Development of QSAAR and QAAR models for predicting fish early-life stage toxicity with a focus on industrial chemicals

ABSTRACT

We developed models for predicting fish early-life stage (ELS) toxicities oriented to industrial chemicals. The training set was constructed without data from the Office of Pesticide Programs Pesticide Ecotoxicity Database, the main source for the pesticide-biased training set used in our previous work (SAR QSAR Environ. Res. 29:9, 725–742). In addition to the descriptors from the previous study, we also used water solubility to develop the new models, which were evaluated against the test set used in our previous study so that we could focus on the effects of the different training set and the additional descriptor. The statistics for the new models were hardly better than those for the previous models, which suggests, contrary to our expectations, that pesticide-biased data can successfully be used to develop models for predicting the fish ELS toxicities oriented to industrial chemicals. Acute Daphnia magna toxicity was important for the predictive QSAARs in both studies. A distance-based method for defining the applicability domains indicated that water solubility was a key indicator for detecting underestimated chemicals. The comparison of fish ELS toxicities for chemicals presented in different literatures revealed the uncertainty of the experimental data, which may lead to the low predictivity.

KEYWORDS:

• Quantitative structure–activity–activity relationship (QSAAR)
• quantitative activity–activity relationship (QAAR)
• linear regression model
• fish early-life stage (ELS) toxicity
• acute Daphnia magna toxicity
• water solubility

Acknowledgements

This work was supported in part by the Japan Society for the Promotion of Science (JSPS KAKENHI Grant No. JP17K00640).

Disclosure statement

No potential conflict of interest was reported by the authors.

Supplementary Material

Supplementary data for this article can be accessed at https://doi.org/10.1080/1062936X.2019.1669707.

Additional information

Funding

This work was supported by the JSPS KAKENHI [Grant Number JP17K00640].