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Combating Antiviral Drug Resistance: A Multipronged Strategy.

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Researchers developed a multipronged strategy to combat viral protease drug resistance, including novel inhibitors and degraders targeting SARS-CoV-2 main protease (Mpro). This approach utilizes computational methods and artificial intelligence to design effective antivirals against resistant mutations.

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Area of Science:

  • Virology
  • Medicinal Chemistry
  • Computational Biology

Background:

  • Viral proteases are crucial for viral replication and are key targets for antiviral drugs.
  • Drug resistance mutations in viral proteases, like SARS-CoV-2 main protease (Mpro), challenge the efficacy of existing therapies.
  • Developing next-generation antivirals requires strategies to overcome or prevent drug resistance.

Purpose of the Study:

  • To develop a comprehensive strategy to combat antiviral drug resistance, focusing on SARS-CoV-2 main protease (Mpro).
  • To design and synthesize novel inhibitors and degraders targeting Mpro, including those effective against drug-resistant variants.
  • To employ advanced computational and AI methods to guide the design and evaluation of antiviral agents.

Main Methods:

  • Screening of an α-ketoamide library to identify initial inhibitors.
  • Design and synthesis of covalent Mpro inhibitors (e.g., H135, H102) using computational and structural insights.
  • Development of the first PROTAC molecule (HP211206) targeting Mpro for protease degradation.
  • Application of computational chemistry (PDLD/S-LRA/β framework, QM calculations) for binding free energy evaluation.
  • Utilizing a vitality strategy to assess inhibitor binding and enzyme catalytic efficiency.
  • Employing kinetic simulations to model time-dependent inhibition.
  • Implementing artificial intelligence (D2Screen) for virtual screening of non-covalent inhibitors.

Main Results:

  • Compound 17 showed initial inhibitory activity against SARS-CoV-2.
  • Covalent inhibitors H135 and H102 demonstrated potent activity against Mpro and various SARS-CoV-2 variants.
  • H102 induced conformational changes in the Mpro catalytic dyad (His41), enhancing the antiresistance profile.
  • HP211206 effectively degraded drug-resistant Mpro mutants.
  • Computational methods provided accurate binding free energy calculations and predicted resistance-prone sites.
  • D2Screen identified quinoline-based inhibitors with anti-drug-resistance activity against Mpro E166V mutant.

Conclusions:

  • A multipronged approach combining synthetic chemistry, structural biology, computational modeling, and AI is effective in combating viral protease drug resistance.
  • Novel covalent inhibitors and proteolysis-targeting chimera (PROTAC) molecules offer promising avenues for next-generation antivirals.
  • Computational and AI tools are essential for accelerating the design of potent and resistance-evading antiviral agents.