Computational Identification of New Dual PAK4 and NAMPT Inhibitors

Dual inhibition of p21-activated kinase 4 (PAK4) and nicotinamide phosphoribosyltransferase (NAMPT) has emerged as a promising therapeutic strategy due to its ability to simultaneously target oncogenic signaling and cellular metabolism in cancer. While existing inhibitors such as KPT9274 and PF-3758309 have demonstrated preclinical activity, their clinical translation has been limited by poor selectivity, suboptimal efficacy, and dose-limiting toxicities. Reliance on a small number of available compounds is therefore insufficient, highlighting the need for systematic optimization to identify candidates with improved therapeutic profiles. Although several dual PAK4 and NAMPT inhibitors such as GNE2861, LCH7749944, and PF3758309 have been identified in our previous study, the number of compounds available is limited. Identification of new candidate compounds would allow researchers to identify those that are the most efficient, those that are the most potent and selective, and those that have optimal pharmacokinetics. In this study, we performed additional large-scale drug screening to identify new candidate dual PAK4 and NAMPT inhibitors and expand the diversity of this inhibitor class. Computational molecular docking was used to evaluate binding affinities toward both targets, followed by drug-protein interaction analyses to assess binding stability and interaction patterns at the molecular level. Several new candidate compounds demonstrated favorable predicted binding to both PAK4 and NAMPT, with distinct interaction profiles compared to previously reported inhibitors. Biochemical assays further demonstrated inhibitory activity against both PAK4 and NAMPT among several selected compounds, supporting their potential as dual-target inhibitors. These findings provide mechanistic insight into dual-target engagement and highlight structural features associated with improved binding behavior. Expanding the pool of dual inhibitors enhances opportunities for preclinical development, supports optimization of pharmacokinetic and safety profiles, and strengthens datasets for drug discovery. Collectively, this work broadens the landscape of dual PAK4 and NAMPT inhibitors and supports their potential application across multiple cancer types beyond triple-negative breast cancer.