Toward Integrative Predictive Toxicology: Advanced Methods for Drug Toxicity and Safety Prediction

The capability of anticipating and mitigating drug toxicity represents one of the most persistent challenges in drug development. Despite rigorous preclinical evaluation, nearly one third of drug candidates fail during the clinical phases due to safety issues, in particular hepatotoxicity and cardiotoxicity. Routine in vitro and in vivo toxicology tests, while essential to define safety margins, are frequently associated with high false‐positive rates and poor translation to human outcomes. While investigative toxicology has improved mechanistic understanding of off‐target interactions and toxicokinetics, the gap between preclinical findings and clinical safety still challenges efficient drug development. To overcome these drawbacks, the field is developing and applying more integrative strategies that combine computational modeling, machine learning, multi‐omics technologies, and advanced in vitro systems. These approaches propose predictive pipelines likely able to identify chemical toxicology issues during early design stages and to characterize mechanisms of adverse events. In this review we provide a critical overview of emerging tools and strategies for drug toxicity prediction, evaluating their current impact, limitations, and translational potential to reduce safety‐related attrition and support the development of safer therapeutics. This article is categorized under: Data Science > Artificial Intelligence/Machine Learning Molecular and Statistical Mechanics > Molecular Mechanics