Machine learning on drug-specific data to predict small molecule teratogenicity.
Chemical structure
Drug development
Drug exposure
High-throughput screening
Informatics
Machine learning
Teratogenicity
Translational medicine
Journal
Reproductive toxicology (Elmsford, N.Y.)
ISSN: 1873-1708
Titre abrégé: Reprod Toxicol
Pays: United States
ID NLM: 8803591
Informations de publication
Date de publication:
08 2020
08 2020
Historique:
received:
05
12
2019
revised:
04
05
2020
accepted:
06
05
2020
pubmed:
20
5
2020
medline:
29
4
2021
entrez:
20
5
2020
Statut:
ppublish
Résumé
Pregnant women are an especially vulnerable population, given the sensitivity of a developing fetus to chemical exposures. However, prescribing behavior for the gravid patient is guided on limited human data and conflicting cases of adverse outcomes due to the exclusion of pregnant populations from randomized, controlled trials. These factors increase risk for adverse drug outcomes and reduce quality of care for pregnant populations. Herein, we propose the application of artificial intelligence to systematically predict the teratogenicity of a prescriptible small molecule from information inherent to the drug. Using unsupervised and supervised machine learning, our model probes all small molecules with known structure and teratogenicity data published in research-amenable formats to identify patterns among structural, meta-structural, and in vitro bioactivity data for each drug and its teratogenicity score. With this workflow, we discovered three chemical functionalities that predispose a drug towards increased teratogenicity and two moieties with potentially protective effects. Our models predict three clinically-relevant classes of teratogenicity with AUC = 0.8 and nearly double the predictive accuracy of a blind control for the same task, suggesting successful modeling. We also present extensive barriers to translational research that restrict data-driven studies in pregnancy and therapeutically "orphan" pregnant populations. Collectively, this work represents a first-in-kind platform for the application of computing to study and predict teratogenicity.
Identifiants
pubmed: 32428651
pii: S0890-6238(20)30132-5
doi: 10.1016/j.reprotox.2020.05.004
pmc: PMC7577422
mid: NIHMS1637386
pii:
doi:
Substances chimiques
Teratogens
0
Types de publication
Journal Article
Langues
eng
Sous-ensembles de citation
IM
Pagination
148-158Subventions
Organisme : NHLBI NIH HHS
ID : K01 HL141771
Pays : United States
Organisme : NHGRI NIH HHS
ID : U54 HG007963
Pays : United States
Organisme : NCATS NIH HHS
ID : UL1 TR002243
Pays : United States
Informations de copyright
Copyright © 2020 The Author(s). Published by Elsevier Inc. All rights reserved.
Déclaration de conflit d'intérêts
Declaration of Competing Interest The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper.
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