Analysis of Artabotrys hexapetalus Stem Bark and Leaf Ethanol Extracts as α-Glucosidase Inhibitors: In Vitro Analysis, LC-MS/MS, Machine Learning, and Molecular Docking

Dela Rosa, Berna Elya, Muhammad Hanafi, Alfi Khatib, Yuniwaty Halim, Muhammad Imam Surya

Research output: Contribution to journalArticlepeer-review

Abstract

Artabotrys hexapetalus (L.f.) Bhandari, Annonaceae, is one of Artabotrys species found in Asia, such as Sri Lanka, India, China, Malaysia, Indonesia, and other Southeast Asian countries. This plant is traditionally used as an herbal medicine. This research explored the potential of A. hexapetalus leaf and stem bark ethanol extract to treat type 2 diabetes by inhibiting the activity of α-glucosidase, including its relationship with the antioxidant activity, phenolic content, and flavonoid content. The analysis used α-glucosidase inhibition assay, 2,2-diphenyl-1-picrylhydrazyl free radical inhibition assay, ferric reducing antioxidant power assay, total phenolic content, total flavonoid content, liquid chromatography-tandem mass chromatography, and molecular docking analysis. Results showed that stem bark extract had a moderate α-glucosidase inhibitory activity with an IC50 value of 47.084 ppm. In contrast, the leaf extract had a weak α-glucosidase inhibitory activity with an IC50 value of 104.755 ppm. Random permutation in random forest simulation was used to predict the factors contributing to the α-glucosidase inhibition. For stem bark and leaf extracts, the α-glucosidase inhibition activity was predicted to be influenced by antioxidant activity and phenolic compounds. Comparisons between various analyses were shown to corroborate the random permutation results. Nevertheless, since the major identified active compound that likely played a role in α-glucosidase inhibition in stem bark was from terpene groups, it was predicted that some major compounds with high α-glucosidase inhibitory and antioxidant activities in stem bark were yet-to-be-identified. Graphical Abstract: [Figure not available: see fulltext.].

Original languageEnglish
Pages (from-to)386-396
Number of pages11
JournalRevista Brasileira de Farmacognosia
Volume34
Issue number2
DOIs
Publication statusAccepted/In press - 2023

Keywords

  • Activity prediction
  • Antioxidants
  • Diabetes
  • Feature importance
  • In silico analysis
  • Random permutation

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