Literature DB >> 33529582

The future of phenotypic drug discovery.

Ellen L Berg1.   

Abstract

Phenotypic drug discovery (PDD) uses biological systems directly for new drug screening. While PDD has proved effective in the discovery of drugs with novel mechanisms, for broader adoption, key challenges need resolution: progression of poorly qualified leads and overloaded pipelines due to lack of effective tools to process and prioritize hits; and advancement of leads with undesirable mechanisms that fail at more expensive stages of discovery. Here I discuss how human-based phenotypic platforms are being applied throughout the discovery process for hit triage and prioritization, for elimination of hits with unsuitable mechanisms, and for supporting clinical strategies through pathway-based decision frameworks. Harnessing the data generated in these platforms can also fuel a deeper understanding of drug efficacy and toxicity mechanisms. As these approaches increase in use, they will gain in power for driving better decisions, generating better leads faster and in turn promoting greater adoption of PDD.
Copyright © 2021 Elsevier Ltd. All rights reserved.

Entities:  

Keywords:  Microphysiological systems; Phenotypic assay; adverse outcome pathway; assay performance; assay qualification; cell-based assays; discovery program outcome pathway; drug discovery; drug discovery innovation; drug discovery productivity; drug safety; drug screening; hit prioritization; hit triage; human cells; lead discovery; mechanism of action; non-animal alternatives; phenotypic drug discovery; phenotypic profiling; primary cells; translational research

Year:  2021        PMID: 33529582     DOI: 10.1016/j.chembiol.2021.01.010

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


  3 in total

1.  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

2.  High-throughput platform for yeast morphological profiling predicts the targets of bioactive compounds.

Authors:  Shinsuke Ohnuki; Itsuki Ogawa; Kaori Itto-Nakama; Fachuang Lu; Ashish Ranjan; Mehdi Kabbage; Abraham Abera Gebre; Masao Yamashita; Sheena C Li; Yoko Yashiroda; Satoshi Yoshida; Takeo Usui; Jeff S Piotrowski; Brenda J Andrews; Charles Boone; Grant W Brown; John Ralph; Yoshikazu Ohya
Journal:  NPJ Syst Biol Appl       Date:  2022-01-27

3.  Phenotypic screening of the ReFRAME drug repurposing library to discover new drugs for treating sickle cell disease.

Authors:  Belhu Metaferia; Troy Cellmer; Emily B Dunkelberger; Quan Li; Eric R Henry; James Hofrichter; Dwayne Staton; Matthew M Hsieh; Anna K Conrey; John F Tisdale; Arnab K Chatterjee; Swee Lay Thein; William A Eaton
Journal:  Proc Natl Acad Sci U S A       Date:  2022-09-26       Impact factor: 12.779

  3 in total

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