Development and assessment of quantitative structure-activity relationship models for bioconcentration factors of organic pollutants

Bioconcentration factors (BCFs) are of great importance for ecological risk assessment of organic chemicals. In this study, a quantitative structure-activity relationship (QSAR) model for fish BCFs of 8 groups of compounds was developed employing partial least squares (PLS) regression, based on linear solvation energy relationship (LSER) theory and theoretical molecular structural descriptors. The guidelines for development and validation of QSAR models proposed by the Organization for Economic Cooperation and Development (OECD) were followed. The model results show that the main factors governing logBCF are Connolly molecular area (CMA), average molecular polarizability (α) and molecular weight (MW). Thus molecular size plays a critical role in affecting the bioconcentration of organic pollutants in fish. For the established model, the multiple correlation coefficient square (RY2) = 0.868, the root mean square error (RMSE) = 0.553 log units, and the leave-many-out cross-validated Q2CUM = 0.860, indicating its good goodness-of-fit and robustness. The model predictivity was evaluated by external validation, with the external explained variance (QE2XT) = 0.755 and RMSE = 0.647 log units. Moreover, the applicability domain of the developed model was assessed and visualized by the Williams plot. The developed QSAR model can be used to predict fish logBCF for organic chemicals within the application domain.

Bioconcentration factors （BCFs） are of great importance for ecological risk assessment of organic chemicals. In this study, a quantitative structure-activity relationship （QSAR） model for fish BCFs of 8 groups of compounds was developed employing partial least squares （PLS） regression, based on linear solvation energy relationship （LSER） theory and theoretical molecular structural descriptors. The guidelines for development and validation of QSAR models proposed by the Organization for Economic Co-operation and Development （OECD） were followed. The model results show that the main factors governing IogBCF are Connolly molecular area （CMA）, average molecular polarizability （α） and molecular weight （Mw）. Thus molecular size plays a critical role in affecting the bioconcentration of organic pollutants in fish. For the established model, the multiple correlation coefficient square （R^2y） = 0.868, the root mean square error （RMSE） = 0.553 log units, and the leave-many-out cross-validated Q^2cuM = 0.860, indicating its good goodness-of-fit and robustness. The model predictivity was evaluated by external validation, with the external explained variance （Q^2EXT） = 0.755 and RMSE = 0.647 log units. Moreover, the applicability domain of the developed model was assessed and visualized by the Williams plot. The developed QSAR model can be used to predict fish IogBCF for organic chemicals within the application domain.