https://f1000research.com/articles/9-944/v1
https://doi.org/10.12688/f1000research.25813.1
https://pubmed.ncbi.nlm.nih.gov/32850117/
TITLE:
Recent advances in phenotypic drug discovery
ALTERNATIVE TITLE:
None
DATE:
Verify – Thu, 27 Aug 2020 06:00:00 -0400
AUTHORS:
David C Swinney,Jonathan A Lee
SOURCE:
F1000Research
DESCRIPTION:
There is a great need for innovative new medicines to treat unmet medical needs. The discovery and development of innovative new medicines is extremely difficult, costly, and inefficient. In the last decade, phenotypic drug discovery (PDD) was reintroduced as a strategy to provide first-in-class medicines. PDD uses empirical, target-agnostic lead generation to identify pharmacologically active molecules and novel therapeutics which work through unprecedented drug mechanisms. The economic and…
CONTENT:
F1000Res. 2020 Aug 7;9:F1000 Faculty Rev-944. doi: 10.12688/f1000research.25813.1. eCollection 2020.
ABSTRACT
There is a great need for innovative new medicines to treat unmet medical needs. The discovery and development of innovative new medicines is extremely difficult, costly, and inefficient. In the last decade, phenotypic drug discovery (PDD) was reintroduced as a strategy to provide first-in-class medicines. PDD uses empirical, target-agnostic lead generation to identify pharmacologically active molecules and novel therapeutics which work through unprecedented drug mechanisms. The economic and scientific value of PDD is exemplified through game-changing medicines for hepatitis C virus, spinal muscular atrophy, and cystic fibrosis. In this short review, recent advances are noted for the implementation and de-risking of PDD (for compound library selection, biomarker development, mechanism identification, and safety studies) and the potential for artificial intelligence. A significant barrier in the decision to implement PDD is balancing the potential impact of a novel mechanism of drug action with an under-defined scientific path forward, with the desire to provide infrastructure and metrics to optimize return on investment, which a known mechanism provides. A means to address this knowledge gap in the future is to empower precompetitive research utilizing the empirical concepts of PDD to identify new mechanisms and pharmacologically active compounds.
PMID:32850117 | PMC:PMC7431967 | DOI:10.12688/f1000research.25813.1
PUBMED ID:
pubmed:32850117
OTHER ID:
pmid:32850117,pmc:PMC7431967,doi:10.12688/f1000research.25813.1
PUBLICATION DATE:
Thu, 27 Aug 2020 06:00:00 -0400
2020-08-28
RETRIEVAL DATE :
08/27/20 07:01AM
LINK – PUBMED:
https://pubmed.ncbi.nlm.nih.gov/32850117/?utm_source=Other&utm_medium=rss&utm_campaign=pubmed-2&utm_content=1l7lJQBQXfTBJkL86rnvYKzafMiKbgcUrlv_X8_D_H5EuRmkjR&fc=20200708141943&ff=20200827070102&v=2.11.5
LINK – DOI:
https://doi.org/10.12688/f1000research.25813.1
LINK – FULL TEXT:
Pending
NOTES:
None