Exploring Drug Repurposing for Influenza A (H3N2) Virus: A Computational Approach to Identifying Commercialized Drugs Targeting Hemagglutinin, Neuraminidase, and Nucleoprotein.

IF 5.3 2区 化学 Q1 CHEMISTRY, MEDICINAL
Nouh Mounadi,Hassan Nour,Kasim Sakran Abass,Mhammed El Kouali,Samir Chtita
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引用次数: 0

Abstract

Influenza A virus (IAV-A) remains a major global health threat, responsible for seasonal epidemics and significant mortality, with estimates ranging from 290 000 to 650 000 deaths annually, due to respiratory complications. The virus is highly mutable, which complicates treatment options and contributes to the emergence of drug resistance. IAV-A's pathogenicity is largely driven by two key surface proteins, hemagglutinin (HA), neuraminidase (NA) and nucleoprotein (NP), which facilitate viral entry and spread within the host. Despite the availability of antiviral treatments such as Oseltamivir, Zanamivir, and more recently, Baloxavir Marboxil, their efficacy is limited by resistance, side effects, and the rapid mutation of the virus. Given these challenges, there is an urgent need for new therapeutic strategies. Drug repositioning offers a promising solution by identifying new uses for existing, approved drugs, thereby reducing the development time and cost. This study aimed to explore the potential of 31 drugs from various therapeutic classes, including FDA-approved compounds, as candidates for treating influenza A (H3N2), with a particular focus on targeting NA, HA, and NP proteins. Through in silico molecular docking studies, we analyzed the energy scores between these drugs and target proteins. ADMET predictions were conducted to evaluate the pharmacokinetics, safety, and bioavailability of the best drug candidates. Following this, molecular dynamics simulations were performed to assess the stability of the drug-protein complexes over time. Finally, free-energy calculations were carried out to assess binding affinities, ranking the drugs based on their potential inhibitory effects and providing critical insights for their further development as antiviral treatments. This integrated computational approach offers a faster, cost-effective pathway for discovering novel influenza therapies with significant implications for pandemic preparedness.
探索甲型流感(H3N2)病毒的药物再利用:一种识别针对血凝素、神经氨酸酶和核蛋白的商业化药物的计算方法。
甲型流感病毒(IAV-A)仍然是一个主要的全球健康威胁,造成季节性流行和大量死亡,估计每年因呼吸道并发症死亡的人数在290 万至65 万之间。该病毒具有高度易变性,使治疗方案复杂化,并导致耐药性的出现。IAV-A的致病性主要由两种关键的表面蛋白驱动,即血凝素(HA)、神经氨酸酶(NA)和核蛋白(NP),它们促进病毒进入并在宿主内传播。尽管有诸如奥司他韦、扎那米韦和最近的巴洛昔韦等抗病毒治疗方法,但它们的疗效受到耐药性、副作用和病毒快速突变的限制。鉴于这些挑战,迫切需要新的治疗策略。药物重新定位提供了一个有前途的解决方案,通过确定现有的,已批准的药物的新用途,从而减少开发时间和成本。本研究旨在探索来自不同治疗类别的31种药物的潜力,包括fda批准的化合物,作为治疗甲型流感(H3N2)的候选药物,特别关注NA, HA和NP蛋白。通过硅分子对接研究,我们分析了这些药物与靶蛋白之间的能量评分。ADMET预测用于评估最佳候选药物的药代动力学、安全性和生物利用度。随后,进行分子动力学模拟以评估药物-蛋白质复合物随时间的稳定性。最后,进行自由能计算以评估结合亲和力,根据药物的潜在抑制作用对药物进行排名,并为其作为抗病毒治疗的进一步开发提供关键见解。这种综合计算方法为发现新型流感疗法提供了一种更快、更具成本效益的途径,对大流行防范具有重大意义。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
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来源期刊
CiteScore
9.80
自引率
10.70%
发文量
529
审稿时长
1.4 months
期刊介绍: The Journal of Chemical Information and Modeling publishes papers reporting new methodology and/or important applications in the fields of chemical informatics and molecular modeling. Specific topics include the representation and computer-based searching of chemical databases, molecular modeling, computer-aided molecular design of new materials, catalysts, or ligands, development of new computational methods or efficient algorithms for chemical software, and biopharmaceutical chemistry including analyses of biological activity and other issues related to drug discovery. Astute chemists, computer scientists, and information specialists look to this monthly’s insightful research studies, programming innovations, and software reviews to keep current with advances in this integral, multidisciplinary field. As a subscriber you’ll stay abreast of database search systems, use of graph theory in chemical problems, substructure search systems, pattern recognition and clustering, analysis of chemical and physical data, molecular modeling, graphics and natural language interfaces, bibliometric and citation analysis, and synthesis design and reactions databases.
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