Abstract

The hepatitis C virus is a communicable disease that gradually harms the liver leading to cirrhosis and hepatocellular carcinoma. Important therapeutic interventions have been reached since the discovery of the disease. However, its resurgence urges the need for new approaches against this malady. The NS4B receptor is one of the important proteins for Hepatitis C Virus RNA replication that acts by mediating different viral properties. In this work, we opt to explore the relationships between the molecular structures of biologically tested NS4B inhibitors and their corresponding inhibitory activities to assist the design of novel and potent NS4B inhibitors. For that, a set of 115 indol-2-ylpyridine-3-sulfonamides (IPSA) compounds with inhibitory activity against NS4B is used. A hybrid genetic algorithm combined with multiple linear regressions (GA-MLR) was implemented to construct a predictive model. This model was further used and applied to a set of compounds that were generated based on a pharmacophore modeling study combined with virtual screening to identify structurally similar lead compounds. Multiple filtrations were implemented for selecting potent hits. The selected hits exhibited advantageous molecular features, allowing for favorable inhibitory activity against HCV. The results showed that 7 out of 1285 screened compounds, were selected as potent candidate hits where Zinc14822482 exhibits the best predicted potency and pharmacophore features. The predictive pharmacokinetic analysis further justified the compounds as potential hit molecules, prompting their recommendation for a confirmatory biological evaluation. We believe that our strategy could help in the design and screening of potential inhibitors in drug discovery.

Communicated by Ramaswamy H. Sarma

摘要

丙型肝炎病毒是一种传染病，它逐渐损害肝脏，导致肝硬化和肝细胞癌。自从发现这种疾病以来，已经达成了重要的治疗干预措施。然而，它的死灰复燃促使人们需要采取新的方法来对抗这种疾病。NS4B 受体是丙型肝炎病毒 RNA 复制的重要蛋白质之一，通过介导不同的病毒特性起作用。在这项工作中，我们选择探索经过生物学测试的 NS4B 抑制剂的分子结构与其相应的抑制活性之间的关系，以帮助设计新型有效的 NS4B 抑制剂。为此，使用了一组 115 种对 NS4B 具有抑制活性的吲哚-2-基吡啶-3-磺胺 (IPSA) 化合物。实施了结合多元线性回归（GA-MLR）的混合遗传算法来构建预测模型。该模型被进一步使用并应用于一组化合物，这些化合物基于药效团建模研究与虚拟筛选相结合，以识别结构相似的先导化合物。实施了多次过滤以选择有效的命中。选择的命中表现出有利的分子特征，允许对 HCV 有良好的抑制活性。结果表明，在 1285 种筛选化合物中，有 7 种被选为有效候选命中，其中 Zinc14822482 表现出最佳预测效力和药效团特征。预测药代动力学分析进一步证明了这些化合物是潜在的命中分子，促使他们建议进行确认性生物学评估。我们相信我们的策略可以帮助设计和筛选药物发现中的潜在抑制剂。

由 Ramaswamy H. Sarma 传达