Artificial Intelligence for Drug Toxicity and Safety

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.

Keywords: adverse drug reactions; deep learning; machine learning; pharmacovigilance.

Figure 1:

Artificial intelligence and machine learning present an opportunity for improving drug safety. These algorithms enable a data-driven approach to toxicity and safety assessments that can identify patterns that otherwise would be overlooked. Traditional machine learning, including methods like logistic regression, random forests, and support vector machines can produce interpretable models with relatively low complexity. These methods are desirable when the goal is to understand how the predictors affect the incidence or risk of an adverse event. A new class of methods, called deep neural networks – and often referred to as “artificial intelligence” – allows for more complex models to be built at the cost of requiring significantly more data. The benefit of using these algorithms is that they can automatically identify non-linear patterns in the data without requiring much manual intervention. Common examples that have been used in drug safety research include convolutional and recurrent neural networks. In both cases, these models have been used to in pre-clinical drug toxicity study, to model patient diversity, and to facilitate lead selection and trial design, and in post-marking surveillance to conduct comparative effectiveness research, identify drug-drug interactions, and to aid in clinical decision making. AI-assisted drug safety and toxicity science remains a nascent and growing field that requires further research to evaluate its potential clinical impact.