Bioprocess Monitoring Using Metabolic Variables for Scale-Up Control
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Solution Overview
Problem
Current bioprocess monitoring and control methods are limited by their reliance on macroscopic process parameters, which restrict flexibility and scalability, as they do not directly account for the metabolic conditions of cells, leading to rigid process design spaces and increased costs and time in scale-up processes.
Innovation Solution
A computer-implemented method that uses multivariate models to monitor bioprocesses by characterizing metabolic conditions, including specific transport rates and reaction rates, allowing for a more flexible and scalable approach by tying product specifications to metabolic properties rather than macroscopic measurements.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Manufacturing precision
If multivariate statistical models (PCA, PLS) are used to identify critical process parameters and establish acceptable ranges, then manufacturing precision of CQAs is improved, but adaptability of process conditions is worsened
Solution Approach 1:
The patent transitions from using macroscopic process parameters (temperature, pH, dissolved oxygen) to using metabolic condition variables (specific transport rates, internal metabolite concentrations, reaction rates) as the basis for monitoring and control. This parameter transformation enables flexible scale-up and process modifications while maintaining CQA compliance, as metabolic conditions can be maintained constant even when macroscopic parameters change during scale-up.
2Manufacturing precision
If macroscopic process parameters are monitored and controlled within predetermined ranges, then manufacturing precision is improved, but device complexity and measurement requirements are worsened
Solution Approach 1:
The patent introduces metabolic condition variables as intermediary parameters that bridge macroscopic process measurements and microscopic cellular responses. By using metabolic variables (specific transport rates, internal metabolite concentrations, reaction rates) as intermediaries, the system can infer cellular metabolic state from macroscopic measurements without requiring direct measurement of intracellular parameters, thus reducing measurement complexity while maintaining control precision.
3Manufacturing precision
If process design space is characterized at each scale using traditional statistical methods, then manufacturing precision is improved, but productivity and time-to-market are worsened
Solution Approach 1:
The patent establishes metabolic condition profiles and multivariate models during early process development at small scale, creating a predictive framework that can be applied at larger scales without requiring complete re-characterization. The metabolic design space is defined upfront based on metabolic variables that remain consistent across scales, enabling parallel scale-up activities and regulatory filings while maintaining process control precision.
Data Source
Figure 1A~1B
Figure 1C
Figure 2
AI summary
A computer implemented method for monitoring a bioprocess comprising a cell culture in a bioreactor is provided. The method including the steps of: obtaining measurements of the amount of biomass and the amount of one or more metabolites in the bioreactor as a function of bioprocess maturity, using the measurements to determining one or more metabolic condition variables; using a pre-trained multivariate model to determine the value of one or more latent variables as a function of bioprocess maturity, wherein the multivariate model is a linear model that uses process variables including the metabolic condition variables as predictor variables and maturity as a response variable; comparing the value(s) of the one or more latent variables to one or more predetermined values as a function of maturity; and determining on the basis of the comparison whether the bioprocess is operating normally.