Computational Toxicology: Expanding Frontiers in Risk Assessment

Dear colleagues,

Emerging technologies and advances in computational toxicology coupled with an improved understanding of mechanistic adverse outcome pathways (AOPs), in a new era of systems-focused thinking, are radically expanding the frontiers for risk assessment. The scientific community is capitalizing on these innovations to transform traditional approaches used for prioritization and standard setting regarding potential toxicity from exposures. Likewise, improved measurement capabilities and computational techniques are better characterizing emissions, transport, and transformation processes from source to media to target site exposures in various species as part of aggregate exposure pathways (AEPs) that can be integrated with AOP key events to refine dose–response analyses. New approach methodologies (NAMs) are increasingly providing biokinetic and mechanistic data from in vitro assays and in silico predictions that can aid evidence integration. A comprehensive characterization of the exposome promises to revolutionize how we approach protection of public health, including consideration of both human and ecological risks, with the help of artificial intelligence and machine learning. Similar computational approaches are used in the medical arena and can be mutually informative. In this Special Issue, we explore this new computational capacity across the source-to-outcome spectrum with examples of specific models and informatics approaches in both the environmental and medical arenas, including strategies for systematic review and harnessing big data. Conceptual constructs as well as quantitative examples are highlighted to illustrate impacts. Cross-cutting challenges such as how to foster the FAIR (findable, accessible, interoperable, and reproducible) principles and management of data repositories are also featured.

Ms. Annie M. Jarabek
Dr. Alicia Paini
Dr. Peter P. Egeghy
Guest Editors

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