Exploring the use of Artificial Intelligence (AI) for extracting and integrating data obtained through New Approach Methodologies (NAMs) for chemical risk assessment

Ecological risk assessors face increasing demands to assess more chemicals, with greater speed and accuracy, and to do so using fewer resources and experimental animals. New approaches in biological and computational sciences may be able to generate mechanistic information that could help in meeting these challenges. However, to use mechanistic data to support chemical assessments, there is a need for effective translation of this information into endpoints meaningful to ecological risk-effects on survival, development, and reproduction in individual organisms and, by extension, impacts on populations. Here we discuss a framework designed for this purpose, the adverse outcome pathway (AOP). An AOP is a conceptual construct that portrays existing knowledge concerning the linkage between a direct molecular initiating event and an adverse outcome at a biological level of organization relevant to risk assessment. The practical utility of AOPs for ecological risk assessment of chemicals is illustrated using five case examples. The examples demonstrate how the AOP concept can focus toxicity testing in terms of species and endpoint selection, enhance across-chemical extrapolation, and support prediction of mixture effects. The examples also show how AOPs facilitate use of molecular or biochemical endpoints (sometimes referred to as biomarkers) for forecasting chemical impacts on individuals and populations. In the concluding sections of the paper, we discuss how AOPs can help to guide research that supports chemical risk assessments and advocate for the incorporation of this approach into a broader systems biology framework.

In June 2005, a World Health Organization (WHO)-International Programme on Chemical Safety expert meeting was held in Geneva during which the toxic equivalency factors (TEFs) for dioxin-like compounds, including some polychlorinated biphenyls (PCBs), were reevaluated. For this reevaluation process, the refined TEF database recently published by Haws et al. (2006, Toxicol. Sci. 89, 4-30) was used as a starting point. Decisions about a TEF value were made based on a combination of unweighted relative effect potency (REP) distributions from this database, expert judgment, and point estimates. Previous TEFs were assigned in increments of 0.01, 0.05, 0.1, etc., but for this reevaluation, it was decided to use half order of magnitude increments on a logarithmic scale of 0.03, 0.1, 0.3, etc. Changes were decided by the expert panel for 2,3,4,7,8-pentachlorodibenzofuran (PeCDF) (TEF = 0.3), 1,2,3,7,8-pentachlorodibenzofuran (PeCDF) (TEF = 0.03), octachlorodibenzo-p-dioxin and octachlorodibenzofuran (TEFs = 0.0003), 3,4,4',5-tetrachlorbiphenyl (PCB 81) (TEF = 0.0003), 3,3',4,4',5,5'-hexachlorobiphenyl (PCB 169) (TEF = 0.03), and a single TEF value (0.00003) for all relevant mono-ortho-substituted PCBs. Additivity, an important prerequisite of the TEF concept was again confirmed by results from recent in vivo mixture studies. Some experimental evidence shows that non-dioxin-like aryl hydrocarbon receptor agonists/antagonists are able to impact the overall toxic potency of 2,3,7,8-tetrachlorodibenzo-p-dioxin (TCDD) and related compounds, and this needs to be investigated further. Certain individual and groups of compounds were identified for possible future inclusion in the TEF concept, including 3,4,4'-TCB (PCB 37), polybrominated dibenzo-p-dioxins and dibenzofurans, mixed polyhalogenated dibenzo-p-dioxins and dibenzofurans, polyhalogenated naphthalenes, and polybrominated biphenyls. Concern was expressed about direct application of the TEF/total toxic equivalency (TEQ) approach to abiotic matrices, such as soil, sediment, etc., for direct application in human risk assessment. This is problematic as the present TEF scheme and TEQ methodology are primarily intended for estimating exposure and risks via oral ingestion (e.g., by dietary intake). A number of future approaches to determine alternative or additional TEFs were also identified. These included the use of a probabilistic methodology to determine TEFs that better describe the associated levels of uncertainty and "systemic" TEFs for blood and adipose tissue and TEQ for body burden.