Glisic Sanja, Stevanovic Kristina, Perdih Andrej, Bukreyeva Natalya, Maruyama Junki, Perovic Vladimir, López-Serrano Sergi, Darji Ayub, Radosevic Draginja, Sencanski Milan, Veljkovic Veljko, Botta Bruno, Mori Mattia, Paessler Slobodan
Laboratory for Bioinformatics and Computational Chemistry, Institute of Nuclear Sciences VINCA, University of Belgrade, 11000 Belgrade, Serbia.
Theory Department, National Institute of Chemistry, 1000 Ljubljana, Slovenia.
Int J Mol Sci. 2025 Jun 21;26(13):5962. doi: 10.3390/ijms26135962.
Influenza is still a chronic global health threat, inducing a sustained search for effective antiviral therapeutics. Computational methods have played a pivotal role in developing small molecule therapeutics. In this study, we applied a combined in silico and in vitro approach to explore the potential anti-influenza activity of cyproheptadine, a clinically used histamine H1 receptor antagonist. Virtual screening based on the average quasivalence number (AQVN) and electron-ion interaction potential (EIIP) descriptors suggests similarities between cyproheptadine and several established anti-influenza agents. The subsequent ligand-based pharmacophore screening of a focused H1 antagonist library was aligned with the bioinformatics prediction, and further experimental in vitro evaluation of cyproheptadine demonstrated its anti-influenza activity. These findings provide proof of concept for cyproheptadine's in silico-predicted antiviral potential and underscore the value of integrating computational predictions with experimental validation. The results of the current study provide a preliminary proof of concept for the predicted anti-influenza potential based on computational analysis and emphasize the utility of integrating in silico screening with experimental validation in the early stages of drug repurposing efforts.
流感仍是全球长期的健康威胁，促使人们不断寻找有效的抗病毒疗法。计算方法在开发小分子疗法中发挥了关键作用。在本研究中，我们采用计算机模拟和体外实验相结合的方法，探索临床使用的组胺H1受体拮抗剂赛庚啶的潜在抗流感活性。基于平均准价数（AQVN）和电子 - 离子相互作用势（EIIP）描述符的虚拟筛选表明，赛庚啶与几种已确立的抗流感药物存在相似性。随后对聚焦的H1拮抗剂库进行基于配体的药效团筛选，与生物信息学预测结果相符，对赛庚啶的进一步体外实验评估证实了其抗流感活性。这些发现为赛庚啶计算机模拟预测的抗病毒潜力提供了概念验证，并强调了将计算预测与实验验证相结合的价值。当前研究结果为基于计算分析预测的抗流感潜力提供了初步概念验证，并强调了在药物重新利用早期阶段将计算机模拟筛选与实验验证相结合的实用性。
