African Genome Center, Mohammed VI Polytechnic University, Ben Guerir 43150, Morocco.
Department of Chemistry, Faculty of Sciences Semlalia, BP 2390, Marrakech 40000, Morocco.
Molecules. 2022 Apr 23;27(9):2729. doi: 10.3390/molecules27092729.
Hepatitis C virus (HCV) is a serious disease that threatens human health. Despite consistent efforts to inhibit the virus, it has infected more than 58 million people, with 300,000 deaths per year. The HCV nonstructural protein NS5A plays a critical role in the viral life cycle, as it is a major contributor to the viral replication and assembly processes. Therefore, its importance is evident in all currently approved HCV combination treatments. The present study identifies new potential compounds for possible medical use against HCV using the quantitative structure-activity relationship (QSAR). In this context, a set of 36 NS5A inhibitors was used to build QSAR models using genetic algorithm multiple linear regression (GA-MLR) and Monte Carlo optimization and were implemented in the software CORAL. The Monte Carlo method was used to build QSAR models using SMILES-based optimal descriptors. Four splits were performed and 24 QSAR models were developed and verified through internal and external validation. The model created for split 3 produced a higher value of the determination coefficients using the validation set (R = 0.991 and Q = 0.943). In addition, this model provides interesting information about the structural features responsible for the increase and decrease of inhibitory activity, which were used to develop eight novel NS5A inhibitors. The constructed GA-MLR model with satisfactory statistical parameters (R = 0.915 and Q = 0.941) confirmed the predicted inhibitory activity for these compounds. The Absorption, Distribution, Metabolism, Elimination, and Toxicity (ADMET) predictions showed that the newly designed compounds were nontoxic and exhibited acceptable pharmacological properties. These results could accelerate the process of discovering new drugs against HCV.
丙型肝炎病毒（HCV）是一种严重威胁人类健康的疾病。尽管人们一直致力于抑制该病毒，但它仍已感染了超过 5800 万人，每年导致 30 万人死亡。HCV 非结构蛋白 NS5A 在病毒生命周期中起着至关重要的作用，因为它是病毒复制和组装过程的主要贡献者。因此，在所有目前批准的 HCV 联合治疗中，它都非常重要。本研究使用定量构效关系（QSAR）确定了针对 HCV 的新的潜在化合物，用于可能的医疗用途。在这种情况下，使用了一组 36 种 NS5A 抑制剂，使用遗传算法多元线性回归（GA-MLR）和蒙特卡罗优化构建 QSAR 模型，并在软件 CORAL 中实施。蒙特卡罗方法用于使用基于 SMILES 的最佳描述符构建 QSAR 模型。进行了四次拆分，开发了 24 个 QSAR 模型，并通过内部和外部验证进行了验证。使用验证集对拆分 3 创建的模型产生了更高的确定系数值（R = 0.991 和 Q = 0.943）。此外，该模型提供了有关负责增加和减少抑制活性的结构特征的有趣信息，这些信息用于开发了八种新型 NS5A 抑制剂。具有令人满意的统计参数（R = 0.915 和 Q = 0.941）的构建的 GA-MLR 模型证实了这些化合物的预测抑制活性。吸收，分布，代谢，消除和毒性（ADMET）预测表明，新设计的化合物没有毒性，并表现出可接受的药理学特性。这些结果可能会加速发现针对 HCV 的新药的过程。
