|How Adverse Outcome Pathways Can Aid the Development and Use of Computational Prediction Models for Regulatory Toxicology.
|Year of Publication
|Wittwehr, C, Aladjov, H, Ankley, G, Byrne, HJ, de Knecht, J, Heinzle, E, Klambauer, G, Landesmann, B, Luijten, M, MacKay, C, Maxwell, G, Meek, MEBette, Paini, A, Perkins, E, Sobanski, T, Villeneuve, D, Waters, KM, Whelan, M
|Adverse Outcome Pathways, Animals, Computer Simulation, Humans, Toxicity Tests, Toxicology
Efforts are underway to transform regulatory toxicology and chemical safety assessment from a largely empirical science based on direct observation of apical toxicity outcomes in whole organism toxicity tests to a predictive one in which outcomes and risk are inferred from accumulated mechanistic understanding. The adverse outcome pathway (AOP) framework provides a systematic approach for organizing knowledge that may support such inference. Likewise, computational models of biological systems at various scales provide another means and platform to integrate current biological understanding to facilitate inference and extrapolation. We argue that the systematic organization of knowledge into AOP frameworks can inform and help direct the design and development of computational prediction models that can further enhance the utility of mechanistic and in silico data for chemical safety assessment. This concept was explored as part of a workshop on AOP-Informed Predictive Modeling Approaches for Regulatory Toxicology held September 24-25, 2015. Examples of AOP-informed model development and its application to the assessment of chemicals for skin sensitization and multiple modes of endocrine disruption are provided. The role of problem formulation, not only as a critical phase of risk assessment, but also as guide for both AOP and complementary model development is described. Finally, a proposal for actively engaging the modeling community in AOP-informed computational model development is made. The contents serve as a vision for how AOPs can be leveraged to facilitate development of computational prediction models needed to support the next generation of chemical safety assessment.
|PubMed Central ID
|P42 ES016465 / ES / NIEHS NIH HHS / United States