Drug Response Profile-Based Machine Learning Enables Strategic Cell Line and Compound Selection for Drug Development.

Abstract

MOTIVATION: Early-stage drug discovery relies on testing compounds across a limited set of cell lines, making it challenging to capture biological diversity while maintaining experimental efficiency. Current predictive approaches often depend on high-dimensional omics data, which can be costly and difficult to interpret. We therefore evaluated whether drug-response panel (DRP) descriptors, which capture sensitivity profiles to a reference set of compounds, can provide an efficient and informative alternative for modelling drug response. RESULTS: Using gradient boosting models across GDSC and CCLE datasets, DRP descriptors consistently outperformed mRNA expression features in predicting drug sensitivity (-log10(IC50)), although performance varied across compounds. Model interpretation recovered known MAPK-associated sensitivity signatures and identified potential biomarkers for MEK1/2 and BTK/MNK inhibitors. Extending this framework, we demonstrated its utility in compound prioritisation by distinguishing between tumourigenic MCF7 and non-tumourigenic MCF10A cells, successfully identifying compounds with selective activity. Together, these results show that DRP-based representations, derived from compact screening panels, support efficient cell line selection, biomarker discovery, and compound prioritisation in early-stage drug development. AVAILABILITY: Code and data uploaded to https://github.com/abbiAR/-Strategic-Cell-Line-and-Compound-Selection-Using-Drug-Response-Profiles. SUPPLEMENTARY INFORMATION: Supplementary data are available at Bioinformatics online.