Phenotypic approaches for CNS drugs.

Abstract

Central nervous system (CNS) drug development is plagued by high clinical failure rate. Phenotypic assays promote clinical translation of drugs by reducing complex brain diseases to measurable, clinically valid phenotypes. We critique recent platforms integrating patient-derived brain cells, which most accurately recapitulate CNS disease phenotypes, with higher throughput models, including immortalized cells, to balance validity and scalability. These platforms were screened with conventional commercial chemogenomic compound libraries. We explore emerging library curation strategies to improve hit rate and quality, and screening novel fragment libraries as alternatives, for more tractable drug target deconvolution. The clinically relevant models used in these platforms could harbor important, unidentified drug targets, so we review evolving agnostic target deconvolution approaches, including chemical proteomics and artificial intelligence (AI), which aid in phenotypic screening hit mechanism elucidation, thereby facilitating rational hit-to-drug optimization.