Dmitriev Alexander V, Rudik Anastassia V, Karasev Dmitry A, Pogodin Pavel V, Lagunin Alexey A, Filimonov Dmitry A, Poroikov Vladimir V
Laboratory of Structure-Function Based Drug Design, Department of Bioinformatics, Institute of Biomedical Chemistry, Pogodinskaya Str. 10, bldg. 8, 119121 Moscow, Russia.
Pharmaceutics. 2021 Apr 13;13(4):538. doi: 10.3390/pharmaceutics13040538.
Drug-drug interactions (DDIs) can cause drug toxicities, reduced pharmacological effects, and adverse drug reactions. Studies aiming to determine the possible DDIs for an investigational drug are part of the drug discovery and development process and include an assessment of the DDIs potential mediated by inhibition or induction of the most important drug-metabolizing cytochrome P450 isoforms. Our study was dedicated to creating a computer model for prediction of the DDIs mediated by the seven most important P450 cytochromes: CYP1A2, CYP2B6, CYP2C19, CYP2C8, CYP2C9, CYP2D6, and CYP3A4. For the creation of structure-activity relationship (SAR) models that predict metabolism-mediated DDIs for pairs of molecules, we applied the Prediction of Activity Spectra for Substances (PASS) software and Pairs of Substances Multilevel Neighborhoods of Atoms (PoSMNA) descriptors calculated based on structural formulas. About 2500 records on DDIs mediated by these cytochromes were used as a training set. Prediction can be carried out both for known drugs and for new, not-yet-synthesized substances. The average accuracy of the prediction of DDIs mediated by various isoforms of cytochrome P450 estimated by leave-one-out cross-validation (LOO CV) procedures was about 0.92. The SAR models created are publicly available as a web resource and provide predictions of DDIs mediated by the most important cytochromes P450.
药物相互作用（DDIs）可导致药物毒性、药理作用减弱以及药物不良反应。旨在确定研究药物可能存在的药物相互作用的研究是药物发现和开发过程的一部分，包括评估由最重要的药物代谢细胞色素P450同工酶的抑制或诱导介导的药物相互作用潜力。我们的研究致力于创建一个计算机模型，用于预测由七种最重要的P450细胞色素介导的药物相互作用：CYP1A2、CYP2B6、CYP2C19、CYP2C8、CYP2C9、CYP2D6和CYP3A4。为了创建预测分子对代谢介导的药物相互作用的构效关系（SAR）模型，我们应用了物质活性谱预测（PASS）软件和基于结构式计算的物质原子对多级邻域（PoSMNA）描述符。大约2500条关于这些细胞色素介导的药物相互作用的记录用作训练集。预测既可以针对已知药物，也可以针对尚未合成的新物质进行。通过留一法交叉验证（LOO CV）程序估计的细胞色素P450各种同工酶介导的药物相互作用预测的平均准确率约为0.92。创建的SAR模型作为网络资源公开可用，并提供由最重要的细胞色素P450介导的药物相互作用的预测。
