Molecular Diversity

, Volume 16, Issue 1, pp 59–72

Combining 2D and 3D in silico methods for rapid selection of potential PDE5 inhibitors from multimillion compounds’ repositories: biological evaluation

  • Tünde Tömöri
  • István Hajdú
  • László Barna
  • Zsolt Lőrincz
  • Sándor Cseh
  • György Dormán
Full-Length Paper

Abstract

Rapid in silico selection of target focused libraries from commercial repositories is an attractive and cost-effective approach when starting new drug discovery projects. If structures of active compounds are available rapid 2D similarity search can be performed on multimillion compounds’ databases. This in silico approach can be combined with physico-chemical parameter filtering based on the property space of the active compounds and 3D virtual screening if the structure of the target protein is available. A multi-step virtual screening procedure was developed and applied to select potential phosphodiesterase 5 (PDE5) inhibitors in real time. The combined 2D/3D in silico method resulted in the identification of 14 novel PDE5 inhibitors with <1 μMIC50 values and the hit rate in the second in silico selection and in vitro screening round exceeded the 20%.

Keywords

2D similarity selection Virtual screening Phosphodiesterase 5 inhibitor 3D docking In vitro screening 

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References

  1. 1.
    Decornez H, Gulyás-Forró A, Papp A, Szabó M, Sármay G, Hajdú I, Cseh S, Dormán G, Kitchen DB (2009) Design, selection, and evaluation of a general kinase-focused library. ChemMedChem 4: 1273–1278. doi:10.1002/cmdc.200900164 PubMedCrossRefGoogle Scholar
  2. 2.
    Stahura FL, Bajorath J (2004) Virtual screening methods that complement high-throughput screening. Comb Chem High Throughput Screen 7: 259–269. doi:10.2174/1386207043328706 PubMedGoogle Scholar
  3. 3.
    Niinivehmas SP, Virtanen SI, Lehtonen JV, Postila PA., Pentikinen OT (2011) Comparison of Virtual High-Throughput Screening Methods for the Identification of Phosphodiesterase-5 Inhibitors. J Chem Inf Model 51: 1353–1363. doi:10.1021/ci1004527 PubMedCrossRefGoogle Scholar
  4. 4.
    Willett P (2006) Similarity-based virtual screening using 2D finger prints. Drug Discov Today 11: 1046–1053. doi:10.1016/j.drudis.2006.10.005 PubMedCrossRefGoogle Scholar
  5. 5.
    Johnson MA, Maggiora GM (1990) Concepts and applications of molecular similarity. Wiley, New YorkGoogle Scholar
  6. 6.
    Tovar A, Eckert H, Bajorath J (2007) Comparison of 2D fingerprint methods for multiple-template similarity searching on compound activity classes of increasing structural diversity. Chem Med Chem 2: 208–217. doi:10.1002/cmdc.200600225 PubMedGoogle Scholar
  7. 7.
    Willett P, Winterman V (1986) A comparison of some measures of intermolecular structural similarity. Quant Struct Act Rel 5: 18–25. doi:10.1002/qsar.19860050105 CrossRefGoogle Scholar
  8. 8.
    Dixon SL, Koehler RT (1999) The hidden component of size in twodimensional fragment descriptors: side effects on sampling in bioactive libraries. J Med Chem 42: 2887–2900. doi:10.1021/jm980708c PubMedCrossRefGoogle Scholar
  9. 9.
    Xue L, Stahura FL, Godden JW, Bajorath J (2001) Fingerprint scaling increases the probability of identifying molecules with similar activity in virtual screening calculations. J Chem Inf Comput Sci 41: 746–753. doi:10.1021/ci000311t PubMedCrossRefGoogle Scholar
  10. 10.
    Schuffenhauer A, Floersheim P, Acklin P, Jacoby E (2003) Similarity metrics for ligands reflecting the similarity of the target proteins. J Chem Inf Comput Sci 43: 391–405. doi:10.1021/ci025569t PubMedCrossRefGoogle Scholar
  11. 11.
    Hert J, Willett P, Wilton DJ, Acklin P, Azzaoui K, Jacoby E, Schuffenhauer A (2005) Enhancing the effectiveness of similarity-based virtual screening using nearest-neighbour information. J Med Chem 48: 7049–7054. doi:10.1021/jm050316n PubMedCrossRefGoogle Scholar
  12. 12.
    Olah M, Mracec M, Ostopovici L, Rad R, Bora A, Hadaruga N, Olah I, Banda M, Simon Z, Mracec M, Oprea TI (2005) WOMBAT: World of Molecular Bioactivity. In: Oprea TI (eds) Chemoinformatics in drug discovery. Wiley-VCH, New York, pp 223–239. doi:10.1002/3527603743.ch9 Google Scholar
  13. 13.
    Raevsky OA (2004) Physicochemical descriptors in property-based drug design. Mini Rev Med Chem 4: 1041–1052. doi:10.2174/1389557043402964 PubMedGoogle Scholar
  14. 14.
    Di L, Kerns EH, Carter GT (2009) Drug-like property concepts in pharmaceutical design. Curr Pharm Des 15: 2184–2194. doi:10.2174/138161209788682479 PubMedCrossRefGoogle Scholar
  15. 15.
    Khanna V, Ranganathan S (2009) Physiochemical property space distribution among human metabolites, drugs and toxins. BMC Bioinform 10: S10. doi:10.1186/1471-2105-10-S15-S10 CrossRefGoogle Scholar
  16. 16.
    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: 3–25. doi:10.1016/S0169-409X(96)00423-1 CrossRefGoogle Scholar
  17. 17.
    Veber DF, Johnson SR, Cheng H-Y, Smith BR, Ward KW, Kenneth DK (2002) Molecular properties that influence the oral bioavailability of drug candidates. J Med Chem 45: 2615–2623. doi:10.1021/jm020017n PubMedCrossRefGoogle Scholar
  18. 18.
    Olah MM, Bologa CG, Oprea TI (2004) Strategies for compound selection. Curr Drug Discov Technol 1: 211–220. doi:10.2174/1570163043334965 PubMedCrossRefGoogle Scholar
  19. 19.
    Tyrchan C, Blomberg N, Engkvist O, Kogej T, Muresan S. (2009) Physicochemical property profiles of marketed drugs, clinical candidates and bioactive compounds. Bioorg Med Chem Lett 19: 6943–6947. doi:10.1016/j.bmcl.2009.10.068 PubMedCrossRefGoogle Scholar
  20. 20.
    Morphy R (2006) The influence of target family and functional activity on the physicochemical properties. J Med Chem 49: 2969–2978. doi:10.1021/jm0512185 PubMedCrossRefGoogle Scholar
  21. 21.
    Darvas F, Keserű G, Papp Á, Dormán G, Ürge L, Krajcsi P (2002) In silico and ex silico ADME approaches for drug discovery. Curr Top Med Chem 2: 1269–1277. doi:10.2174/1568026023392841 CrossRefGoogle Scholar
  22. 22.
    Jayashankar L, Syama Sundar B (2010) Computational studies on phosphodiesterase-5 inhibitors to design novel lead compounds for the treatment of erectile dysfunction. J Pharm Sci Technol 2: 156–169Google Scholar
  23. 23.
    Chen G, Wang H, Robinson H, Cai J, Wan Y, Ke H (2008) An insight into the pharmacophores of phosphodiesterase-5 inhibitors from synthetic and crystal structural studies. Biochem Pharmacol 75: 1717–1728. doi:10.1016/j.bcp.2008.01.019 PubMedCrossRefGoogle Scholar
  24. 24.
    Ke H, Wang H (2007) Crystal structures of phosphodiesterases and implications on substrate specificity and inhibitor selectivity. Curr Top Med Chem 7: 391–403. doi:10.2174/156802607779941242 PubMedCrossRefGoogle Scholar
  25. 25.
    Palmer MJ, Bell AS, Fox DNA, Brown DG (2007) Design of second generation phosphodiesterase 5 inhibitors. Curr Top Med Chem 7: 405–419. doi:10.2174/156802607779941288 PubMedCrossRefGoogle Scholar
  26. 26.
    Reddy AS, Pati SP, Kumar PP, Pradeep HN, Sastry GN (2007) Virtual screening in drug discovery—a computational perspective. Curr Protein Pept Sci 8: 329–351. doi:10.2174/138920307781369427 PubMedCrossRefGoogle Scholar
  27. 27.
    Kiss R, Kiss B, Szalai F, Szalai F, Jelinek I, László V, Noszál B, Falus A, Keseru GM (2008) Discovery of novel human histamine H4 receptor ligands by large-scale structure-based virtual screening. J Med Chem 51: 3145–3153. doi:10.1021/jm7014777 PubMedCrossRefGoogle Scholar
  28. 28.
    http://www.chemaxon.com (accessed April, 2010); InstJChem v. 5.3.1, 2010 was used for structure searching and chemical database access and management: Marvin v. 5.3.1, 2010 was used for drawing, displaying, and characterizing chemical structures and substructures: fingerprints are explained at http://www.chemaxon.com/jchem/doc/user/fingerprint.html
  29. 29.
    Adams SE, Glen RC (2006) Similarity metrics and descriptor spaces—which combinations to choose. QSAR Comb Sci 26: 1133–1142. doi:10.1002/qsar.200610097 Google Scholar
  30. 30.
    Loughney K, Hill TR, Florio VA, Uher L, Rosman GJ, Wolda SL, Jones BA, Howard ML, McAllister-Lucas LM, Sonnenburg WK, Francis SH, Corbin JD, Beavo JA, Ferguson K (1998) Isolation and characterization of cDNAs encoding PDE5A, a human cGMP-binding, cGMP-specific 3’,5’-cyclic nucleotide phosphodiesterase. Gene 216: 139–147. doi:10.1016/S0378-1119(98)00303-5 PubMedCrossRefGoogle Scholar
  31. 31.
    Thompson WJ, Appleman MM (1971) Multiple cyclic nucleotide phosphodiesterase activities from rat brain. Biochemistry 10: 311–316. doi:10.1021/bi00778a018 PubMedCrossRefGoogle Scholar
  32. 32.
    Thomas MK, Francis SH, Corbin JD (1990) Characterization of a purified bovine lung cGMP-binding cGMP phosphodiesterase. J Biol Chem 265: 14964–14970PubMedGoogle Scholar
  33. 33.
    Lugnier C, Schini-Kerth V (2006) yclic nucleotide phosphodiesterase (PDE) superfamily: a new target for the development of specific therapeutic agents. Pharmacol Therapeut 109: 366–398. doi:pharmthera.2005.07.003 CrossRefGoogle Scholar
  34. 34.
    Chen G, Wang H, Robinson H, Cai J, Wana Y, Ke H (2008) An insight into the pharmacophores of phosphodiesterase-5 inhibitors from synthetic and crystal structural studies. Biochem Pharmacol 75: 1717–1728. doi:10.1016/j.bcp.2008.01.019 PubMedCrossRefGoogle Scholar
  35. 35.
    Medina-Franco JL, Martínez-Mayorga K, Giulianotti MA, Houghten RA, Pinilla C (2008) Visualization of the chemical space in drug discovery. Curr Comput Aid Drug 4: 322–333. doi:10.2174/157340908786786010 CrossRefGoogle Scholar
  36. 36.
    PharmaProjects, www.pharmaprojects.com/
  37. 37.
    Irwin JJ, Shoichet BK (2005) ZINC—a free database of commercially available compounds for virtual screening. J Chem Inf Model 45: 177–182. doi:10.1021/ci049714 PubMedCrossRefGoogle Scholar
  38. 38.
  39. 39.
    Zhao H (2007) Scaffold selection and scaffold hopping in lead generation: a medicinal chemistry perspective. Drug Discov Today 12: 149–155. doi:10.1016/j.drudis.2006.12.003 PubMedCrossRefGoogle Scholar

Copyright information

© Springer Science+Business Media B.V. 2011

Authors and Affiliations

  • Tünde Tömöri
    • 1
  • István Hajdú
    • 1
  • László Barna
    • 2
  • Zsolt Lőrincz
    • 1
  • Sándor Cseh
    • 1
  • György Dormán
    • 1
  1. 1.TargetExDunakesziHungary
  2. 2.Institute of Experimental MedicineBudapestHungary

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