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QSAR modeling of PET imaging agents for the diagnosis of Parkinson’s disease targeting dopamine receptor

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Abstract

Dopamine (D2) receptor has emerged as a potent drug target for the diagnosis and treatment of Parkinson’s disease (PD). Radiolabelled imaging such as positron emission tomography (PET) has been recognized as an important tool in medicinal chemistry useful for the early diagnosis of PD. The present study explores quantitative structure—activity relationship analysis of 34 PET imaging agents targeted toward dopamine D2 receptor. The dataset division into training and test sets was done using Euclidean distance division method, while the feature selection was done by double cross-validation-genetic algorithm method. Finally, a five-descriptor partial least squares regression model was derived after carrying out the best subset selection applied on the significant descriptors. The developed model showed robustness in terms of statistical parameters. Finally, the structural information derived from the model descriptors gives an insight for the development of new candidate D2-PET imaging for the use in PD.

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References

  1. Parkinson's Foundation (2020) Understanding Parkinson's, Statistics. https://www.parkinson.org/Understanding-Parkinsons/Statistics. Accessed on 02 July 2020

  2. Jankovic J (2008) Parkinson’s disease: clinical features and diagnosis. J Neurol Neurosurg Psychiatry 79(4):368–376

    Article  CAS  Google Scholar 

  3. Barone P (2010) Neurotransmission in Parkinson’s disease: beyond dopamine. Eur J Neurol 17(3):364–376

    Article  CAS  Google Scholar 

  4. Antonini A, Moresco R, Gobbo C, De Notaris R, Panzacchi A, Barone P, Calzetti S, Negrotti A, Pezzoli G, Fazio F (2001) The status of dopamine nerve terminals in Parkinson’s disease and essential tremor: a PET study with the tracer [11-C] FE-CIT. Neurol Sci 22(1):47–48

    Article  CAS  Google Scholar 

  5. Politis M, Piccini P (2012) Positron emission tomography imaging in neurological disorders. J Neurol 259(9):1769–1780

    Article  Google Scholar 

  6. De P, Roy J, Bhattacharyya D, Roy K (2020) Chemometric modeling of PET imaging agents for diagnosis of Parkinson’s disease: a QSAR approach. Struct Chem. https://doi.org/10.1007/s11224-020-01560-6

    Article  Google Scholar 

  7. Heiss WD, Hilker R (2004) The sensitivity of 18-fluorodopa positron emission tomography and magnetic resonance imaging in Parkinson’s disease. Eur J Neurol 11(1):5–12

    Article  Google Scholar 

  8. Wu L, Liu FT, Ge JJ, Zhao J, Tang YL, Yu WB, Yu H, Anderson T, Zuo CT, Chen L (2018) Clinical characteristics of cognitive impairment in patients with Parkinson’s disease and its related pattern in 18F-FDG PET imaging. Hum Brain Mapp 39(12):4652–4662

    Article  Google Scholar 

  9. Glaab E, Trezzi JP, Greuel A, Jäger C, Hodak Z, Drzezga A, Timmermann L, Tittgemeyer M, Diederich NJ, Eggers C (2019) Integrative analysis of blood metabolomics and PET brain neuroimaging data for Parkinson’s disease. Neurobiol Dis 124:555–556

    Article  CAS  Google Scholar 

  10. Roy K (2018) Quantitative structure-activity relationships (QSARs): a few validation methods and software tools developed at the DTC laboratory. J Indian Chem Soc 95(12):1497–2150

    CAS  Google Scholar 

  11. Gramatica P (2020) Principles of QSAR modeling: comments and suggestions from personal experience. IJQSPR 5(3):61–97

    Google Scholar 

  12. MarvinSketch software (2020). https://www.chemaxon.com Accessed on 25 May 2020

  13. Sipos A, Kiss B, Schmidt É, Greiner I, Berényi S (2008) Synthesis and neuropharmacological evaluation of 2-aryl-and alkylapomorphines. Bioorg Med Chem 16(7):3773–3779

    Article  CAS  Google Scholar 

  14. Gao Y, Baldessarini RJ, Kula NS, Neumeyer JL (1990) Synthesis and dopamine receptor affinities of enantiomers of 2-substituted apomorphines and their N-n-propyl analogs. J Med Chem 33(6):1800–1805

    Article  CAS  Google Scholar 

  15. Tóth M, Berényi S, Csutorás C, Kula NS, Zhang K, Baldessarini RJ, Neumeyer JL (2006) Synthesis and dopamine receptor binding of sulfur-containing aporphines. Bioorg Med Chem 14(6):1918–1923

    Article  Google Scholar 

  16. Søndergaard K, Kristensen JL, Palner M, Gillings N, Knudsen GM, Roth BL, Begtrup M (2005) Synthesis and binding studies of 2-arylapomorphines. Org Biomol Chem 3(22):4077–4081

    Article  Google Scholar 

  17. Gao Y, Ram VJ, Campbell A, Kula NS, Baldessarini RJ, Neumeyer JL (1990) Synthesis and structural requirements of N-substituted norapomorphines for affinity and activity at dopamine D-1, D-2, and agonist receptor sites in rat brain. J Med Chem 33(1):39–44

    Article  CAS  Google Scholar 

  18. Baldessarini R, Kula N, Gao Y, Campbell A, Neumeyer J (1991) R (−) 2-fluoro-nn-propylnorapomorphine: a very potent and D2-selective dopamine agonist. Neuropharmacology 30(1):97–99

    Article  CAS  Google Scholar 

  19. Vasdev N, Natesan S, Galineau L, Garcia A, Stableford WT, McCormick P, Seeman P, Houle S, Wilson AA (2006) Radiosynthesis, ex vivo and in vivo evaluation of [11C] preclamol as a partial dopamine D2 agonist radioligand for positron emission tomography. Synapse 60(4):314–331

    Article  CAS  Google Scholar 

  20. Chumpradit S, Kung M, Billings J, Mach R, Kung H (1993) Fluorinated and iodinated dopamine agents: D2 imaging agents for PET and SPECT. J Med Chem 36(2):221–228

    Article  CAS  Google Scholar 

  21. Murphy RA, Kung HF, Kung MP, Billings J (1990) Synthesis and characterization of iodobenzamide analogs: potential D-2 dopamine receptor imaging agents. J Med Chem 33(1):171–178

    Article  CAS  Google Scholar 

  22. Dragon version 7 (2016) Kodesrl, Milan, Italy. https://www.talete.mi.it/index.htm. Accessed on 26 May 2020

  23. Tropsha A (2010) Best practices for QSAR model development, validation, and exploitation. Mol Inform 29(6–7):476–488

    Article  CAS  Google Scholar 

  24. Golmohammadi H, Dashtbozorgi Z, Acree WE Jr (2012) Quantitative structure–activity relationship prediction of blood-to-brain partitioning behavior using support vector machine. Eur J Pharm Sci 47(2):421–429

    Article  CAS  Google Scholar 

  25. Roy K, Ambure P (2016) The “double cross-validation” software tool for MLR QSAR model development. Chemom Intell Lab Syst 159:108–126

    Article  CAS  Google Scholar 

  26. Devillers J (1996) Genetic algorithms in molecular modeling. Academic Press, Cornwall, Great Britain

    Google Scholar 

  27. Khan PM, Roy K (2018) Current approaches for choosing feature selection and learning algorithms in quantitative structure–activity relationships (QSAR). Expert Opin Drug Discov 13(12):1075–1089

    Article  CAS  Google Scholar 

  28. Wold S, Sjöström M, Eriksson L (2001) PLS-regression: a basic tool of chemometrics. Chemom Intell Lab Syst 58(2):109–130

    Article  CAS  Google Scholar 

  29. Baumann D, Baumann K (2014) Reliable estimation of prediction errors for QSAR models under model uncertainty using double cross-validation. J Cheminform 6(1):47

    Article  Google Scholar 

  30. Roy K, Mitra I (2011) On various metrics used for validation of predictive QSAR models with applications in virtual screening and focused library design. Comb Chem High Throughput Screen 14(6):450–474

    Article  CAS  Google Scholar 

  31. Ojha PK, Mitra I, Das RN, Roy K (2011) Further exploring rm2 metrics for validation of QSPR models. Chemom Intell Lab Syst 107(1):194–205

    Article  CAS  Google Scholar 

  32. 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 

  33. Akarachantachote N, Chadcham S, Saithanu K (2014) Cutoff threshold of variable importance in projection for variable selection. Int J Pure Appl Math 94(3):307–322

    Article  Google Scholar 

  34. Finnema SJ, Bang-Andersen B, Wikstrom HV, Halldin C (2010) Current state of agonist radioligands for imaging of brain dopamine D2/D3 receptors in vivo with positron emission tomography. Curr Top Med Chem 10(15):1477–1498

    Article  CAS  Google Scholar 

  35. De P, Aher RB, Roy K (2018) Chemometric modeling of larvicidal activity of plant derived compounds against zika virus vector Aedes aegypti: application of ETA indices. RSC Adv 8(9):4662–5467

    Article  CAS  Google Scholar 

  36. Jackson JE (2005) A user’s guide to principal components, vol 587. Wiley, United States of America

    Google Scholar 

  37. Topliss JG, Edwards RP (1979) Chance factors in studies of quantitative structure-activity relationships. J Med Chem 22(10):1238–1244

    Article  CAS  Google Scholar 

  38. Gadaleta D, Mangiatordi GF, Catto M, Carotti A, Nicolotti O (2016) Applicability domain for QSAR models: where theory meets reality. IJQSPR 1(1):45–63

    Google Scholar 

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Acknowledgements

Special issue to Celebrate 80th Birthday of Prof Ramon Carbó-Dorca

Funding

PD thanks Indian Council of Medical Research, New Delhi, for awarding with a Senior Research Fellowship. KR thanks Science and Engineering Research Board (SERB), New Delhi, for financial assistance under the MATRICS scheme (File number MTR/2019/000008). Financial assistance from DAE-BRNS under the scheme 36 (3)/14/08/2017-BRNS is also thankfully acknowledged.

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  1. Drug Theoretics and Cheminformatics Laboratory, Department of Pharmaceutical Technology, Jadavpur University, Kolkata, 700032, India

    Priyanka De & Kunal Roy

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  1. Priyanka De
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  2. Kunal Roy
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Correspondence to Kunal Roy.

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Published as part of the special collection of articles “Festschrift in honour of Prof. Ramon Carbó-Dorca”.

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De, P., Roy, K. QSAR modeling of PET imaging agents for the diagnosis of Parkinson’s disease targeting dopamine receptor. Theor Chem Acc 139, 176 (2020). https://doi.org/10.1007/s00214-020-02687-9

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