You are here:
How adverse outcome pathways can aid the development and use of computational prediction models for regulatory toxicology
Citation:
Wittwehr, C., H. Aladjov, G. Ankley, H. Byrne, J. de Knecht, E. Heinzle, G. Klambauer, B. Landesmann, M. Luijten, C. MacKay, G. Maxwell, M. Meek, A. Paini, E. Perkins, T. Sobanski, Dan Villeneuve, K. Waters, AND M. Whelan. How adverse outcome pathways can aid the development and use of computational prediction models for regulatory toxicology. TOXICOLOGICAL SCIENCES. Society of Toxicology, RESTON, VA, 155(2):326-336, (2017).
Impact/Purpose:
The present manuscript reports on expert opinion and case studies that came out of a European Commission, Joint Research Centre-sponsored workshop on AOP-informed predictive modelling approaches for regulatory toxicology that was held 24-25, September 2015. The workshop aimed to address the role that adverse outcome pathways (AOPs) and particularly quantitative AOPs, consisting of computational prediction models aligned with AOPs, could play in the future of toxicology. This is a critical scientific challenge as it related to broadening the scope of risk-based regulatory decision-making contexts in which mechanistic toxicity data, including data from high throughput toxicology programs, can be effectively employed. The workshop brought together toxicologists, AOP developers, risk assessment professionals, and computational modelers. Participants discussed 1) how the AOP framework could inform predictive model development, 2) case examples in which those principles had been applied, 3) how the existing risk assessment paradigm, particularly the problem formulation phase interfaces with AOP and model development and 4) ideas for fostering greater engagement of the computational modeling community in addressing chemical safety assessment challenges. Results of this workshop help support on-going research being conducted as part of CSS Project 17.01, AOP discovery and development. Results contribute to an FY16 product “Quantitative (q)AOP for aromatase inhibition as case study to advance qAOP development practice.”
Description:
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 has emerged as a systematic approach for organizing knowledge that supports such inference. We argue that this systematic organization of knowledge 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. 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 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.
