IN SILICO APPROACH FOR SCREENING OF THE INDONESIAN MEDICINAL PLANTS DATABASE TO DISCOVER POTENTIAL DIPEPTIDYL PEPTIDASE-4 INHIBITORS
DOI:
https://doi.org/10.22159/ijap.2020.v12s1.FF008Keywords:
Dipeptidyl peptidase-4, Virtual screening, Pharmacophore-based, Molecular docking, In silico, DiabetesAbstract
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.
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References
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molecular structure. J Chem Inf Comput Sci 2004;44:1000-5.
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for drug discovery. 1. A qualitative and quantitative characterization of
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30. Veber DF, Johnson SR, Cheng HY, Smith BR, Ward KW, Kopple KD.
Molecular properties that influence the oral bioavailability of drug
candidates. J Med Chem 2002;45:2615-23.
31. Egan WJ, Merz KM Jr, Baldwin JJ. Prediction of drug absorption using
multivariate statistics. J Med Chem 2000;43:3867-77.
32. Muegge I, Heald SL, Brittelli D. Simple selection criteria for drug-like
chemical matter. J Med Chem 2001;44:1841-6.
33. Martin YC. A bioavailability score. J Med Chem 2005;48:3164-70.
34. Ertl P, Schuffenhauer A. Estimation of synthetic accessibility score
of drug-like molecules based on molecular complexity and fragment
contributions. J Cheminform 2009;1:8.
inhibitors on cardiovascular disease risk in type 2 diabetes mellitus. Int
J Pharm Pharm Sci 2016;9:254-9.
2. Drucker DJ. Dipeptidyl peptidase-4 inhibition and the treatment of
type 2 diabetes: Preclinical biology and mechanisms of action. Diabetes
Care 2007;30:1335-43.
3. Kang NS, Ahn JH, Kim SS, Chae CH, Yoo SE. Docking-based
3D-QSAR study for selectivity of DPP4, DPP8, and DPP9 inhibitors.
Bioorg Med Chem Lett 2007;17:3716-21.
4. Lankas GR, Leiting B, Roy RS, Eiermann GJ, Beconi MG, Biftu T,
et al. Dipeptidyl peptidase IV inhibition for the treatment of type 2
diabetes: Potential importance of selectivity over dipeptidyl peptidases
8 and 9. Diabetes 2005;54:2988-94.
5. Patel BD, Ghate MD. Recent approaches to medicinal chemistry and
therapeutic potential of dipeptidyl peptidase-4 (DPP-4) inhibitors. Eur
J Med Chem 2014;74:574-605.
6. Sneha P, Doss CG. Gliptins in managing diabetes reviewing
computational strategy. Life Sci 2016;166:108-20.
7. Wermuth CG. Pharmacophores: Historical perspective and viewpoint
from a medicinal chemist. In: Langern T, Hoffmann RD, editors.
Pharmacophores and Pharmacophore Searches. Weinheim, Germany:
Wiley-VCH; 2006. p. 3-13.
8. Wolber G, Langer T. LigandScout: 3-D pharmacophores derived from
protein-bound ligands and their use as virtual screening filters. J Chem
Inf Model 2005;45:160-9.
9. Seidel T, Ibis G, Bendix F, Wolber G. Strategies for 3D pharmacophorebased
virtual screening. Drug Discov Today Technol 2010;7:221-8.
10. Umashankar V, Gurunathan S. Drug discovery: An appraisal. Int J
Pharm Pharm Sci 2015;7:59-66.
11. Pissarnitski DA, Zhao Z, Cole D, Wu WL, Domalski M, Clader JW,
et al. Scaffold-hopping from xanthines to tricyclic guanines: A case
study of dipeptidyl peptidase 4 (DPP4) inhibitors. Bioorg Med Chem
2016;24:5534-45.
12. Daina A, Michielin O, Zoete V. SwissADME: A free web tool to
evaluate pharmacokinetics, drug-likeness and medicinal chemistry
friendliness of small molecules. Sci Rep 2017;7:e42717.
13. Arulmozhiraja S, Matsuo N, Ishitsubo E, Okazaki S, Shimano H,
Tokiwa H. Comparative binding analysis of dipeptidyl peptidase IV
(DPP-4) with antidiabetic drugs an ab initio fragment molecular orbital
study. PLoS One 2016;11:e0166275.
14. Nojima H, Kanou K, Terashi G, Takeda-Shitaka M, Inoue G, Atsuda K,
et al. Comprehensive analysis of the Co-structures of dipeptidyl
peptidase IV and its inhibitor. BMC Struct Biol 2016;16:11.
15. Shamsara J. Correlation between virtual screening performance
and binding site descriptors of protein targets. Int J Med Chem
2018;2018:3829307.
16. Nabeno M, Akahoshi F, Kishida H, Miyaguchi I, Tanaka Y, Ishii S,
et al. A comparative study of the binding modes of recently launched
dipeptidyl peptidase IV inhibitors in the active site. Biochem Biophys
Res Commun 2013;434:191-6.
17. Desiraju GR, Steiner T. The Weak Hydrogen Bond. Oxford: Oxford
University Press; 2001. p. 1-28.
18. Baell JB, Holloway GA. New substructure filters for removal of pan
assay interference compounds (PAINS) from screening libraries and for
their exclusion in bioassays. J Med Chem 2010;53:2719-40.
19. Brenk R, Schipani A, James D, Krasowski A, Gilbert IH, Frearson J,
et al. Lessons learnt from assembling screening libraries for drug
discovery for neglected diseases. ChemMedChem 2008;3:435-44.
20. Schultz TW, Yarbrough JW, Hunter RS, Aptula AO. Verification
of the structural alerts for Michael acceptors. Chem Res Toxicol
2007;20:1359-63.
21. Daina A, Zoete V. A BOILED-egg to predict gastrointestinal
absorption and brain penetration of small molecules. ChemMedChem
2016;11:1117-21.
22. Ertl P, Rohde B, Selzer P. Fast calculation of molecular polar surface
area as a sum of fragment-based contributions and its application to the
prediction of drug transport properties. J Med Chem 2000;43:3714-7.
23. Daina A, Michielin O, Zoete V. iLOGP: A simple, robust, and efficient
description of n-octanol/water partition coefficient for drug design
using the GB/SA approach. J Chem Inf Model 2014;54:3284-301.
24. Cheng T, Zhao Y, Li X, Lin F, Xu Y, Zhang X, et al. Computation of
octanol-water partition coefficients by guiding an additive model with
knowledge. J Chem Inf Model 2007;47:2140-8.
25. Moriguchi I, Hirono S, Liu Q, Nakagome I, Matsushita Y. Simple
method of calculating octanol/water partition coefficient. Chem Pharm
Bull 1992;40:127-30.
26. Moriguchi I, Hirono S, Nakagome I, Hirano H. Comparison of reliability
of log p values for drugs calculated by several methods. Chem Pharm
Bull 1994;42:976-8.
27. Delaney JS. ESOL: Estimating aqueous solubility directly from
molecular structure. J Chem Inf Comput Sci 2004;44:1000-5.
28. Ali J, Camilleri P, Brown MB, Hutt AJ, Kirton SB. Revisiting the
general solubility equation: In silico prediction of aqueous solubility
incorporating the effect of topographical polar surface area. J Chem Inf
Model 2012;52:420-8.
29. Ghose AK, Viswanadhan VN, Wendoloski JJ. A knowledge-based
approach in designing combinatorial or medicinal chemistry libraries
for drug discovery. 1. A qualitative and quantitative characterization of
known drug databases. J Comb Chem 1999;1:55-68.
30. Veber DF, Johnson SR, Cheng HY, Smith BR, Ward KW, Kopple KD.
Molecular properties that influence the oral bioavailability of drug
candidates. J Med Chem 2002;45:2615-23.
31. Egan WJ, Merz KM Jr, Baldwin JJ. Prediction of drug absorption using
multivariate statistics. J Med Chem 2000;43:3867-77.
32. Muegge I, Heald SL, Brittelli D. Simple selection criteria for drug-like
chemical matter. J Med Chem 2001;44:1841-6.
33. Martin YC. A bioavailability score. J Med Chem 2005;48:3164-70.
34. Ertl P, Schuffenhauer A. Estimation of synthetic accessibility score
of drug-like molecules based on molecular complexity and fragment
contributions. J Cheminform 2009;1:8.
Published
23-03-2020
How to Cite
FARKHANI, A., SAURIASARI, R., & YANUAR, A. (2020). IN SILICO APPROACH FOR SCREENING OF THE INDONESIAN MEDICINAL PLANTS DATABASE TO DISCOVER POTENTIAL DIPEPTIDYL PEPTIDASE-4 INHIBITORS. International Journal of Applied Pharmaceutics, 12(1), 60–68. https://doi.org/10.22159/ijap.2020.v12s1.FF008
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