Opportunities and challenges in phenotypic drug discovery: an industry perspective

There has been a resurgence in interest in phenotypic drug discovery (PDD) approaches in recent years based on their potential to address the incompletely understood complexity of diseases and their promise of delivering first-in-class drugs. However, PDD approaches can also present considerable challenges, and this article focuses on the lessons learned by researchers engaged in PDD in the pharmaceutical industry, and discusses how PDD can best deliver value to drug discovery portfolios. Phenotypic drug discovery (PDD) approaches do not rely on knowledge of the identity of a specific drug target or a hypothesis about its role in disease, in contrast to the target-based strategies that have been widely used in the pharmaceutical industry in the past three decades. However, in recent years, there has been a resurgence in interest in PDD approaches based on their potential to address the incompletely understood complexity of diseases and their promise of delivering first-in-class drugs, as well as major advances in the tools for cell-based phenotypic screening. Nevertheless, PDD approaches also have considerable challenges, such as hit validation and target deconvolution. This article focuses on the lessons learned by researchers engaged in PDD in the pharmaceutical industry and considers the impact of 'omics' knowledge in defining a cellular disease phenotype in the era of precision medicine, introducing the concept of a chain of translatability. We particularly aim to identify features and areas in which PDD can best deliver value to drug discovery portfolios and can contribute to the identification and the development of novel medicines, and to illustrate the challenges and uncertainties that are associated with PDD in order to help set realistic expectations with regard to its benefits and costs.