Discovery of NLRP3 inhibitors using machine learning: Identification of a hit compound to treat NLRP3 activation-driven diseases

NLRP3 is vital in developing many human diseases as one of the most critical inflammasomes. Developing related inhibitors has been instrumental in advancing the development of therapies for associated diseases. To date, there are no NLRP3 inhibitors on the market. This study identified a series of NLRP3 inhibitors using the self-developed machine learning model. Among them, CSC-6 was validated as the hit molecule with optimal activity and significantly inhibited IL-1β secreted by PMA-THP-1 cells (IC50 = 2.3 ± 0.38 μM). The results show that CSC-6 specifically binds NLRP3 and inhibits NLRP3 activation by blocking ASC oligomerization during NLRP3 assembly. In vivo experiments have demonstrated that CSC-6 effectively reduces the symptoms of NLRP3 overactivation-mediated sepsis and Gout in mouse models. Importantly, CSC-6 has lower cytotoxicity and exhibits better stability in human-derived liver microsomes, which is more favorable for the drug to maintain its efficacy in vivo for longer. The discovery of CSC-6 may contribute to the design and discovery of related NLRP3 inhibitors. [Display omitted] •A new set of NLRP3 inhibitors were identified by self-developed machine learning model.•Mechanism of action of optimally active CSC-6 elucidated in detail.•CSC-6 has good drug properties and has shown therapeutic efficacy in a variety of NLRP3-driven diseases.