Integrating Computational Methods in Network Pharmacology and In Silico Screening to Uncover Multi-targeting...

Glycoproteins are an underexploited drug target for cancer therapeutics. In this work, we integrated computational methods in network pharmacology and in silico 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, Manilkara zapota (sapodilla/chico), Mangifera indica (mango), Annona muricata (soursop/guyabano), Artocarpus heterophyllus (jackfruit/langka), Lansium domesticum (langsat/lanzones), and Antidesma bunius (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 (Mangifera indica), cyanomaclurin (Artocarpus heterophyllus), genistein (Annona muricata), kaempferol (Annona muricata and Antidesma bunius), norartocarpetin (Artocarpus heterophyllus), quercetin (Annona muricata, Antidesma bunius, Manilkara zapota, Mangifera indica), rutin (Annona muricata, Antidesma bunius, Lansium domesticum), and ellagic acid (Antidesma bunius and Mangifera indica). 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. In vitro cytotoxicity assays of the plant extracts showed that the n-hexane, ethyl acetate, and methanol leaf extracts from A. muricata, L. domesticum and M. indica 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.