tetano
Editor, Senior Moderator
J Chem Inf Model
. 2025 Oct 18.
doi: 10.1021/acs.jcim.5c00992. Online ahead of print. Exploring Drug Repurposing for Influenza A (H3N2) Virus: A Computational Approach to Identifying Commercialized Drugs Targeting Hemagglutinin, Neuraminidase, and Nucleoprotein
Nouh Mounadi[SUP] 1 [/SUP], Hassan Nour[SUP] 1 [/SUP], Kasim Sakran Abass[SUP] 2 [/SUP], Mhammed El Kouali[SUP] 1 [/SUP], Samir Chtita[SUP] 1 [/SUP]
Affiliations
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.
. 2025 Oct 18.
doi: 10.1021/acs.jcim.5c00992. Online ahead of print. Exploring Drug Repurposing for Influenza A (H3N2) Virus: A Computational Approach to Identifying Commercialized Drugs Targeting Hemagglutinin, Neuraminidase, and Nucleoprotein
Nouh Mounadi[SUP] 1 [/SUP], Hassan Nour[SUP] 1 [/SUP], Kasim Sakran Abass[SUP] 2 [/SUP], Mhammed El Kouali[SUP] 1 [/SUP], Samir Chtita[SUP] 1 [/SUP]
Affiliations
- PMID: 41108225
- DOI: 10.1021/acs.jcim.5c00992
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.