Structure-based drug repurposing for CDK6: Molecular docking and molecular dynamics characterization of FDA-approved...

Cyclin-dependent kinase 6 (CDK6) contributes to G1-to-S cell-cycle progression and represents an established therapeutic target in hormone receptor-positive breast cancer. However, adverse effects and acquired resistance associated with current CDK4/6 inhibitors support continued investigation of alternative chemical scaffolds. In this study, a total of 319 FDA-approved non-oncological drugs were screened by molecular docking against the ATP-binding site of a predicted human CDK6-Cyclin D1 complex. The three highest-ranked compounds, dutasteride, danazol, and azelastine, were subsequently subjected to 100-ns molecular dynamics simulations, principal component analysis, MM-PBSA calculations, per-residue energy decomposition, and in silico pharmacokinetic profiling, with abemaciclib included as a reference inhibitor. All three repurposed candidates remained accommodated within the CDK6 binding pocket during their individual trajectories, although they exhibited different degrees of positional and conformational rearrangement and distinct energetic profiles. MM-PBSA ΔGtotal values were -20.47 ± 6.87 kcal/mol for dutasteride, -24.57 ± 5.09 kcal/mol for danazol, and -39.48 ± 6.10 kcal/mol for azelastine, compared with -41.79 ± 5.43 kcal/mol for abemaciclib. Per-residue decomposition showed that the candidates engaged overlapping regions of the CDK6 binding pocket while exhibiting distinct residue-level energetic contributions. Pharmacokinetic predictions further indicated differences in lipophilicity, gastrointestinal absorption, blood-brain barrier permeability, and P-glycoprotein substrate status among the selected compounds. Collectively, these findings provide structural, conformational, energetic, and pharmacokinetic characterization of dutasteride, danazol, and azelastine as candidate CDK6-binding scaffolds. Direct biochemical binding, kinase-inhibition, and cellular target-engagement studies are required to determine whether these predicted interactions have biological relevance.