Information about acute fish toxicity is routinely required in many jurisdictions for environmental risk assessment of chemicals. This information is typically obtained using a 96-hour juvenile fish test for lethality according to OECD test guideline (TG) 203 or equivalent regional guidelines. However, TG 203 has never been validated using criteria currently required for new test methods including alternative methods. Characterization of the practicality and validity of TG 203 is important to provide a benchmark for alternative methods. This contribution systematically summarizes the available knowledge about limitations and uncertainties of TG 203, based on methodological, statistical, and biological considerations. Uncertainties stem from the historic flexibility (e.g. use of a broad range of species) and constraints of the basic test design (e.g., no replication). Other sources of uncertainty arise from environmental safety extrapolation based on TG 203 data. Environmental extrapolation models, combined with data from alternative methods, including mechanistic indicators of toxicity, may provide at least the same level of environmental protection. Yet, most importantly the 3R advantages of alternative methods allow a better standardization, characterization and an improved basic study design. This can enhance data reliability and thus facilitate the comparison of chemical toxicity, as well as the environmental classifications and prediction of no-effect concentrations of chemicals. Combined with the 3R gains and the potential for higher throughput, a reliable assessment of more chemicals can be achieved, leading to improved environmental protection.

A calculation estimating the effect concentration (EL/LL50) of a water-accommodated fraction (WAF) for mixture toxicity is proposed. The method is based on chemical activity where the activity of a molecule is its effective concentration taking into account intermolecular interactions. First, the thermodynamic influence of each constituent on the solubility of the others within the mixture (i.e. the concentration of each constituent in the “loading rate”) is determined. Then, the non-bioavailable fraction is determined and removed to calculate the true concentration of each constituent exerting toxicity. Finally, the loading rate is adjusted until the sum of activities of the bioavailable fractions is equal to the fraction-weighted average of toxic activity of each constituent. This process is a mechanistic interpretation of experimental WAF tests. The methodology has been validated comparing toxic loading rates of 13 reliable experimental WAF studies on fish, daphnids, and algae. The predictions were all within a factor of 2 of the study outcomes and can be considered as accurate as the laboratory studies. This is in contrast to the standard additivity method which consistently overestimates the toxicity of these mixtures by at least a factor of 2 up to over an order of magnitude or even more.

ABSTRACTIn silico methods are typically underrated in the current risk assessment paradigm, as evidenced by the recent document from the European Chemicals Agency (ECHA) on animal alternatives, in which quantitative structure–activity relationships (QSARs) were practically used only as a last resort. Their primary use is still to provide supporting evidence for read‐across strategies or to add credence to experimental results of unknown or limited validity (old studies, studies without good laboratory practices [GLPs], limited information reported, etc.) in hazard assessment, but under the pressure of increasing burdens of testing, industry and regulators alike are at last warming to them. Nevertheless, their true potential for data‐gap filling and for resolving sticking points in risk assessment methodology and beyond has yet to be recognized. We postulate that it is possible to go beyond the level of simply increasing confidence to the point of using in silico approaches to accurately predict results that cannot be resolved analytically. For example, under certain conditions it is possible to obtain meaningful results by in silico extrapolation for tests that would be technically impossible to conduct in the laboratory or at least extremely challenging to obtain reliable results. The following and other concepts are explored in this article: the mechanism of action (MechoA) of the substance should be determined, as an aid verifying that the QSAR model is applicable to the substance under review; accurate QSARs should be built with high‐quality data that were not only curated but also validated with expert judgment; although a rule of thumb for acute to chronic ratios appears applicable for nonpolar narcotics, it seems unlikely that a “one‐value‐fits‐all” answer exists for other MechoAs; a holistic approach to QSARs can be employed (via reverse engineering) to help validate or invalidate an experimental endpoint value on the basis of multiple experimental studies. Integr Environ Assess Manag 2019;15:40–50. © 2018 SETAC

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