Artificial Intelligence in Toxicology and Pharmacology

Abstract

Methods that utilize machine learning and artificial intelligence have transformed a wide variety of fields, including the field of toxicology. Physiologically based pharmacokinetic (PBPK) modeling, quantitative structure-activity relationship modeling for toxicity prediction, adverse outcome pathway analysis, high-throughput screening, toxicogenomics, big data, and toxicological databases are just some of the areas that are covered in this review. By leveraging machine learning and artificial intelligence approaches, it is now possible to develop PBPK models for hundreds of chemicals in an efficient manner, to create in silico models to predict toxicity for a large number of chemicals with similar accuracies compared with In vivo animal experiments, and to analyze a large amount of data of various types (toxicogenomics, high-content image data, etc.) to generate new insights into toxicity mechanisms rapidly, which was previously impossible. This is an improvement over the previous situation The field of toxicological sciences faces a number of challenges that must be overcome before it can make further progress. These challenges include the following: (1) not all machine learning models are equally useful for a particular type of toxicology data; therefore, it is important to test different methods to determine the optimal approach; (2) the current toxicity prediction is primarily based on bioactivity classification (yes/no); therefore, additional studies are required to predict the intensity of effect or dose-response relationship.