Technology-Driven Evolution of Modern Toxicology: Role of Computational Toxicology

Toxicology is the study of the harmful effects of chemicals, substances, or environmental agents on living systems. Toxicity studies are essential for determining the health impact and risk-assessment of new drug molecules, cosmetics, healthcare and consumer products, and environmental chemicals on humans. Traditional toxicity testing relied more on observational toxicology, and included rigorous bioassay employing large number of animals (in vivo tests). However, such tests were crippled by many scientific, ethical, and regulatory issues. Soon, in vitro and in silico models were considered as more sustainable alternatives. Dramatic revolution in hardware and software technologies prompted the evolution of modern toxicology that was evidenced by more mechanism based toxicity studies, new biochemical pathways, and molecular interactions at cellular and molecular levels. The speed, precision, and reproducibility of toxicity tests were enhanced by many new technologies like high-throughput screening, omics, microfluidic devices, computational modeling, etc. Computational modelling when applied to toxicology is referred as computational toxicology, that integrates data science, artificial engineering, algorithm, machine learning, and deep learning to analyze complex multifaceted toxicity data sets, and in conjunction with in silico models can simulate biological perturbations to predict toxicological outcome. New drug discovery, SARs (structure-activity relationship), molecular docking, and ADME/T (absorption, distribution, metabolism, elimination and toxicity) are largely dependent on computational toxicology. This article reviews the role of computational toxicology in technology-driven evolution of modern toxicology, and the future of toxicology and possible challenges.