Author:
Garnsey Michelle R.,Robinson Matthew C.,Nguyen Luong T.,Cardin Rhonda,Tillotson Joseph,Mashalidis Ellene,Aijia Yu,Aschenbrenner Lisa,Balesano Amanda,Behzadi Amin,Boras Britton,Chang Jeanne S.,Eng Heather,Ephron Andrew,Foley Tim,Ford Kristen K.,Frick James M.,Gibson Scott,Hao Li,Hurst Brett,Kalgutkar Amit S.,Korczynska Magdalena,Lengyel-Zhand Zsofia,Liping Gao,Meredith Hannah R.,Patel Nandini C,Polivkova Jana,Rai Devendra,Rose Colin R.,Rothan Hussin,Sakata Sylvie K.,Vargo Thomas R.,Wenying Qi,Wu Huixian,Yiping Liu,Yurgelonis Irina,Zhang Jinzhi,Zhu Yuao,Zhang Lei,Lee Alpha A.
Abstract
AbstractVaccines and first-generation antiviral therapeutics have provided important protection against coronavirus disease 2019 (COVID-19) caused by severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2). However, there remains a need for additional therapeutic options that provide enhanced efficacy and protection against potential viral resistance. The SARS-CoV-2 papain-like protease (PLpro) is one of two essential cysteine proteases involved in viral replication. While inhibitors of the SARS-CoV-2 main protease (Mpro) have demonstrated clinical efficacy, known PLproinhibitors have to date lacked the inhibitory potency and requisite pharmacokinetics to demonstrate that targeting PLprotranslates toin vivoefficacy in a preclinical setting. Herein, we report the machine learning-driven discovery of potent, selective, and orally available SARS-CoV-2 PLproinhibitors, with lead compound PF-07957472 (4) providing robust efficacy in a mouse-adapted model of COVID-19 infection.
Publisher
Cold Spring Harbor Laboratory