A Computational Modelling Study of Phytoconstituents of Curcuma longa Rhizome with IGF-1R Protein in Ovarian Cancer

Background: Ovarian cance,r is a huge concern due to its high mortality rate and resis,tance tothe conventional therapies. Recent studies have focused on the insulin-like growth factor 1 receptor (IGF-1R) as a therapeutic tphytochemicalsits ron the prolifa eration of cancer cells. Phytoco tituents from Curcuma longa ar known for their anti-cancer propertisignallingg them potential candidates for ovarian cancer treatment. This computational study aims to evaluate the molecular docking and pharmacokinetic profiles of ,phytochemicals in Curcuma longa against IGF-1R to explore their potential to inhicer ,progression. Results: Molecular docking analysis showed that digalloyl-hexoside had the highest binding affinity with IGF-1R, with a docking score of -8.849 kcal/mol. Hyperoside and valoneic acid dilactone followed closely, showing strong interactions as well. All three compounds exhibited six hydrogen bonds with key residues like ASP 1056 and LYS 1003, contributing to their strong binding potential. Caffeic acid showed the lowest affinity with a docking score of -7.985 kcal/mol. ADMET analysis reveal ed that caffeic acid had the highest gastrointestinal absorption and skin permeability, adhering to Lipinski’s rule of five. In contrast, digalloyl-hexoside, hyperoside, and valoneic acid dilactone had lower bioavailability and gastrointestinal absorption but exhibited acceptable pharmacokinetic properties. Conclusion : Digalloyl-hexoside exhibited the highest binding affinity, followed by hyperoside and valoneic acid dilactone, though all compounds had lower docking scores than the reference ligand. ADMET analysis revealed that while caffeic acid had the highest absorption, the other compounds showed better overall pharmacokinetic profiles. However, low gastrointestinal absorption limits their bioavailability. Further research, including structural modifications and additional validation, is recommended.