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Predicting the cytotoxicity of disinfection by-products to Chinese hamster ovary by using linear quantitative

Li-Tang Qin1,2,3, Xin Zhang1, Yu-Han Chen1

  • 1College of Environmental Science and Engineering, Guilin University of Technology, Guilin, 541004, China.

Environmental Science and Pollution Research International
|April 17, 2019
PubMed

Insights

Predicting disinfection by-product (DBP) toxicity is crucial. This study developed reliable linear quantitative structure-activity relationship (QSAR) models to accurately forecast the cytotoxicity of emerging DBPs to Chinese hamster ovary (CHO) cells.

Area of Science:

  • Environmental Chemistry
  • Toxicology
  • Computational Chemistry

Background:

  • Disinfection by-products (DBPs) are emerging contaminants with potential health risks.
  • Accurate prediction of DBP toxicity is essential for risk assessment and management.
  • Existing models for DBP toxicity prediction require further development and validation.

Purpose of the Study:

  • To establish a reliable quantitative structure-activity relationship (QSAR) model for predicting the cytotoxicity of disinfection by-products (DBPs).
  • To assess the predictive performance of linear QSAR models using Chinese hamster ovary (CHO) cell data.
  • To ensure the developed models have wide applicability domains for diverse DBPs.

Main Methods:

  • Collected cytotoxicity data for 74 DBPs using Chinese hamster ovary (CHO) cells as the endpoint.
  • Developed linear QSAR models utilizing multiple linear regression (MLR) analysis.
  • Validated the models through internal (LOOCV, LMOOCV, bootstrapping) and external validation techniques.

Main Results:

  • The developed MLR-based QSAR models demonstrated high goodness of fit (R² = 0.763-0.799).
  • Models exhibited excellent robustness (Q²LOO = 0.718-0.745) and predictive ability (CCC = 0.806-0.848).
  • Williams plot analysis confirmed the models' wide application domains and coverage of 74 structurally diverse DBPs.

Conclusions:

  • Linear QSAR models provide a reliable approach for predicting the cytotoxicity of disinfection by-products (DBPs) to Chinese hamster ovary (CHO) cells.
  • The developed models are robust and possess strong predictive capabilities for a wide range of DBPs.
  • These QSAR models can aid in the risk assessment of emerging DBPs in water treatment and environmental monitoring.
Keywords:
Chinese hamster ovaryCytotoxicityDisinfection by-productsQuantitative structure-activity relationship

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