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Artificial Intelligence for Drug Toxicity and Safety

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Basile, Anna O., Yahi, Alexandre, Tatonetti, Nicholas P.·2019·Trends in Pharmacological Sciences

DOI 10.1016/j.tips.2019.07.005

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Abstract

Interventional pharmacology is one of medicine’s most potent weapons against disease. These drugs, however, can result in damaging side effects and must be closely monitored. Pharmacovigilance is the field of science that monitors, detects, and prevents adverse drug effects. Safety efforts begin during the development process, using in vivo and in vitro studies, continue through clinical trials, and extend to post-marketing surveillance of ADRs in real-world populations. Future toxicity and safety challenges, including increased polypharmacy and patient diversity, stress the limits of these traditional tools. Massive amounts of newly available data present an opportunity for using artificial intelligence and machine learning to improve drug safety science. Here, we explore recent advances as applied to pre-clinical drug safety and post-marketing surveillance with a specific focus on machine and deep learning approaches.

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Alizadehsani, R., Oyelere, S. S., Hussain, S., Jagatheesaperumal, S. K., Calixto, R. R., Rahouti, M., Roshanzamir, M., & De Albuquerque, V. H. C. (2024) · IEEE Access · doi:10.1109/access.2024.3373195 · Cited by 124

See all 363

Authors: Basile, A. O., Yahi, A., & Tatonetti, N. P.
Venue: Trends in Pharmacological Sciences
Publisher: Elsevier BV
Published: 2019-09
Volume: 40
Issue: 9
Pages: 624-635
ISSN: 0165-6147 (ISSN-L)
DOI: 10.1016/j.tips.2019.07.005
OpenAlex: W2966109107
Cited by: 363
References: 95
FWCI: 9.94
Type: journal article
Language: en
Open access: green

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Registry, retraction, Cites and statistics results are computed, not judged, with one exception: a printed reference that no index lists is read once by a model before it counts as a citation error. Claims are extracted by a model and kept only when their quote appears verbatim in the text; each cited source is then checked, with a model’s help, for whether it says what the claim relies on. Those verdicts can be wrong — read the claim’s quote and the cited source before relying on one.

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Bibliographic data: Semantic Scholar, OpenAlex, Crossref.

Contains information from the Semantic Scholar Academic Graph Datasets, made available under the ODC Attribution License (ODC-BY 1.0).

Semantic Scholar’s data is described in: Kinney, R., et al. (2023). The Semantic Scholar Open Data Platform. arXiv:2301.10140.

Artificial Intelligence for Drug Toxicity and Safety

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