An orally bioavailable SARS-CoV-2 main protease inhibitor exhibits improved affinity and reduced sensitivity to mutations

Abstract

Inhibitors of the severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) main protease (M pro ) such as nirmatrelvir (NTV) and ensitrelvir (ETV) have proven effective in reducing the severity of COVID-19, but the presence of resistance-conferring mutations in sequenced viral genomes raises concerns about future drug resistance. Second-generation oral drugs that retain function against these mutants are thus urgently needed. We hypothesized that the covalent hepatitis C virus protease inhibitor boceprevir (BPV) could serve as the basis for orally bioavailable drugs that inhibit SARS-CoV-2 M pro more efficiently than existing drugs. Performing structure-guided modifications of BPV, we developed a picomolar-affinity inhibitor, ML2006a4, with antiviral activity, oral pharmacokinetics, and therapeutic efficacy similar or superior to those of NTV. A crucial feature of ML2006a4 is a derivatization of the ketoamide reactive group that improves cell permeability and oral bioavailability. Last, ML2006a4 was found to be less sensitive to several mutations that cause resistance to NTV or ETV and occur in the natural SARS-CoV-2 population. Thus, anticipatory design can preemptively address potential resistance mechanisms to expand future treatment options against coronavirus variants.

Rational Design Problematics of Peptide Nucleic Acids as SARS-CoV-2 Inhibitors

“…Thus, the authors , started from the structures of crystallized inhibitors, proposing new inhibitory peptidomimetics. Modifications of the known hepatitis C virus protease inhibitor boceprevir allowed obtaining orally bioavailable compounds, , and it was shown that they may be less sensitive to several mutations that cause resistance in the natural SARS-CoV-2 population . As the availability of computer modeling methods increases, researchers are gradually supplementing them with modern technologies of artificial intelligence and deep learning; ,, however, approaches based on screening of chemical libraries are also on top of their relevancy.…”