CN-115175982-B - Computer-implemented method, computer program product, and system for simulating a cell culture process

CN115175982BCN 115175982 BCN115175982 BCN 115175982BCN-115175982-B

Abstract

A computer-implemented method for simulating a cell culture process is provided. The method comprises obtaining (S10) measurable parameter values measured for at least one operation of the cell culture process, the measurable parameter values being values of measurable parameters in a cell culture process model, estimating (S20) values of non-measurable parameters in the model using Bayesian inference using the obtained measurable parameter values, wherein the model describes the cell culture process using a normal differential equation comprising a coupling of the measurable parameters and the non-measurable parameters, wherein one or more of the non-measurable parameters relate to cells lysed in the cell culture process, receiving (S30) one or more new measurable parameter values related to the one or more operating conditions of the cell culture process, simulating (S40) the cell culture process using the model of the cell culture process, the pre-estimated values of the non-measurable parameters in the model, and the accepted one or more measurable parameter values.

Inventors

Oliver Klorek
Christopher McCready

Assignees

赛多利斯司特蒂姆数据分析公司

Dates

Publication Date: 20260505
Application Date: 20210219
Priority Date: 20200221

Claims (7)

1. A computer-implemented method for simulating a cell culture process, comprising: S30, receiving one or more measurable parameter values associated with one or more operating conditions of the cell culture process, the one or more operating conditions including one or more of fresh medium feed flow rate, fresh medium composition, cell outflow flow rate, harvest flow rate, temperature, pH, dissolved oxygen, agitation rate, living cell targets, top gas, operating volume; s40 the cell culture process was simulated using the following: A model of the cell culture process; An estimate of an unmeasurable parameter in the model, and The received one or more measurable parameter values; receiving information indicative of a desired cell growth during cell culture, and Based on the results of the simulated cell culture process, optimal operating conditions for obtaining the desired cell growth are determined, Wherein the model of the cell culture process describes the cell culture process using a normal differential equation comprising a coupling of measurable parameters and the non-measurable parameters, one or more of the measurable parameters relating to the one or more operating conditions of the cell culture process; Wherein one or more of the non-measurable parameters relates to lysed cells during the cell culture; Wherein one or more of the non-measurable parameters include one or more of: Concentration of lysed cells during cell culture, The concentration of biological material that has a toxic effect on living cells, Wherein the concentration of lysed cells is tracked by modeling the breakdown of dead cells, Wherein the production of toxic biological material is modeled as a function of viable cell density, Wherein the modeling of the dissociation of dead cells involves cell death rate adjusted by the concentration of lysed cells or the concentration of toxic biological material, and Wherein the predicted value of the non-measurable parameter is estimated using bayesian inference using a measurable parameter value measured for at least one operation of the cell culture process.
2. The computer-implemented method of claim 1, further comprising: S10, obtaining a measurable parameter value measured for at least one operation of the cell culture process; S20, estimating the value of an unmeasurable parameter in the model by using Bayesian inference by using the obtained measurable parameter value, wherein the value of the unmeasurable parameter is used as a predicted value of the unmeasurable parameter in the simulated cell culture process.
3. The method of claim 1 or 2, further comprising: Generating one or more control signals for controlling one or more devices performing the cell culture process to operate under the determined optimal operating conditions, and The generated one or more control signals are output.
4. A computer program product comprising computer readable instructions which, when loaded and run on a computer, cause the computer to perform the method of any of claims 1-3.
5. A system for simulating a cell culture process, comprising: A storage medium (30) storing a model of the cell culture process describing the cell culture process using a normal differential equation comprising a coupling of measurable and non-measurable parameters, wherein one or more of the measurable parameters relate to one or more operating conditions of the cell culture process, and one or more of the non-measurable parameters relate to lysed cells in the cell culture process, the one or more operating conditions including one or more of fresh medium feed flow rate, fresh medium composition, cell outflow flow rate, harvest flow rate, temperature, pH, dissolved oxygen, agitation rate, living cell targets, top gas, operating volume, and A processor configured to perform the operations of: s30, receiving one or more measurable parameter values associated with the one or more operating conditions of the cell culture process; s40 the cell culture process was simulated using the following: A model of the cell culture process; An estimate of an unmeasurable parameter in the model, wherein the estimate of the unmeasurable parameter is estimated using Bayesian inference using a measurable parameter value measured for at least one operation of the cell culture process, and The received one or more new measurable parameter values; receiving information indicative of a desired cell growth during cell culture, and Based on the results of the simulated cell culture process, optimal operating conditions for obtaining the desired cell growth are determined, Wherein one or more of the non-measurable parameters include one or more of: Concentration of lysed cells during cell culture, The concentration of biological material that has a toxic effect on living cells, Wherein the concentration of lysed cells is tracked by modeling the breakdown of dead cells, Wherein the production of toxic biological material is modeled as a function of viable cell density, Wherein the modeling of the breakdown of dead cells involves cell death rate adjusted by the concentration of lysed cells or the concentration of toxic biological material.
6. The system of claim 5, wherein the processor is further configured to: S10, obtaining a measurable parameter value measured for at least one operation of the cell culture process; S20, estimating the value of an unmeasurable parameter in the model by using Bayesian inference by using the obtained measurable parameter value, wherein the value of the unmeasurable parameter is used as a predicted value of the unmeasurable parameter in the simulated cell culture process.
7. The system of claim 5 or 6, wherein the processor is further configured to: Generating one or more control signals for controlling one or more devices performing the cell culture process to operate under the determined optimal operating conditions, and The generated one or more control signals are output.

Description

Computer-implemented method, computer program product, and system for simulating a cell culture process The present application relates to computer-implemented methods, computer program products and systems for simulating and/or controlling a cell culture process. Background Cell growth during cell culture can be described by a mathematical model. For example, the kinetics of growth of a cell culture process can be described using the kinetics of moruo growth. A model of a cell culture process may be used to simulate the cell culture process. In some cases, the simulation results may be used to control the cell culture process to obtain a desired cell growth during the cell culture process. When modeling different types and/or phases of a cell culture process, separate models or separate sets of model parameters (e.g., model coefficients) corresponding to the respective types and/or phases of the cell culture process may be constructed. For example, a cell culture process in a batch/fed-batch operation may require a different model than a cell culture process model in a continuous medium exchange operation. Furthermore, for example, different phases in a fed-batch operation, such as an exponential growth phase and a stationary phase, may require different models or at least different sets of model parameters. Summary of The Invention According to one aspect, the problem relates to providing a reliable model of a cell culture process suitable for different types and/or different phases of cell culture processes, thereby facilitating control of the cell culture process. This problem is solved by the features disclosed in the independent claims. Further exemplary embodiments are defined by the dependent claims. According to one aspect, a computer-implemented method for simulating a cell culture process is provided. The method comprises the following steps: obtaining a measurable parameter value for at least one operational measurement of a cell culture process, the measurable parameter value being a value of a measurable parameter in a cell culture process model, wherein one or more measurable parameters relate to one or more operational conditions of the cell culture process; Estimating a value of an unmeasurable parameter in a model using bayesian inference using the obtained values of the measurable parameters, wherein the model describes a cell culture process using a normal differential equation comprising a coupling of the measurable parameter and the unmeasurable parameter, wherein one or more of the unmeasurable parameters relate to lysed cells in the cell culture process; Receiving one or more new measurable parameter values associated with the one or more operating conditions of the cell culture process; the cell culture process was simulated using the following: a model of a cell culture process; an estimate of an unmeasurable parameter in the model, and One or more new measurable parameter values are received. In the present disclosure, the term "measurable parameter" may denote a parameter, the value of which may be measured and/or quantified with respect to the operation of the cell culture process. For example, the value of the measurable parameter may be measured with a suitable sensor during operation of the cell culture process. Further, for example, the values of the measurable parameters may be quantified based on calculations using values measured with one or more sensors during operation of the cell culture process. Examples of measurable parameters in the present disclosure may include, but are not limited to, fresh medium feed flow rate, fresh medium metabolite concentration, cell outflow flow rate, harvest flow rate, recirculation flow rate, bulk solution volume, viable cell density, viability (e.g., percentage of viable cells), visible dead cell concentration, metabolite concentration, temperature, pH, lysed oxygen, and the like. Furthermore, in the present disclosure, the term "non-measurable parameter" may refer to a parameter whose value cannot be directly measured and/or quantified in a manner similar to that described above for measuring and/or quantifying the value of a measurable parameter. In the method according to the above aspect, the one or more non-measurable parameters may include a concentration of lysed cells during cell culture and/or a concentration of biological material having a toxic effect on living cells. In this disclosure, "lysed cells" may be understood as dead cells that have been broken down and become part of a bulk fluid. Other examples of non-measurable parameters in the present disclosure may include, but are not limited to, maximum growth rate, cell death rate in the absence of lysed cells, toxicity of lysed cells or biological material, lower substrate values for inhibited cell growth, sensitivity to quadratic terms deviating from optimal conditions, higher values for inhibited terms for inhibited growth, and the like. In various embodiments and examp