Combating Antiviral Drug Resistance: A Multipronged Strategy.

ConspectusViral proteases are essential enzymes required for viral replication and assembly, making them prime antiviral drug targets. However, under the selective pressure of protease inhibitors, viruses can acquire mutations that reduce drug binding efficacy, posing significant challenges in both chronic infections (e.g., HIV, HCV) and acute infections like COVID-19, where mutations in the SARS-CoV-2 main protease (M) have been reported to compromise the efficacy of drugs such as nirmatrelvir. To address these challenges, mainstream strategies in combating viral protease drug resistance mutations include combination therapies and targeting evolutionarily conserved regions of viral proteases. By disrupting multiple stages of the viral lifecycle or focusing on functionally indispensable residues, these strategies aim to develop next-generation antivirals that remain effective against evolving viral mutations.Here we provide an account of our laboratory's journey with our collaborators of the past 5 years, started during the COVID-19 pandemic and continued beyond it, in developing a multipronged strategy to combat antiviral drug resistance. The journey began with the discovery of compound from screening our in-house α-ketoamide library with inhibitory activity against both the proteasome and SARS-CoV-2. We subsequently designed more specific covalent M inhibitors with different warheads, such as H135 and H102 displaying potent activity, using computational and structural insights. H135 exhibited broad anti-SARS-CoV-2 activity, including alpha, delta, XBB.1.5, BA.5.2, EG.5.1, and JN.1.1 variants, in VeroE6-TMPRSS2 cells. Particularly, we observed an unusual distortion of the geometry of the catalytic dyad of M in the X-ray crystal structure of H102, in which H102 induced conformational change of the catalytic residue His41. Using H102 as a model compound, we demonstrated that inducing conformational changes in His41 slightly enhances the antiresistance profile of inhibitors. Besides conventional protease inhibition, we attempted a new alternative strategy of protease degradation and developed HP211206, the first reported PROTAC molecule targeting SARS-CoV-2 M, which is capable of degrading known drug-resistant M mutants. To aid the design of these viral protease inhibitors and degraders, computational chemistry was used to develop an efficient method integrating the PDLD/S-LRA/β framework with quantum-mechanical calculations to evaluate both non-covalent and covalent contributions to absolute binding free energy between protein mutants and various inhibitors, which can help find inhibitors or degraders with high target binding. A vitality strategy was also employed to evaluate both the binding free energy of inhibitors () and the enzyme's catalytic efficiency parameters ( and ) for natural substrates, which can help predict sites on the viral protease prone to drug resistance mutations and thus to be avoided in drug targeting. Additionally, a kinetic simulation framework was used to model the time dependence of inhibition (IC()) for nirmatrelvir analogues, providing valuable insights into their time-dependent efficacy. Most recently, artificial intelligence was applied in the development of D2Screen, which incorporates deep learning into conventional virtual screening and enabled us to identify quinoline-based non-covalent M inhibitors exhibiting anti-drug-resistance activity against the E166V M mutant. Together, these synthetic, computational, structural, and biological studies illustrate a multipronged strategy for developing more effective therapeutics that are less susceptible to drug resistant mutation.