Identification of Novel Tubulin Dimer Inhibitors for Pancreatic Cancer Using an Integrated Computational Approach

Despite advances in medical science, pancreatic cancer is still among the deadliest forms of cancer, and only 10% of patients survive because they are usually diagnosed late and do not respond to standard treatments. Inhibition of microtubule behaviour by disrupting their dimeric structures is a potential treatment for pancreatic cancer. This study aims to identify new inhibitors that will bind to the tubulin dimer using an integrative computational drug discovery approach.

An advanced series of in silico experiments was conducted using molecular docking, Density Functional Theory (DFT), Molecular Dynamics (MD) simulations, and Principal Component Analysis (PCA). Compounds from DrugBank were selected based on their strong interactions with the tubulin dimer. Moreover, DFT was utilized to investigate electronic interactions, whereas MD simulations (1 μs) were used to understand the stability and behaviour of the studied complex.

The molecules Bisphosphonate-8 and Fenoldopam were selected as inhibitors, whereas Paclitaxel was chosen as a reference compound. Analysis of docking and binding free energy showed that Bisphosphonate-8 has approximately twice the binding affinity for the α-β tubulin dimer compared to Paclitaxel. The DFT calculations indicated a high interaction capacity. MD simulation showed that Bisphosphonate-8 forms a stable complex during the simulation, whereas Paclitaxel demonstrated notable instability. Principal component analysis corroborated that Bisphosphonate-8 is highly adaptable and stable.

This computational approach successfully identified Bisphosphonate-8 as an inhibitor of the α-β tubulin dimer, with better binding affinity and stability than well-known compounds and desirable pharmacokinetic properties.

Based on the results presented above, Bisphosphonate-8 is a promising candidate for targeting the α-β tubulin dimer in pancreatic cancer, with stronger interactions and greater conformational adaptability. The computational data provide an excellent starting point for further experiments aimed at developing novel therapeutic approaches to target the α-β tubulin heterodimer in pancreatic cancer.