Recent developments in pharmacophore-based modeling of Ca2+/Calmodulin-dependent protein kinase II delta (CaMkIIδ) inhibitors for heart failure therapy.

Abstract

Introduction: Calcium/calmodulin-dependent protein kinase II delta (CaMKIIδ) regulates cardiac excitation - contraction coupling and contributes to heart failure onset and progression. Sustained CaMKIIδ activation promotes sarcoplasmic reticulum Ca2+ leak, arrhythmias, maladaptive remodeling, and contractile dysfunction, making CaMKIIδ inhibition an attractive therapeutic strategy.

Areas covered: This narrative review surveys computational approaches for discovering CaMKIIδ inhibitors, emphasizing pharmacophore modeling. The authors summarize ligand- and structure-based pharmacophore methods, their coupling to docking and QSAR, and their use in virtual screening and scaffold hopping, highlighting studies that prospectively identified and synthesized new inhibitors. They also cover machine learning - assisted discovery and drug-repurposing efforts that nominated approved agents (e.g. ruxolitinib and hesperadin) as CaMKIIδ inhibitors, and outline major ATP-site - targeting chemotypes reported to date. Literature was searched in PubMed, Scopus, Web of Science, and Google Scholar (January 2000-December 2025) using 'CaMKII inhibitor,' 'CaMKIIδ pharmacophore,' 'CaMKII molecular modeling,' 'CaMKII virtual screening,' and 'CaMKII drug discovery,' followed by manual reference mining.

Expert opinion: Pharmacophore-driven CaMKIIδ modeling remains underused. Progress should integrate AI/deep learning, isoform-aware selectivity filters, state- and PTM-specific targeting, and rigorous experimental validation to deliver potent, selective, clinically viable inhibitors for heart failure. Standardized benchmarking, transparent negative results, and selectivity panels against CaMKIIα/β/γ and kinome off-targets will aid translation substantially.