Ibuprofen: Fish Short-Term Reproduction Assay with Zebrafish (Danio rerio) Based on an Extended OECD 229 Protocol.

A study was conducted to understand the potential for ibuprofen to impact the hypothalamus-pituitary-gonadal endocrine axis resulting in disruption of fish reproduction. The Good Laboratory Practice study was conducted according to the Organisation for Economic Co-operation and Development 229 Protocol, Fish Short-Term Reproduction Assay, and extended an additional 4 d to evaluate hatching success in the F1 generation. Test organisms were exposed to nominal test concentrations of 0.5, 2.4, 11.5, 55.3, and 265.4 µg ibuprofen/L and a negative control (dilution water). To strengthen the statistical power of the study, twice the number of replicates were used in the negative control versus individual treatment levels. A 21-d pre-exposure to identify groups of actively spawning fish was immediately followed by a 36-d exposure. Results for apical endpoints of survival, growth, and reproduction (fecundity and fertility), as well as the biomarker vitellogenin in the F0 generation and time to hatch and hatching success in the F1 generation are presented. Based on mean measured exposure concentrations and effects on fecundity in the F0 generation and hatching success in the F1 generation, overall no-observed-effect concentration and lowest-observed-effect concentration for the present study were 55.2 and 265.9 µg ibuprofen/L, respectively. Results from the present study indicate a lack of endocrine-mediated reproductive effects in zebrafish at environmentally relevant concentrations of ibuprofen. Environ Toxicol Chem 2020;39:1534-1545. © 2020 SETAC.

Aquatic hazard, bioaccumulation and screening risk assessment for 6:2 fluorotelomer sulfonate.

This study assessed the aquatic toxicity and bioaccumulation potential of 6:2 fluorotelomer sulfonate (6:2 FTSA). Acute and chronic aquatic hazard endpoints indicate 6:2 FTSA is not classified for aquatic hazard according to GHS or European CLP legislation. The aqueous bioconcentration factors for 6:2 FTSA were <40 and the dietary assimilation efficiency, growth corrected half-life and dietary biomagnification factor (BMF) were 0.435, 23.1d and 0.295, respectively. These data indicate that 6:2 FTSA is not bioaccumulative in aquatic organisms. Comparison of PNECs with the reported surface water concentrations (non-spill situations) suggests low risk to aquatic organisms from 6:2 FTSA. Future studies are needed to elucidate the biotic and abiotic fate of commercial AFFF surfactants in the environment.

Improving uncertainty analysis in kinetic evaluations using iteratively reweighted least squares.

Kinetic parameters of environmental fate processes are usually inferred by fitting appropriate kinetic models to the data using standard nonlinear least squares (NLS) approaches. Although NLS is appropriate to estimate the optimum parameter values, it implies restrictive assumptions on data variances when the confidence limits of the parameters must also be determined. Particularly in the case of degradation and metabolite formation, the assumption of equal error variance is often not realistic because the parent data usually show higher variances than those of the metabolites. Conventionally, such problems would be tackled by weighted NLS regression, which requires prior knowledge about the data errors. Instead of implicitly assuming equal error variances or giving arbitrary weights decided by the researcher, we use an iteratively reweighted least squares (IRLS) algorithm to obtain the maximum likelihood estimates of the model parameters and the error variances specific for the different species in a model. A study with simulated data shows that IRLS gives reliable results in the case of both unequal and equal error variances. We also compared results obtained by NLS and IRLS, with probability distributions of the parameters inferred with a Markov-Chain Monte-Carlo (MCMC) approach for data from aerobic transformation of different chemicals in soil. Confidence intervals obtained by IRLS and MCMC are consistent, whereas NLS leads to very different results when the error variances are distinctly different between different species. Because the MCMC results can be assumed to reflect the real parameter distribution imposed by the observed data, we conclude that IRLS generally yields more realistic estimates of confidence intervals for model parameters than NLS.