Lung cancer is the leading cause of cancer-related fatalities worldwide, with non-small cell lung cancer (NSCLC) comprising 85% of cases. Despite advancements in treatment, EGFR mutations remain prevalent, contributing to treatment resistance. Recent investigations have unveiled promising insights on curcumin, derived from turmeric, and cucurbit [2] uril (CB[2]UN) as potential multitargeted inhibitors for EGFR-mutated NSCLC.
This study employs dodecagonal computational methods combining molecular docking and molecular dynamics to assess the interaction of curcumin and CB[2]UN with NSCLC proteins. The efficacies of these compounds were evaluated using sophisticated techniques, leading to the proposal of curcumin and CB[2]UN as viable options to combat aggressive forms of lung cancer.
The molecular docking studies revealed significant binding affinities of curcumin (-6.9 kcal/mol) and CB[2]UN (-8.1 kcal/mol) with selected EGFR-mutant NSCLC proteins. This suggests strong potential for these compounds to effectively disrupt the signaling pathways associated with tumor progression.
Further analysis involved 50 ns molecular dynamics simulations assessing kinetic stability, where interactions were continuously monitored, showcasing stable protein-ligand complexes. Their binding free energies, calculated through MMPBSA and MMGBSA methods, confirmed the thermodynamic stability of the complexes, indicating favorable conditions for therapeutic applications.
Curcumin’s multi-faceted mechanisms of action encompass the regulation of important signaling pathways, promotion of apoptosis, and interruption of cell cycling which all contribute to its anticancer properties. Similarly, CB[2]UN plays roles beyond mere encapsulation, with the potential to alter cellular uptake and signaling pathways.
By exploring these compounds through innovative computational strategies, the study emphasizes their prospective clinical significance as adjuncts or alternatives to existing therapies for NSCLC patients harboring EGFR mutations.
Overall, the findings highlight the competitive advantage of integrating computational drug design approaches with traditional pharmacological research, paving the way for future advancements and investigations.