沉香
传统医学
淀粉酶
酶抑制
生物
化学
食品科学
植物
医学
酶
生物化学
替代医学
病理
作者
Syahputra Wibowo,Sunia Kusuma Wardhani,Lisna Hidayati,Nastiti Wijayanti,Koichi Matsuo,Jessica Costa,Yudhi Nugraha,Josephine Elizabeth Siregar,Tri Rini Nuringtyas
标识
DOI:10.1016/j.bcab.2024.103152
摘要
Diabetes mellitus is a metabolic disorder characterized by high blood sugar. The most common treatment is taking oral hypoglycemic drugs such as acarbose, but long-term use of acarbose has dangerous side effects. Thus, alternative therapies using herbal medicines are of interest to researchers. Aquilaria malaccensis leaves have been used as an herbal tea for their high antioxidant and antimicrobial activity. This research aimed to analyze the antidiabetic potential of A. malaccensis leaves extract in vitro and in silico. The research utilized a methodology that included the analysis of the inhibitory activity of α-amylase and α-glucosidase enzymes through spectrophotometry. Furthermore, the study examined the inhibition of glucose diffusion in the dialysis bag. Additionally, an in silico investigation on α-glucosidase inhibition was conducted, involving toxicity analysis of selected compounds, molecular docking, molecular dynamic simulation, and density functional theory. The results showed that chloroform extract of A. malaccensis leaves had the best result on each test parameter with an IC50 of 3.22 mg/mL for α-amylase inhibition and 3.65 mg/mL for α-glucosidase inhibition. The chloroform extract of A. malaccensis leaves is the best for inhibiting glucose diffusion in the first 30 min. All in silico models supported by density functional theory showed that some secondary metabolites of A. malaccensis leaves extract, such as 5-Hydroxy-4′,7-dimethoxyflavone and followed by epifriedelanol, are better than acarbose in terms of inhibiting α-glucosidase. Based on the results, agarwood's extract demonstrates anti-diabetic potential through the inhibition of α-amylase and exhibits a slightly superior efficacy in inhibiting α-glucosidase activity. This observation is substantiated by both in-laboratory and in-silico data.
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