Molecular Diversity ( IF 3.9 ) Pub Date : 2020-02-11 , DOI: 10.1007/s11030-020-10047-9 Minh-Tri Le 1, 2 , Thien-Vy Phan 3 , Viet-Khoa Tran-Nguyen 1 , Thanh-Dao Tran 1 , Khac-Minh Thai 1
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Abstract
The overexpression of ABCC2/MRP2, an ATP-binding cassette transporter, contributes to multidrug resistance in cancer cells. In this study, a quantitative structure–activity relationship (QSAR) analysis on ABCC2 inhibitors has been carried out, aiming to establish a computational prediction model for ABCC2 modulators. Seven classification models and two regression models were built by SONNIA 4.2, and two other regression models were built by MOE 2008.10 based on a data set comprising 372 compounds collected from 16 relevant publications. The CPG-C iABCC2 model for classifying ABCC2 inhibitors has total accuracy of 0.88 and Matthews correlation coefficient MCC = 0.75. The CPG-C iEG model for classifying ABCC2 inhibitors (substrate EG: β-estradiol 17-β-d-glucuronide) has total accuracy of 0.91 and MCC = 0.82. The regression model PLS EG-IC50 for predicting ABCC2 inhibitors (substrate EG) gave root-mean-square error RMSE = 0.26, Q2 = 0.73 and \( R_{\text{pred}}^{2} = 0.63 \). The regression model PLS CDCF-IC50 for predicting ABCC2 inhibitors [substrate CDCF: 5(6)-carboxy-2′,7′-dichlorofluorescein] gave RMSE = 0.31, Q2 = 0.74 and \( R_{\text{pred}}^{2} = 0.67 \). Four 2D-QSAR models were applied to 1661 compounds, with results indicating 369 compounds having the ability to reverse the efflux of both EG and CDCF by ABCC2, 152 among them having IC50 < 100 µM.
Graphic abstract
中文翻译：
人类 ABCC2/MRP2 外排泵抑制剂的预测模型：一项 QSAR 研究
摘要
ABCC2/MRP2（一种 ATP 结合盒式转运蛋白）的过度表达有助于癌细胞的多药耐药性。在本研究中，对 ABCC2 抑制剂进行了定量构效关系 (QSAR) 分析，旨在建立 ABCC2 调节剂的计算预测模型。SONNIA 4.2 建立了七个分类模型和两个回归模型，MOE 2008.10 建立了另外两个回归模型，该模型包含从 16 篇相关出版物中收集的 372 种化合物的数据集。用于分类 ABCC2 抑制剂的 CPG-C iABCC2 模型的总准确度为 0.88，马修斯相关系数 MCC = 0.75。用于分类ABCC2抑制剂的CPG-C iEG模型（底物EG：β-雌二醇17-β- d-glucuronide) 的总准确度为 0.91，MCC = 0.82。用于预测 ABCC2 抑制剂（底物 EG）的回归模型 PLS EG-IC 50给出均方根误差 RMSE = 0.26，Q 2 = 0.73 和\( R_{\text{pred}}^{2} = 0.63 \) . 用于预测 ABCC2 抑制剂的回归模型 PLS CDCF -IC 50 [底物 CDCF：5(6)-carboxy-2',7'-dichlorofluorescein] 给出 RMSE = 0.31，Q 2 = 0.74 和\( R_{\text{pred} }^{2} = 0.67 \)。四个 2D-QSAR 模型应用于 1661 种化合物，结果表明 369 种化合物具有通过 ABCC2 逆转 EG 和 CDCF 流出的能力，其中 152 种的 IC 50 < 100 µM。
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