• AULIA FARKHANI Laboratory of Biomedical Computation and Drug Design, Faculty of Pharmacy, Universitas Indonesia, Depok, West Java, Indonesia.
  • RANI SAURIASARI Laboratory of Clinical and Community Pharmacy, Faculty of Pharmacy, Universitas Indonesia, Depok, West Java, Indonesia.
  • ARRY YANUAR Laboratory of Biomedical Computation and Drug Design, Faculty of Pharmacy, Universitas Indonesia, Depok, West Java, Indonesia


Background: Dipeptidyl peptidase-4 (DPP4) is an enzyme responsible for inactivating the hormone incretin, which potentiates insulin secretion and
glucagon inhibition; inhibitors of DPP4 are used as therapeutic drugs for type-2 diabetes.
Objective: In this study, we evaluated potential DPP4 inhibitors from the Indonesian Medicinal Plants Database using an in silico approach.
Methods: A ligand-based pharmacophore model was used for screening the database using LigandScout 4.2. This model was validated using several
parameters of enrichment metrics, including receiver operating characteristics, area under curve (AUC), and enrichment factor (EF). Hit compounds
were also docked with DPP4 to calculate the free binding energy and analyze the interaction between the ligand and DPP4. In addition, bioavailability
and medicinal chemistry predictions were performed for the hit compounds.
Results: The best pharmacophore model demonstrated AUC100% and EF1% values of 0.82 and 33.8, respectively. The pharmacophore features of the
model included hydrogen bond donors, hydrogen bonds, hydrophobic interactions, and positive ionization areas. Based on our results of virtual
screening and molecular docking, six hit compounds were ultimately identified, namely, L-noradrenaline, octopamine, Nb-demethylechitamine, alliin,
isoalliin, and subaphylline.
Conclusion: Collectively, our findings indicate that subaphylline is the most promising compound for further studies, including in vitro and in vivo
experiments and those focused on molecular dynamics and structural modification.

Keywords: Dipeptidyl peptidase-4, Virtual screening, Pharmacophore-based, Molecular docking, In silico, Diabetes


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