Integrating in silico molecular docking, ADMET analysis of C.verticillata with diabetic markers and in vitro anti-inflammatory activity

Abstract

Abstract Background Over the past decade, various research studies have proved the interconnection between the inflammatory pathways and diabetes complication in clinical condition. The present study evaluated the anti-inflammatory and antioxidant activity. Further, the sample was tested for its pharmacokinetics properties and the best compounds were docked with the diabetic markers (DPP IV (PDB-ID: IJ2E) and SGLT2 (PDB-ID: 7VSI)). Results C.verticillata showed a good hydrogen peroxide (78.3 ± 0.34%, IC50 = 287.81 µg/ml) and superoxide scavenging activity (52.7 ± 1.26%, IC50 = 796.15 µg/ml). In addition, the sample was checked for its anti-inflammatory activity with protein denaturation (57.4 ± 0.19%, IC50 = 471.5 µg/ml) and proteinase inhibition assay (68.3 ± 0.48%, IC50 = 213.42 µg/ml). Further, the bioactive compounds detected from HPLC-ESI-MS/MS analyzed sample were checked for its drug likeliness by checking its ADME properties and toxicological parameters. It has been observed that except Loliolide, all the other compounds have followed the physicochemical parameters and proved to exhibit drug likeliness characteristics. The bioactive compounds that follow the Lipinski’s rule were taken further for in silico molecular docking analysis with the diabetic protein markers (DPP IV and SGLT2). Docking results revealed that Pyro pheophorbide a with DPP IV and Dihydromonacolin L acid with SGLT2 have recorded a maximum docking score of (− 9.4 kcal/mol) and (− 9.2 kcal/mol), respectively. Conclusion The observed results suggest that the identified and selected bioactive compounds from C.verticillata can be considered as a potential target molecule for the management of oxidative stress induced diabetic condition. Furthermore, the study also provides an insight on the effectiveness of the compounds on reducing the inflammation as well.

Keywords

• Marine seaweeds
• Inflammation
• Oxidative stress
• Diabetes
• ADMET analysis
• Toxicity prediction