Abstract
Substitution of serum and other clinically incompatible reagents is requisite for controlling product quality in a therapeutic cell manufacturing process. However, substitution with chemically defined compounds creates a complex, large-scale optimization problem due to the large number of possible factors and dose levels, making conventional process optimization methods ineffective. We present a framework for high-dimensional optimization of serum-free formulations for the expansion of human hematopoietic cells. Our model-free approach utilizes evolutionary computing principles to drive an experiment-based feedback control platform. We validate this method by optimizing serum-free formulations for first, TF-1 cells and second, primary T-cells. For each cell type, we successfully identify a set of serum-free formulations that support cell expansions similar to the serum-containing conditions commonly used to culture these cells, by experimentally testing less than 1 × 10−5 % of the total search space. We also demonstrate how this iterative search process can provide insights into factor interactions that contribute to supporting cell expansion.
Highlights
Substitution of serum and other clinically incompatible reagents is requisite for controlling product quality in a therapeutic cell manufacturing process
A performance score was calculated, which was the cell expansion obtained with the encountered test formulations normalized to the maximum cell expansion achieved by the serum-containing “Positive Control” (PC) i.e. cell expansion achieved using commonly used serumcontaining culture formulation for a given cell type
The proof-of-concept study presented here demonstrated the ability of high dimensional-Differential Evolution (HD-DE), a closed feedback optimization system to identify serum-free culture formulations for human hematopoietic cells from a large, complex solution space
Summary
Substitution of serum and other clinically incompatible reagents is requisite for controlling product quality in a therapeutic cell manufacturing process. The incorporation of algorithmic optimization methods (including Differential Evolution principles) have been shown to be a feasible approach for the optimization of drug combinations based on in vitro cell culture data[13,16,17,18,19,20] This strategy is especially befitting in cases where discovery of combinations of multiple compounds are advantageous, but have only been applied to small scale optimization involving fewer factors (4–8 factors), requiring selective screening of multiple groups of factors, or dependent on a process that involves heavy human intervention. We refer to this approach as high dimensional-Differential Evolution (HD-DE) This strategy enables an automated, efficient optimization strategy for serum-free culture formulations that support cell expansion. We illustrate how the data generated during the optimization process can be used to gain insights into factor potency, synergies, and dose-dependent effects
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