Pharmaceutical process optimisation: Decision support under high uncertainty
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2023-02-01
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Computers and Chemical Engineering
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Elsevier
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Computing and Information Systems
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Journal Article
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Pham, T. D., Manapragada, C., Rajan, N. & Aickelin, U. (2023). Pharmaceutical process optimisation: Decision support under high uncertainty. Computers and Chemical Engineering, 170, https://doi.org/10.1016/j.compchemeng.2022.108100.
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Abstract
Optimisation of cell culture processes is an extremely challenging problem for the biomanufacturing industry. Limited data availability, coupled with biological complexity in modelling highly variable living cells, necessitates a decision support methodology that is performant under high levels of uncertainty. Raw materials, experimental & manufacturing facilities, and human expertise are all steeply expensive and availability is tightly constrained — planning their allocation is subject to the core uncertainties underlying the behaviour of living cells.
This paper presents a novel decision support methodology for optimisation under high uncertainty. Optimisation techniques require an “objective function” that maps decision variables to the quantity being optimised, so that the decision space can be explored to find an optimum. By learning multiple types of objective function candidates with different levels of fidelity to real-world processes, our method mitigates the risk of picking a poor approximation of the objective function due to sampling effects and algorithmic randomness.
Wet lab experimentation on a biomanufacturing feed optimisation problem verified that inferred candidates can successfully support domain experts in designing a new optimised feed strategy with significantly higher product yield than the current industrial control strategy. Our results indicate potential for extending our methodology to the optimisation of other complex industrial processes.