Anti-inflammatory phytocompounds from Crateva adansonii DC leaf extracts were identified by GCMS analysis and its anti-inflammatory potential was evaluated by in silico molecular docking study against inflammatory molecular targets. Three different (Aqueous, Methanol and Petroleum ether) dried leaf extracts of Crateva adansonii were obtained from soxhlet extraction method. Preliminary phytoconstituents analysis of three different leaf extracts of C. adansonii confirmed the presence of various major classes of bioactive phytoconstituents such as polyphenols (tannins and flavonoids), steroids, alkaloid, coumarin, carbohydrate and terpenoids. Among three leaf extracts, methanolic leaf extract possess highest total phenolic content of 77 ± 1.65 µg gallic acid equivalent (GAE)/g of dry weight of leaf extract, subsequently methanolic leaf extract also shows maximal in vitro antioxidant activity (DPPH scavenging activity) of 71.22 ± 1.32% among three different leaf extracts. GC–MS analysis of petroleum ether leaf extract revealed the presence of nine phytocompounds representing 95.43% peak area percentage, among nine identified phytocompounds three phytocompounds of C. adansonii possess anti-inflammatory property namely phytol, 1-Hexyl-2-Nitrohexane and 2-Isopropyl-5-Methylcyclohexyl 3-(1-(4-Chlorophenyl)-3-Oxobutyl)-Coumarin-4-Yl Carbonate were chosen for in silico molecular docking study against four inflammatory receptor targets (COX-2, TNFα IL-1β and IL-6) and they shows less binding energy with highest docking score ranging from −15.9500 to 5.0869. The present study substantially indicated and proven that anti-inflammatory potential of phytocompounds from C. adansonii leaf extracts which can be exploited for commercial designing of novel anti-inflammatory drug to treat various inflammatory disorders.
Thirumalaisamy, R., Ammashi, S., & Muthusamy, G. (2018). Screening of anti-inflammatory phytocompounds from Crateva adansonii leaf extracts and its validation by in silico modeling. Journal of Genetic Engineering and Biotechnology, 16(2), 711–719. https://doi.org/10.1016/j.jgeb.2018.03.004