The main protease (M) enzyme has an imperative function in disease progression and the life cycle of the SARS-CoV-2 virus. Although the orally active drug nirmatrelvir (co-administered with ritonavir as paxlovid) has been approved for emergency use as the frontline antiviral agent, there are a number of limitations that necessitate the discovery of new drug scaffolds, such as poor pharmacokinetics and susceptibility to proteolytic degradation due to its peptidomimetic nature. This study utilized a novel virtual screening workflow that combines pharmacophore modelling, multiple-receptor covalent docking, and biological evaluation in order to find new M inhibitors. After filtering and analysing ∼66,000 ligands from three different electrophilic libraries, 29 compounds were shortlisted for experimental testing, and two of them exhibited ≥20% inhibition at 100 μM. Our top candidate, GF04, is a benzylpyrrolyl compound that exhibited the highest inhibition activity of 38.3%, with a relatively small size (<350 Da) and leadlike character. Interestingly, our approach also identified another hit, DR07, a pyrimidoindol with a non-peptide character, and a molecular weight of 438.9 Da, reporting an inhibition of 26.3%. The established approach detailed in this study, in conjunction with the discovered inhibitors, has the capacity to yield novel perspectives for devising covalent inhibitors targeting the COVID-19 M enzyme and other comparable targets.
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http://dx.doi.org/10.1016/j.jmgm.2023.108672 | DOI Listing |
JAMA Netw Open
January 2025
Erasmus MC Cancer Institute, University Medical Center Rotterdam, Rotterdam, the Netherlands.
Importance: Active surveillance (AS) for patients with prostate cancer (PC) often includes fixed repeat prostate biopsies that do not account for the varying risk of reclassification to significant disease. Given the invasive nature and potential complications of biopsies, a personalized approach is needed to balance the burden of biopsies with the risk of missing disease progression.
Objective: To develop and externally validate a dynamic model that predicts an individual's risk of PC reclassification during AS.
J Chem Inf Model
January 2025
Division of Preclinical Innovation, National Center for Advancing Translational Sciences (NCATS), National Institutes of Health, Rockville, Maryland 20850, United States.
The global impact of SARS-CoV-2 highlights the need for treatments beyond vaccination, given the limited availability of effective medications. While Pfizer introduced , an FDA-approved antiviral targeting the SARS-CoV-2 main protease (Mpro), this study focuses on designing new antivirals against another protease, papain-like protease (PLpro), which is crucial for viral replication and immune suppression. NCATS/NIH performed a high-throughput screen of ∼15,000 molecules from an internal molecular library, identifying initial hits with a 0.
View Article and Find Full Text PDFInt J Biol Macromol
January 2025
Department of Basic Medical Sciences, Faculty of Medicine, Istanbul Medipol University, Istanbul 34815, Türkiye.
The COVID-19 pandemic began in March 2020 and has affected many countries and infected over a million people. It has had a serious impact on people's physical and mental health, daily life and the global economy. Today, many drugs show limited efficacy in the treatment of COVID-19 and studies to develop effective drugs continue.
View Article and Find Full Text PDFJ Med Chem
January 2025
Department of Chemical Biology, Helmholtz Centre for Infection Research, Inhoffenstr. 7, Braunschweig 38124, Germany.
The main protease M is a clinically validated target to treat infections by the coronavirus SARS-CoV-2. Among the first reported M inhibitors was the peptidomimetic α-ketoamide , whose cocrystal structure with M paved the way for multiple lead-finding studies. We established structure-activity relationships for the series by modifying residues at the P1', P3, and P4 sites.
View Article and Find Full Text PDFDigit Discov
January 2025
School of Natural and Environmental Sciences, Newcastle University Newcastle Upon Tyne NE1 7RU UK
FEgrow is an open-source software package for building congeneric series of compounds in protein binding pockets. For a given ligand core and receptor structure, it employs hybrid machine learning/molecular mechanics potential energy functions to optimise the bioactive conformers of supplied linkers and functional groups. Here, we introduce significant new functionality to automate, parallelise and accelerate the building and scoring of compound suggestions, such that it can be used for automated design.
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