Method for Predicting Outcome of an Modelling of a Process in a Bioreactor

Patent metadata

Field	Value
Publication number	US-2025068794-A1
Application number	US-202418948020-A
Country	US
Kind code	A1
Filing date	Nov 14, 2024
Priority date	Jun 16, 2017
Publication date	Feb 27, 2025
Grant date	—

Abstract

A method for predicting outcome of a process used for manufacturing a sample in a bioreactor, the process belonging to a category. The method comprises selecting a process model based on the category; accessing historic data related to past process runs for manufacturing the sample; accessing current data obtained from a current process run of the process. The obtained current data, which is based on the selected process model, comprises: process strategy data, bioreactor instrument data, data from online sensors and/or data from offline sensors. The method further comprises predicting an outcome of at least one selected parameter of the current process run for manufacturing the sample based on the accessed historic data and current data.

First claim

What is claimed is:

1 . A method for predicting outcome of a process used for manufacturing a sample in a bioreactor, wherein the method comprises:
selecting a process model corresponding to a category associated with a type of process; accessing historic data related to at least one past process run for manufacturing the sample; accessing current data obtained from a current process run of the process, wherein the obtained current data, which is based on the selected process model, comprises: process strategy data, bioreactor instrument data, data from online sensors and/or data from offline sensors; subselecting historic data of at least one previous process run similar to the current process run from the accessed historic data; and predicting an outcome of at least one selected parameter of the current process run for manufacturing the sample based on the subselected historic data and current data.

2 . The method according to claim 1 , wherein the method further comprises consolidating historic data, current data and data related to the process model in a database.

3 . The method according to claim 1 , wherein obtaining current data further comprises:
selecting parameters based on the base model used, and reading data of selected parameters.

4 . The method according to claim 1 , wherein the method further comprises handling of missing data in the current data obtained from the current process run.

5 . The method according to claim 4 , wherein handling of missing data further comprises:
replacing missing data values with imputed values, or removing data with missing data values.

6 . The method according to claim 1 , wherein the method further comprises adapting the process model in real-time based on the current data.

7 . The method according to claim 1 , wherein the updated model is determined in response to completion of the current process run.

8 . The method according to claim 6 , wherein the process used in the current process run is determined to belong to a new category and the step of adapting the process run further comprises:
assigning the process to the new category; and storing the process model as a new process model.

9 . The method according to claim 1 , wherein the step of predicting the outcome of at least one selected parameter further comprises:
predicting a forecast of the at least one parameter; and/or predicting a forecast of anomalies; and/or determining and recommending actions to obtain improved conditions for the current process run.

10 . The method according to claim 1 , wherein the at least one selected parameter is selected from cell viability, cell count, product titre, and product quality of the current process run.

11 . The method according to claim 1 , wherein the at least one process parameter is cell viability or cell count.

12 . The method according to claim 1 , further comprising
automatically determining an updated model for predicting outcomes of future process runs, wherein the updated model is determined based at least in part on additional data from the current process run; and applying the updated model to future process runs of other samples.

13 . The method according to claim 12 , wherein the process belongs to a multi-level knowledge tree, and the selecting a process model is based on the multi-level knowledge tree, wherein each level of the knowledge tree corresponds to the category associated with the type of process, and wherein the updated model is determined based at least in part on the process model based on the multi-level knowledge tree.

14 . A non-transitory computer-readable storage medium carrying a computer program predicting outcome of a current process run for manufacturing a sample in a bioreactor according to claim 1 .

15 . A method for modelling of a process used for manufacturing a sample in a bioreactor, wherein the method comprises:
selecting a process model corresponding to a category associated with a type of process; accessing historic data related to at least one past process run for manufacturing the sample; accessing current data obtained from a current process run of the process, wherein the current data, which is based on the selected process model, comprises: process strategy data, bioreactor instrument data, data from online sensors and/or data from offline sensors; monitoring at least one parameter of the current process run for manufacturing the sample; and subselecting historic data of at least one previous process run similar to the current process run from the accessed historic data.

16 . The method according to claim 15 , wherein the method further comprises consolidating historic data, current data and data related to the process model in a database.

17 . The method according to claim 15 , wherein obtaining current data further comprises:
selecting parameters based on the base model used, and reading data of selected parameters.

18 . The method according to claim 15 , wherein the method further comprises handling of missing data in the current data obtained from the current process run.

19 . The method according to claim 18 , wherein handling of missing data further comprises:
replacing missing data values with imputed values, or removing data with missing data values.

20 . The method according to claim 15 , wherein the step of adapting the process model further comprises updating the process model for the category.

21 . The method according to claim 15 , wherein the process used in the current process run is determined to belong to a new category and the step of adapting the process model further comprises creating a new process model by:
assigning the process to the new category; and storing the process model as a new process model.

22 . The method according to claim 15 , wherein the at least one selected parameter is selected from cell viability, cell count, product titre, and product quality of the current process run.

23 . The method according to claim 15 , wherein the at least one process parameter is cell viability or cell count.

24 . The method according to claim 15 , further comprising
automatically determining an updated model for predicting outcomes of future process runs, wherein the updated model is determined based at least in part on additional data from the current process run; and applying the updated model to future process runs of other samples.

25 . The method according to claim 24 , wherein the process belongs to a multi-level knowledge tree, and the selecting a process model is based on the multi-level knowledge tree, wherein each level of the knowledge tree corresponds to the category associated with the type of process, and wherein the updated model is determined based at least in part on the process model based on the multi-level knowledge tree.

26 . A non-transitory computer-readable storage medium carrying a computer program for modelling of a process used for manufacturing a sample in a bioreactor according to claim 15 .

27 . A control system for controlling a process used for manufacturing a sample in a bioreactor, wherein the control system is configured to model the process and is further configured to:
select a process model corresponding to a category associated with a type of process; access historic data related to at least one past process run for manufacturing the

Assignees

• Cytiva Sweden Ab

Inventors

• Swaminathan Krishna Kumar
• Dasaratha Sridhar
• George Sittal
• Jammu Vinay Bhaskar
• Chauhan Alok Singh
• Konduru Nagaraju
• Boddapati Neelima
• Bhatia Ankita
• Mohanty Mithilesh
• Aeron Harsha
• Castan Andreas

Classifications

• G05B13/04

  involving the use of models or simulators · CPC title

• G05B17/02

  electric · CPC title

• G06F30/20 Primary

  Design optimisation, verification or simulation (optimisation, verification or simulation of circuit designs G06F30/30) · CPC title

• C12M41/48 Primary

  Automatic or computerized control (automatic analysis G01N35/00) · CPC title