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EDITOR'S NOTE:This article was generated from the session "Predictive models in ecotoxicology: Bridging the gap between scientific progress and regulatory applicability," presented at the 27th SETAC Europe Annual Meeting (May 2017, Brussels, Belgium). The session considered approaches used in ecotoxicology for understanding and predicting the effects of chemicals, from QSAR to ecological modelling. This series aims to critically analyze and debate application examples and future developments to increase the acceptability of predictive models by regulators, managers, NGOs, and other stakeholders. 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. C 2018 SETAC

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.

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