Sun Guohui, Bai Peiying, Fan Tengjiao, Zhao Lijiao, Zhong Rugang, McElhinney R Stanley, McMurry T Brian H, Donnelly Dorothy J, McCormick Joan E, Kelly Jane, Margison Geoffrey P
Beijing Key Laboratory of Environmental and Viral Oncology, Faculty of Environment and Life, Beijing University of Technology, Beijing 100124, China.
Department of Medical Technology, Beijing Pharmaceutical University of Staff and Workers, Beijing 100079, China.
Pharmaceutics. 2023 Aug 21;15(8):2170. doi: 10.3390/pharmaceutics15082170.
-methylguanine-DNA methyltransferase (MGMT) constitutes an important cellular mechanism for repairing potentially cytotoxic DNA damage induced by guanine -alkylating agents and can render cells highly resistant to certain cancer chemotherapeutic drugs. A wide variety of potential MGMT inactivators have been designed and synthesized for the purpose of overcoming MGMT-mediated tumor resistance. We determined the inactivation potency of these compounds against human recombinant MGMT using [H]-methylated-DNA-based MGMT inactivation assays and calculated the IC values. Using the results of 370 compounds, we performed quantitative structure-activity relationship (QSAR) modeling to identify the correlation between the chemical structure and MGMT-inactivating ability. Modeling was based on subdividing the sorted pIC values or on chemical structures or was random. A total of nine molecular descriptors were presented in the model equation, in which the mechanistic interpretation indicated that the status of nitrogen atoms, aliphatic primary amino groups, the presence of O-S at topological distance 3, the presence of Al-O-Ar/Ar-O-Ar/R..O..R/R-O-C=X, the ionization potential and hydrogen bond donors are the main factors responsible for inactivation ability. The final model was of high internal robustness, goodness of fit and prediction ability ( = 0.7474, = 0.7375-0.7437, = 0.8530). After the best splitting model was decided, we established the full model based on the entire set of compounds using the same descriptor combination. We also used a similarity-based read-across technique to further improve the external predictive ability of the model ( = 0.7528, = 0.7387-0.7449, = 0.8560). The prediction quality of 66 true external compounds was checked using the "Prediction Reliability Indicator" tool. In summary, we defined key structural features associated with MGMT inactivation, thus allowing for the design of MGMT inactivators that might improve clinical outcomes in cancer treatment.
O^6-甲基鸟嘌呤-DNA甲基转移酶（MGMT）是修复鸟嘌呤烷基化剂诱导的潜在细胞毒性DNA损伤的重要细胞机制，可使细胞对某些癌症化疗药物产生高度抗性。为了克服MGMT介导的肿瘤耐药性，人们设计并合成了多种潜在的MGMT失活剂。我们使用基于[H]-甲基化DNA的MGMT失活测定法测定了这些化合物对人重组MGMT的失活效力，并计算了IC值。利用370种化合物的结果，我们进行了定量构效关系（QSAR）建模，以确定化学结构与MGMT失活能力之间的相关性。建模基于对排序后的pIC值进行细分、基于化学结构或随机进行。模型方程中总共呈现了九个分子描述符，其中机理解释表明氮原子的状态、脂肪族伯氨基、拓扑距离为3处O-S的存在、Al-O-Ar/Ar-O-Ar/R..O..R/R-O-C=X的存在、电离势和氢键供体是决定失活能力的主要因素。最终模型具有较高的内部稳健性、拟合优度和预测能力（R^2 = 0.7474，Q^2 = 0.7375 - 0.7437，R_pred^2 = 0.8530）。确定最佳拆分模型后，我们使用相同的描述符组合基于整个化合物集建立了完整模型。我们还使用基于相似性的类推技术进一步提高了模型的外部预测能力（R^2 = 0.7528，Q^2 = 0.7387 - 0.7449，R_pred^2 = 0.8560）。使用“预测可靠性指标”工具检查了66种真实外部化合物的预测质量。总之，我们定义了与MGMT失活相关的关键结构特征，从而有助于设计可能改善癌症治疗临床结果的MGMT失活剂。
