Productive CHO cell lines selection in biopharm process development through machine learning on metabolomic dynamics

Abstract

The identification of highly productive cell lines is crucial in the development of bioprocesses for the production of therapeutic monoclonal antibodies (mAbs). Metabolomics data provide valuable information for cell line selection and allow the study of the relationship with mAb productivity and product quality attributes. We propose a novel robust machine learning procedure which, exploiting dynamic metabolomic data from the Ambr®15 scale, supports the selection of highly productive cell lines during biopharmaceutical bioprocess development and scale-up. The metabolomic profiles dynamics allows to identify the cell lines with high productivity, already in the early stages of experimentation, and the biomarkers that are the most related to mAb productivity, finding at the same time the key metabolic pathways for discriminating mAb productivity. Specifically, tricarboxylic acid cycle pathways are predominant in the early stages of the cultivation, while amino and nucleotide sugar pathways influence in the late stages of the culture.