Medicinal Chemistry Research

, Volume 26, Issue 11, pp 3000–3014 | Cite as

Identification of potential type 4 cAMP phosphodiesterase inhibitors via 3D pharmacophore modeling, virtual screening, DFT and structural bioisostere design

  • Dhrubajyoti Gogoi
  • Amrita K. Chaliha
  • Diganta Sarma
  • Bibhuti B. Kakoti
  • Alak K. BuragohainEmail author
Original Research


Cyclic nucleotide phosphodiesterase Type 4 specifically metabolizes Cyclic Adenosine Monophosphate and has widespread distribution across the human body. The aim of this study was to generate a well-validated ligand-based 3D Quantitative Structure Activity Relationship pharmacophore model to identify potential phosphodiesterase Type 4 inhibitors using a set of 18 known chemically diverse phosphodiesterase Type 4 inhibitors. The HypoGen module of Discovery Studio v4.1 was used to generate the aforementioned pharmacophore model which was then employed as 3D query for virtual screening of four chemical and two natural product databases. The top hits were evaluated for their drug-like properties. The binding orientations of the best fits were predicted by molecular docking. Orbital energies were predicted for top hits and the density functional theory based minimum energy gap (Highest Occupied Molecular Orbital–Lowest Unoccupied Molecular Orbital gap) was used to further cull the selection and identify the most potential phosphodiesterase Type 4 inhibitors. Chemical similarity search was performed and structural analogs of the best hits were designed to discover novel potential phosphodiesterase Type 4 inhibitors. Use of Hypo1 as 3D query for virtual screening yielded 1243 compounds and subsequent molecular docking studies narrowed it down to 19 potential phosphodiesterase Type 4 inhibitors while a density functional theory-based study further culled this selection down to six most potential inhibitors. Six structurally diverse chemical structures with novel scaffolds and six analogs of the best hits were identified using pharmacophore modeling to be potential phosphodiesterase Type 4 inhibitors.


Pharmacophore PDE4 Molecular docking Virtual screening Quantitative structure-activity relationship Density function theory 



Authors thankfully acknowledge Department of Biotechnology, Government of India for providing financial support for the Bioinformatics Infrastructure Facility (BIF) and the DBT e-library Consortium (DelCON) facility at the Centre for Biotechnology and Bioinformatics, Dibrugarh University in which the present work has been performed. The authors are also grateful to Dr. R. L. Bezbaruah, Chief Scientist (Retired) and Coordinator, BIF, CSIR-NEIST, Jorhat for his advice in the study. The Authors also thankful to Dr. Bulumoni Kalita, Assistant Professor, Department of Physics and Mr. Vishwa Jyoti Baruah, Assistant Professor, Centre for Biotechnology and Bioinformatics, Dibrugarh University for their valuable support in the present work.

Compliance with ethical standards

Conflict of Interests

The authors declares that they have no competing interests.

Supplementary material

44_2017_1998_MOESM1_ESM.doc (1.9 mb)
Supplementary Material


  1. Arooj M, Sakkiah S, Kim S, Arulalapperumal V, Lee KW (2013) A combination of receptor-based pharmacophore modeling & QM techniques for identification of human chymase inhibitors. PLoS One 8(4):e63030CrossRefPubMedPubMedCentralGoogle Scholar
  2. Arooj M, Thangapandian S, John S, Hwang S, Park JK, Lee KW (2011) 3D QSAR pharmacophore modeling, in silico screening, and density functional theory (DFT) approaches for identification of human chymase inhibitors. Int J Mol Sci 12(12):9236–9264CrossRefPubMedPubMedCentralGoogle Scholar
  3. Azevedo MF, Faucz FR, Bimpaki E, Horvath A, Levy I, de Alexandre RB, Ahmad F, Manganiello V, Stratakis CA (2013) Clinical and molecular genetics of the phosphodiesterases (PDEs). Endocr Rev 35(2):195–233CrossRefPubMedPubMedCentralGoogle Scholar
  4. Beghè B, Rabe KF, Fabbri LM (2013) Phosphodiesterase-4 inhibitor therapy for lung diseases. Am J Respir Crit Care Med 188(3):271–278CrossRefPubMedGoogle Scholar
  5. Brogi S, Kladi M, Vagias C, Papazafiri P, Roussis V, Tafi A (2009) Pharmacophore modeling for qualitative prediction of antiestrogenic activity. J Chem Inf Model 49(11):2489–2497CrossRefPubMedGoogle Scholar
  6. Buckley GM, Cooper N, Davenport RJ, Dyke HJ, Galleway FP, Gowers L, Haughan AF, Kendall HJ, Lowe C, Montana JG, Oxford J (2002a) 7-Methoxyfuro [2, 3-c] pyridine-4-carboxamides as PDE4 inhibitors: A potential treatment for asthma. Bioorg Med Chem Lett 12(3):509–512CrossRefPubMedGoogle Scholar
  7. Buckley GM, Cooper N, Dyke HJ, Galleway FP, Gowers L, Haughan AF, Kendall HJ, Lowe C, Maxey R, Montana JG, Naylor R (2002b) 8-Methoxyquinoline-5-carboxamides as PDE4 inhibitors: a potential treatment for asthma. Bioorg Med Chem Lett 12(12):1613–1615CrossRefPubMedGoogle Scholar
  8. Castro LR, Gervasi N, Guiot E, Cavellini L, Nikolaev VO, Paupardin-Tritsch D, Vincent P (2010) Type 4 phosphodiesterase plays different integrating roles in different cellular domains in pyramidal cortical neurons. J Neurosci Methods 30(17):6143–6151CrossRefGoogle Scholar
  9. Chong J, Leung B, Poole P (2013) Phosphodiesterase 4 inhibitors for chronic obstructive pulmonary disease. Cochrane Database Syst Rev 11:CD002309Google Scholar
  10. Davies M, Nowotka M, Papadatos G, Dedman N, Gaulton A, Atkinson F, Bellis L, Overington JP (2015) ChEMBL web services: streamlining access to drug discovery data and utilities. Nucleic Acids Res 43:W612–W620CrossRefPubMedPubMedCentralGoogle Scholar
  11. De Azevedo J, Walter F (2010) MolDock applied to structure-based virtual screening. Curr Drug Targets 11(3):327–334CrossRefPubMedGoogle Scholar
  12. Debnath AK (2002) Pharmacophore mapping of a series of 2, 4-diamino-5-deazapteridine inhibitors of Mycobacterium avium complex dihydrofolate reductase. J Med Chem 45(1):41–53CrossRefPubMedGoogle Scholar
  13. Devereux M, Popelier PL, McLay IM (2009) Quantum isostere database: a web-based tool using quantum chemical topology to predict bioisosteric replacements for drug design. J Chem Inf Model 49(6):1497–1513CrossRefPubMedGoogle Scholar
  14. Gilbert AM, Caltabiano S, Roberts D, Sum SF, Francisco GD, Lim K, Asselin M, Ellingboe JW, Kharode Y, Cannistraci A, Francis R (2000) Novel and selective calcitonin-inducing agents. J Med Chem 43(6):1223–1233CrossRefPubMedGoogle Scholar
  15. Jin SL, Ding SL, Lin SC (2012) Phosphodiesterase 4 and its inhibitors in inflammatory diseases. Chang Gung Med J 35(3):197–210PubMedGoogle Scholar
  16. John S, Thangapandian S, Arooj M, Hong JC, Kim KD, Lee KW (2011) Development, evaluation and application of 3D QSAR pharmacophore model in the discovery of potential human renin inhibitors. BMC Bioinform 12(14):S4CrossRefGoogle Scholar
  17. Kansal N, Silakari O, Ravikumar M (2010) Three dimensional pharmacophore modelling for c-Kit receptor tyrosine kinase inhibitors. Eur J Med Chem 45(1):393–404CrossRefPubMedGoogle Scholar
  18. Kumar R, Son M, Bavi R, Lee Y, Park C, Arulalapperumal V, Cao GP, Kim HH, Suh JK, Kim YS, Kwon YJ (2015) Novel chemical scaffolds of the tumor marker AKR1B10 inhibitors discovered by 3D QSAR pharmacophore modeling. Acta Pharmacol Sin 36(8):998–1012CrossRefPubMedPubMedCentralGoogle Scholar
  19. Lee C, Yang W, Parr RG (1988) Development of the Colle-Salvetti correlation-energy formula into a functional of the electron density. Phys Rev B 37(2):785CrossRefGoogle Scholar
  20. Li Y, Evans JN (1995) The Fukui function: a key concept linking frontier molecular orbital theory and the hard-soft-acid-base principle. J Am Chem Soc 117(29):7756–7759CrossRefGoogle Scholar
  21. Lipinski CA, Lombardo F, Dominy BW, Feeney PJ (1997) Experimental and computational approaches to estimate solubility and permeability in drug discovery and development settings. Adv Drug Deliv Rev 23(1-3):3–25CrossRefGoogle Scholar
  22. Lugnier C (2006) Cyclic nucleotide phosphodiesterase (PDE) superfamily: a new target for the development of specific therapeutic agents. Pharmacol Ther 109(3):366–398CrossRefPubMedGoogle Scholar
  23. Lugnier C (2011) PDE inhibitors: a new approach to treat metabolic syndrome ? Curr Opin Pharmacol 11(6):698–706CrossRefPubMedGoogle Scholar
  24. Maurice DH, Ke H, Ahmad F, Wang Y, Chung J, Manganiello VC (2014) Advances in targeting cyclic nucleotide phosphodiesterases. Nat Rev Drug Discov 13(4):290–314CrossRefPubMedPubMedCentralGoogle Scholar
  25. Mortelmans K, Zeiger E (2000) The ames salmonella/microsome mutagenicity assay. Mutat Res Fund Mol Mech Mut 455(1):29–60CrossRefGoogle Scholar
  26. Muller GW, Shire MG, Wong LM, Corral LG, Patterson RT, Chen Y, Stirling DI (1998) Thalidomide analogs and PDE4 inhibition. Bioorg Med Chem Lett 8:2669–2674CrossRefPubMedGoogle Scholar
  27. Niu M, Dong F, Tang S, Fida G, Qin J, Qiu J, Liu K, Gao W, Gu Y (2013) Pharmacophore modeling and virtual screening for the discovery of new type 4 cAMP phosphodiesterase (PDE4) inhibitors. PLoS One 8(12):e82360CrossRefPubMedPubMedCentralGoogle Scholar
  28. Ochiai H, Ohtani T, Ishida A, Kishikawa K, Obata T, Nakai H, Toda M (2004) Orally active PDE4 inhibitors with therapeutic potential. Bioorg Med Chem Lett 14(5):1323–1327CrossRefPubMedGoogle Scholar
  29. Panchmatia PM, Ali ME, Sanyal B, Oppeneer PM (2010) Halide ligated iron porphines: a DFT + U and UB3LYP study. J Phys Chem A 114(51):13381–13387CrossRefPubMedGoogle Scholar
  30. Parker KA, Ressa M, Skelley S, Smith DK (1992) Community resources in obese care. J Fla Med Assoc 79(6):389–391PubMedGoogle Scholar
  31. Provins L, Christophe B, Danhaive P, Dulieu J, Durieu V, Gillard M, Lebon F, Lengelé S, Quéré L, van Keulen B (2006) First dual M 3 antagonists-PDE4 inhibitors: synthesis and SAR of 4, 6-diaminopyrimidine derivatives. Bioorg Med Chem Lett 16(7):1834–1839CrossRefPubMedGoogle Scholar
  32. Richter W, Menniti FS, Zhang HT, Conti M (2013) PDE4 as a target for cognition enhancement. Exp Opin Ther Targets 17(9):1011–1027CrossRefGoogle Scholar
  33. Sakkiah S, Lee KW (2012) Pharmacophore-based virtual screening and density functional theory approach to identifying novel butyrylcholinesterase inhibitors. Acta Pharmacol Sin 33(7):964–978CrossRefPubMedPubMedCentralGoogle Scholar
  34. Thomsen R, Christensen MH (2006) MolDock: a new technique for high-accuracy molecular docking. J Med Chem 49(11):3315–3321CrossRefPubMedGoogle Scholar
  35. Veber DF, Johnson SR, Cheng HY, Smith BR, Ward KW, Kopple KD (2002) Molecular properties that influence the oral bioavailability of drug candidates. J Med Chem 45(12):2615–2623CrossRefGoogle Scholar
  36. Wirth M, Zoete V, Michielin O, Sauer WH (2012) SwissBioisostere: a database of molecular replacements for ligand design. Nucleic Acids Res 41(D1):D1137–D1143CrossRefPubMedPubMedCentralGoogle Scholar

Copyright information

© Springer Science+Business Media, LLC 2017

Authors and Affiliations

  • Dhrubajyoti Gogoi
    • 1
  • Amrita K. Chaliha
    • 2
  • Diganta Sarma
    • 3
  • Bibhuti B. Kakoti
    • 1
  • Alak K. Buragohain
    • 2
    Email author
  1. 1.DBT-Bioinformatics Infrastructure Facility, Centre for Biotechnology and Bioinformatics, School of Science and EngineeringDibrugarh UniversityDibrugarhIndia
  2. 2.Centre for Biotechnology and Bioinformatics, School of Science and EngineeringDibrugarh UniversityDibrugarhIndia
  3. 3.Department of ChemistrySchool of Science and EngineeringDibrugarhIndia

Personalised recommendations