Using chemical and biological data to predict drug toxicity.

Cell painting Chemical structure Chemoinformatics Gene expression Machine learning Toxicity prediction

Journal

SLAS discovery : advancing life sciences R & D
ISSN: 2472-5560
Titre abrégé: SLAS Discov
Pays: United States
ID NLM: 101697563

Informations de publication

Date de publication:
04 2023
Historique:
received: 30 10 2022
revised: 19 12 2022
accepted: 31 12 2022
medline: 25 4 2023
pubmed: 14 1 2023
entrez: 13 1 2023
Statut: ppublish

Résumé

Various sources of information can be used to better understand and predict compound activity and safety-related endpoints, including biological data such as gene expression and cell morphology. In this review, we first introduce types of chemical, in vitro and in vivo information that can be used to describe compounds and adverse effects. We then explore how compound descriptors based on chemical structure or biological perturbation response can be used to predict safety-related endpoints, and how especially biological data can help us to better understand adverse effects mechanistically. Overall, the described applications demonstrate how large-scale biological information presents new opportunities to anticipate and understand the biological effects of compounds, and how this can support predictive toxicology and drug discovery projects.

Identifiants

pubmed: 36639032
pii: S2472-5552(22)13714-7
doi: 10.1016/j.slasd.2022.12.003
pii:
doi:

Types de publication

Journal Article Review Research Support, Non-U.S. Gov't

Langues

eng

Sous-ensembles de citation

IM

Pagination

53-64

Informations de copyright

Copyright © 2023 The Authors. Published by Elsevier Inc. All rights reserved.

Déclaration de conflit d'intérêts

Declaration of Competing Interest The authors declare the following financial interests/personal relationships which may be considered as potential competing interests: Anika Liu reports financial support was provided by GlaxoSmithKline. Srijit Seal reports financial support was provided by Cambridge Commonwealth, European and International Trust, Boak Student Support Fund (Clare Hall), Jawaharlal Nehru Memorial Fund, Allen, Meek and Read Fund, and Trinity Henry Barlow (Trinity College). Srijit Seal reports financial support was provided by Cambridge Centre for Data Driven Discovery and Accelerate Programme for Scientific Discovery made possible by a donation from Schmidt Futures. Hongbin Yang reports financial support was provided by Cambridge Alliance on Medicines Safety (CAMS). Anika Liu reports a relationship with Boehringer Ingelheim Pharma GmbH & Co. KG that includes: employment. Andreas Bender reports a relationship with Healx Ltd that includes: equity or stocks. Andreas Bender reports a relationship with PharmEnable Ltd that includes: equity or stocks. Andreas Bender reports a relationship with Pangea Botanica that includes: employment. Srijit Seal reports a relationship with AbsoluteAI Ltd that includes: employment.

Auteurs

Anika Liu (A)

Yusuf Hamied Department of Chemistry, University of Cambridge, Lensfield Road, CB2 1EW, Cambridge, United Kingdom; Milner Therapeutics Institute, University of Cambridge, Puddicombe Way, CB2 0AW, Cambridge, United Kingdom.

Srijit Seal (S)

Yusuf Hamied Department of Chemistry, University of Cambridge, Lensfield Road, CB2 1EW, Cambridge, United Kingdom.

Hongbin Yang (H)

Yusuf Hamied Department of Chemistry, University of Cambridge, Lensfield Road, CB2 1EW, Cambridge, United Kingdom.

Andreas Bender (A)

Yusuf Hamied Department of Chemistry, University of Cambridge, Lensfield Road, CB2 1EW, Cambridge, United Kingdom. Electronic address: ab454@cam.ac.uk.

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Classifications MeSH