Department of Medicinal Chemistry, Faculty of Pharmacy, University of Medicine and Pharmacy at Ho Chi Minh City, Ho Chi Minh City, 700000, Vietnam.
School of Medicine, Vietnam National University Ho Chi Minh City, 700000, Ho Chi Minh City, Vietnam.
Mol Divers. 2021 May;25(2):741-751. doi: 10.1007/s11030-020-10047-9. Epub 2020 Feb 11.
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-IC for predicting ABCC2 inhibitors (substrate EG) gave root-mean-square error RMSE = 0.26, Q = 0.73 and [Formula: see text]. The regression model PLS CDCF-IC for predicting ABCC2 inhibitors [substrate CDCF: 5(6)-carboxy-2',7'-dichlorofluorescein] gave RMSE = 0.31, Q = 0.74 and [Formula: see text]. 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 IC < 100 µM.
ABCC2/MRP2 的过表达导致癌细胞的多药耐药。本研究对 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。通过 SONNIA 4.2 构建了 7 个分类模型和 2 个回归模型，通过 MOE 2008.10 构建了另外 2 个回归模型，数据集由 16 篇相关文献中收集的 372 种化合物组成。CPG-C iABCC2 模型用于分类 ABCC2 抑制剂的总准确率为 0.88，马修斯相关系数 MCC=0.75。CPG-C iEG 模型用于分类 ABCC2 抑制剂（底物 EG：β-雌二醇 17-β-D-葡萄糖醛酸苷）的总准确率为 0.91，MCC=0.82。用于预测 ABCC2 抑制剂（底物 EG）的 PLS EG-IC 回归模型给出了 RMSE=0.26、Q=0.73 和 [Formula: see text]。用于预测 ABCC2 抑制剂[底物 CDCF：5(6)-羧基-2',7'-二氯荧光素]的 PLS CDCF-IC 回归模型给出了 RMSE=0.31、Q=0.74 和 [Formula: see text]。将四个 2D-QSAR 模型应用于 1661 种化合物，结果表明 369 种化合物具有逆转 ABCC2 对 EG 和 CDCF 外排的能力，其中 152 种化合物的 IC < 100 μM。
