Integrating Computational Methods in Network Pharmacology and In Silico Screening to Uncover Multi-targeting Phytochemicals against Aberrant Protein Glycosylation in Lung Cancer.

Glycoproteins are an underexploited drug target for cancer therapeutics. In this work, we integrated computational methods in network pharmacology and docking approaches to identify phytochemical compounds that could potentially interact with several cancer-associated glycoproteins. We first created a database of phytochemicals from selected plant species, (sapodilla/chico), (mango), (soursop/guyabano), (jackfruit/langka), (langsat/lanzones), and (bignay), and performed pharmacokinetic analysis to determine their drug-likeness properties. We then constructed a phytochemical-glycoprotein interaction network and characterized the degree of interactions between the phytochemical compounds and with cancer-associated glycoproteins and other glycosylation-related proteins. We found a high degree of interactions from α-pinene (), cyanomaclurin (), genistein (), kaempferol ( and ), norartocarpetin (), quercetin (, , , ), rutin (, , ), and ellagic acid ( and ). Subsequent docking analysis confirmed that these compounds could potentially bind to EGFR, AKT1, KDR, MMP2, MMP9, ERBB2, IGF1R, MTOR, and HRAS proteins, which are known cancer biomarkers. cytotoxicity assays of the plant extracts showed that the -hexane, ethyl acetate, and methanol leaf extracts from , and gave the highest growth inhibitory activity against A549 lung cancer cells. These may help further explain the reported cytotoxic activities of select compounds from these plant species.