Skip to main content

Advertisement

SpringerLink
Log in

Quasi 4D-QSAR and 3D-QSAR study of the pan class I phosphoinositide-3-kinase (PI3K) inhibitors

  • Original Research
  • Published:
Medicinal Chemistry Research Aims and scope Submit manuscript

Abstract

The class I phosphoinositide-3-kinases (PI3Ks) is currently investigated and attracted as a promising target toward anticancer therapies. The quasi 4D-QSAR model is developed by a training set of 30 pan class I PI3K inhibitors. This methodology is based on the generation of a conformational ensemble profile for each compound instead of only one conformation, followed by the calculation of intermolecular interaction energies at each grid point considering probes and all aligned conformations resulting from molecular dynamic simulations. A comparison of the proposed methodology with comparative molecular field analysis (CoMFA) formalism has been performed. This paradigm explores jointly the main features of CoMFA and 4D-QSAR models. The best 4D-QSAR model is checked for free from chance correlation, reliability and robustness by leave-N-out cross-validation and Y-randomization in addition to analysis of the independent test set. Statistical parameters of the best 4D-QSAR model are R 2 = 0.871, q 2LOO  = 0.661, and R 2Pred  = 0.751. The results of the suggested model are in good agreement with docking study that was previously reported by Rewcastle et al. (J Med Chem 54:7105–7126, 2011).

This is a preview of subscription content, log in via an institution to check access.

Access this article

Log in via an institution

Price excludes VAT (USA)
Tax calculation will be finalised during checkout.

Instant access to the full article PDF.

Institutional subscriptions

Fig. 1
Fig. 2
Fig. 3
Fig. 4
Fig. 5
Fig. 6

Similar content being viewed by others

References

  • Andrade CH, Pasqualoto KFM, Ferreira EI, Hopfinger AJ (2010) 4D-QSAR: perspectives in drug design. Molecules 15:3281–3294

    Article  PubMed  CAS  Google Scholar 

  • Berendsen HJC, Postma JPM, Van Gunsteren WF, DiNola A, Haak J (1984) Molecular dynamics with coupling to an external bath. J Chem Phys 81:3684

    Article  CAS  Google Scholar 

  • Bhonsle JB, Wang Z, Tamamura H, Fujii N, Peiper SC, Trent JO (2005) A simple, automated quasi-4D-QSAR, quasi-multi way PLS approach to develop highly predictive QSAR models for highly flexible CXCR4 inhibitor Cyclic Pentapeptide Ligands Using Scripted Common Molecular Modeling Tools. QSAR Comb Sci 24:620–630

    Article  CAS  Google Scholar 

  • Cantley LC (2002) The phosphoinositide 3-kinase pathway. Science 296:1655

    Article  PubMed  CAS  Google Scholar 

  • Clark M, Cramer RD III, Van Opdenbosch N (1989) Validation of the general purpose Tripos 5.2 force field. J Comput Chem 10:982–1012

    Article  CAS  Google Scholar 

  • Courtney KD, Corcoran RB, Engelman JA (2010) The PI3K pathway as drug target in human cancer. J Clin Oncol 28:1075

    Article  PubMed  CAS  Google Scholar 

  • Cramer RD III, Patterson DE, Bunce JD (1988) Comparative molecular field analysis (CoMFA). 1. Effect of shape on binding of steroids to carrier proteins. J Am Chem Soc 110:5959–5967

    Article  PubMed  CAS  Google Scholar 

  • Darden T, York D, Pedersen L (1993) Particle mesh Ewald: An N log (N) method for Ewald sums in large systems. J Chem Phys 98:10089–10092

    Article  CAS  Google Scholar 

  • Dimitrov S, Dimitrova G, Pavlov T, Dimitrova N, Patlewicz G, Niemela J, Mekenyan O (2005) A stepwise approach for defining the applicability domain of SAR and QSAR models. J Chem Inf Model 45:839–849

    Article  PubMed  CAS  Google Scholar 

  • Doucet JP, Panaye A (2009) Three dimensional QSAR: applications in pharmacology & toxicology (QSAR in environmental & health sciences). CRC Press, New York

    Google Scholar 

  • Doweyko AM (2004) 3D-QSAR illusions. J Comput Aided Mol Des 18:587–596

    Article  PubMed  CAS  Google Scholar 

  • Dragos H, Gilles M, Alexandre V (2009) Predicting the predictability: a unified approach to the applicability domain problem of QSAR models. J Chem Inf Model 49:1762–1776

    Article  PubMed  CAS  Google Scholar 

  • Frédérick R, Denny WA (2008) Phosphoinositide-3-kinases (PI3 Ks): combined comparative modeling and 3D-QSAR to rationalize the inhibition of p110α. J Chem Inf Model 48:629–638

    Article  PubMed  Google Scholar 

  • Fruman DA, Bismuth G (2009) Fine tuning the immune response with PI3 K. Immunol Rev 228:253–272

    Article  PubMed  CAS  Google Scholar 

  • Ghasemi JB, Safavi-Sohi R (2011) 4D-LQTA-QSAR and docking study on potent gram-negative specific LpxC inhibitors: a comparison to CoMFA modeling. Mol Divers. doi:10.1007/s11030-011-9340-3

  • Ghasemi JB, Salahinejad M, Rofouei MK (2011) Review of the quantitative structure–activity relationship modelling methods on estimation of formation constants of macrocyclic compounds with different guest molecules. Supramol Chem 23:615–631. doi:10.1080/10610278.2011.581281

    Article  CAS  Google Scholar 

  • Golbraikh A, Tropsha A (2002a) Beware of q2! J Mol Graph Model 20:269–276

    Article  PubMed  CAS  Google Scholar 

  • Golbraikh A, Tropsha A (2002b) Predictive QSAR modeling based on diversity sampling of experimental datasets for the training and test set selection. J Comput Aided Mol Des 16:357–369

    Article  PubMed  CAS  Google Scholar 

  • Hopfinger A, Wang S, Tokarski JS, Jin B, Albuquerque M, Madhav PJ, Duraiswami C (1997) Construction of 3D-QSAR models using the 4D-QSAR analysis formalism. J Am Chem Soc 119:10509–10524

    Article  CAS  Google Scholar 

  • Huang CH, Mandelker D, Schmidt-Kittler O, Samuels Y, Velculescu VE, Kinzler KW, Vogelstein B, Gabelli SB, Amzel LM (2007) The structure of a human p110a/p85a complex elucidates the effects of oncogenic PI3Ka mutations. Science 318:1744–1747

    Article  PubMed  CAS  Google Scholar 

  • Itai A, Tomioka N (1993) In: Kubinyi H (ed) 3D QSAR in drug design: theory, methods and applications, vol 1. Kluwer Academic Publishers, Leiden, pp 200–206

    Google Scholar 

  • Jiang BH, Liu LZ (2008) PI3 K/PTEN signaling in tumorigenesis and angiogenesis. Biochim Biophys Acta, Proteins Proteomics 1784:150–158

    Article  PubMed  CAS  Google Scholar 

  • Kennard RW, Stone LA (1969) Computer aided design of experiments. Technometrics 11:137–148

    Article  Google Scholar 

  • Kiralj R, Ferreira MMC (2009) Basic validation procedures for regression models in QSAR and QSPR studies: theory and application. J Braz Chem Soc 20:770–787

    Article  CAS  Google Scholar 

  • Kong D, Yamori T (2008) Phosphatidylinositol 3-kinase inhibitors: promising drug candidates for cancer therapy. Cancer Sci 99:1734–1740

    Article  PubMed  CAS  Google Scholar 

  • Kusalik PG, Svishchev IM (1994) The spatial structure in liquid water. Science 265:1219

    Google Scholar 

  • Maira SM, Stauffer F, Schnell C, García-Echeverría C (2009) PI3 K inhibitors for cancer treatment: where do we stand. Biochem Soc Trans 37:265–272

    Article  PubMed  CAS  Google Scholar 

  • Martins JPA, Barbosa EG, Pasqualoto KFM, Ferreira MMC (2009) LQTA-QSAR: a new 4D-QSAR methodology. J Chem Inf Model 49:1428–1436

    Article  PubMed  CAS  Google Scholar 

  • Matthews DJ, Gerritsen ME (2010) Targeting protein kinases for cancer therapy, Intracellular signal transduction cascades, Targeting protein kinases for cancer therapy. Wiley, Hoboken, pp 266–270

    Book  Google Scholar 

  • Parrinello M, Rahman A (1980) Crystal structure and pair potentials: A molecular-dynamics study. Phys Rev Lett 45:1196–1199

    Article  CAS  Google Scholar 

  • Rewcastle GW, Denny WA (2009) Inhibitors of phosphatidylinositol 3-kinases: the next wave of anti-cancer drugs? Chem New Zeal 73:9–11

    CAS  Google Scholar 

  • Rewcastle GW, Gamage SA, Flanagan JU, Frederick R, Denny WA, Baguley BC, Kestell P, Singh R, Kendall JD, Marshall ES (2011) Synthesis and biological evaluation of novel analogues of the pan class I phosphatidylinositol 3-kinase (PI3 K) inhibitor 2-(difluoromethyl)-1-[4,6-di (4-morpholinyl)-1,3,5-triazin-2-yl]-1H-benzimidazole (ZSTK474). J Med Chem 54:7105–7126. doi:10.1021/jm200688y

    Article  PubMed  CAS  Google Scholar 

  • Ricketts EM, Bradshaw J, Hann M, Hayes F, Tanna N, Ricketts DM (1993) Comparison of conformations of small molecule structures from the Protein Data Bank with those generated by Concord, Cobra, ChemDBS-3D, and Converter and those extracted from the Cambridge Structural Database. J Chem Inf Model 33:905–925

    Article  CAS  Google Scholar 

  • Rusinko A III, Sheridan RP, Nilakantan R, Haraki KS, Bauman N, Venkataraghavan R (1989) Using CONCORD to construct a large database of three-dimensional coordinates from connection tables. J Chem Inf Model 29:251–255

    Article  CAS  Google Scholar 

  • Santos-Filho OA, Hopfinger AJ (2002) The 4D-QSAR Paradigm: Application to a Novel Set of Non-peptidic HIV Protease Inhibitors. Quant Struc Act Rel 21:369–381

    Article  CAS  Google Scholar 

  • Shim J, MacKerell AD Jr (2011) Computational ligand-based rational design: role of conformational sampling and force fields in model development. Med Chem Commun 2:356–370

    Article  CAS  Google Scholar 

  • Tropsha A, Gramatica P, Gombar VK (2003) The importance of being earnest: validation is the absolute essential for successful application and interpretation of QSPR models. QSAR Comb Sci 22:69–77

    Article  CAS  Google Scholar 

  • Verma J, Khedkar VM, Coutinho EC (2010) 3D-QSAR in drug design-A review. Curr Top Med Chem 10:95–115

    Article  PubMed  CAS  Google Scholar 

  • Wang R, Gao Y, Liu L, Lai L (1998) All-orientation search and all-placement search in comparative molecular field analysis. J Mol Model 4:276–283

    Article  CAS  Google Scholar 

  • Ward SG, Finan P (2003) Isoform-specific phosphoinositide 3-kinase inhibitors as therapeutic agents. Curr Opin Pharmacol 3:426–434

    Article  PubMed  CAS  Google Scholar 

  • Ward S, Sotsios Y, Dowden J, Bruce I, Finan P (2003) Therapeutic potential of phosphoinositide 3-kinase inhibitors. Chem Biol 10:207–213

    Article  PubMed  CAS  Google Scholar 

Download references

Acknowledgments

We acknowledge Iranian National Science Foundation (INSF) for financial support of Grant No 90004061.

Author information

Authors and Affiliations

  1. Chemistry Department, Faculty of Sciences, K. N. Toosi University of Technology, Tehran, Iran

    Reihaneh Safavi-Sohi & Jahan B. Ghasemi

Authors
  1. Reihaneh Safavi-Sohi
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Jahan B. Ghasemi
    View author publications

    You can also search for this author in PubMed Google Scholar

Corresponding author

Correspondence to Jahan B. Ghasemi.

Rights and permissions

Reprints and permissions

About this article

Cite this article

Safavi-Sohi, R., Ghasemi, J.B. Quasi 4D-QSAR and 3D-QSAR study of the pan class I phosphoinositide-3-kinase (PI3K) inhibitors. Med Chem Res 22, 1587–1596 (2013). https://doi.org/10.1007/s00044-012-0151-6

Download citation

  • Received:

  • Accepted:

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.1007/s00044-012-0151-6

Keywords

Search

Navigation