Literature DB >> 33740435

High-content phenotypic and pathway profiling to advance drug discovery in diseases of unmet need.

Rebecca E Hughes1, Richard J R Elliott1, John C Dawson1, Neil O Carragher2.   

Abstract

Conventional thinking in modern drug discovery postulates that the design of highly selective molecules which act on a single disease-associated target will yield safer and more effective drugs. However, high clinical attrition rates and the lack of progress in developing new effective treatments for many important diseases of unmet therapeutic need challenge this hypothesis. This assumption also impinges upon the efficiency of target agnostic phenotypic drug discovery strategies, where early target deconvolution is seen as a critical step to progress phenotypic hits. In this review we provide an overview of how emerging phenotypic and pathway-profiling technologies integrate to deconvolute the mechanism-of-action of phenotypic hits. We propose that such in-depth mechanistic profiling may support more efficient phenotypic drug discovery strategies that are designed to more appropriately address complex heterogeneous diseases of unmet need.
Copyright © 2021 Elsevier Ltd. All rights reserved.

Entities:  

Keywords:  esophageal cancer; glioblastoma; high-content imaging; machine learning; network pharmacology; structural similarity

Mesh:

Substances:

Year:  2021        PMID: 33740435     DOI: 10.1016/j.chembiol.2021.02.015

Source DB:  PubMed          Journal:  Cell Chem Biol        ISSN: 2451-9448            Impact factor:   8.116


  3 in total

Review 1.  Phenotypic drug discovery: recent successes, lessons learned and new directions.

Authors:  Fabien Vincent; Arsenio Nueda; Jonathan Lee; Monica Schenone; Marco Prunotto; Mark Mercola
Journal:  Nat Rev Drug Discov       Date:  2022-05-30       Impact factor: 112.288

2.  Machine Learning Enables Accurate and Rapid Prediction of Active Molecules Against Breast Cancer Cells.

Authors:  Shuyun He; Duancheng Zhao; Yanle Ling; Hanxuan Cai; Yike Cai; Jiquan Zhang; Ling Wang
Journal:  Front Pharmacol       Date:  2021-12-17       Impact factor: 5.810

3.  BowSaw: Inferring Higher-Order Trait Interactions Associated With Complex Biological Phenotypes.

Authors:  Demetrius DiMucci; Mark Kon; Daniel Segrè
Journal:  Front Mol Biosci       Date:  2021-06-17
  3 in total

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