Opportunities and challenges in phenotypic drug discovery: an industry perspective

Abstract

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.

Self‐Assembly of Therapeutic Peptide into Stimuli‐Responsive Clustered Nanohybrids for Cancer‐Targeted Therapy

“…Intracellular protein–protein interactions (PPIs) is a crucial and yet inadequately exploited class of therapeutic targets for their essential roles in most of cellular processes and disease initiation and progression . Small molecule drugs have been demonstrated to be highly successful in targeting enzymes, receptors, and ion channels for clinical benefits; however, they generally failed to target PPIs due to their own physicochemical limitations . Peptides, on the other hand, can potently and specifically inhibit PPIs, aided by their large interacting surface of diverse topological structure .…”

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“…High Content Imaging (HCI) is a popular and basic tool of early drug discovery in multiple disease research areas (Carpenter, 2007). HCI was developed with the contribution of major advances in robotics, imaging and automated image processing (Moffat et al, 2017). HCI is already a fundamental technology for antiviral screening and validation (Bernatchez et al, 2018;Hoenen, 2017;Lowen et al, 2018;Mudhasani et al, 2014;Panchal et al, 2010;Tan et al, 2014).…”

Elucidating Compound Mechanism of Action and Predicting Cytotoxicity Using Machine Learning Approaches, Taking Prediction Confidence into Account

“…In phenotypic screening, the response of test compounds is evaluated either on diseaserelevant biological readouts (Moffat, Vincent, Lee, Eder, & Prunotto, 2017), or on multidimensional measurements of complex systems (e.g., live cell imaging), from cultured cell lines to animal models (Feng, Mitchison, Bender, Young, & Tallarico, 2009;Haasen et al, 2017;Priest & Erdemli, 2014). This usually leads to noisier readouts than those from biochemical assays that are often hard to interpret.…”