.

[0034] Figure 3 This is a flowchart of a glucose algorithm based on one aspect of the disclosed technology.

[0035] Figure 4 This is an example of an automated process for feeding glucose, based on one aspect of the disclosed technology.

[0036] Figure 5It is a block diagram of a nutrient feeding control system based on one aspect of the disclosed technology.

[0037] Figure 6A This is an exemplary table showing, according to one aspect of the disclosed technology, the residual glucose target determined experimentally by culture day.

[0038] Figure 6B This is another exemplary table showing, according to one aspect of the disclosed technology, the residual glucose target determined experimentally by culture day.

[0039] Figure 6C This is an exemplary table illustrating cell line counts and bioreactor operation for calculating expected cell growth behavior, according to one aspect of the disclosed technology.

[0040] Figure 7 It is a graph showing the median and IQR of the multiple change of ΔIVCD over time according to one aspect of the disclosed technology.

[0041] Figure 8 This is a graph showing the median ΔIVCD / VCD and IQR over time according to one aspect of the disclosed technology.

[0042] Figure 9A Figure B shows a graph of the percentage error of VCD prediction over time according to one aspect of the disclosed technology.

[0043] Figures 10A-10C A graph showing residual glucose levels over time for different cell lines according to one aspect of the disclosed technique is presented.

[0044] Figures 11A-11B Additional graphs showing residual glucose levels over time for different cell lines according to one aspect of the disclosed technique are shown.

[0045] Figure 12 A graph showing residual glucose levels over time, measured by RSV according to one aspect of the disclosed technique, is presented.

[0046] Figure 13 A graph showing the residual glucose level of cell line CHO6 measured over time according to one aspect of the disclosed technique is presented.

[0047] Figure 14 The median expected value calculated from the Janssen cell line database is shown. and median expectation value.

[0048] Figure 15 It is a flowchart of a process executed by a nutrient feeding control system according to one aspect of the disclosed technology.

[0049] Figure 16 This is an exemplary illustration of data input associated with a bioreactor based on one aspect of the disclosed technology.

[0050] Figure 17 This is an exemplary illustration of data input associated with a bioreactor based on one aspect of the disclosed technology.

[0051] Figure 18 This is another exemplary illustration of data input associated with a bioreactor based on one aspect of the disclosed technology.

[0052] Figure 19 This is yet another exemplary illustration of data input associated with a bioreactor based on one aspect of the disclosed technology.

[0053] Figure 20 This is an exemplary illustration of selecting various data import files based on one aspect of the disclosed technology.

[0054] Figure 21 This is an additional exemplary illustration of data input associated with a bioreactor based on one aspect of the disclosed technology.

[0055] Figure 22 This is another exemplary illustration of data input associated with a bioreactor based on one aspect of the disclosed technology.

[0056] Figure 23 This is yet another exemplary illustration of data input associated with a bioreactor based on one aspect of the disclosed technology.

[0057] Figure 24 This is an exemplary illustration of a user interface related to data input associated with a bioreactor, based on one aspect of the disclosed technology.

[0058] Figure 25 This is another exemplary illustration of a user interface related to data input associated with a bioreactor, based on one aspect of the disclosed technology.

[0059] Figure 26 This is yet another exemplary illustration of a user interface related to data input associated with a bioreactor, based on one aspect of the disclosed technology.

[0060] Figure 27 This is an exemplary illustration of a user interface related to data input associated with a bioreactor, based on one aspect of the disclosed technology.

[0061] Figure 28 This is an exemplary illustration of data input associated with a bioreactor based on one aspect of the disclosed technology.

[0062] Figure 29 This is another exemplary illustration of data input associated with a bioreactor based on one aspect of the disclosed technology.

[0063] Figure 30 This is yet another exemplary illustration of data input associated with a bioreactor based on one aspect of the disclosed technology.

[0064] Figure 31 This is an exemplary illustration of data input associated with a bioreactor based on one aspect of the disclosed technology.

[0065] Figure 32 This is another exemplary illustration of data input associated with a bioreactor based on one aspect of the disclosed technology.

[0066] Figure 33 This is yet another exemplary illustration of data input associated with a bioreactor based on one aspect of the disclosed technology.

[0067] Figure 34 This is an exemplary illustration of selecting various data import files based on one aspect of the disclosed technology.

[0068] Figure 35 This is an exemplary illustration of data input associated with a bioreactor based on one aspect of the disclosed technology.

[0069] Figure 36 This is another exemplary illustration of data input associated with a bioreactor based on one aspect of the disclosed technology.

[0070] Figure 37 This is an exemplary flowchart illustrating the process of controlling nutrient feeding during cell culture.

[0071] Figure 38 This is another exemplary flowchart illustrating the process of controlling nutrient feeding during cell culture.

[0072] Figure 39 This is an exemplary flowchart illustrating the process of balancing glucose feed during cell growth.

[0073] Figure 40 This is an exemplary flowchart illustrating the process of controlling glucose feeding during cell culture.

[0074] Figure 41 This is an exemplary flowchart illustrating the process of adjusting the amount of reagent saccharification during cell culture.

[0075] Figure 42This is an exemplary flowchart illustrating the process of controlling glucose feeding during cell culture.

[0076] Figure 43 This is another exemplary flowchart illustrating the process of controlling glucose feeding during cell culture. Detailed Implementation

[0077] Some embodiments of the disclosed technology will be described more fully with reference to the accompanying drawings. However, the disclosed technology may be embodied in many different forms and should not be construed as limited to the embodiments set forth herein. The various elements and components described below as constituting the disclosed technology are intended to be illustrative rather than restrictive. Many suitable components that will perform the same or similar functions as those described herein are intended to be covered within the scope of the disclosed electronic devices and methods. Such other components not described herein may include, for example, components developed after the development of the disclosed technology.

[0078] It should also be understood that mentioning one or more method steps does not preclude the existence of additional method steps or intermediate method steps between those explicitly identified steps.

[0079] It should be noted that, unless the context clearly indicates otherwise, the term “a” or “an” entity refers to one or more of that entity; for example, “amino acid” should be understood to mean one or more proteins. Therefore, the terms “a” (or “an”), “one or more” and “at least one” are used interchangeably herein.

[0080] The term "nutrient" can refer to any compound, molecule, or substance used by an organism to survive, grow, or otherwise add biomass. Examples of nutrients may include carbohydrate sources (e.g., common sugars such as glucose, galactose, maltose, or fructose, or more complex sugars), amino acids, and vitamins (e.g., B vitamins (e.g., B12), vitamin A, vitamin E, riboflavin, thiamine, and biotin). In this invention, one or more nutrients may be used as substitute molecules to determine the amount of total nutrient culture medium to be added to the bioreactor. In some embodiments, the term "nutrient" may refer to common sugars, vitamins, and amino acids.

[0081] The term "amino acid" can refer to any one of the twenty 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 their racemic mixtures. The term "amino acid" can also refer to known non-standard amino acids, such as 4-hydroxyproline, hydroxyproline, S-sulfocysteine, phosphotyrosine, ε-N,N,N-trimethyllysine, 3-methylhistidine, 5-hydroxylysine, O-phosphoserine, γ-carboxyglutamic acid, 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, diazoserine, and S-adenosylmethionine. In some embodiments, the amino acid is glutamic acid, glutamine, lysine, tyrosine, or valine. In some embodiments, the amino acid is glutamic acid or glutamine.

[0082] The terms "nutrient medium," "supplemented medium," "supplement," "total supplement," and "total nutrient medium" are used interchangeably and can include "complete" media used for growth, propagation, and the addition of biomass to cell lines. A nutrient medium can be distinguished from substances or simple media that are insufficient on their own to grow and propagate cell lines. Therefore, for example, glucose or ordinary sugars are not nutrient media on their own because they are insufficient to grow and propagate cell lines without other required nutrients.

[0083] In one aspect, the present invention teaches a carbohydrate control algorithm for balancing glucose feed in bioreactors, such as CHO cell bioreactors. The disclosed system has the advantage over existing technologies in that it utilizes (automatically or non-automatically) collected data to adjust glucose in the bioreactor, thereby enhancing glycation prevention, reducing bioreactor collapse due to insufficient glucose feed, dynamically measuring cellular responses to glucose, and adjusting glucose in any organism (e.g., CHO). It is semi-automatic or fully automatic and organism-independent, thus allowing the use of other mammalian cells (e.g., CAR-T, CHO, etc.). The system is dynamic, particularly in that it learns periodically from, for example, glucose measurements within the system. The external (bioreactor) system contains the algorithm guiding the glucose feed system (standard off-the-shelf kits are suitable for this system). Off-the-shelf glucose feed kits can be configured in various ways (possibly multiple feeds, different locations, possibly continuous or discontinuous feeds). Continuous systems, such as pre-programmed, non-feedback-controlled continuous feeds of cell cultures (the system will simply separate glucose clumps over time), can be used. This allows glucose data to be automatically fed to the algorithm. The glucose feed is adjusted based on data obtained using a glucose analyzer. In some embodiments, the disclosed system is used to produce commercial chemicals other than glycoproteins using mammalian cells, yeast, or bacteria.

[0084] In one embodiment, the disclosed system includes an automated process with feedback control during cell culture.

[0085] In one implementation, lactate and glucose are measured, and the glucose target is modified stepwise (0.5 g / L each time).

[0086] In one implementation, only glucose is measured. This simplifies the laboratory process by using only glucose and makes it easier for bioreactor operators to use algorithms and feed glucose appropriately.

[0087] In one implementation, the algorithm uses only cell density measurements and has pre-programmed global glucose consumption values.

[0088] In one implementation, a glucose target is calculated daily. The algorithm can be updated to allow for multiple measurements, and the target is used for the next 24 hours.

[0089] The algorithm concept originates from adopting the thought process used by human operators and transforming it into a flowchart that uses measured glucose and lactate to make step changes to increase or decrease glucose targets.

[0090] The disclosed glucose algorithm is often more accurate than a human operator because human operators tend to underestimate glucose (leading to depletion events) or overestimate the glucose target to ensure that glucose is not depleted. The disclosed algorithm performs better than a human operator in maintaining glucose at the desired level. The algorithm has the desired glucose level to maintain, for example, it can be set to a value between 1 g / L and 2 g / L.

[0091] The disclosed method can be used for other monosaccharides, nutrients, etc. Non-limiting examples of such monosaccharides or nutrients include glutamic acid, galactose, lactic acid, and glutamine.

[0092] The disclosed technology can minimize glucose to avoid glycation.

[0093] In some embodiments, the methods disclosed herein can increase the quantity of bioproducts produced in bioreactor cell cultures for bioproduct production, or reduce the bioproduct production time. The disclosed methods may include (a) intermittently or continuously analyzing the concentration of one or more nutrients in the bioreactor cell culture; and (b) adding additional nutrient culture medium to the bioreactor cell culture when the concentration of one or more nutrients falls below a target value.

[0094] In some implementations, additional nutrient culture medium may be added to the bioreactor cell culture in an amount sufficient to maintain a substantially stable amino acid concentration throughout the bioreactor process.

[0095] In some embodiments, the bioreactor cell culture may comprise Chinese hamster ovary (CHO) cells, HEK-293 cells, or VERO cells. In some embodiments, the bioproduct may be an antibody or an antibody-like peptide.

[0096] In one embodiment, the method of the present invention can be carried out in the presence of any cell culture medium. For example, the bioreactor process can 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 a medium with a defined chemical composition.

[0097] Various analytical devices can be used in this invention. The analytical device may include any instrument or process capable of detecting and / or quantifying substitute molecules or markers (e.g., amino acids or other substitutes in cell culture media (e.g., vitamins, minerals, ions, sugars, etc.)). The analytical device may be a device for performing gas chromatography, HPLC, cation exchange chromatography, anion exchange chromatography, size exclusion chromatography, enzyme-catalyzed assays, and / or chemical reaction assays.

[0098] like Figure 1As shown, production reactor 102 may include mammalian cell cultures. Production reactor 102 may be a bioreactor, cell culture reactor, or sample bioreactor. Production reactor 102 may be at least one of the following: well plate, shake flask, benchtop container, and commercial-scale (e.g., 15 kL) stainless steel reactor. Reaction samples may be removed from production reactor 102 and fed to nutrient feed control system 110. Nutrient feed control system 110 may include glucose measurement system 104 that performs glucose measurement. Glucose measurement may be performed offline or online. Nutrient feed control system 110 may also include glucose target prediction system 106, which receives glucose measurement values ​​from glucose measurement system 104 and performs glucose target prediction. Nutrient feed control system 110 may include glucose calculation system 108, which can then use the predicted glucose target to calculate the amount of glucose to be added and send instructions to nutrient feed system 120. In one embodiment, the process performed by glucose measurement system 104, glucose target prediction system 106, and glucose calculation system 108 may be performed by one or more processors.

[0099] The nutrient feeding system 120 may include a pump 111 that feeds the correct amount of glucose from glucose feed 112 into the production reactor 102.

[0100] Figure 2 A schematic diagram of an exemplary environment that can be used to implement one or more embodiments of the present disclosure is shown. A nutrient feed control system 110 can communicate with the production reactor 102 and the nutrient feed system 120 via a network 180. The nutrient feed control system 110 can direct the nutrient feed system 120 to feed one or more nutrients into the production reactor 102.

[0101] Figure 3 A flowchart of the glucose algorithm is shown. At 302, production reactor 102 can provide samples. At 304, glucose measurement system 104 can receive samples and measure glucose levels. At 306, glucose target prediction system 106 can predict how much glucose to add to production reactor 102. At 308, glucose calculation system 108 can calculate and output the correct volume of glucose to be added. At 310, the correct volume of glucose can be fed into production reactor 102. This algorithm is applicable to pre-culture. For example, this algorithm can be used to feed glucose into enhanced inoculation sequences. This algorithm is also applicable to N-1 perfusion processes and production perfusion processes.

[0102] Figure 4An example of an automated process is illustrated. At 402, production reactor 102 can provide samples. At 404, glucose measurements can be performed, such as online glucose measurement (e.g., NovaFlex) at 404a or Raman probe measurement at 404b. Instruments can be used to measure offline pH as well as glucose and lactate. At 406, nutrient feed control system 110 can execute a predicted glucose feed target. At 408, reactor control station can process the predicted glucose feed target. At 410, controller can calculate the glucose feed volume. At 412, controller can feed glucose into production reactor 102.

[0103] The methods disclosed herein can increase the yield of subsequent bioreactor cell cultures. In some embodiments, the method can increase the amount of antibodies (or other bioproducts) produced in bioreactor cell cultures that produce antibodies (or other bioproducts), or reduce the time required for antibody (or other bioproduct) production. The method may include analyzing culture samples (with or without extracting samples from the bioreactor) using automated sampling devices (e.g., via offline, online, in-line, or near-line sample analysis). The method may include analyzing culture samples (e.g., the concentration of residual glucose) using automated analytical devices to generate data representing the amount of nutrients (or other alternative markers). The method may include processing the generated data (e.g., by analyzing residual glucose in the sample) using algorithms or computer-based processing programs, wherein the processed data is used to determine the amount of additional nutrient medium to be added to the bioreactor. The method may include adding the determined amount of nutrient medium to the bioreactor using an automated feeder. The method may include recording the time and amount of each nutrient medium addition.

[0104] Mammalian cells can include any mammalian cell capable of growing in a culture. Exemplary mammalian cells include, for example, CHO cells (including CHO-K1, ...). CHO DUKX-B11, CHO DG44), VERO, BHK, HeLa, CV1 (including Cos; Cos-7), MDCK, 293, 3T3, C127, myeloma cell lines (especially mouse), PC12, HEK-293 cells (including HEK-293T and HEK-293E), PER C6, Sp2 / 0, NSO, and W138 cells. Mammalian cells derived from any of the aforementioned cell types may also be used. In some embodiments, the bioreactor cell culture may comprise Chinese hamster ovary (CHO) cells, HEK-293 cells, or VERO cells.

[0105] The steps of the disclosed methods are repeatable and can occur at various intervals. In some embodiments, the steps disclosed herein can be repeated more than 10 times throughout the bioreactor process, or repeated 10 to 1000 times, 20 to 500 times, or 30 to 100 times throughout the bioreactor process. In some embodiments, the steps can be repeated approximately 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 approximately every 4 to 18 hours or approximately every 10 minutes to approximately every 6 hours throughout the bioreactor process. In a specific embodiment, the method includes measuring the amount of residual nutrients (e.g., residual glucose) once daily, producing a concentration of the target nutrient (glucose) after approximately one day or approximately 24 hours. In some embodiments, the method includes measuring the amount of residual nutrients (e.g., residual glucose) multiple times daily (e.g., twice, three, or four times daily), producing a concentration of the target nutrient (glucose) during approximately 24 hours of measurement.

[0106] The steps of the methods disclosed herein can be performed in a relatively short time; that is, the sampling, analysis, and addition of supplemental nutrient culture media can occur relatively rapidly. In some embodiments, the steps of the disclosed methods are performed in approximately 1 minute to approximately 2 hours.

[0107] In some embodiments, the steps of the disclosed method are performed by one or more automated devices. The terms “automatic,” “automatically,” or “automatically operated” describe one or more mechanical devices that perform one or more tasks without any human intervention or action (other than any human intervention or action necessary to initially prepare one or more devices for task execution or that may be necessary to maintain the automated operation of one or more devices). The “mechanical device” that automatically performs one or more tasks may optionally include a computer and the necessary instructions (code) therein to process collected data that can be used for decision-making purposes to control and direct the performance of one or more devices, such as controlling the timing, duration, frequency, type, and / or characteristics of the tasks to be performed.

[0108] In various implementations, "offline" analysis refers to permanently removing samples from the production process and analyzing them at a later point in time, so that the data analysis does not convey real-time or near-real-time information about process conditions. In some implementations, one or more analytical devices are used offline.

[0109] In one implementation, the analytical device (or the sensor portion connected thereto) may be introduced directly into the bioreactor or purification unit, or the device or sensor portion may be isolated from the bioreactor or purification unit by a suitable barrier or membrane.

[0110] In some embodiments, the analytical device may be a kit, such as a test strip, which can be placed in contact with a sample to rapidly determine cell concentration. In some embodiments, the kit may contain a substrate that generates a chemical and / or enzyme-linked reaction in the presence of an alternative marker or a specific concentration of an alternative marker to produce a detectable signal. The detectable signal may include, for example, a color change or other visual signal. In some embodiments, the analytical device may be a disposable analytical device, such as a disposable test strip. Such kits may be useful compared to other larger and more complex analytical devices due to their ease of operation and reduced cost. Such kits can also be used for small-scale cell culture propagation to determine optimal health and productivity of cultures.

[0111] "Conventional production processes" may include (a) adding nutrient culture medium to a bioreactor in a bolus feed at specified time points, or (b) adding glucose (or other single nutrient) to the bioreactor as glucose (or other single nutrient) is consumed. Conventional production processes may result in lower bioproduct yields and / or lower bioproduct production efficiency. In one embodiment, the disclosed system may utilize a feedback control method in which the concentration of one or more nutrients is monitored, and an appropriate amount of total culture medium is added to the bioreactor based on the concentration of that nutrient. Monitoring can be performed automatically and frequently, thereby significantly improving bioproduct yields.

[0112] In some embodiments, the quantity of biological products produced can be significantly increased relative to conventional production processes. In some embodiments, the quantity of biological products produced can be 10% to 100% greater than that produced through conventional production processes. In some embodiments, the quantity of biological products produced by the method of the present invention can be 10%, 15%, 20%, 25%, 30%, 35%, 40%, 45%, 50%, 55%, 60%, 65%, 70%, 80%, 90%, or 100% greater than that produced through conventional production processes.

[0113] In some implementations, the degree of saccharification of the bioproduct is reduced by at least 10%, at least 15%, at least 20%, at least 25%, at least 30%, at least 35%, at least 40%, or at least 45% compared to the degree of saccharification of the bioproduct produced through conventional production processes.

[0114] In various implementation schemes, the glucose algorithms and methods described herein can effectively achieve residual glucose levels of 0 g / L to 3 g / L, 0.5 g / L to 2 g / L, 2 g / L to 5 g / L, less than 1 g / L, or less than 2 g / L one day after feeding.

[0115] Various biological products are conceivable in this invention. In some embodiments, the biological product may be an antibody, recombinant protein, glycoprotein, or fusion protein. In some embodiments, the biological product may be a soluble protein. In some embodiments, the biological product may be an antibody, antibody fragment, or modified antibody (e.g., multivalent antibody, domain-deleted antibody, multimeric antibody, hinge-modified antibody, stabilized antibody, multispecific antibody, linear antibody, scFv, linked scFv antibody, multivalent linear antibody, Fc-free multivalent antibody, Fab, multivalent Fab, etc.).

[0116] Exemplary embodiments of the disclosed technology will now be referenced in detail, examples of which are shown in the accompanying drawings and disclosed herein. Where convenient, the same reference numerals will be used throughout the drawings to refer to the same or similar parts.

[0117] Figure 5 This is a block diagram of a nutrient feed control system 110 according to one aspect of the disclosed technology. The nutrient feed control system 110 may include one or more processors 510. Processes executed by the glucose measurement system 104, the glucose target prediction system 106, and the glucose calculation system 108 may be performed by one or more processors 510.

[0118] refer to Figure 5 Processor 510 may include one or more of a microprocessor, microcontroller, digital signal processor, coprocessor, etc., or combinations thereof, capable of executing stored instructions and manipulating stored data. Processor 510 may be one or more known processing devices, such as those derived from Intel. TM Manufactured Pentium TM Series or by AMD TM Turion manufactured TM This is a series of microprocessors. Processor 510 can be configured as a single-core or multi-core processor that performs parallel processing simultaneously. For example, processor 510 can be a single-core processor configured with virtualization technology. In some embodiments, processor 510 can use a logical processor to execute and control multiple processes simultaneously. Processor 510 can implement virtual machine technology or other similar known technologies to provide the ability to execute, control, run, manipulate, store, etc., multiple software processes, applications, programs, etc. Those skilled in the art will understand that other types of processor arrangements that provide the capabilities disclosed herein can be implemented.

[0119] In some embodiments, the non-transitory computer-readable medium 520 may 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), disk, optical disk, floppy disk, hard disk, removable magnetic tape, flash memory, redundant array of independent disks (RAID), etc.) for storing files including an operating system 522, applications (including, for example, web browser applications, widget or gadget engines and / or other applications if necessary), executable instructions, and data. In one embodiment, the processing techniques described herein are implemented as a combination of executable instructions and data within the non-transitory computer-readable medium 520. The non-transitory computer-readable medium 520 may include one or more memory devices storing data and instructions for performing one or more features of the disclosed embodiments. 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, Microsoft... TM SQL database, SharePoint TM Database, Oracle TM Database, Sybase TM Database or other relational or non-relational database. Non-transitory computer-readable medium 520 may include software components that, when executed by processor 510, perform one or more processes consistent with the disclosed embodiments. In some embodiments, non-transitory computer-readable medium 520 may include database 524 to perform one or more processes and functions associated with the disclosed embodiments. Non-transitory computer-readable medium 520 may include one or more programs 526 to perform one or more functions of the disclosed embodiments. Furthermore, processor 510 may execute one or more programs 526 remotely from system 110. For example, system 110 may access one or more remote programs 526 that, when executed, perform functions associated with the disclosed embodiments.

[0120] System 110 may also include one or more I / O devices 560, which may include one or more interfaces for receiving signals or inputs from devices and providing signals or outputs to one or more devices, thereby allowing system 110 to receive and / or send data. For example, system 110 may include interface components that can provide interfaces to one or more input devices, such as one or more keyboards, mice, touchscreens, touchpads, trackballs, scroll wheels, digital cameras, microphones, sensors, etc., enabling system 110 to receive data from one or more users. System 110 may include displays, screens, touchpads, etc., for displaying images, videos, data, or other information. I / O devices 560 may include a graphical user interface 562.

[0121] In exemplary embodiments of the disclosed technology, system 110 may include any number of hardware and / or software applications executed to facilitate any operation. One or more I / O interfaces 560 may be used to receive or collect data and / or user instructions from a variety of input devices. The received data may be processed by one or more computer processors and / or stored in one or more memory devices as needed in various embodiments of the disclosed technology.

[0122] Network 180 may include a network of interconnected computing devices, more commonly referred to as the Internet. Network 180 can be of any suitable type, including individual connections via the Internet such as cellular or WiFi networks. In some implementations, network 180 may use direct connections such as radio frequency identification (RFID), near field communication (NFC), or Bluetooth. TM Low-energy Bluetooth TM (BLE), WiFi TM ZigBee TMThe network 180 can connect terminals, services, and mobile devices using backscatter communication (ABC) protocols, USB, WAN, or LAN. Because the transmitted information may be personal or confidential, security concerns may require encryption or other protection of these types of connections. However, in some implementations, the information being transmitted may be less personal, and therefore the network connection may be chosen for convenience rather than security. Network 180 can include any type of computer networking arrangement for exchanging data. For example, network 180 can be the Internet, a private data network, a virtual private network using a public network, and / or other suitable connections that enable 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 a local network that includes any type of computer networking arrangement for exchanging data in a local area, such as WiFi, Bluetooth, etc. TM Ethernet, and other suitable network connections that enable components of the system environment to interact with each other.

[0123] Example 1

[0124] Glucose feed is a crucial parameter for bioreactor process optimization. Furthermore, high cell density processes may require significant amounts of glucose (ranging from 5 g / L to 10 g / L per day), and the daily requirement varies with cell density. Therefore, a variable glucose algorithm can be developed to predict the daily glucose target requirement. This algorithm uses live cell density (VCD) and residual glucose measurements to calculate the daily glucose target. Implementation of the glucose algorithm has shown the potential to achieve residual glucose levels between 0 g / L and 3 g / L one day after feed in six cell lines, with most residual glucose levels falling between 0.5 g / L and 2 g / L.

[0125] The glucose feed target for the day (d) can be calculated from the residual glucose target for the second day (d+1) and the sum of the predicted glucose consumption between day d and day d+1 (Equation 1).

[0126] glucose target d =Predicting glucose consumption d+1 +Residual glucose target d+1 [1]

[0127] Figure 6A -B shows two tables, each illustrating the experimentally determined residual glucose targets for the glucose algorithm by culture day. The residual glucose targets are experimentally determined and vary between 0.5 g / L and 1.5 g / L with culture days.

[0128] Predicted glucose consumption can be defined as the predicted VCD (VCD) for the next culture day. d+1 Multiply by the daily glucose consumption rate (d) (Equation 2).

[0129] Predicting glucose consumption d+1 =

[0130] glucose consumption rate d *Predicting VCDs d+1 [2]

[0131] In this context, the specific glucose consumption rate can be defined as the amount of glucose consumed per cell per day between day d-1 and day d. The glucose concentration consumed can be calculated as the difference between the glucose target at day d-1 and the measured glucose concentration at day d. The glucose concentration consumed can then be expressed as the cumulative change in cell density (ΔIVCD) from day d-1 to day d. d Multiply by the time elapsed between d-1 and d to normalize (Equation 3).

[0132]

[0133] In most cases, the logarithmic average method can be used to approximate IVCD. When VCD remains constant from d-1 to d, the simple average method can be used.

[0134] d+1 prediction VCD (VCD) d+1 IVCD can be defined as the change in IVCD from d-1 to d (ΔIVCD) d Multiply by the median expected multiple change of ΔIVCD from d to d+1 ( and ΔIVCD of d+1 d+1 With VCD d+1 The reciprocal of the difference in median expected multiples between (Equation 4).

[0135]

[0136] Using ΔIVCD d Replace VCD d As a measure of viable cell density, individual VCD measurements can be significantly overestimated or underestimated due to experimental errors associated with cell counting instruments. Reducing the impact of individual measurement errors on IVCD, and consequently reducing their impact on glucose target prediction, is crucial.

[0137] Parameters characterizing cell line growth in high-titer media and fed conditions can be estimated from a database of 162 5L bioreactor runs, specifically the median expected fold change in ΔIVCD and ΔIVCD / VCD on day d+1. This can include six parameters. (Lonza Sales AG) cell lines, which overexpress different proteins and have different phenotypes, are named CHO1, CHO2, CHO3, CHO4, CHO5 and CHO6 for brevity. Figure 6C A table is shown showing cell line counts and bioreactor operations used to calculate expected cell growth behavior. Note that the target inoculation density for these reactors can be 500,000 live cells / mL.

[0138] The fold change of ΔIVCD from d to d+1 can be calculated as a function of culture days from all runs in the database, regardless of the cell line or feed used. Figure 7 The calculated mean and IQR of the fold change in ΔIVCD from day d to day d+1 of culture for 162 runs of six cell lines with high-titer medium and feed are shown. The median and IQR of the data can then be calculated to determine the median expected value.

[0139] The fold difference between ΔIVCD and VCD at d+1 can be calculated as a function of culture day from all runs in the database, regardless of the cell line or feed used. Figure 8 The calculated mean and IQR of ΔIVCD / VCD at day d+1 of culture day (d) are shown for 162 runs of 5L bioreactors from six cell lines with high-titer medium and feed. The median and IQR of the data can then be calculated to determine the median expected value.

[0140] The error in VCD prediction can be calculated as the difference between the predicted VCD and the measured VCD on day d+1 (Equation 5).

[0141]

[0142] The percentage error in VCD prediction is centered at zero and varies with incubation days, averaging between 0% and 9%. Figures 9A-9B The percentage error in VCD prediction is shown to be less than 10% on average.

[0143] The glucose algorithm performance can be achieved in 5L (e.g., with five cell lines (CHO1, CHO2, CHO3, CHO4, CHO5, and CHO6) and different high-titer feeds (JMF-1, JMF-5, and JaMS)). Figures 10A-10C (as shown) and AMBR250 (as shown) Figures 11A-11B , Figure 12 and Figure 13The study was conducted in a bioreactor (as shown). Residual glucose could be measured approximately one day after feeding, and the data indicated that the algorithm maintained glucose levels between 0.5 g / L and 2 g / L in most cases.

[0144] In another implementation, equations 2 through 4 can be replaced by the following equations. For example, glucose consumption can be predicted based on the definition in equation 2-1. d+1 The algorithm assumes a time frame of one day.

[0145] Predicting glucose consumption d+1 = Glucose consumption rate d *VCD d+1 The change in the predicted multiple * 1 day [2-1]

[0146] The glucose consumption rate can be defined according to Equation 3-1. d .

[0147]

[0148] VCD can be defined in Equation 4-1 d+1 The algorithm predicts the multiple change. It assumes a time period of one day.

[0149]

[0150] The values ​​in Equation 4-1 can be calculated from the Janssen cell line database with high-titer culture media and feed, such as... Figure 14 As shown. This database can consist of 162 runs of a 5L bioreactor. It can include six cell lines with different phenotypes: CHO1, CHO2, CHO3, CHO4, CHO5, and CHO6.

[0151] Equation 5 predicts VCD d+1 VCD can be defined as Equation 4-1 d+1 The predicted change multiplied by ΔIVCD d .

[0152] Residual glucose can be measured approximately one day after feeding, and data indicate that the algorithm keeps glucose levels between 0.5 g / L and 2 g / L in most cases.

[0153] Glucose levels can usually be controlled between 0.5 g / L and 2 g / L. Figure 15An exemplary flow for calculating a glucose target is shown. At 1502, a residual glucose measurement, VCD, and sampling time can be received as inputs. At 1504, the processor can determine whether the residual glucose level is greater than the target glucose level. If yes, at 1506, the glucose consumption is determined by determining the amount of glucose consumed between the previous day and the current day during the cell culture process. If not, at 1508, the glucose consumption is determined based on the difference between the predetermined glucose target and the residual glucose level. At 1510, the integrated viable cell density (IVCD) is calculated. At 1512, the predetermined viable cell density for the next day is calculated based on the integrated viable cell density. At 1514, the specific glucose consumption rate is calculated by dividing the glucose consumption by ΔIVCD. At 1516, the predicted glucose consumption is calculated by multiplying the specific glucose consumption rate by the predetermined viable cell density for the next day. At 1518, the glucose target is calculated by adding the predicted glucose consumption and the predetermined minimum glucose level.

[0154] 2.0 Variable Glucose Feed Schedule Description

[0155] 2.1 AMBR Description of 250 bioreactor

[0156] 2.1.1 First step

[0157] The feed sheet can be found on the Resources for High Tier Implementation SharePoint site. The link to that site is on the API-LM SharePoint page.

[0158] 2.1.2 Second step

[0159] Before starting the run, enter the SQL ID 1602 for each bioreactor and culture day, such as... Figure 16 As shown. According to... Figure 17 The instructions shown use a file macro to add the bioreactor SQL ID. First, on the "Batch" tab, enter the bioreactor ID in column B under Batch Name. Second, when finished, press the "Update Batch ID" button 1702. Third, populate the "Input and Feed Target" table with the bioreactor ID.

[0160] 2.1.3 Third step

[0161] For each AMBR250 bioreactor and culture day, enter the sampling time 1802 (in 24-hour format hh:mm), and manually or using a macro to measure VCD 1804 (10^6 cells / mL) and glucose concentration 1806 (g / L) in the bioreactor before feeding. Figure 18 As shown.

[0162] Macros in Excel files allow importing external readings from Vi-CELL, BioHT, MetaFLEX, and AMBR250 instruments. Sample naming requirements detailed below must be followed to accurately import values ​​for each instrument file type. Vi-CELL multifiles or AMBR250 datasheets for VCD measurements can be imported. MetaFLEX or BioHT files for glucose measurements can be imported, such as... Figure 19 As shown.

[0163] 2.1.3.1 Vi-CELL Multiple Files

[0164] First, a bioprocess should be created for each bioreactor before obtaining sampling readings. Second, the "Remarks" section should be used to indicate the culture day named D# (culture day 1 example = D1). Third, all readings should be saved to a single Excel multi-file. The multi-file should be created using Vi-CELL software.

[0165] 2.1.3.2 BioHT text file

[0166] BioHT sample names follow the Malvern API-LM format: "Bioreactor ID" _D#.

[0167] 2.1.3.3 MetaFLEX file

[0168] First, enter the "Bioreactor ID" in the "Patient ID" field on MetaFLEX. Second, enter the culture day number (enter only #, not D#) in the "Patient Name" field on MetaFLEX.

[0169] 2.1.3.4 AMBR250 Vi-CELL value

[0170] Vi-CELL values ​​from the AMBR250 are exported to a daily table via the AMBR250 software. This table contains only the bioreactor, "Batch Name," and "Viable Cell Density" columns from the AMBR250 software.

[0171] Press the "Input and Feed Target" table as shown below. Figure 19After clicking the "Import Data File" button (1902), the "Data to Import" window (2000) appears, as shown. Figure 20 As shown. You can select the file from which to add cell counts and glucose values ​​by pressing the appropriate "Browse" button 2002 and navigating to the desired file, such as a ViCELL multifile or AMBR250 ViCELL CSV file for ViCell, a MetaFLEX CSV file for MetaFlex, and a BioHT text file for BioHT. After selecting the file, press the "Upload Data" button 2004. The VCD and glucose values ​​will populate the "Input and Feed Target" table.

[0172] For AMBR250, fill in the pH and sampling time when importing using MetaFLEX.

[0173] AMB250 does not export the culture date. When importing an AMBR250 Vi-CELL file, an additional dialog box will open asking for the culture date. Enter the numeric culture date in this window (culture date 1 example = 1).

[0174] 2.1.4 Step 4

[0175] The feed table will calculate the glucose target (g / L) for each AMBR250 vessel. Operators can enter the glucose target into the SQL under column 2102 of "Glucose_Target," and can either manually enter it into the AMBR250 table or use a macro for semi-automatic entry. Figure 21 As shown.

[0176] For AMBR250, when importing the MetaFLEX file, add pH 2202 and time 2204 to the glucose feed table, as follows: Figure 22 As shown. This can then be used to import daily pH offset values ​​into the AMBR250. This only applies to MetaFLEX imports, not BioHT imports. pH is included in... Figure 22 The diagrams are provided to simplify the operation of some microbioreactors. Online pH measurements, determined by the controller, can be verified using offline pH measurements. If these differ, the online measurements are corrected. By including pH values ​​in a table, users can easily import offline measurements for "pH offset" calculations. pH is not required for glucose target calculations.

[0177] 2.1.5 Step 5

[0178] By using the "Export pH and Glucose" macro 2302, such as Figure 23As shown, the glucose table will export two CSV files: a pH offline measurement file for the selected date and a glucose measurement / glucose target file. Save both tables to a flash drive.

[0179] If MetaFLEX is not used to measure glucose / pH, the pH file will be blank.

[0180] 2.1.6 Step Six

[0181] To input the glucose target value into the AMBR software, click the "Table" tab 2402 on the left navigation bar, as shown below. Figure 24 As shown.

[0182] 2.1.7 Step Seven

[0183] Then, on the right side of the screen, click the "Data" tab 2502, as shown. Figure 25 As shown.

[0184] 2.1.8 Step 8

[0185] Next, click the "Enter data..." button in the lower right corner of the screen, as shown in Figure 2602. Figure 26 As shown, a dialog box will pop up. (Two methods—use an export macro or manually enter the data).

[0186] 2.1.9 Step Nine

[0187] In the dialog box, if using the semi-automatic macro method: "Click" "Import Data Using Template" link 2702 (available if the glucose file can be exported from step 2.1.5), as follows... Figure 27 As shown. If using the manual method: "Click" "Enter data value".

[0188] 2.1.10 Step 10

[0189] If using the manual method, skip to step 2.1.12. If using the semi-automatic macro method, after selecting "Import data using template" link 2702, a new dialog box 2800 will appear, as follows: Figure 28 As shown. Select the "Load Data" button 2802 on the left. After loading the table, "highlight" all cells (click the first cell and drag to the last cell), then select the "AutoDetect" button 2804.

[0190] Step 11, Section 2.1.11

[0191] The cells are filled with color codes, depending on the data type in each cell (bioreactor, data header, and data parameters). If all the cell codes are correct, click "Save Data".

[0192] If using a MetaFLEX instrument, the variable glucose algorithm feed table can export the measured pH to a second saved file. In the left-hand navigation bar, select "pH Offset" and then select all reactors, then select "Correct pH Offset". In the dialog box, select "Import Data File" in the lower left corner of the screen, then select "Exported pH File". After filling in the data, remove all inactive bioreactors and select the "Save Data" button 2902, as shown. Figure 29 As shown.

[0193] 2.1.12 Step Twelve

[0194] If the export button in 2.1.5 does not work, under "Enter data value": In the upper right corner of the next window, click "Use current date and time" 3002, as shown. Figure 30 As shown. For each AMBR bioreactor, the measured glucose value (g / L) 3004 and the target glucose value (g / L) 3006 are entered into the table.

[0195] 2.1.13 Step Thirteen

[0196] Finally, click "Save Data" in the bottom right corner of the screen (3008). Figure 30 As shown.

[0197] 2.2 Description of a 5L bioreactor

[0198] 2.2.1 First step

[0199] The feed sheet can be found on the Resources for High Tier Implementation SharePoint site. The link to that site is on the API-LM SharePoint page.

[0200] 2.2.2 Second step

[0201] Before starting the run, enter the SQL bioreactor ID for each reactor and culture day. The SQL bioreactor ID 3102 can be added using an embedded macro, such as... Figure 31 As shown. For information on the use of macros for filling bioreactor IDs, see Section 2.1.2.

[0202] 2.2.3 Third step

[0203] For each 5L bioreactor and culture day, input sampling time 3202 (in 24-hour format hh:mm), and measure VCD 3204 (10^6 cells / mL) and glucose concentration 3206 (g / L) in the bioreactor before feeding. Figure 32 As shown.

[0204] Macros in the Excel file allow the import of external readings from Vi-CELL, BioHT, MetaFLEX, and AMBR250 instruments, such as... Figure 33 As shown. The sample naming requirements detailed below must be followed to accurately import values ​​for each instrument file type. Vi-CELL multifiles or AMBR250 datasheets for VCD measurements can be imported. MetaFLEX or BioHT files for glucose measurements can be imported.

[0205] 2.2.3.1 Vi-CELL Multiple Files

[0206] A bioprocess should be created for each bioreactor before obtaining sampling readings. The "Remarks" section should be used to indicate the culture day named D# (culture day 1 example = D1). Save all readings to a single Excel multi-file. Create the multi-file using Vi-CELL software.

[0207] 2.2.3.2 BioHT text file

[0208] BioHT sample names follow the Malvern API-LM format: "Bioreactor ID" _D#.

[0209] 2.2.3.3 MetaFLEX file

[0210] In the "Patient ID" field on MetaFLEX, enter the "Bioreactor ID". In the "Patient Name" field on MetaFLEX, enter the culture day number (enter only #, not D#).

[0211] After pressing the "Import Data File" button in the "Input and Feed Target" table, the "Data to Import" window (3400) appears, such as... Figure 34 As shown. You can select the file from which to add cell counts and glucose values ​​by pressing the appropriate "Browse" button 3402 and navigating to the desired file, such as a ViCELL multifile for ViCell, a ViCELL CSV file, a MetaFLEX CSV file for MetaFLEX, and a BioHT text file for BioHT. After selecting the file, press the "Upload Data" button. The VCD and glucose values ​​will populate the "Input and Feed Target" table.

[0212] 2.2.4 Step 4

[0213] The feed order will calculate the glucose target (g / L) for each 5L container, which the operator will enter into the SQL under column 3502 of "Glucose_Target". Figure 35 As shown.

[0214] 2.2.5 Step 5

[0215] In SQL (v 2.28 or later), enter the measured glucose (g / L) in column "Glucose_G" (3600) and the glucose target in column "Glucose_Target" (3602). Calculate the volume of glucose to be added to the bioreactor in column "Feed2_Target" (3604). Record the actual volume of glucose fed into the bioreactor (3606). Figure 36 As shown.

[0216] Figure 37 This is an exemplary flowchart illustrating the process of controlling nutrient feed during cell culture. At 3702, a sample can be received from a bioreactor containing the cell culture. At 3704, viable cell density and residual nutrient measurements can be determined from the received sample. At 3706, a daily nutrient feed target can be calculated based on the viable cell density and residual nutrient measurements. At 3708, nutrients can be fed into the bioreactor according to the calculated daily nutrient feed target.

[0217] In one embodiment, the process may further include maintaining the daily residual nutrient concentration in the bioreactor within a predetermined range.

[0218] In one implementation, the daily nutrient feed target can be recalculated based on daily measurements of live cell density and residual nutrients.

[0219] In one implementation, the nutrient may be selected from glucose, glutamic acid, galactose, lactic acid, and glutamine.

[0220] In one implementation, the nutrient may include one or more monosaccharides.

[0221] In one embodiment, residual nutrient measurement may include determining the concentration of nutrients in the bioreactor.

[0222] In one implementation, residual nutrient measurement may include performing one or more of offline and online nutrient measurements.

[0223] In one implementation, residual nutrient measurement can be performed by one or more of the following: a NovaFlex device and a Raman probe.

[0224] In one embodiment, the bioreactor can be one or more of the following: a Chinese hamster ovary (CHO) cell bioreactor and a 5L bioreactor. Other mammalian cell types besides CHO can be used for the production of biopharmaceuticals, including recombinant cells. Non-limiting examples of such mammalian cell types include HEK, 293, and PerC6. The process can also be used with other non-mammalian cell types, such as yeast and bacteria.

[0225] In one implementation, the cells in the bioreactor may be mammalian cells.

[0226] In one implementation, the cells are CHO cells.

[0227] In one implementation, the daily nutrient feed target can be calculated at least in part based on the global average consumption values ​​of at least six cell lines and growth curves predetermined from multiple runs of the bioreactor.

[0228] Figure 38 This is another exemplary flowchart illustrating the process of controlling nutrient feeding during cell culture. At 3802, a sample can be received from a container containing the cell culture. At 3804, viable cell density and residual nutrient measurements can be determined from the received sample. At 3806, a daily nutrient feeding target can be calculated based on the viable cell density and residual nutrient measurements. At 3808, nutrients can be fed into the container according to the calculated daily nutrient feeding target. In one embodiment, the container may be a culture flask.

[0229] In one implementation, the nutrient may be selected from glucose, glutamic acid, galactose, lactic acid, and glutamine.

[0230] Figure 39 This is an exemplary flowchart illustrating the process of balancing glucose feed during cell growth. At 3902, the viable cell density and glucose concentration measured during the cell growth process can be determined periodically. At 3904, the glucose feed target of the nutrient can be adjusted periodically based on the viable cell density and glucose concentration. At 3906, glucose can be periodically fed into the cell growth process according to the glucose feed target.

[0231] Figure 40This is an exemplary flowchart illustrating the process of controlling glucose feed during cell culture. At 4002, a sample can be received from the production reactor containing the cell culture. At 4004, the residual amount of glucose can be measured from the received sample. At 4006, the sampling time when the sample is received from the production reactor can be determined. At 4008, the residual amount of glucose can be compared with a predetermined glucose target. At 4010, the amount of glucose consumed can be calculated. For example, when the residual amount of glucose is greater than the predetermined glucose target, the amount of glucose consumed can be determined by determining the amount of glucose consumed between the previous day and the current day during the cell culture process. In another example, when the residual amount of glucose is not greater than the predetermined glucose target, the amount of glucose consumed can be determined based on the difference between the predetermined glucose target and the residual amount of glucose.

[0232] At 4012, the integral viable cell density can be calculated. At 4014, the predetermined viable cell density for the next day can be calculated based on the integral viable cell density. At 4016, the specific glucose consumption rate can be calculated based on the glucose consumption and the integral viable cell density. At 4018, the predicted glucose consumption can be calculated by multiplying the specific glucose consumption rate by the predetermined viable cell density for the next day. At 4020, the glucose target can be calculated by adding the predetermined glucose consumption and the predetermined minimum glucose amount. At 4022, glucose can be fed into the production reactor according to the glucose target.

[0233] In one implementation scheme, feeding can be carried out daily.

[0234] Figure 41 This is an exemplary flowchart illustrating the process of adjusting the amount of reagent saccharification during cell culture. At 4102, a sample can be received from the production reactor containing the cell culture. At 4104, the residual amount of nutrients can be measured from the received sample. At 4106, the amount of nutrients consumed since the last feed can be determined based on the residual amount of nutrients. At 4108, the viable cell density can be determined from the received sample. At 4110, the predicted amount of nutrients to be consumed before the next feed can be calculated based on the nutrient consumption and viable cell density. At 4112, the target amount of nutrients for this feed can be calculated based on the predicted nutrient consumption before the next feed and a predetermined residual nutrient target. At 4114, nutrients can be fed into the bioreactor according to the calculated target amount of nutrients.

[0235] In one implementation, the predicted live cell density between the current feed and the next feed can be determined at least in part based on the determined live cell density. The nutrient consumption rate can be determined at least in part based on the amount of nutrient consumed. The predicted consumption rate can be calculated based on the predicted live cell density and the nutrient consumption rate.

[0236] In one implementation scheme, feeding can be carried out daily.

[0237] In one implementation, the nutrient may be selected from glucose, glutamic acid, galactose, lactic acid, and glutamine.

[0238] In one implementation, the nutrient may include one or more monosaccharides.

[0239] Figure 42 This is an exemplary flowchart illustrating the process of controlling glucose feed during cell culture. At 4202, a glucose measurement can be determined. At 4204, a lactate measurement can be determined. At 4206, the current culture day can be determined. At 4208, a glucose target can be determined based on a combination of the glucose measurement, lactate measurement, and the current culture day.

[0240] In one implementation, the glucose target can be 5 g / L when the glucose measurement is less than 1 g / L, the lactate measurement is less than 1 g / L, and the current culture day is day 5.

[0241] In one implementation, the glucose target can be 4.5 g / L when the glucose measurement is less than 1 g / L, the lactate measurement is greater than 1 g / L and less than 3 g / L, and the current culture day is day 5.

[0242] Figure 43This is another exemplary flowchart illustrating the process of controlling glucose feed during cell culture. At 4302, a sample can be received from the production reactor containing the cell culture. At 4304, the residual amount of glucose can be measured from the received sample. At 4306, the residual amount of glucose can be compared with a predetermined glucose target. At 4308, glucose consumption can be calculated. For example, when the residual amount of glucose is greater than the predetermined glucose target, glucose consumption can be determined by determining the amount of glucose consumed between the previous day and the current day during the cell culture process. In another example, when the residual amount of glucose is not greater than the predetermined glucose target, glucose consumption can be determined based on the difference between the predetermined glucose target and the residual amount of glucose. At 4310, the viable cell density for the current day can be determined. At 4312, the viable cell density for the previous day can be determined. At 4314, the growth rate can be estimated based on the viable cell density for the current day and the viable cell density for the previous day. At 4316, the integral viable cell density for the next day can be predicted based on the estimated growth rate. At 4318, the predetermined live cell density for the next day can be calculated based on the integrated live cell density. At 4320, the specific glucose consumption rate can be calculated based on the glucose consumption and the integrated live cell density. At 4322, the predicted glucose consumption can be calculated by multiplying the specific glucose consumption rate by the predetermined live cell density for the next day. At 4324, the glucose target can be calculated by adding the predetermined glucose consumption and the predetermined minimum glucose amount. At 4326, glucose can be fed into the production reactor according to the glucose target.

[0243] The following is a list of abbreviations and definitions.

[0244] d can refer to the cell culture day.

[0245] d+1 can refer to the next cell culture day.

[0246] d-1 can refer to the previous cell culture day.

[0247] VCD can refer to live cell density (number of cells per mL).

[0248] IVCD can refer to integral viable cell density (cells / mL*day).

[0249] ΔIVCD d It can refer to the change in IVCD from day d-1 to day d (cell count * day / mL).

[0250] VCD d+1 It could refer to the VCD on day d+1.

[0251] ΔIVCD d+1It can refer to the change in IVCD from day d to day d+1 (cell count * day / mL).

[0252] IQR can refer to the interquartile range.

[0253] glucose target d This can refer to the target concentration (g / L) of glucose in the feed reactor.

[0254] glucose consumption rate d It can refer to the amount of glucose consumed each day ((g / L) / day) from time d-1 to time d.

[0255] glucose consumption rate d It can refer to the amount of glucose consumed by the cells each day from time d-1 to time d (pg / (cell number* / day)).

[0256] Residual glucose target d+1 It can refer to the required theoretical concentration (g / L) of residual glucose in the reactor at time d+1.

[0257] Predicting glucose consumption d+1 It can refer to the predicted glucose (g / L) consumed at time d+1.

[0258] VCD d+1 The change in prediction multiple can refer to the change in prediction multiple in the VCD exported from the database from time d to time d+1. Assume that one day has passed (unitless).

[0259] Predicting glucose consumption d+1 It can refer to the predicted glucose concentration (g / L) consumed by the cell culture from time d to time d+1.

[0260] Measuring glucose d It can refer to the glucose concentration d measured in the bioreactor on that day.

[0261] ΔIVCD d+1 / VCD d+1 It can refer to the multiple difference between ΔIVCD and VCD from d to d+1. If ΔIVCD = VCD, then it equals 1.

[0262] ΔIVCD d+1 / ΔIVCD d It can refer to the multiple change of ΔIVCD from d to d+1.

[0263] Measurement of VCD d+1 It can refer to the VCD measured at d+1.

[0264] This written description uses examples to disclose certain embodiments of the disclosed technology, including best practices, and also enables any person skilled in the art to practice certain embodiments of the disclosed technology, including making and using any apparatus or system and performing any incorporated methods. The patentable scope of certain embodiments of the disclosed technology is defined in the claims and may include other examples that would occur to a person skilled in the art. Such other examples are intended to be within the scope of the claims if they have structural elements identical to the literal language of the claims, or if they include equivalent structural elements that are not substantially different from the literal language of the claims.

[0265] While some embodiments of the disclosed technology have been described in conjunction with various embodiments currently considered to be most practical, it should be understood that the disclosed technology is not limited to the disclosed embodiments, but rather is intended to cover various modifications and equivalent arrangements included within the scope of the appended claims. Although specific terminology is used herein, it is used only in a general and descriptive sense and not for limiting purposes.

[0266] The foregoing description of some embodiments of the disclosed technology, with reference to block diagrams and flowcharts of systems and / or computer program products according to exemplary embodiments of the disclosed technology, illustrates certain implementations of the disclosed technology. It should be understood that one or more blocks in the block diagrams and flowcharts, as well as combinations of blocks in the block diagrams and flowcharts, can be implemented by computer-executable program instructions, respectively. Similarly, according to some embodiments of the disclosed technology, some blocks in the block diagrams and flowcharts may not need to be executed in the order presented, or may not need to be executed at all.

[0267] These computer program instructions may also be stored in a computer-readable storage medium that can instruct a computer or other programmable data processing device to operate in a particular manner, such that the instructions stored in the computer-readable storage medium produce an article of writing including instruction means that implement one or more functions specified in one or more flowchart blocks.

[0268] Implementations of the disclosed technology can provide a computer program product comprising a computer-usable medium having computer-readable program code or program instructions contained therein, the computer-readable program code being adapted to be executed to implement one or more functions specified in one or more flowchart blocks. The computer program instructions can also be loaded onto a computer or other programmable data processing apparatus to cause a series of operational elements or steps to be performed on the computer or other programmable apparatus, thereby producing a computer-implemented process, such that the instructions, which execute on the computer or other programmable apparatus, provide elements or steps for implementing the functions specified in one or more flowchart blocks.

[0269] Therefore, the boxes in block diagrams and flowcharts support combinations of means for performing a specified function, combinations of elements or steps for performing a specified function, and program instruction means for performing a specified function. It should also be understood that each box in a block diagram and flowchart, and combinations of boxes in block diagrams and flowcharts, can be implemented by a hardware-based dedicated computer system, or a combination of dedicated hardware and computer instructions, that performs the specified function, element, or step.

Claims

1. A method for controlling glucose feeding during cell culture, comprising: Samples are received from a bioreactor containing cell cultures; The residual amount of glucose was measured from the received sample; Determine the sampling time when the sample is received from the bioreactor; The residual amount of glucose is compared with a predetermined glucose target; Calculate glucose consumption using the following steps: When the residual amount of glucose is greater than the predetermined glucose target, the amount of glucose consumed is determined by determining the amount of glucose consumed between the previous day and the current day during the cell culture process. When the residual amount of glucose is not greater than the predetermined glucose target, the amount of glucose consumed is determined based on the difference between the predetermined glucose target and the residual amount of glucose. Calculate the integral live cell density; The predetermined live cell density for the next day is calculated based on the integrated live cell density. The specific glucose consumption rate is calculated based on the amount of glucose consumed and the integral viable cell density. The predicted glucose consumption is calculated by multiplying the specific glucose consumption rate by the predetermined live cell density on the second day; The glucose target is calculated by adding the predetermined glucose consumption and the predetermined minimum glucose amount; and Glucose is fed into the bioreactor according to the glucose target.

2. The method of claim 1, wherein the feeding is performed daily.

3. A method for adjusting the amount of saccharification of reagents during cell culture, comprising: Samples are received from a bioreactor containing cell cultures; The residual amount of nutrients was measured from the received samples; The amount of the nutrient consumed since the last feed is determined based on the residual amount of the nutrient. Determine the live cell density from the received samples; The predicted consumption of the nutrients to be consumed before the next feed is calculated based on the consumption of the nutrients and the live cell density. The target amount of nutrients in this feed is calculated based on the predicted consumption of the nutrients before the next feed and the predetermined residual nutrient target. as well as The nutrients are fed into the bioreactor according to the calculated target amount of the nutrients.

4. The method according to claim 3, further comprising: The predicted live cell density between the current feed and the next feed is determined at least in part based on the determined live cell density. The rate of nutrient consumption is determined at least in part based on the amount of the nutrient consumed; and The predicted consumption rate is calculated based on the predicted live cell density and the nutrient consumption rate.

5. The method of claim 3, wherein the feeding is performed daily.

6. The method according to any one of claims 3 to 5, wherein the nutrient is selected from glucose, glutamic acid, galactose, lactic acid and glutamine.

7. The method according to any one of claims 3 to 5, wherein the nutrient comprises one or more monosaccharides.

8. A method for controlling glucose feeding during cell culture, comprising: Samples are received from a bioreactor containing cell cultures; The residual amount of glucose was measured from the received sample; The residual amount of glucose is compared with a predetermined glucose target; Calculate glucose consumption using the following steps: When the residual amount of glucose is greater than the predetermined glucose target, the amount of glucose consumed is determined by determining the amount of glucose consumed between the previous day and the current day during the cell culture process. When the residual amount of glucose is not greater than the predetermined glucose target, the amount of glucose consumed is determined based on the difference between the predetermined glucose target and the residual amount of glucose. Determine the live cell density for that day; Determine the live cell density of the previous day; The growth rate is estimated based on the live cell density of the day and the live cell density of the previous day; The integral live cell density for the second day is predicted based on the estimated growth rate. The predetermined live cell density for the second day is calculated based on the integral live cell density. The specific glucose consumption rate is calculated based on the amount of glucose consumed and the integral viable cell density. The predicted glucose consumption is calculated by multiplying the specific glucose consumption rate by the predetermined live cell density on the second day; The glucose target is calculated by adding the predetermined glucose consumption and the predetermined minimum glucose amount; and Glucose is fed into the bioreactor according to the glucose target.

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