Recent advances in phenotypic drug discovery [version 1; peer review: 2 approved]

Abstract

There is a great need for innovative new medicines to treat unmet medical needs. The discovery and development of innovative new medicines is extremely difficult, costly, and inefficient. In the last decade, phenotypic drug discovery (PDD) was reintroduced as a strategy to provide first-in-class medicines. PDD uses empirical, target-agnostic lead generation to identify pharmacologically active molecules and novel therapeutics which work through unprecedented drug mechanisms. The economic and scientific value of PDD is exemplified through game-changing medicines for hepatitis C virus, spinal muscular atrophy, and cystic fibrosis. In this short review, recent advances are noted for the implementation and de-risking of PDD (for compound library selection, biomarker development, mechanism identification, and safety studies) and the potential for artificial intelligence. A significant barrier in the decision to implement PDD is balancing the potential impact of a novel mechanism of drug action with an under-defined scientific path forward, with the desire to provide infrastructure and metrics to optimize return on investment, which a known mechanism provides. A means to address this knowledge gap in the future is to empower precompetitive research utilizing the empirical concepts of PDD to identify new mechanisms and pharmacologically active compounds.