Quantifying Uncertainty in Ecotoxicological Risk Assessment: MUST, a Modular Uncertainty Scoring Tool.

Whether conducting a risk, hazard, or alternatives assessment, one invariably struggles with the task of reconciling multiple available values of toxicological thresholds into a single outcome. When combining multiple pieces of evidence from many different sources, it is important to consider the role of data uncertainty. Uncertainty is inherent to all scientific data. However, in toxicological assessments, controversies and uncertainties are typically understated; they lack methodological transparency; or they poorly integrate qualitative and quantitative sources of information. Similarly, in model development, data curation is rarely performed with sufficient rigor, particularly when applying big data statistics. To overcome the hurdles of a decision process that must reconcile divergent data, we developed an uncertainty scoring tool that can be trained to reproduce specific decision-making paradigms and ensure consistency in the practitioner's judgment across complex scenarios. While designed to aid with ecotoxicological assessments and predictive model development, the tool's applicability extends to any decision-making process that calls for synthesis of incongruent data. Here, we highlight the development process, as well as demonstrate the method's utility in several prototypical ecotoxicological case studies.

Toward a Universal Acute Fish Threshold of Toxicological Concern.

Threshold of toxicological concern (TTC) is a concept that has been around for decades in human health sciences. Ecotoxicology recently adopted a variant of this concept as eco-TTC. Adoption of the concept of TTC considerably reduces the amount of animal testing required for regulatory purposes. We provide an application of a universal TTC for the entirety of acute fish toxicity data (i.e., establishment of an exposure level below which there would be minimal probability of acute fish toxicity for any chemical, without consideration of mechanism of action). We calculated TTC values for a number of subgroups using various approaches. These approaches were evaluated using data from a cohort of 69 999 acute fish toxicological assays. This database was normalized/curated for units, exposure duration, quality assurance/control, and duplicates, which reduced it to 47 694 assays. Data were not normally but log-normally distributed, making geometric means the most appropriate statistical parameter. Thus, we developed descriptive statistics using geometric means with 95, 99, and 99.9% confidence intervals. Various assessment factors (akin to predicted-no-effect concentration derivation) were applied to the geometric means to derive TTCs. Other approaches employed were the calculation of y = 0 intercepts as well as development of 95 and 99.75% cutoffs of cumulative data as well as modular uncertainty scoring tool (MUST) analysis. All of the methodologies derived highly congruent TTCs ranging from to 2 to 8 µg/L except for the 99.75th percentile cutoff of 0.3 µg/L. The data would be most useful in making a binary testing/no testing required decision. For acute fish toxicity, a TTC value of 2 µg/L was most appropriate, based on the 95th percentile of data distribution without any assessment factor. Environ Toxicol Chem 2021;40:1740-1749. © 2021 SETAC.