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

机译：有机污染物生物富集因子定量构效关系模型的建立与评估

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摘要

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 logBCF are Connolly molecular area (CMA), average molecular polarizability (alpha) and molecular weight (M (W)). 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 (Y) (2)) = 0.868, the root mean square error (RMSE) = 0.553 log units, and the leave-many-out cross-validated Q (CUM) (2) = 0.860, indicating its good goodness-of-fit and robustness. The model predictivity was evaluated by external validation, with the external explained variance (Q (EXT) (2)) = 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.
机译：生物浓缩因子（BCF）对于有机化学品的生态风险评估非常重要。在这项研究中，基于线性溶剂化能量关系（LSER）理论和理论分子结构描述符，使用偏最小二乘（PLS）回归，建立了8组化合物鱼BCF的定量构效关系（QSAR）模型。遵循了经济合作与发展组织（OECD）提出的开发和验证QSAR模型的指南。模型结果表明，控制logBCF的主要因素是Connolly分子面积（CMA），平均分子极化率（α）和分子量（M（W））。因此，分子大小在影响鱼类中有机污染物的生物浓度中起着至关重要的作用。对于已建立的模型，多重相关系数平方（R（Y）（2））= 0.868，均方根误差（RMSE）= 0.553 log个单位，并留有多个交叉验证的Q（CUM）（ 2）= 0.860，表明其良好的拟合优度和鲁棒性。通过外部验证评估模型的可预测性，外部解释方差（Q（EXT）（2））= 0.755，RMSE = 0.647对数单位。此外，已开发模型的适用范围通过Williams图进行了评估和可视化。开发的QSAR模型可用于预测应用领域内有机化学物质的鱼类logBCF。

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