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Tiered Approaches to Incorporate the Adverse Outcome Pathway Framework into Chemical-Specific Risk-Based Decision Making
Citation:
Leonard, J., S. Bell, N. Oki, M. Nelms, Y. Tan, AND S. Edwards. Tiered Approaches to Incorporate the Adverse Outcome Pathway Framework into Chemical-Specific Risk-Based Decision Making. Chapter 12, Natàlia Garcia-Reyero, Cheryl A. Murphy (ed.), A Systems Biology Approach to Advancing Adverse Outcome Pathways for Risk Assessment. Springer International Publishing AG, Cham (ZG), Switzerland, , 235-261, (2018).
Impact/Purpose:
High throughput toxicity testing holds the promise of providing data for tens of thousands of chemicals that currently have no data due to the cost and time required for animal testing. Interpretation of these results require information linking the perturbations seen in vitro with adverse outcomes in vivo and requires knowledge of how estimated exposure to the chemicals compare to the in vitro concentrations that show an effect. This chapter describes methods for generating AOPs and ADME predictions at differing levels of detail to provide these connections. Lower tier AOPs and ADME predictions provide limited information but have limited input data requirements. Higher tiers provide more information, including quantitative dosimetry and dose-response information, but require additional data and modeling. Lower tiers are designed to inform experimental approaches to fill data gaps and provide the information needed for higher tier analyses in an efficient manner.
Description:
The concept of Adverse Outcome Pathways (AOPs) arose as a means of addressing the challenges associated with establishing relationships between high-throughout (HT) in vitro dose response data and in vivo biological outcomes. However, AOP development has also been met with challenges of its own, such as the time, effort, and expertise necessary to achieve a scientifically sound construct able to support ecotoxicology and human health risk assessment. Thus, the development process is a continuum that matches the level of maturity of the AOP with the decision context in which it will be used. This approach allows for prioritization of AOPs for detailed evidence evaluation based on the anticipated application of the AOP. In addition, through advances in computational analytical methodologies that allow for capturing the vast amount of HT data (e.g., transcriptomic data) spanning a broad chemical and biological space, computationally predicted AOPs can be rapidly generated to help accelerate the curation of AOPs. AOPs are chemical agnostic to allow a single AOP to be coupled with in vitro dose-response information from a variety of chemicals. To predict an in vivo outcome, however, exposure and pharmacokinetic characteristics (i.e., absorption, metabolism, distribution, and elimination) must be considered. The availability of data and the individual needs of investigators determine the steps, or “tiers” that are appropriate for different situations, such that use of lower tiers allows for broader coverage when data is lacking, while use of higher tiers allows for greater confidence when data is abundant. Integration of AOPs with chemical-specific exposure and pharmacokinetic considerations, even at different tiers or stages of development, can more rapidly and efficiently inform a chemical’s mode of action that leads to a toxicological outcome.
