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Toxicity Rank Order (TRO) As a New Approach for Toxicity Prediction by QSAR Models

Author

Listed:
  • Yuting Chen

    (College of Environment and Resource, Dalian Minzu University, Dalian 116600, China)

  • Yuying Dong

    (College of Environment and Resource, Dalian Minzu University, Dalian 116600, China)

  • Le Li

    (College of Environment and Resource, Dalian Minzu University, Dalian 116600, China)

  • Jian Jiao

    (College of Environment and Resource, Dalian Minzu University, Dalian 116600, China)

  • Sitong Liu

    (College of Environment and Resource, Dalian Minzu University, Dalian 116600, China)

  • Xuejun Zou

    (College of Environment and Resource, Dalian Minzu University, Dalian 116600, China)

Abstract

Quantitative Structure–Activity Relationship (QSAR) models are commonly used for risk assessment of emerging contaminants. The objective of this study was to use a toxicity rank order (TRO) as an integrating parameter to improve the toxicity prediction by QSAR models. TRO for each contaminant was calculated from collected toxicity data including acute toxicity concentration and no observed effect concentration. TRO values associated with toxicity mechanisms were used to classify pollutants into three modes of action consisting of narcosis, transition and reactivity. The selection principle of parameters for QSAR models was established and verified. It showed a reasonable prediction of toxicities caused by organophosphates and benzene derivatives, especially. Compared with traditional procedures, incorporating TRO showed an improved correlation coefficient of QSAR models by approximately 10%. Our study indicated that the proposed procedure can be used for screening modeling parameter data and improve the toxicity prediction by QSAR models, and this could facilitate prediction and evaluation of environmental contaminant toxicity.

Suggested Citation

  • Yuting Chen & Yuying Dong & Le Li & Jian Jiao & Sitong Liu & Xuejun Zou, 2022. "Toxicity Rank Order (TRO) As a New Approach for Toxicity Prediction by QSAR Models," IJERPH, MDPI, vol. 20(1), pages 1-10, December.
    • Handle: RePEc:gam:jijerp:v:20:y:2022:i:1:p:701-:d:1020593
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    References listed on IDEAS

    as
    1. Zhixing Ren & Yingwei Wang & Haihong Xu & Yufei Li & Song Han, 2019. "Fuzzy Comprehensive Evaluation Assistant 3D-QSAR of Environmentally Friendly FQs to Reduce ADRs," IJERPH, MDPI, vol. 16(17), pages 1-20, August.
    2. Lu-ze Yang & Miao Liu, 2020. "A Double-Activity (Green Algae Toxicity and Bacterial Genotoxicity) 3D-QSAR Model Based on the Comprehensive Index Method and Its Application in Fluoroquinolones’ Modification," IJERPH, MDPI, vol. 17(3), pages 1-14, February.
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