Integrative Approaches in Translational Modelling for Human Health Risk from Environmental Chemicals and Drugs Across Real-World Exposure Scenarios

Translational modelling has emerged as a critical approach in environmental health and pharmacology, offering new pathways to predict human health risks arising from exposure to chemicals and drugs. Traditionally, risk assessment relied heavily on animal studies or observational epidemiology, but these methods often struggle to capture the complexities of human biology and diverse population responses. In recent years, translational modelling has begun to bridge this gap, integrating mechanistic insights, computational tools, and a wealth of experimental and real-world data. As regulatory and research communities seek alternatives to animal testing and strive for more accurate, human-relevant predictions, challenges persist regarding the harmonization of various data streams, the identification of sensitive subpopulations, and the quantitative translation of experimental findings into actionable health policies.Importantly, the exposure component of risk is not limited to ambient environmental pathways. For many substances, exposure is driven by context-specific scenarios such as pharmaceutical administration and dosing regimens, occupational exposure under workplace conditions, and dietary intake (including food additives), alongside consumer-product use and other relevant routes. A central need across these scenarios is to connect external exposure (or use patterns) to internal dose and biological response in a way that is quantitative, transparent, and suitable for decision-making.Recent advances are enabling the field to move beyond traditional toxicological endpoints by harnessing multi-omics data, high-throughput screening results, and precise human biomonitoring. These innovations allow the detection of subtle, mechanism-based effects—such as those involving epigenetic changes—and provide an unprecedented ability to characterize aggregate, cumulative, and mixture exposures across routes and settings. Modern translational modelling is also increasingly aligning with computational advances such as machine learning and artificial intelligence, which offer powerful opportunities for pattern recognition and causal inference in large, complex datasets. However, several key challenges remain: ensuring interoperability between diverse models, establishing standards for model validation, and addressing uncertainties related to real-world exposure variability, human heterogeneity, and data limitations.This Research Topic aims to advance the integration of translational modelling techniques for the robust prediction of human health risks from drugs and chemicals across real-world exposure contexts. It seeks to foster discussion and innovation around multi-level data integration, mechanistic inference, and practical application in regulatory science and policy. Central objectives include exploring new modelling frameworks, identifying best practices for combining experimental evidence with population and use/exposure data, and clarifying pathways for translating scientific advances into regulatory and public health decision-making.To gather further insights into the scope and application of translational modelling in this field, we welcome articles that address, but are not limited to, the following themes:Mechanistic and data‑driven modelling for human health risk predictionIntegration of multi-omics, biomonitoring, and exposure/use data (e.g., clinical, occupational, dietary, consumer)Identification and assessment of sensitive populations under cumulative riskAdvancements in in vitro, in vivo, and in silico approaches for translational health scienceApplication of translational models in regulatory assessments and public health policyAppendix: We invite original research articles, reviews, brief research reports, and perspectives. Topic editors: DEEPIKA DEEPIKA, M Wahajuddin, PhD, FBPhS, FHEA, FRSC, FRSB, Vikas Kumar, Carolina Vogs Source: https://www.frontiersin.org/research-topics/75457/integrative-approaches-in-translational-modelling-for-human-health-risk-from-environmental-chemicals-and-drugs-across-real-world-exposure-scenarios