Caffeic Acid Phenethyl Ester Effects: In Silico Study of its Osteo-immunological Mechanisms - Zhao, Yuhao; Pang, Xiaokun; Nepal, Akriti; Jiang, Xincan; Xu, Xiaoxin; Zhao, Dongbin; Murtaza, Ghulam; Ma, Yanxu (Letters in Drug Design & Discovery, 2020)
Background: Biological system complexity impedes the drug target identification by biological experiments. Thus drugs, rather than acting on target site only, can interact with the entire biological system. Study of this phenomenon, known as network pharmacology, provides grounds for biological target identification of new drugs or acts as a foundation for the discovery of new targets of present drugs. No publication is available on the interaction network of CAPE. Aim: This study was aimed at the investigation of the candidate targets and possible interactions of caffeic acid phenethyl ester (CAPE) involved in its osteoimmunological effects. Methods: This study encompasses the investigation of candidate targets and possible interactions of CAPE by analyzing through PASS Prediction and constructing a biological network of CAPE. Results: In response to input (CAPE), PASS Prediction generated a network of 1723 targets. While selecting the probability to be active (Pa) value greater than 0.7 brought forth only 27 targets for CAPE. Most of these targets predicted the therapeutic role of CAPE as an osteoimmunological agent. Apart from this, this network pharmacology also identified 10 potential anti-cancer targets for CAPE, out of which 7 targets have been used efficiently in developing potent osteoimmunological drugs. Conclusion: This study provides scientific prediction of the mechanisms involved in osteoimmunological effects of CAPE, presenting its promising use in the development of a natural therapeutic agent for the pharmaceutical industry. CAPE targets identified by web-based online databases and network pharmacology need additional in silico assessment such as docking and MD simulation studies and experimental verification to authenticate these results.
Keywords: in silico prediction