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

Abstract

Introduction: Phosphoinositide 3-kinase gamma (PI3Kγ) has been regarded as a promising drug target for the treatment of various diseases, and the diverse physiological roles of class I PI3K isoforms (α, β, δ, and γ) highlight the importance of isoform selectivity in the development of PI3Kγ inhibitors. However, the high structural conservation among the PI3K family makes it a big challenge to develop selective PI3Kγ inhibitors.

Objectives: A novel machine learning-based virtual screening with multiple PI3Kγ protein structures was developed to discover novel PI3Kγ 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-KI3. The selective inhibition mechanism of JN-KI3 against PI3Kγ was uncovered by a theoretical study.

Results: 49 hits were identified through virtual screening, and the cell-free enzymatic studies found that JN-KI3 selectively inhibited PI3Kγ 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-KI3 potently blocked the PI3K signaling, finally led to distinct apoptosis of hematologic cell lines at a low concentration. Lastly, the key residues of PI3Kγ and the structural characteristics of JN-KI3, which both would influence γ isoform-selective inhibition, were highlighted by systematic theoretical studies.

Conclusion: The developed virtual screening model strongly manifests the robustness to find novel PI3Kγ inhibitors. JN-KI3 displays a specific cytotoxicity on hematologic tumor cells, and significantly promotes apoptosis associated with the inhibition of the PI3K signaling, which depicts PI3Kγ as a potential target for the hematologic tumor therapy. The theoretical results reveal that those key residues interacting with JN-KI3 are less common compared to most of the reported PI3Kγ inhibitors, indicating that JN-KI3 has novel structural characteristics as a selective PIK3γ inhibitor.

Keywords: ADMET, absorption, distribution, metabolism, excretion, and toxicity; AKT, protein kinase B; AUC, area under receiver operations characteristic curve; Badapple, bioactivity data associative promiscuity pattern learning engine; CADD, computer-aided drug design; CDRA, confirmatory dose–response assays; DMEM, Dulbecco’s Modified Eagle Medium; DS3.5, discovery studio 3.5; FBS, fetal bovine serum; GPCR, G protein-coupled receptors; H-bond, hydrogen bond; Hematologic malignancies; IMDM, Iscove’s Modified Dulbecco’s Medium; Ionic, ionic interactions; JN-KI3; MD, molecular dynamics; MM/GBSA, molecular mechanics/generalized born surface area; Molecular dynamics simulation; NBC, naive Bayesian classifier; PAGE, polyacrylamide gel electrophoresis; PAINS, pan-assay interference compounds; PARP, poly ADP-ribose polymerase; PDB, protein data bank; PI3K, Phosphoinositide 3-kinase; PI3Kγ; PSA, primary screening assays; REOS, rapid elimination of swill; RMSD, root-mean-squared-deviation; RMSF, root-mean-squared-fluctuation; ROC, receiver operations characteristic; RTK, receptor tyrosine kinases; SD, standard deviation; SMILES, simplified molecular input line entry specification; SP, standard precision; Selective inhibitor; VS, virtual screening; Virtual screening; Water Bridge, hydrogen bonds through water molecular bridge; XP, extra precision.