Identification of Novel Tubulin Dimer Inhibitors for Pancreatic Cancer: An Integrated Computational Approach

Pancreatic cancer remains one of the most lethal malignancies, with mortality rates exceeding 90% in males and 89% in females, largely due to late-stage diagnosis and chemotherapy resistance. Despite advances in cancer research, effective treatment options for pancreatic cancer remain limited, highlighting the urgent need for novel therapeutic strategies. Identification of potent tubulin dimer inhibitors, disrupting the Alpha-Beta dimer complex essential for microtubule dynamics and cancer cell proliferation, offers a promising therapeutic avenue. Computational drug discovery provides an effective strategy to find novel inhibitors with enhanced binding efficiency, stability, and adaptability for personalized treatments. We implemented a multi-faceted computational approach combining molecular docking, density functional theory (DFT), molecular dynamics (MD) simulations, and principal component analysis (PCA). DrugBank screening identified Bisphosphonate-8 and Fenoldopam as potential inhibitors, with Paclitaxel as a reference drug. DFT calculations provided quantum mechanical insights into ligand-tubulin interactions, while binding energy analysis revealed Bisphosphonate-8 exhibiting a nearly 2-fold higher binding affinity than Paclitaxel. Microsecond-scale MD simulations assessed the stability and flexibility of ligand-protein complexes, and PCA analysis of MD simulations trajectories demonstrated significant conformational adaptability, reinforcing the potential of Bisphosphonate-8 and Fenoldopam as effective tubulin dimer inhibitors. Bisphosphonate-8, demonstrating superior binding properties in silico, emerges as a promising candidate for preclinical evaluation in pancreatic cancer, offering translational potential for improved targeted therapy.