Synthesis and Structure-Activity Relationship of Covalent Inhibitors of SARS-CoV-2 Papain-Like Protease with Antiviral Potency

The papain-like protease (PLpro) is a highly conserved domain encoded by the coronavirus (CoV) genome and it plays an essential role in the replication and maturation of the virus in addition to weakening host immune response. Due to the virus's reliance on PLpro for survival and propagation, small-molecule inhibitors of PLpro serve as an attractive model for direct-acting antiviral therapeutic agents against SARS-CoV-2. Building upon existing work aimed at designing covalent inhibitors against PLpro, we report the synthesis and structure-activity relationship of analogs based on the known covalent inhibitor 1 (Sanders, et al.2023). To evaluate the efficacy of synthesized derivatives, we conducted enzymatic inhibition assays, SARS-CoV-2/HeLa-ACE2 cellular potency and toxicity assays, and profiled the most promising analogs via in vitro ADME and in vivo pharmacokinetic studies. Additionally, we describe computational docking of profiled compounds bound to PLpro to elucidate the structure-activity relationship of compounds based on 1 and offer suggestions for optimizing the potency and selectivity of the electrophilic warhead and improving ADME and PK properties for this chemotype. Relative to the parent compound, new designs demonstrate comparable potency and target selectivity for PLpro. The accomplished SAR campaign provides novel insight for future development of antivirals against SARS-CoV-2.

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Publication Details

Journal
Bioorganic & Medicinal Chemistry Letters
Published
2024-11-21
DOI
https://doi.org/10.1016/j.bmcl.2024.130034
Citations
2
Primary Topic
Computational Drug Discovery Methods
Type
article
Field-Weighted Citation Impact
0.72
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article

Synthesis and Structure-Activity Relationship of Covalent Inhibitors of SARS-CoV-2 Papain-Like Protease with Antiviral Potency

Laura Riva, Rebecca Greene-Cramer, Case W. McNamara, Connor McCarty et al.
2 citations
Bioorganic & Medicinal Chemistry Letters
Computational Drug Discovery Methods
0.72
article

Synthesis and Structure-Activity Relationship of Covalent Inhibitors of SARS-CoV-2 Papain-Like Protease with Antiviral Potency

Laura Riva, Rebecca Greene-Cramer, Case W. McNamara, Connor McCarty, Gennadii A. Grabovyi, Arnab K. Chatterjee, Anna De Falco, Mina Heacock, G.T. Montelione, Lirui Song, Alan Chu, Kristen Johnson, C. Rouch, Karen C. Wolff
article en
2 citations

Abstract

The papain-like protease (PLpro) is a highly conserved domain encoded by the coronavirus (CoV) genome and it plays an essential role in the replication and maturation of the virus in addition to weakening host immune response. Due to the virus's reliance on PLpro for survival and propagation, small-molecule inhibitors of PLpro serve as an attractive model for direct-acting antiviral therapeutic agents against SARS-CoV-2. Building upon existing work aimed at designing covalent inhibitors against PLpro, we report the synthesis and structure-activity relationship of analogs based on the known covalent inhibitor 1 (Sanders, et al.2023). To evaluate the efficacy of synthesized derivatives, we conducted enzymatic inhibition assays, SARS-CoV-2/HeLa-ACE2 cellular potency and toxicity assays, and profiled the most promising analogs via in vitro ADME and in vivo pharmacokinetic studies. Additionally, we describe computational docking of profiled compounds bound to PLpro to elucidate the structure-activity relationship of compounds based on 1 and offer suggestions for optimizing the potency and selectivity of the electrophilic warhead and improving ADME and PK properties for this chemotype. Relative to the parent compound, new designs demonstrate comparable potency and target selectivity for PLpro. The accomplished SAR campaign provides novel insight for future development of antivirals against SARS-CoV-2.

Bioorganic & Medicinal Chemistry LettersVol. 116
Scripps Research Institute (US), Rensselaer Polytechnic Institute (US)
Zero hunger
Openalex Percentile: Top 25%
Computational Drug Discovery Methods
0.72
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