One-Time Optimization of Advanced T Cell Culture Media Using a Machine Learning Pipeline.

ABSTRACT: The growing application of cell and gene therapies in humans leads to a need for cell type-optimized culture media. Design of Experiments (DoE) is a successful and well known tool for the development and optimization of cell culture media for bioprocessing. When optimizing culture media for primary cells used in cell and gene therapy, traditional DoE approaches that depend on interpretable models will not always provide reliable predictions due to high donor variability. Here we present the implementation of a machine learning pipeline into the DoE-based design of cell culture media to optimize T cell cultures in one experimental step (one-time optimization). We applied a definitive screening design from the DoE toolbox to screen 12 major media components, resulting in 25 (2k + 1) m

SUBMITTER: Grzesik P 

PROVIDER: S-EPMC8320393 | biostudies-literature | 2021

REPOSITORIES: biostudies-literature