Machine-learning and structure-based discovery of SARS-CoV-2 papain-like protease (PLpro) inhibitors with efficacy in a murine infection model

TL;DR

Researchers used machine learning and structural insights to discover effective SARS-CoV-2 PLpro inhibitors that work in a mouse model of COVID-19.

Contribution

Integration of machine learning and structure-based design led to potent, orally available PLpro inhibitors with in vivo efficacy.

Findings

Lead compound PF-07957472 showed robust efficacy in a mouse-adapted model of COVID-19.

X-ray crystallography revealed structural insights for improved potency over GRL0617.

Additional compounds were designed with reduced off-target liabilities like hERG and CYP3A4 TDI.

Abstract

First-generation antiviral therapeutics have provided important protection against COVID-19 caused by severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2). However, additional therapeutic mechanisms are needed that provide enhanced efficacy and protection against potential viral resistance. The SARS-CoV-2 papain-like protease (PLpro) is one of the two essential cysteine proteases involved in viral replication. While inhibitors of the SARS-CoV-2 main protease have demonstrated clinical efficacy, previously reported PLpro inhibitors like GRL0617 have lacked the cellular inhibitory potency to demonstrate that targeting PLpro translates to in vivo efficacy in a preclinical setting. Here, we report the machine learning–driven discovery of potent, selective, and orally available SARS-CoV-2 PLpro inhibitors, with lead compound PF-07957472 providing robust efficacy in a mouse-adapted model of COVID-19 infection. Structural elucidation of PF-07957472 bound to SARS-CoV-2 PLpro by X-ray crystallography provides rationale for the enhanced potency observed over GRL0617. We used this structural understanding and platform as a basis to design additional potent compounds with improved off-target liabilities, such as human ether-a-go-go (hERG) and CYP3A4 time-dependent inhibition (TDI). This work demonstrates the value of integrating machine-learning methods with traditional structure-based drug design to rapidly arrive at potent antiviral compounds.