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<p>The Hepatitis C virus (HCV) poses a significant universal health threat, with around 3 million new infections annually. The NS3/4A protease is a suitable target for drugs to treat the viral infection hepatitis C (HCV). This study aims to search and identify potent HCV NS3/4A protease inhibitors by applying comprehensive computational techniques. An e-pharmacophore model was created employing the best docked Voxilaprevir-6NZT complex. Based on the selected hypothesis, compounds predicted to bind to the HCV NS3/4A protease were chosen. Molecular docking identified five hits, each demonstrating key interactions with the target. The effectiveness of the molecular docking protocol was evaluated using the enrichment calculation approach, which demonstrated the highest accuracy of the method. Absorption, distribution, metabolism, excretion and toxicity (ADMET) prediction was used to assess the safety profile of the top hits. Four leads exhibited antiviral and favorable ADME properties. The DFT study was carried out to predict the chemical reactivity of the best compounds, which revealed the potential of compound CID 142714408 to inhibit HCV NS3/4A protease more efficiently than the other compounds. Additionally, MD simulations of top two compounds were performed to assess their stability. The results will pave the way for the discovery of novel potential HCV NS3/4A protease inhibitors with good ADME profiles and low toxicity.</p>

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Integrated in silico approach for discovering novel HCV NS3/4A protease inhibitors: virtual screening, docking, and dynamic simulation

  • 期刊论文

  • 发表日期：

    2025年4月19日

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  • Afaf Zekri,
  • Mebarka Ouassaf,
  • Shafi Ullah Khan,
  • Kannan R. R. Rengasamy,
  • Bader Y. Alhatlani

摘要

The Hepatitis C virus (HCV) poses a significant universal health threat, with around 3 million new infections annually. The NS3/4A protease is a suitable target for drugs to treat the viral infection hepatitis C (HCV). This study aims to search and identify potent HCV NS3/4A protease inhibitors by applying comprehensive computational techniques. An e-pharmacophore model was created employing the best docked Voxilaprevir-6NZT complex. Based on the selected hypothesis, compounds predicted to bind to the HCV NS3/4A protease were chosen. Molecular docking identified five hits, each demonstrating key interactions with the target. The effectiveness of the molecular docking protocol was evaluated using the enrichment calculation approach, which demonstrated the highest accuracy of the method. Absorption, distribution, metabolism, excretion and toxicity (ADMET) prediction was used to assess the safety profile of the top hits. Four leads exhibited antiviral and favorable ADME properties. The DFT study was carried out to predict the chemical reactivity of the best compounds, which revealed the potential of compound CID 142714408 to inhibit HCV NS3/4A protease more efficiently than the other compounds. Additionally, MD simulations of top two compounds were performed to assess their stability. The results will pave the way for the discovery of novel potential HCV NS3/4A protease inhibitors with good ADME profiles and low toxicity.