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Developing confidence in adverse outcome pathway-based toxicity predictions effects of the fungicide imazalil on fathead minnow reproduction
Villeneuve, Dan, B. Blackwell, J. Cavallin, W. Cheng, D. Feifarek, K. Jensen, M. Kahl, S. Poole, E. Randolph, T. Saari, K. Watanabe, AND G. Ankley. Developing confidence in adverse outcome pathway-based toxicity predictions effects of the fungicide imazalil on fathead minnow reproduction. SETAC Europe, Brussels, BELGIUM, May 07 - 11, 2017.
The vision for toxicity testing in the 21st century is to enhance our ability to predict hazard and risk based on data that can be acquired more efficiently and cost effectively than is possible using traditional whole organism toxicity testing. The adverse outcome pathway (AOP) framework was developed to aid the translation of mechanistic data (e.g., effects of chemicals on enzyme activities, gene expression, receptor binding, etc.) into information that’s meaningful to risk managers (e.g., impacts on survival, growth, reproduction, disease, etc.). However, in order for regulators and others in the scientific community to accept these new/alternative approaches to chemical safety assessment, they need to develop confidence, through case studies, that alternative data and AOPs can provide reliable predictions of hazard and risk. The present case study is aimed at establishing that confidence for an important mechanism of endocrine disruption and its potential ecological consequences. The results show that AOP-based approaches were useful for predicting the effects of a previously untested chemical and that the accuracy of quantitative predictions was within the range of variability observed in whole organism studies.
An adverse outcome pathway (AOP) description linking inhibition of aromatase (cytochrome P450 [cyp] 19) to reproductive dysfunction was reviewed for scientific and technical quality and endorsed by the OECD (https://aopwiki.org/wiki/index.php/Aop:25). An intended application of the AOP framework is to support the use of mechanistic or pathway-based data to infer or predict chemical hazards and apical adverse outcomes. As part of this work, ToxCast high throughput screening data were used to identify a chemicals’ ability to inhibit aromatase activity in vitro. Twenty-four hour in vivo exposures, focused on effects on production and circulating concentrations of 17â-estradiol (E2), key events in the AOP, were conducted to verify in vivo activity. Based on these results, imazalil was selected as a case study chemical to test an AOP-based hazard prediction. A computational model of the fish hypothalamic-pituitary-gonadal-liver axis and a statistically-based model of oocyte growth dynamics were used to predict impacts of different concentrations of imazalil on multiple key events along the AOP, assuming continuous exposure for 21 d. Results of the model simulations were used to select test concentrations and design a fathead minnow reproduction study in which fish were exposed to 20, 60, or 200 µg imazalil/L for durations of 2.5, 10, or 21d. Within 60 h of exposure, female fathead minnows showed significant reductions in ex vivo production of E2, circulating E2 concentrations, and significant increases in the ovarian expression of mRNA transcripts coding for cyp19a1a, cyp11a, and cyp17. A concentration-dependent decrease in cumulative fecundity was also detected for fathead minnow pairs exposed continuously for 21 d. Overall, results of the study provide strong support for the qualitative relationships represented in the AOP and provide further evidence of concentration-response and temporal concordance among key events. The quantitative models over-estimated the in vivo potency, suggesting that additional chemical-specific properties must be taken into consideration in order to reliably use this quantitative AOP construct for predictive risk assessment.