Network-Based Analysis and ADMET Profiling of Vinblastine and Vincristine: Insights into Their Therapeutic Potential for Cancer Treatment

Cancer remains one of the leading global health challenges, necessitating targeted and personalized treatment approaches. Vinblastine and vincristine, microtubule-disrupting vinca alkaloids, present strong potential for cancer therapy based on computationally modeled molecular interactions and pharmacological profiles. This study aims to explore the therapeutic potential of vinblastine and vincristine by mapping their molecular targets through bioinformatics analysis. Protein-protein interaction networks were constructed using STRING and visualized with Cytoscape. Key clusters were identified through K-means and MCODE algorithms. SwissTarget Prediction was used for gene-drug interaction analysis. SwissADME profiled pharmacokinetics and drug-likeness, while ProTox-II assessed toxicity risk. Additional pathway enrichment and functional annotation were conducted using publicly available bioinformatics databases. All results derive from theoretical and computational workflows without experimental validation. The analysis identified PIK3CA, MCHR1, BDKRB1, and CHRM1 as crucial biological targets associated with oncogenic signaling, angiogenesis, and immune modulation. Clustering algorithms revealed subnetworks linked to mitotic control and cancer-related pathways. Vinblastine and vincristine demonstrated drug-like properties with minimal violations of Lipinski’s rule. SwissADME profiling confirmed acceptable solubility, gastrointestinal absorption, and bioavailability. ProTox-II results suggested favorable safety margins with low predicted toxicity. These findings indicate potential for selective targeting of cancer-related molecular mechanisms and support further investigation of these compounds in precision oncology. Vinblastine and vincristine demonstrate promising computational profiles for anticancer activity, with strong molecular target interactions and favorable ADME and toxicity parameters. While these results are based on in silico analysis, they support future experimental validation. The study underscores the value of bioinformatics tools in identifying precision therapies and optimizing drug development strategies for cancer treatment.