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Molecular modeling of potential PET imaging agents for adenosine receptor in Parkinson’s disease

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Abstract

The adenosine receptors have appeared as potent and selective drug target in various diseases especially for central nervous system diseases. Adenosine receptor A2A antagonists have been known as potential treatment for Parkinson’s disease (PD). Radiolabeled A2AR antagonists can be used as positron emission tomography (PET) tracers and diagnostic tools for PD. In the present investigation, we perform the quantitative structure–activity relationship (QSAR) analysis and docking studies of a series of PET tracers as ligands and Adenosine receptors (A2AR) binding affinity, to elucidate the structural properties required for A2AR antagonist in treatment for PD. Several variable-selection methods were used to choose the descriptors that would lead to good QSAR model. Among several models developed, the best model was a five-variable multiple linear regression (MLR) equation with statistical parameters of squared correlation coefficient R 2 = 0.90 ± 0.01 and cross-validated correlation coefficient Q 2 = 0.84 ± 0.02. The QSAR models were also constructed for A2AR selectivity to tracer ligands, that yielded a four-variable model with R 2 = 0.94 ± 0.01 and Q 2 = 0.89 ± 0.02. The most important variables contributed in models construction involved: partial charge, hydrophobic atoms, rotatable bonds, polar van der Waals surface area, potential energy, and conformation-dependent charge descriptors. Finally, molecular docking analysis was carried out to better understand the interactions between ligands and Adenosine receptors. The importance of π-π stacking interactions between aromatic moiety of the ligands and triazine core of A2AR antagonist was confirmed.

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References

  1. Müller CE, Scior T (1993) Adenosine receptors and their modulators. Pharm Acta Helv 68(2):77–111

    Article  Google Scholar 

  2. Sachdeva S, Gupta M (2013) Adenosine and its receptors as therapeutic targets: an overview. Saudi Pharm J 21(3):245–253

    Article  Google Scholar 

  3. Dunwiddie TV, Masino SA (2001) The role and regulation of adenosine in the central nervous system. Annu Rev Neurosci 24(1):31–55

    Article  CAS  Google Scholar 

  4. Fredholm BB, Abbracchio MP, Burnstock G, Daly JW, Harden TK, Jacobson KA, Leff P, Williams M (1994) Nomenclature and classification of purinoceptors. Pharmacol Rev 46(2):143–156

    CAS  Google Scholar 

  5. Haas HL, Selbach O (2000) Functions of neuronal adenosine receptors. Naunyn-Schmiedeberg’s arch. Pharmacology 362(4–5):375–381

    CAS  Google Scholar 

  6. Poulsen S-A, Quinn RJ (1998) Adenosine receptors: new opportunities for future drugs. Bioorg Med Chem 6(6):619–641

    Article  CAS  Google Scholar 

  7. Kim DS, Palmiter RD (2003) Adenosine receptor blockade reverses hypophagia and enhances locomotor activity of dopamine-deficient mice. Proc Natl Acad Sci 100(3):1346–1351

    Article  CAS  Google Scholar 

  8. Pinna A (2014) Adenosine A2A receptor antagonists in Parkinson’s disease: progress in clinical trials from the newly approved istradefylline to drugs in early development and those already discontinued. CNS Drugs 28(5):455–474

    Article  CAS  Google Scholar 

  9. Jacobson KA, Van Galen PJ, Williams M (1992) Adenosine receptors: pharmacology, structure-activity relationships, and therapeutic potential. J Med Chem 35(3):407–422

    Article  CAS  Google Scholar 

  10. Cools AR, Rossum JV (1976) Excitation-mediating and inhibition-mediating dopamine-receptors: a new concept towards a better understanding of electrophysiological, biochemical, pharmacological, functional and clinical data. Psychopharmacology 45(3):243–254

    Article  CAS  Google Scholar 

  11. Ongini E, Adami M, Ferri C, Bertorelli R (1997) Adenosine A2A receptors and neuroprotection. Ann N Y Acad Sci 825(1):30–48

    Article  CAS  Google Scholar 

  12. Richardson PJ, Kase H, Jenner PG (1997) Adenosine A2A receptor antagonists as new agents for the treatment of Parkinson’s disease. Trends Pharmacol Sci 18(4):338–344

    Article  CAS  Google Scholar 

  13. Fenu S, Pinna A, Ongini E, Morelli M (1997) Adenosine a 2A receptor antagonism potentiates L-DOPA-induced turning behaviour and c-fos expression in 6-hydroxydopamine-lesioned rats. Eur J Pharmacol 321(2):143–147

    Article  CAS  Google Scholar 

  14. Kanda T, Jackson MJ, Smith LA, Pearce RK, Nakamura J, Kase H, Kuwana Y, Jenner P (1998) Adenosine A2A antagonist: a novel antiparkinsonian agent that does not provoke dyskinesia in parkinsonian monkeys. Ann Neurol 43(4):507–513

    Article  CAS  Google Scholar 

  15. Kanda T, Shiozaki S, Shimada J, Suzuki F, Nakamura J (1994) KF17837: a novel selective adenosine A2A receptor antagonist with anticataleptic activity. Eur J Pharmacol 256(3):263–268

    Article  CAS  Google Scholar 

  16. Mally J, Stone TW (1996) Potential role of adenosine antagonist therapy in pathological tremor disorders. Pharmacol Ther 72(3):243–250

    Article  CAS  Google Scholar 

  17. Shimada J, Koike N, Nonaka H, Shiozaki S, Yanagawa K, Kanda T, Kobayashi H, Ichimura M, Nakamura J, Kase H (1997) Adenosine A 2A antagonists with potent anti-cataleptic activity. Med Chem Lett 7(18):2349–2352

    Article  CAS  Google Scholar 

  18. Shiozaki S, Ichikawa S, Nakamura J, Kitamura S, Yamada K, Kuwana Y (1999) Actions of adenosine A2A receptor antagonist KW-6002 on drug-induced catalepsy and hypokinesia caused by reserpine or MPTP. Psychopharmacology 147(1):90–95

    Article  CAS  Google Scholar 

  19. Segovia F, Górriz J, Ramírez J, Salas-Gonzalez D, Álvarez I, López M, Chaves R, Initiative AsDN (2012) A comparative study of feature extraction methods for the diagnosis of Alzheimer’s disease using the ADNI database. Neurocomputing 75(1):64–71

    Article  Google Scholar 

  20. Zhang D, Wang Y, Zhou L, Yuan H, Shen D, Initiative AsDN (2011) Multimodal classification of Alzheimer’s disease and mild cognitive impairment. NeuroImage 55(3):856–867

    Article  Google Scholar 

  21. Alavi A, Basu S (2008) Planar and SPECT imaging in the era of PET and PET–CT: can it survive the test of time? Eur J Nucl Med Mol Imaging 35(8):1554–1559

    Article  Google Scholar 

  22. Rahmim A, Zaidi H (2008) PET versus SPECT: strengths, limitations and challenges. Nucl Med Commun 29(3):193–207

    Article  Google Scholar 

  23. Holschbach MH, Olsson RA (2002) Applications of adenosine receptor ligands in medical imaging by positron emission tomography. Curr Pharm Des 8(26):2345–2352

    Article  CAS  Google Scholar 

  24. Elsinga PH, Hatano K, Ishiwata K (2006) PET tracers for imaging of the dopaminergic system. Curr Med Chem 13(18):2139–2153

    Article  CAS  Google Scholar 

  25. Ishiwata K, Kimura Y, de Vries J, Erik F, Elsinga PH (2007) PET tracers for mapping adenosine receptors as probes for diagnosis of CNS disorders. Cent Nerv Syst Agents Med Chem 7(1):57–77

    Article  CAS  Google Scholar 

  26. Ghasemi J, Salahinejad M, Rofouei M (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(9):614–629

    Article  Google Scholar 

  27. Salahinejad M, Ghasemi J (2014) 3D-QSAR studies on the toxicity of substituted benzenes to Tetrahymena pyriformis: CoMFA, CoMSIA and VolSurf approaches. Ecotoxicol Environ Saf 105:128–134

    Article  CAS  Google Scholar 

  28. Du Q-S, Huang R-B, Chou K-C (2008) Recent advances in QSAR and their applications in predicting the activities of chemical molecules, peptides and proteins for drug design. Curr Protein Pept Sci 9(3):248–259

    Article  CAS  Google Scholar 

  29. Khanapur S, van Waarde A, Ishiwata K, Leenders KL, Dierckx RA, Elsinga PH (2014) Adenosine A2A receptor antagonists as positron emission tomography (PET) tracers. Curr Med Chem 21(3):312–328

    Article  CAS  Google Scholar 

  30. Guyon I, Elisseeff A (2003) An introduction to variable and feature selection. J Mach Learn Res 3:1157–1182

    Google Scholar 

  31. Holland JH (1992) Adaptation in natural and artificial systems: an introductory analysis with applications to biology, control, and artificial intelligence. MIT Press, Cambridge,

    Google Scholar 

  32. Ayers JT, Clauset A, Schmitt JD, Dwoskin LP, Crooks PA (2005) Molecular modeling of mono-and bis-quaternary ammonium salts as ligands at the α4β2 nicotinic acetylcholine receptor subtype using nonlinear techniques. AAPS J 7(3):E678–E685

    Article  CAS  Google Scholar 

  33. Vadlamudi SM, Kulkarni VM (2003) 3D-QSAR of protein tyrosine phosphatase 1B inhibitors by genetic function approximation. Internet Electron J Mol Des 2:000

  34. Mercader AG, Duchowicz PR, Fernández FM, Castro EA (2008) Modified and enhanced replacement method for the selection of molecular descriptors in QSAR and QSPR theories. Chemom Intell Lab Syst 92(2):138–144

    Article  CAS  Google Scholar 

  35. Duchowicz PR, Castro EA, Fernández FM (2006) Alternative algorithm for the search of an optimal set of descriptors in QSAR-QSPR studies. MATCH Commun Math Comput Chem 55:179–192

    CAS  Google Scholar 

  36. Duchowicz PR, Fernández M, Caballero J, Castro EA, Fernández FM (2006) QSAR for non-nucleoside inhibitors of HIV-1 reverse transcriptase. Bioorg Med Chem 14(17):5876–5889

    Article  CAS  Google Scholar 

  37. Rücker C, Rücker G, Meringer M (2007) Y-randomization and its variants in QSPR/QSAR. J Chem Inf Model 47(6):2345–2357

    Article  Google Scholar 

  38. Roy K, Das RN, Ambure P, Aher RB (2016) Be aware of error measures. Further studies on validation of predictive QSAR models. Chemom Intell Lab Syst 152:18–33

    Article  CAS  Google Scholar 

  39. Roy K, Kar S, Ambure P (2015) On a simple approach for determining applicability domain of QSAR models. Chemom Intell Lab Syst 145:22–29

    Article  CAS  Google Scholar 

  40. Choi H, Lee DS (2015) PET and SPECT of neurobiological systems. J Nucl Med 56(11):1805–1805

    Article  Google Scholar 

  41. Chirico N, Gramatica P (2011) Real external predictivity of QSAR models: how to evaluate it? Comparison of different validation criteria and proposal of using the concordance correlation coefficient. J Chem Inf Model 51(9):2320–2335

    Article  CAS  Google Scholar 

  42. Stigliani J-L, Bernardes-Génisson V, Bernadou J, Pratviel G (2012) Cross-docking study on InhA inhibitors: a combination of Autodock Vina and PM6-DH2 simulations to retrieve bio-active conformations. Org Biomol Chem 10(31):6341–6349

    Article  CAS  Google Scholar 

Download references

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Correspondence to M. Salahinejad.

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Tamiji, Z., Salahinejad, M. & Niazi, A. Molecular modeling of potential PET imaging agents for adenosine receptor in Parkinson’s disease. Struct Chem 29, 467–479 (2018). https://doi.org/10.1007/s11224-017-1044-6

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  • DOI: https://doi.org/10.1007/s11224-017-1044-6

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