Overview of Toxicogenomic and Personalised Medicine

Background: Toxicogenomics, which integrates genomic, transcriptomic, proteomic, and metabolomic data, provides critical insight into how individuals respond to drugs and environmental toxicants. Its convergence with personalised medicine enables more accurate prediction of adverse effects, individualized drug dosing, and improved therapeutic outcomes. Aim: this review outlines the fundamental principles of toxicogenomics, highlights key technologies driving the field, and examines its clinical applications, including biomarker discovery, toxicity prediction, and patient stratification. The integration of multi-omics data with advanced computational tools, particularly artificial intelligence, is accelerating the development of predictive models that support personalized risk assessment and precision treatment. Results: despite significant progress, challenges such as data standardization, regulatory acceptance, and limited understanding of gene–environment interactions continue to impede clinical translation. Emerging technologies—such as organ-on-a-chip systems, single-cell omics, and exposomics—offer promising avenues for future advancement. Conclusion: toxicogenomics is pivotal in advancing personalized medicine by integrating genetic and molecular profiles with environmental exposure data to predict individual responses to toxicants and therapies.