Nouh Mounadi,Hassan Nour,Kasim Sakran Abass,Mhammed El Kouali,Samir Chtita
{"title":"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。","authors":"Nouh Mounadi,Hassan Nour,Kasim Sakran Abass,Mhammed El Kouali,Samir Chtita","doi":"10.1021/acs.jcim.5c00992","DOIUrl":null,"url":null,"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.","PeriodicalId":44,"journal":{"name":"Journal of Chemical Information and Modeling ","volume":"22 1","pages":""},"PeriodicalIF":5.3000,"publicationDate":"2025-10-18","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"0","resultStr":"{\"title\":\"Exploring Drug Repurposing for Influenza A (H3N2) Virus: A Computational Approach to Identifying Commercialized Drugs Targeting Hemagglutinin, Neuraminidase, and Nucleoprotein.\",\"authors\":\"Nouh Mounadi,Hassan Nour,Kasim Sakran Abass,Mhammed El Kouali,Samir Chtita\",\"doi\":\"10.1021/acs.jcim.5c00992\",\"DOIUrl\":null,\"url\":null,\"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.\",\"PeriodicalId\":44,\"journal\":{\"name\":\"Journal of Chemical Information and Modeling \",\"volume\":\"22 1\",\"pages\":\"\"},\"PeriodicalIF\":5.3000,\"publicationDate\":\"2025-10-18\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"\",\"citationCount\":\"0\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"Journal of Chemical Information and Modeling \",\"FirstCategoryId\":\"92\",\"ListUrlMain\":\"https://doi.org/10.1021/acs.jcim.5c00992\",\"RegionNum\":2,\"RegionCategory\":\"化学\",\"ArticlePicture\":[],\"TitleCN\":null,\"AbstractTextCN\":null,\"PMCID\":null,\"EPubDate\":\"\",\"PubModel\":\"\",\"JCR\":\"Q1\",\"JCRName\":\"CHEMISTRY, MEDICINAL\",\"Score\":null,\"Total\":0}","platform":"Semanticscholar","paperid":null,"PeriodicalName":"Journal of Chemical Information and Modeling ","FirstCategoryId":"92","ListUrlMain":"https://doi.org/10.1021/acs.jcim.5c00992","RegionNum":2,"RegionCategory":"化学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"Q1","JCRName":"CHEMISTRY, MEDICINAL","Score":null,"Total":0}
Exploring Drug Repurposing for Influenza A (H3N2) Virus: A Computational Approach to Identifying Commercialized Drugs Targeting Hemagglutinin, Neuraminidase, and Nucleoprotein.
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.
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.