Experimental evaluation and GC–MS guided computational identification of potential antidiabetic compounds from Heritiera fomes leaves

Type 2 diabetes mellitus (T2DM) remains a major global health burden, while existing therapies are often associated with adverse effects and limited long-term efficacy, underscoring the need for safer and more effective antidiabetic agents. In this context, understudied mangrove-derived phytochemicals represent promising natural sources for the discovery of novel antidiabetic therapeutics. Following plant material collection and extract preparation, qualitative and quantitative phytochemical characterization was performed. Antioxidant activity was assessed via DPPH free radical scavenging and ferricyanide reducing power assays. Anti-inflammatory potential was determined through bovine serum and egg albumin denaturation inhibition assays. Antidiabetic activity was evaluated in vitro using α-amylase and α-glucosidase inhibition assays. Prior to in vivo studies, the extract's safety profile was established through invertebrate and mammalian toxicity models. Antihyperglycemic efficacy was subsequently assessed using the oral glucose tolerance test (OGTT) and an alloxan-induced diabetic rat model. Metabolite profiling was conducted by FTIR and GC–MS analyses. Identified bioactive constituents were further subjected to molecular docking, ADMET profiling, density functional theory (DFT) calculations, and molecular dynamics (MD) simulations to prioritize lead candidates for antidiabetic drug development. The extract exhibited significant antioxidant and anti-inflammatory activities and demonstrated potent inhibition of α-amylase (IC 50 = 40.07 ± 0.18 µg/mL) and α-glucosidase (IC 50 = 35.75 ± 1.42 µg/mL), surpassing that of acarbose. Toxicological assessment revealed moderate cytotoxicity in the brine shrimp assay and low acute toxicity in vivo. In oral glucose tolerance and alloxan-induced diabetic models, the extract significantly improved glucose tolerance and reduced chronic hyperglycemia, outperforming metformin. FTIR analysis enabled functional group characterization, whereas GC–MS facilitated the identification of individual constituents, leading to the detection of 97 secondary metabolites. Comprehensive computational screening, including molecular docking, ADMET prediction, density functional theory-based reactivity assessment, and 100 ns molecular dynamics simulations, identified compounds 61, 78, and 88 as promising candidates. Collectively, these findings establish H. fomes leaves as a promising natural source for the discovery of potential antidiabetic agents and further pharmacological development.