Predicting target interactomes and prognostic biomarkers of Quizartinib in acute myeloid leukemia: An integrative network pharmacology and In-silico approach.

Quizartinib, a second-generation FLT3 inhibitor, has shown clinical efficacy in FLT3 mutated Acute Myeloid Leukemia (AML). However, emerging evidence suggests its pharmacological profile extends beyond FLT3 inhibition, contributing to both therapeutic efficacy and off-target effects. This study employs a computational framework to comprehensively characterize the multi-target interactome of Quizartinib in AML. Potential targets of Quizartinib were predicted using SwissTargetPrediction, SEA, and STITCH, while AML-specific targets were compiled from GeneCards and refined through stringent filtering. Intersection analysis identified mutual targets, which were used to construct a PPI network via STRING and analyzed for KEGG pathway enrichment. Hub genes were identified through topological analysis in Cytoscape. Clinical relevance was assessed through differential expression and survival analysis using GEPIA. Molecular docking studies with AutoDock Vina were performed to validate binding interactions. Intersection analysis identified 40 mutual targets, refined to 11 AML-relevant pathways. Topological analysis prioritized ten hub genes: CSF1R, KIT, FLT3, PDGFRA, PDGFRB, NTRK1, ABL1, LYN, FLT1, and KDR. Survival analysis identified LYN, FLT3, and PDGFRA as prognostic biomarkers (p < 0.05). Molecular docking demonstrated strong binding affinities (-8.2 to -12.6 kcal/mol) with Z-score normalization identifying KIT (Z = -4.685), FLT3 (Z = -3.885), and PDGFRA (Z = -3.785) as the most significant interactions. This study reveals a multi-target kinase interactome for Quizartinib encompassing FLT3, KIT, PDGFRA, LYN, and VEGFR family members. These findings provide a rationale for patient stratification and support exploring combination strategies targeting these off-targets, particularly in addressing the persistent challenge of drug resistance in FLT3-mutated AML.