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Opportunities and challenges in phenotypic drug discovery: an industry perspective

Nature Reviews Drug Discovery volume 16, pages 531543 (2017) | Download Citation

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.

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Acknowledgements

The authors thank A. Subramanian and J. Rudolph for helpful discussions and comments and acknowledge the invaluable contributions of the participants of the 2015 New York Academy of Sciences Symposium and the 2016 Keystone Symposium on phenotypic drug discovery. The idea to gather and share experiences from multiple companies coalesced from conversations with many outstanding scientists during the breaks of those two landmark conferences.

Author information

Author notes

    • Marco Prunotto

    Phenotype and Target ID, Chemical Biology, pRED, Roche, 4070 Basel, Switzerland. Present address: Office of Innovation, Immunology, Infectious Diseases & Ophthalmology (I2O), Roche Late Stage Development, 124 Grenzacherstrasse, 4070 Basel, Switzerland.

Affiliations

  1. Biochemical & Cellular Pharmacology, Genentech, South San Francisco, California 94080, USA.

    • John G. Moffat
  2. Discovery Sciences, Primary Pharmacology Group, Pfizer, Groton, Connecticut 06340, USA.

    • Fabien Vincent
  3. Department of Quantitative Biology, Eli Lilly and Company, Indianapolis, Indiana 46285, USA.

    • Jonathan A. Lee
  4. Novartis Institutes for Biomedical Research, 4002 Basel, Switzerland.

    • Jörg Eder

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Competing interests

John G. Moffat is an employee of Genentech. Fabien Vincent is an employee of Pfizer. Jonathan A. Lee is an employee of Eli Lilly. Jörg Eder is an employee of Novartis. Marco Prunotto is an employee of Roche.

Corresponding author

Correspondence to Marco Prunotto.

Glossary

Chain of translatability

A molecular-level association between the mechanisms that drive the assay phenotype, the preclinical disease model and the human disease.

Molecular phenotype

Gene-level and pathway-level 'omics' signatures shared by disease model and disease state that correspond to and are predictive of disease state versus normal state.

Organoids

In vitro 3D cellular clusters derived from primary tissue or stem cells that show similar characteristics to the tissue of origin; for example, beating cardiomyocytes.

PCSK9

(Proprotein convertase subtilisin/kexin type 9). A secreted protein mainly expressed in the liver. Studies of naturally occurring human genetic variants in PCSK9 provided strong evidence that PCSK9 inhibitors could reduce plasma levels of low-density lipoprotein cholesterol and reduce cardiovascular risk.

Pharmacophores

Groups of molecular features that mediate interactions between a compound and a particular biological target macromolecule and trigger (or block) its biological response.

RNA-seq

Uses rapid sequencing technologies to identify the presence and quantity of RNAs in a biological sample at a given moment in time.

Rule of 3

Three technical factors that influence the probability that a phenotypic assay will identify relevant molecules that affect relevant disease mechanisms: biological system, stimulus and readout.

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https://doi.org/10.1038/nrd.2017.111

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