Breakthroughs in Computational Tools for Predicting Human and Ecological Exposures to Chemical Substances

Dear Colleagues,

New approach methods (NAMs) in exposure science are expanding our ability to predict human and ecological exposures to chemical substances. Computational approaches embracing tools related to informatics, data science, machine learning, artificial intelligence, and mathematical and statistical modeling are essential for expanding the chemical universe for which exposures can be predicted. One of the greatest research needs in this field is the development of robust models that can integrate diverse data streams, including chemical, biological, physical, and social determinants of health. Current models often struggle with the sheer volume and variety of data, necessitating advancements in data fusion techniques that can seamlessly combine information from disparate sources. Development of interoperable platforms and standardized protocols is facilitating data sharing and collaboration, allowing researchers to build upon each other’s work and accelerate breakthroughs. The creation of user-friendly tools and visualizations is facilitating effective communication of exposure risks and inform decision-making processes. Risk assessment is transitioning from a paradigm in which exposure assessment is the final step before risk characterization to one in which an initial understanding of exposure potential and kinetic considerations can serve to efficiently direct resources towards hazard testing (including advanced toxicity NAMs), producing more practical and relevant assessment of risk. In this Special Issue, we explore computational approaches across the source-to-dose exposure continuum, allowing rapid, transparent, interpretable predictions of chemical substance exposures. We examine the transformation of exposomic data into actionable knowledge, ultimately guiding public health policies to mitigate, or even prevent, adverse health effects linked to environmental exposures.

Dr. Peter P. Egeghy
Dr. Caroline L. Ring
Guest Editors

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• exposure science
• computational modeling
• new approach methods
• data science
• risk assessment
• decision support
• environmental epidemiology
• data visualization
• wearable devices
• aggregate exposure pathway