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Combination of Read-Across and QSAR for Ecotoxicity Prediction: A Case Study of Green Algae Growth Inhibition Toxicity Data

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Ecotoxicological QSARs

Part of the book series: Methods in Pharmacology and Toxicology ((MIPT))

Abstract

Effective prediction of the ecotoxicity of chemicals is important for environmental hazard and risk assessment. A previously reported three-step strategy for predicting 72-h growth inhibition toxicity against the green alga Pseudokirchneriella subcapitata has potential utility as a general framework for algal toxicity prediction. This strategy, which combines read-across and quantitative structure–activity relationship (QSAR), consists of a pre-screening process followed by three steps. At Step 1, an interspecies QSAR is used to predict the toxicities of chemicals that satisfy a log D-based criterion. At Step 2, the toxicities of nonpolar and polar narcotic chemicals (Class 1 and Class 2, respectively) are predicted with QSARs. At Step 3, read-across based on defined categories of chemicals is used for any remaining compounds. In this case study, the generalizability of the three-step strategy was evaluated by applying it to a recently published data set of 48-h growth inhibition toxicities against Pseudokirchneriella subcapitata. At the pre-screening stage, new category definitions were required for each endpoint having different test conditions used to obtain the data that were used to develop the strategy. Because the interspecies QSAR used at Step 1 requires 48-h acute Daphnia magna toxicity (immobilization or mortality) as a descriptor, the fact that Daphnia magna data were lacking or unreliable for some of the compounds in the data set limited the utility of the three-step strategy. To circumvent this problem, read-across or local QSAR could be used instead of the interspecies QSAR at Step 1. At Step 2, the QSAR for nonpolar narcotic chemicals developed for the three-step strategy was applicable to the 48-h toxicity data set used in this case study; in contrast, the QSAR for polar narcotics showed unreliable predictivity when tested on the 48-h toxicity data set. Therefore, the polar narcotic QSAR was reconstructed so that it was applicable to the 48-h toxicity data. At Step 3, new categories for read-across were introduced to deal with the 48-h toxicity data; specifically, the chemical categories were classified into three types: Type A for toxic categories, Type B for categories applicable for read-across, and Type C for categories that were difficult to classify for read-across.

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Acknowledgments

The authors thank Drs. Y. Aoki, T.I. Hayashi, and H. Yamamoto, Professor N. Tatarazako, and Mr. K. Hasunuma for their helpful discussions about the interspecies QSAR and about the three-step strategy.

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Authors and Affiliations

  1. Center for Health and Environmental Risk Research, National Institute for Environmental Studies (NIES), Tsukuba, Ibaraki, Japan

    Ayako Furuhama

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  1. Ayako Furuhama
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Correspondence to Ayako Furuhama .

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Editors and Affiliations

  1. Drug Theoretics and Cheminformatics Laboratory, Department of Pharmaceutical Technology, Jadavpur University, Kolkata, India

    Kunal Roy

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Furuhama, A. (2020). Combination of Read-Across and QSAR for Ecotoxicity Prediction: A Case Study of Green Algae Growth Inhibition Toxicity Data. In: Roy, K. (eds) Ecotoxicological QSARs. Methods in Pharmacology and Toxicology. Humana, New York, NY. https://doi.org/10.1007/978-1-0716-0150-1_24

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  • DOI: https://doi.org/10.1007/978-1-0716-0150-1_24

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  • Publisher Name: Humana, New York, NY

  • Print ISBN: 978-1-0716-0149-5

  • Online ISBN: 978-1-0716-0150-1

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