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.

An important goal in toxicology is the development of new ways to increase the speed, accuracy, and applicability of chemical hazard and risk assessment approaches. A promising route is the integration of in vitro assays with biological pathway information. We examined how the adverse outcome pathway (AOP) framework can be used to develop pathway‐based quantitative models useful for regulatory chemical safety assessment. By using AOPs as initial conceptual models and the AOP knowledge base as a source of data on key event relationships, different methods can be applied to develop computational quantitative AOP models (qAOPs) relevant for decision making. A qAOP model may not necessarily have the same structure as the AOP it is based on. Useful AOP modeling methods range from statistical, Bayesian networks, regression, and ordinary differential equations to individual‐based models and should be chosen according to the questions being asked and the data available. We discuss the need for toxicokinetic models to provide linkages between exposure and qAOPs, to extrapolate from in vitro to in vivo, and to extrapolate across species. Finally, we identify best practices for modeling and model building and the necessity for transparent and comprehensive documentation to gain confidence in the use of qAOP models and ultimately their use in regulatory applications. Environ Toxicol Chem 2019;38:1850–1865. © 2019 The Authors. Environmental Toxicology and Chemistry published by Wiley Periodicals, Inc. on behalf of SETAC.

Summary Consumer safety risk assessment of skin sensitization requires information on both consumer exposure

No abstract

Among the factors determining the propensity of a chemical to induce skin allergy are the penetration into skin and the kinetics of ingress. Confocal Raman spectroscopy can provide such information as it enables direct, spatially resolved measurement of the skin and of any chemical uptake. Several chemicals can be monitored at once, and the method is non-destructive (light in, light out) so that the skin can be kept intact for repeated and continuous measurement. Raman spectroscopy was used to follow the penetration of 2.5 weight percent trans-cinnamaldehyde and its delivery vehicle into skin in vitro, up to 24 h after topical application. A custom-made Bronaugh-type diffusion cell that was suitable for the Raman experiment was used. Four different vehicles were tested: absolute ethanol, 50% aqueous ethanol, propylene glycol and acetone:olive oil (4:1); these gave different time scales for cinnamaldehyde penetration. The acetone:olive oil vehicle phase-separated on the skin surface and the cinnamaldehyde penetrated at different rates in the different phases, which may be of significance since this is the preferred solvent for the local lymph node assay (an in vivo animal test used to generate hazard information on skin sensitization). In conclusion, the Raman method gives valuable detailed information on chemical ingress, clearly differentiates between different delivery rates and allows solvent monitoring alongside the chemical of interest.