Discovery of novel selective PI3Kγ inhibitors through combining machine learning-based virtual screening with multiple protein structures and bio-evaluation

Introduction: Phosphoinositide 3-kinase gamma (P13K gamma) has been regarded as a promising drug target for the treatment of various diseases, and the diverse physiological roles of class I PI3K isoforms (alpha, beta, delta, and gamma) highlight the importance of isoform selectivity in the development of PI3K gamma inhibitors. However, the high structural conservation among the PI3K family makes it a big challenge to develop selective PI3K gamma inhibitors. Objectives: A novel machine learning-based virtual screening with multiple PI3K gamma protein structures was developed to discover novel PI3K gamma inhibitors. Methods: A large chemical database was screened using the virtual screening model, the top-ranked compounds were then subjected to a series of bio-evaluations, which led to the discovery of JN-K13. The selective inhibition mechanism of JN-K13 against PI3K gamma was uncovered by a theoretical study. Results: 49 hits were identified through virtual screening, and the cell-free enzymatic studies found that JN-K13 selectively inhibited PI3K gamma at a concentration as low as 3,873 nM but had no inhibitory effect on Class IA PI3Ks, leading to the selective cytotoxicity on hematologic cancer cells. Meanwhile, JN-K13 potently blocked the P13K signaling, finally led to distinct apoptosis of hematologic cell lines at a low concentration. Lastly, the key residues of PI3K gamma and the structural characteristics of JN-K13, which both would influence gamma isoform-selective inhibition, were highlighted by systematic theoretical studies. Conclusion: The developed virtual screening model strongly manifests the robustness to find novel PI3K gamma inhibitors. JN-K13 displays a specific cytotoxicity on hematologic tumor cells, and significantly promotes apoptosis associated with the inhibition of the P13K signaling, which depicts PI3K gamma as a potential target for the hematologic tumor therapy. The theoretical results reveal that those key residues interacting with JN-K13 are less common compared to most of the reported PI3K gamma inhibitors, indicating that JN-K13 has novel structural characteristics as a selective PIK3 gamma inhibitor. (C) 2021 The Authors. Published by Elsevier B.V. on behalf of Cairo University.

Automated summary

A novel machine learning-based virtual screening model was developed to discover new PI3K gamma inhibitors. Among the identified inhibitors, JN-K13 displayed selective cytotoxicity on hematologic tumor cells at low concentrations and promoted apoptosis through the inhibition of PI3K signaling. This study suggests that PI3K gamma could be a potential target for hematologic tumor therapy.