Artificial Intelligence for Drug Toxicity and Safety. (September 2019)

• Record Type: Journal Article
• Authors: Basile, Anna O.; Yahi, Alexandre; Tatonetti, Nicholas P.
• Is Part Of: Trends in pharmacological sciences. Volume 40:Number 9(2019)
• Page Start: 624
• Page End: 635
• Publication Date: 2019-09
• Subjects: pharmacovigilance -- machine learning -- deep learning -- adverse drug reactions; Pharmacology -- Periodicals; Pharmacology -- trends -- Periodicals; Pharmacologie -- Périodiques; Pharmacology; Electronic journals; Periodicals; 615.1
• DOI: 10.1016/j.tips.2019.07.005
• Languages: English
• ISSNs: 0165-6147

Abstract

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 reactions (ADRs). Safety efforts begin during the development process, using in vivo and in vitro studies, continue through clinical trials, and extend to postmarketing 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 (AI) and machine learning to improve drug safety science. Here, we explore recent advances as applied to preclinical drug safety and postmarketing surveillance with a specific focus on machine and deep learning (DL) approaches. Highlights: The expansion of publicly available resources and the adoption of electronic health records (EHRs) has enabled the use of AI methods for pharmacovigilance. Traditional methods for assessing preclinical safety, like quantitative structure–activity relationships (QSAR), are largely moving toward ensemble machine learning (ML) and deep learning (DL) approaches. Postmarketing pharmacovigilance relies on a variety of data sources such as molecular, chemoinformatic, and clinical databases, as well as social media and biomedical literature. DL-powered natural language processing (NLP) methods, including word embedding and attention mechanisms, are the techniques of choice to extract drug–adverse event (AE) relationships in text data.