Precision Toxicity Prediction and Disease Risk Assessment through Integrative Toxicology and Multi-Omics Analysis

Traditional toxicology has focused on single compounds and pathways, limiting its ability to explain complex responses to real-world mixed exposures. Advances in high-throughput omics, exposomics, and computational methods are shifting the field toward integrated, systems-level approaches that incorporate molecular networks, regulatory pathways, and system dynamics. Increasingly, research aims to move beyond associations toward mechanistic, experimentally verifiable, and clinically translatable insights. The integration of multi-omics data with machine learning enables deeper understanding of toxicity mechanisms, causal inference, and target identification, while accounting for genetic susceptibility and gene–environment interactions. However, integrating heterogeneous data while maintaining interpretability and predictive performance remains a major challenge. Bridging computational findings with experimental and clinical validation is essential to advance precision toxicology.This Research Topic aims to develop an integrated, mechanism-oriented framework combining modern toxicology, multi-omics data, machine learning, and bioinformatics to elucidate how complex endogenous and exogenous exposures drive disease initiation and progression. By integrating multidimensional omics profiles with exposure data, it seeks to identify causal molecular pathways, key regulatory nodes, and system-level perturbations, and to validate these findings through experimental, clinical, and population-based studies.Emphasis is placed on genetic and epigenetic susceptibility, gene–environment interactions, and population heterogeneity to better understand differential toxicity responses and disease risk. The Topic also prioritizes the development of interpretable, biologically informed, and clinically actionable predictive models linking exposures to biological responses and health outcomes.Ultimately, this Topic aims to advance precision toxicology by enabling robust mechanistic insights, validated biomarkers, and translational tools for risk assessment, disease stratification, and personalized prevention and intervention across cancer and non-cancer diseases, promoting the integration of systems toxicology into precision medicine and public health.Topics include, but are not limited to:• Multi-omics–based mechanistic elucidation and experimental validation of toxicological processes• Integration of exposomics with genomics and epigenomics to study gene–environment interactions and susceptibility• Machine learning and bioinformatics approaches for causal inference and mechanism-informed toxicity modeling• Identification and validation of key toxicological pathways, regulatory modules, and biomarkers in cancer and non-cancer diseases• Studies addressing inter-individual variability, including genetic, epigenetic, and environmental determinants of toxicity responses• Development of interpretable and clinically translatable predictive models linking exposures, biological mechanisms, and disease outcomes• Systems and network-based approaches for toxicity prediction and pathway-level analysis• Applications of toxicology research in precision medicine, precision public health, and risk stratification• Explainable artificial intelligence (XAI) for mechanism interpretation and reliable toxicity prediction• Systems toxicology studies incorporating real-world data, longitudinal cohorts, and multi-cohort validation• Methodological innovations emphasizing biological validation, reproducibility, and translational applicability Topic editors: Chao Ma, Jinzhu Hu, Zhiwen Cao Source: https://www.frontiersin.org/research-topics/79608/precision-toxicity-prediction-and-disease-risk-assessment-through-integrative-toxicology-and-multi-omics-analysis