Limitations of toxicity characterization in life cycle assessment: Can adverse outcome pathways provide a new foundation?

Life cycle assessment (LCA) has considerable merit for holistic evaluation of product planning, development, production, and disposal, with the inherent benefit of providing a forecast of potential health and environmental impacts. However, a technical review of current life cycle impact assessment (LCIA) methods revealed limitations within the biological effects assessment protocols, including: simplistic assessment approaches and models; an inability to integrate emerging types of toxicity data; a reliance on linear impact assessment models; a lack of methods to mitigate uncertainty; and no explicit consideration of effects in species of concern. The purpose of the current study is to demonstrate that a new concept in toxicological and regulatory assessment, the adverse outcome pathway (AOP), has many useful attributes of potential use to ameliorate many of these problems, to expand data utility and model robustness, and to enable more accurate and defensible biological effects assessments within LCIA. Background, context, and examples have been provided to demonstrate these potential benefits. We additionally propose that these benefits can be most effectively realized through development of quantitative AOPs (qAOPs) crafted to meet the needs of the LCIA framework. As a means to stimulate qAOP research and development in support of LCIA, we propose 3 conceptual classes of qAOP, each with unique inherent attributes for supporting LCIA: 1) mechanistic, including computational toxicology models; 2) probabilistic, including Bayesian networks and supervised machine learning models; and 3) weight of evidence, including models built using decision-analytic methods. Overall, we have highlighted a number of potential applications of qAOPs that can refine and add value to LCIA. As the AOP concept and support framework matures, we see the potential for qAOPs to serve a foundational role for next-generation effects characterization within LCIA. Integr Environ Assess Manag 2016;12:580–590. Published 2015. This article is a US Government work and is in the public domain in the USA. Key Points ID of key limitations in biological effects assessment in LCA: simplistic assessment and models; inability to utilize next-generation toxicity data; use of linear impact assessment models; no methods to mitigate uncertainty; and no explicit consideration of interspeciestoxicity extrapolations. The present study proposes that the adverse outcome pathway (AOP) can be employed to ameliorate many of these problems, to expand data utility and model robustness, and to enable more accurate and defensible biological effects assessments within LCA. We propose 3 conceptual classes of qAOP in support of LCA: (1) mechanistic, including computational toxicology models; (2) probabilistic, including Bayesian networks and supervised machine learning models; and (3) weight of evidence, including models built using decision-analytic methods. Overall, we have demonstrated that qAOPs have the potential to serve as the foundation for next-generation effects assessment within LCA and have highlighted the key attributes required of qAOP models to successfully transition to the greater LCA framework.