Assessing predictive uncertainty in comparative toxicity potentials of triazoles.

Comparative toxicity potentials (CTPs) quantify the potential ecotoxicological impacts of chemicals per unit of emission. They are the product of a substance's environmental fate, exposure, and hazardous concentration. When empirical data are lacking, substance properties can be predicted. The goal of the present study was to assess the influence of predictive uncertainty in substance property predictions on the CTPs of triazoles. Physicochemical and toxic properties were predicted with quantitative structure-activity relationships (QSARs), and uncertainty in the predictions was quantified with use of the data underlying the QSARs. Degradation half-lives were based on a probability distribution representing experimental half-lives of triazoles. Uncertainty related to the species' sample size that was present in the prediction of the hazardous aquatic concentration was also included. All parameter uncertainties were treated as probability distributions, and propagated by Monte Carlo simulations. The 90% confidence interval of the CTPs typically spanned nearly 4 orders of magnitude. The CTP uncertainty was mainly determined by uncertainty in soil sorption and soil degradation rates, together with the small number of species sampled. In contrast, uncertainty in species-specific toxicity predictions contributed relatively little. The findings imply that the reliability of CTP predictions for the chemicals studied can be improved particularly by including experimental data for soil sorption and soil degradation, and by developing toxicity QSARs for more species.

Arguments for considering uncertainty in QSAR predictions in hazard and risk assessments.

Chemical regulation allows non-in vivo testing (i.e. in silico-derived and in vitro-derived) information to replace experimental values from in vivo studies in hazard and risk assessments. Although non-in vitro testing information on chemical activities or properties is subject to added uncertainty as compared to in vivo testing information, this uncertainty is commonly not (fully) taken into account. Considering uncertainty in predictions from quantitative structure-activity relationships (QSARs), which are a form of non-in vivo testing information, may improve the way that QSARs support chemical safety assessment under the EU Registration, Evaluation, Authorisation and Restriction of Chemicals (REACH) system. We argue that it is useful to consider uncertainty in QSAR predictions, as it: a) supports rational decision-making; b) facilitates cautious risk management; c) informs uncertainty analysis in probabilistic risk assessment; d) may aid the evaluation of QSAR predictions in weight-of-evidence approaches; and e) provides a probabilistic model to verify the experimental data used in risk assessment. The discussion is illustrated by using case studies of QSAR integrated hazard and risk assessment from the EU-financed CADASTER project.

The chemical and environmental property space of REACH chemicals.

The European regulation on chemicals, REACH (Registration, Evaluation, Authorisation and Restriction of Chemicals), came into force on 1 June 2007. With pre-registration complete in 2008, data for these substances may provide an overview of the expected chemical space and its characteristics. In this paper, using various in silico computation tools, we evaluate 48782 neutral organic compounds from the list to identify hazardous and safe compounds. Two different classification schemes (modified Verhaar and ECOSAR) identified between 17% and 25% of the compounds as expressing only baseline toxicity (narcosis). A smaller portion could be identified as reactive (19%) or specifically acting (2.7%), while the majority were non-assigned (61%). Overall environmental persistence, bioaccumulation and long-range transport potential were evaluated using structure-activity relationships and a multimedia fugacity-based model. A surprisingly high proportion of compounds (20%), mainly aromatic and halogenated, had a very high estimated persistence (>195 d). The proportion of compounds with a very high estimated bioconcentration or bioaccumulation factor (>5000) was substantially less (6.9%). Finally, a list was compiled of those compounds within the applicability domain of the models used, meeting both persistence and bioaccumulation criteria, and with a long-range transport potential comparable to PCB. This list of 68 potential persistent organic pollutants contained many well-known compounds (all halogenated), but notably also five fluorinated compounds that were not included in the EINECS inventory. This study demonstrates the usability of in silico tools for identification of potentially environmentally hazardous chemicals.