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1.
ACS Med Chem Lett ; 12(8): 1325-1332, 2021 Aug 12.
Article in English | MEDLINE | ID: covidwho-1345534

ABSTRACT

Non-covalent inhibitors of the main protease (Mpro) of SARS-CoV-2 having a pyridinone core were previously reported with IC50 values as low as 0.018 µM for inhibition of enzymatic activity and EC50 values as low as 0.8 µM for inhibition of viral replication in Vero E6 cells. The series has now been further advanced by consideration of placement of substituted five-membered-ring heterocycles in the S4 pocket of Mpro and N-methylation of a uracil ring. Free energy perturbation calculations provided guidance on the choice of the heterocycles, and protein crystallography confirmed the desired S4 placement. Here we report inhibitors with EC50 values as low as 0.080 µM, while remdesivir yields values of 0.5-2 µM in side-by-side testing with infectious SARS-CoV-2. A key factor in the improvement is enhanced cell permeability, as reflected in PAMPA measurements. Compounds 19 and 21 are particularly promising as potential therapies for COVID-19, featuring IC50 values of 0.044-0.061 µM, EC50 values of ca. 0.1 µM, good aqueous solubility, and no cytotoxicity.

2.
ACS Cent Sci ; 7(3): 467-475, 2021 Mar 24.
Article in English | MEDLINE | ID: covidwho-1132027

ABSTRACT

Starting from our previous finding of 14 known drugs as inhibitors of the main protease (Mpro) of SARS-CoV-2, the virus responsible for COVID-19, we have redesigned the weak hit perampanel to yield multiple noncovalent, nonpeptidic inhibitors with ca. 20 nM IC50 values in a kinetic assay. Free-energy perturbation (FEP) calculations for Mpro-ligand complexes provided valuable guidance on beneficial modifications that rapidly delivered the potent analogues. The design efforts were confirmed and augmented by determination of high-resolution X-ray crystal structures for five analogues bound to Mpro. Results of cell-based antiviral assays further demonstrated the potential of the compounds for treatment of COVID-19. In addition to the possible therapeutic significance, the work clearly demonstrates the power of computational chemistry for drug discovery, especially FEP-guided lead optimization.

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