Targeting SARS-CoV-2 papain-like protease in the postvaccine era. (November 2022)

• Record Type: Journal Article
• Authors: Ton, Anh-Tien; Pandey, Mohit; Smith, Jason R.; Ban, Fuqiang; Fernandez, Michael; Cherkasov, Artem
• Is Part Of: Trends in pharmacological sciences. Volume 43:Number 11(2022)
• Page Start: 906
• Page End: 919
• Publication Date: 2022-11
• Subjects: papain-like protease -- artificial intelligence -- small molecules -- computer-aided drug design -- COVID-19 -- SARS-CoV-2; Pharmacology -- Periodicals; Pharmacology -- trends -- Periodicals; Pharmacologie -- Périodiques; Pharmacology; Electronic journals; Periodicals; 615.1
• DOI: 10.1016/j.tips.2022.08.008
• Languages: English
• ISSNs: 0165-6147

Abstract

Abstract : While vaccines remain at the forefront of global healthcare responses, pioneering therapeutics against SARS-CoV-2 are expected to fill the gaps for waning immunity. Rapid development and approval of orally available direct-acting antivirals targeting crucial SARS-CoV-2 proteins marked the beginning of the era of small-molecule drugs for COVID-19. In that regard, the papain-like protease (PLpro) can be considered a major SARS-CoV-2 therapeutic target due to its dual biological role in suppressing host innate immune responses and in ensuring viral replication. Here, we summarize the challenges of targeting PLpro and innovative early-stage PLpro-specific small molecules. We propose that state-of-the-art computer-aided drug design (CADD) methodologies will play a critical role in the discovery of PLpro compounds as a novel class of COVID-19 drugs. Highlights: New emergency-approved drugs against COVID-19 represent the beginning of powerful oral anti-COVID-19 inhibitors in the postvaccine era. Vaccines remain at the forefront of global healthcare response, but oral drugs can fill the gaps when immunity starts waning. The papain-like protease (PLpro) ensures viral replication by processing viral polyproteins and host post-translational modifications. However, a lack of diverse high-quality scaffolds against the protease hinders drug development. Promising compounds are derived from an older noncovalent compound previously identified for SARS-CoV-1. There is a methodological transition focusing on computational screening complimented by experimental validation of inhibitory activity and on-target effects. Deploying machine-learning-enhanced in silico methods should increase the success of identifying diverse and new scaffolds against PLpro.