Repurposing FDA-approved drugs as NLRP3 inhibitors against inflammatory diseases: machine learning and molecular simulation approaches

Abstract

Activation of NLRP3 (NOD-like receptor family, pyrin domain-containing protein 3) has been associated with multiple chronic pathologies, including diabetes, atherosclerosis, and rheumatoid arthritis. Moreover, histone deacetylases (HDACs), specifically HDAC6 is required for the NLRP3 inflammasome to assemble and activate. Thus, NLRP3 serves as an attractive target for the development of novel therapeutic approaches. Several companies are now attempting to develop specific modulators of the NLRP3 inflammasome, but only a handful of small molecules of NLRP3 inflammasome inhibitors, such as MCC950 and Tranilast, are currently available for clinical use. However, their use is limited due to severe side effects and short half-lives. Thus, the repurposing of FDA-approved drugs with NLRP3 inhibitory activity is needed. The present study was aimed at repurposing preexisting drugs that might act as safe and effective NLRP3 inhibitors. A library of 2,697 FDA-approved drugs was screened for binding with NLRP3 (PDB: 7ALV) using Glide (Schrödinger). The top seven FDA-approved drugs with potential binding affinities were selected based on docking scores and subjected to ADMET profiling using pkCSM and SwissADME. The binding of the ADMET-favorable FDA-approved drugs to NLRP3 was validated using MMGBSA (Prime) and Molecular Dynamics (Desmond) in the Schrödinger suite. ADMET profiling revealed that of the seven best docking drugs, empagliflozin and citicoline had good drug-likeness properties. Moreover, MMGBSA analysis and molecular dynamics demonstrated that empagliflozin and citicoline exhibited stable ligand-NLRP3 interactions in the presence of solvents. This study sheds light on the ability of various FDA-approved drugs to act as NLRP3 inhibitors.

Communicated by Ramaswamy H. Sarma

Keywords:

• Molecular docking
• in-silico and ADMET
• NLRP3 and histone deacetylases
• MMGBSA
• molecular dynamics simulation

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Data availability statement

Data will be available on request.

Disclosure statement

No potential conflict of interest was reported by the author(s).

Additional information

Funding

The authors extend their appreciation to the Researchers Supporting Project number (RSPD2024R677), King Saud University, Riyadh, Saudi Arabia for financial support and Basic Science Research Program through the National Research Foundation of the Republic of Korea (NRF) funded by the Ministry of Education (grant number 2021R1A6A1A-03044296) and this research was also supported by the NRF (RS-2023-00248980).