Insights into Predictive Toxicology and Exposomics 2025

This Research Topic spotlights pioneering work at the intersection of exposome science, functional genomics, and precision health. We invite contributions that move beyond associations to deliver mechanistic insight, predictive capability, and translational impact, illuminating how diverse exposures across the lifespan interact with the human genome and epigenome to shape biology and health.

This collection seeks studies that integrate multi-omics (genomics, epigenomics, transcriptomics, proteomics, metabolomics) with exposomics to resolve the complexity of interindividual variability in toxicological responses. We especially welcome approaches that bridge endogenous exposures (e.g., diet-derived metabolites, microbiome-produced molecules, stress physiology) with exogenous toxicants (e.g., PFAS, phthalates, pesticides, metals) using modern measurement platforms capable of simultaneously detecting natural metabolites and environmental contaminants. Contributions leveraging high-throughput functional genomics (such as CRISPR screens and single-cell omics), advanced computational modeling and machine learning, and multi-scale systems frameworks are strongly encouraged.

We aim to curate research that generates actionable, testable predictions; uncovers causal pathways and mediators; and validates personalized biomarkers for risk stratification, early detection, and intervention. We also encourage submissions that harness real-world data streams, wearable sensors, and digital health readouts; build translational pipelines from discovery to clinical or public health practice; and address ethical, regulatory, and equity dimensions, including genetic privacy, algorithmic fairness, and access to precision environmental health.

Topics of interest include, but are not limited to:

o Construction and validation of multi-omics signatures, including genetic and epigenetic markers for toxicity prediction and precision risk assessment;

o Integration of exposome and genomics analytics to forecast disease risk or adverse outcomes;

o Novel computational and machine learning models for toxicological prediction, including genetically stratified and mechanistically informed approaches;

o Functional genomics to identify causal mediators and mechanisms in toxicological pathways (e.g., gene editing, single-cell and spatial technologies);

o Use of real-world data, wearable sensors, and digital phenotyping in exposome and gene–environment interaction research;

o Predictive paradigms that connect endogenous (metabolic, microbial, neuroendocrine/psychological) and exogenous (environmental, chemical, lifestyle) exposures within the context of genetic variation;

o Development of in vitro, in vivo, and in silico models enabling precision toxicology, dose–response inference, and cross-species translation;

o Translational and clinical studies linking predictive signatures to individualized interventions, surveillance, and policy-relevant risk assessment;

o Ethical, regulatory, and societal implications, including genetic privacy, transparency, and equity in predictive toxicology;

o Enabling technologies and methodologies for high-resolution exposome profiling and integrative multi-omics characterization;

Submissions focused solely on single-analyte predictors, isolated pathways, or descriptive associations without mechanistic, predictive, or translational relevance will not be considered.

Our vision is to establish a global forum for rigorous, integrative science that advances predictive toxicology and the multi-signature exposome.