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Structural findings of cinnolines as anti-schizophrenic PDE10A inhibitors through comparative chemometric modeling

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Abstract

Schizophrenia is a complex psychiatric disorder associated with the distortion of striatopallidal neurotransmission of central nervous system. Phosphodiesterase10A (PDE10A) enzyme plays crucial role in cellular signaling pathways in schizophrenia. Inhibition of this enzyme may facilitate better treatment of this disease. 2D-QSAR, HQSAR, pharmacophore mapping, molecular docking, and 3D-QSAR analyses were performed on 81 cinnoline derivatives having PDE10A inhibitory activity. 2D-QSAR models were developed by multiple linear regression and partial least square analyses using both atom based and whole molecular descriptors. The best model, having considerable internal (\(q^{2} = 0.812\)) and external (\({R}^{2}_{\mathrm{pred}}=0.691\)) predictabilities, demonstrated importance of atom-based topological and whole molecular E-state as well as 3D topological indices. The best HQSAR model was also found to be statistically significant (\(q^{2} = 0.664, {R}^{2}_{\mathrm{pred} }= 0.513\)) and it highlighted some important structural features. PHASE-based pharmacophore hypothesis showed the importance of three hydrogen bond acceptor and one each of ring aromatic and hydrophobic features for higher activity. 3D-QSAR CoMFA and CoMSIA models were generated on two different types of alignment procedures—(1) pharmacophore (PHASE) based and (2) docking (GLIDE) based. GLIDE-based alignment produced better results for both CoMFA (\(Q^{2} = 0.578; {R}^{2}_{\mathrm{pred}}=0.841\))and CoMSIA (\(Q^{2} = 0.610; {R}^{2}_{\mathrm{pred}}=0.824\)) methods. Molecular dynamics (MDs) simulations were performed for two ligand–receptor complexes and these simulations explored some crucial factors for higher activity. These findings of MD simulations were consistent with the interpretations obtained from other methods of analyses. The current study may help in designing new PDE10A inhibitors of this class.

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Abbreviations

\(\hbox {IC}_{50}\) :

Inhibitory activity

\(k\)-MCA:

k-Means cluster analysis

PRESS:

Predicted residual sum of squares

QSAR:

Quantitative structure–activity relationship

SDEP:

Standard deviation of error of prediction

HQSAR:

Hologram quantitative structure–activity relationship

CoMFA:

Comparative molecular field analysis

CoMSIA:

Comparative molecular similarity analysis

PDE:

Phosphodiesterase

PLS:

Partial least square

MDs:

Molecular dynamics

References

  1. Perez-Costas E, Melendez-Rerro M, Roberts R (2010) Basal ganglia pathology in schizophrenia: dopamine connections and anomalies. J Neurochem 113:287–302. doi:10.1111/j.1471-4159.2010.06604.x

    Article  CAS  PubMed Central  PubMed  Google Scholar 

  2. Winterer G, Carver FW, Musso F, Mattay V, Weinberger DR, Coppola R (2007) Complex relationship between BOLD signal and synchronization/desynchronization of human brain MEG oscillations. Hum Brain Mapp 28:805–816. doi:10.1002/hbm.20322

  3. Manallack DT, Hughes RA, Thompson PE (2005) The next generation of phosphodiesterase inhibitors: structural clues to ligand and substrate selectivity of phosphodiesterases. J Med Chem 48:3449–3462. doi:10.1021/jm040217u

    Article  CAS  PubMed  Google Scholar 

  4. Menniti FS, Faraci WS, Schmidt CJ (2006) Phosphodiesterases in the CNS: targets for drug development. Nat Rev Drug Discovery 5:660–670. doi:10.1038/nrd2058

    Article  CAS  Google Scholar 

  5. Conti M, Beavo J (2007) Biochemistry and physiology of cyclic nucleotide phosphodiesterases: essential components in cyclic nucleotide signaling. Annu Rev Biochem 76:481–511. doi:10.1146/annurev.biochem.76.060305.150444

    Article  CAS  PubMed  Google Scholar 

  6. Soderling SH, Bayuga SJ, Beavo JA (1999) Isolation and characterization of a dual-substrate phosphodiesterase gene family: PDE10A. Proc Natl Acad Sci USA 96:7071–7076. doi:10.1073/pnas.96.12.7071

    Article  CAS  PubMed Central  PubMed  Google Scholar 

  7. Nishi A, Kuroiwa M, Miller DB, O’Callaghan JP, Bateup HS, Shuto T, Sotogaku N, Fukuda T, Heintz N, Greengard P, Snyder GL (2008) Distinct roles of PDE4 and PDE10A in the regulation of cAMP/PKA signaling in the striatum. J Neurosci 28:10460–10471. doi:10.1523/JNEUROSCI.2518-08.2008

    Article  CAS  PubMed Central  PubMed  Google Scholar 

  8. Schmidt CJ, Chapin DS, Cianfrogna J, Corman ML, Hajos M, Harms JF, Hoffman WE, Lebel LA, McCarthy SA, Nelson FR, Proulx-LaFrance C, Majchrzak MJ, Ramirez AD, Schmidt K, Seymour PA, Siuciak JA, Tingley FD III, Williams RD, Verhoest PR, Menniti FS (2008) Preclinical characterization of selective phosphodiesterase 10A inhibitors: a new therapeutic approach to the treatment of schizophrenia. J Pharmacol Exp Ther 325:681–690. doi:10.1124/jpet.107.132910

    Article  CAS  PubMed  Google Scholar 

  9. Harvey RA, Champe PC (2009) In: Finkel R, Clark MA, Cubeddu LX (eds) Lippincott’s illustrated reviewes: pharmacology, 4th edn. Lippincott Williams & Wilkins, Philadelphia, PA

  10. Young RA, Ward A, Milrinone A (1988) Preliminary review of its pharmacological properties and therapeutic use. Drugs 36:158–192. doi:10.2165/00003495-198836020-00003

    Article  CAS  PubMed  Google Scholar 

  11. Hu E, Kunz RK, Rumfelt S, Chen N, Burli R, Li C, Andrews KL, Zhang J, Chmait S, Kogan J, Lindstrom M, Hitchcock SA, Treanor J (2012) Discovery of potent, selective, and metabolically stable 4-(pyridin-3-yl) cinnolines as novel phosphodiesterase 10A (PDE10A) inhibitors. Bioorg Med Chem Lett 22:2262–2265. doi:10.1016/j.bmcl.2012.01.086

    Article  PubMed  Google Scholar 

  12. Hu E, Ma J, Biorn C, Zeiner DL, Cho R, Rumfelt S, Kunz RK, Nixey T, Michelsen K, Miller S, Shi J, Wong J, Puppa GHD, Able J, Talreja S, Hwang DR, Hitchcock SA, Porter A, Immke D, Allen JR, Treanor J, Chen H (2012) Rapid identification of a novel small molecule phosphodiesterase 10A (PDE10A) tracer. J Med Chem 55:4776–4787. doi:10.1021/jm3002372

    Article  CAS  PubMed  Google Scholar 

  13. Hitchcock SA, Liu R, Arrington MP, Hopper AT, Conticello RD, Nguyen TM, Danca MD, Gauss CM (2007) Cinnoline derivatives as phosphodiesterase 10 inhibitors. US Patent 20070265270A1. 1–42

  14. Adhikari N, Halder AK, Mondal C, Jha T (2013) Exploring structural requirements of aurone derivatives as antimalarials by validated DFT-based QSAR, HQSAR, and COMFA–COMSIA approach. Med Chem Res 22:6029–6045. doi:10.1007/s00044-013-0590-8

    Article  CAS  Google Scholar 

  15. Adhikari N, Halder AK, Mondal C, Jha T (2013) Ligand based validated comparative chemometric modeling and pharmacophore mapping of aurone derivatives as antimalarial agents. Curr Comput Aided Drug Des 8:417–432. doi:10.2174/15734099113099990014

    Article  Google Scholar 

  16. Mondal C, Halder AK, Adhikari N, Jha T (2013) Cholesteryl ester transfer protein inhibitors in coronary heart disease: validated comparative QSAR modeling of \(N{,}N\)-disubstituted trifluoro-3-amino-2-propanols. Comput Biol Med 43:1545–1555. doi: 10.1016/j.compbiomed.2013.07.034

    Article  CAS  PubMed  Google Scholar 

  17. Halder AK, Saha A, Jha T (2013) Exploring QSAR and pharmacophore mapping of structurally diverse selective matrix metalloproteinase-2 inhibitors. J Pharm Pharmacol 65:1541–1554. doi:10.1111/jphp.12133

    Article  CAS  PubMed  Google Scholar 

  18. Halder AK, Saha A, Jha T (2013) Exploration of structural and physicochemical requirements and search of virtual hits for aminopeptidase N inhibitors. Mol Divers 17:123–137. doi:10.1007/s11030-013-9422-5

    Article  CAS  PubMed  Google Scholar 

  19. Adhikari N, Jana D, Halder AK, Mondal C, Maiti MK, Jha T (2012) Chemometric modeling of 5-phenylthiophenecarboxylic acid derivatives as anti-rheumatic agents. Curr Comput Aided Drug Des 8:182–195. doi:10.2174/157340912801619067

    Article  CAS  PubMed  Google Scholar 

  20. Adhikari N, Maiti MK, Jha T (2010) Exploring structural requirements of 1-N-substituted thiocarbamoyl-3-phenyl-2-pyrazolines as antiamoebic agents using comparative QSAR modelling. Bioorg Med Chem Lett 20:4021–4026. doi:10.1016/j.bmcl.2010.05.098

    Article  CAS  PubMed  Google Scholar 

  21. Halder AK, Adhikary N, Maity MK, Jha T (2010) Synthesis, pharmacological activity and comparative QSAR modeling of 1,5-\(N{,}N^\prime \)-substituted-2-(substituted naphthalenesulphonyl) glutamamides as possible anticancer agents. Eur J Med Chem 45:1760–1771. doi: 10.1016/j.ejmech.2010.01.008

    Article  CAS  PubMed  Google Scholar 

  22. Adhikari N, Maiti MK, Jha T (2010) Predictive comparative QSAR modelling of (phenylpiperazinyl-alkyl) oxindoles as selective 5-HT1A antagonists by stepwise regression, PCRA, FA-MLR and PLS techniques. Eur J Med Chem 45:1119–1127. doi:10.1016/j.ejmech.2009.12.011

    Article  CAS  PubMed  Google Scholar 

  23. Chappie TA, Helal CJ, Hou X (2012) Current landscape of phosphodiesterase 10A (PDE10A) inhibition. J Med Chem 55:7299–7331. doi:10.1021/jm30049761

    Article  CAS  PubMed  Google Scholar 

  24. Chem3D Pro Version 5.0 and ChemDraw Ultra Version 5.0 are software programs developed by Cambridge Soft Corporation, USA

  25. Ghose AK, Pritchett A, Crippen GM (1988) Atomic physicochemical parameters for three-dimensional structure-directed quantitative structure–activity relationships. III. Modeling hydrophobic interactions. J Comput Chem 9:80–90. doi:10.1002/jcc.540090111

    Article  CAS  Google Scholar 

  26. Mouse is a computer program written in \(\text{ C }^{++}\) language by Jadavpur University

  27. DRAGON Web version 2.1 is a software developed by Milano Chemometrics and QSAR Research group, Dipartimento di scienzedell’Ambiente e del Territorio Universitadegli Studi di Milano-Bicocca

  28. Accelrys Inc. (2011) Discovery Studio 3.0, San Diego, USA

  29. Sendecor GW, Cochran WG (1967) Multiple regression in statistical methods, 6th edn. Oxford & IBH, New Delhi

    Google Scholar 

  30. Hemmatateenejad B (2004) Optimal QSAR analysis of the carcinogenic activity of drugs by correlation ranking and genetic algorithm-based PCR. J Chemometr 18:475–485. doi:10.1002/cem.891

    Article  Google Scholar 

  31. Tropsha A (2003) Recent trends in quantitative structure–activity relationships. In: Abraham DJ (ed) Burger’s medicinal chemistry and drug discovery, vol 1. Wiley, Hoboken, NJ, pp 49–75

    Google Scholar 

  32. Tetko IV, Tanchuk VY, Villa AE (2001) Prediction of \(n\)-octanol/water partion coefficients from PHYSPROP database using artificial neural networks and E-state indices. J Chem Inf Comput Sci 41:1407–1421. doi: 10.1021/ci010368v

    Article  CAS  PubMed  Google Scholar 

  33. Wold S, Sjostrom M, Eriksson L (2001) PLS-regression: a basic tool of chemometrics. Chemometr Intell Lab Syst 58:109–130. doi:10.1016/S0169-7439(01)00155-1

    Article  CAS  Google Scholar 

  34. Jaiswal M, Khadikar PV, Scozzafava A, Supuran CT (2004) Carbonic anhydrase inhibitors: the first QSAR study on inhibition of tumor-associated isoenzyme IX with aromatic and heterocyclic sulfonamides. Bioorg Med Chem Lett 14:3283–3290. doi:10.1016/j.bmcl.2004.03.099

    Article  CAS  PubMed  Google Scholar 

  35. Tropsha A, Gramatica P, Gomber 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. doi:10.1002/qsar.200390007

    Article  CAS  Google Scholar 

  36. Walker JD, Jaworska J, Comber MH, Schultz TW, Dearden JC (2003) Guidelines for developing and using quantitative structure–activity relationships. Environ Toxicol Chem 22:1653–1665. doi:10.1897/01-627

    Article  CAS  PubMed  Google Scholar 

  37. Golbraikh A, Tropsha A (2002) Beware of q2!. J Mol Graph Model 20:269–276. doi:10.1016/S1093-3263(01)00123-1

    Article  CAS  PubMed  Google Scholar 

  38. Roy PP, Roy K (2008) On some aspects of variable selection for partial least squares regression models. QSAR Comb Sci 27:302–313. doi:10.1002/qsar.200710043

    Article  CAS  Google Scholar 

  39. Roy PP, Paul S, Mitra I, Roy K (2009) On two novel parameters for validation of predictive QSAR models. Molecules 14:1660–1701. doi:10.3390/molecules14051660

  40. Tong W, Lowis DR, Perkins R, Chen Y, Welsh WJ, Goddette DW, Heritage TW, Sheehan DM (1998) Evaluation of quantitative structure–activity relationship methods for large-scale prediction of chemicals binding to the estrogen receptor. J Chem Inf Comput Sci 38:669–677. doi:10.1021/ci980008g

  41. SYBYL-X 2.0 Tripos Inc 1699 South Hanley Road. St Louis, MO 63144, USA

  42. Dixon SL, Smondyrev AM, Knoll EH, Rao SN, Shaw DE, Friesner RA (2006) PHASE: a new engine for pharmacophore perception, 3D QSAR model development, and 3D database screening: 1. Methodology and preliminary results. J Comput Aided Mol Des 20:647–671. doi:10.1007/s10822-006-9087-6

    Article  CAS  PubMed  Google Scholar 

  43. Cramer RD, 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. doi:10.1021/ja00226a005

    Article  CAS  PubMed  Google Scholar 

  44. Klebe G, Abraham U, Mietzner T (1994) Molecular similarity indices in a comparative analysis (CoMSIA) of drug molecules to correlate and predict their biological activity. J Med Chem 37:4130–4146. doi:10.1021/jm00050a010

    Article  CAS  PubMed  Google Scholar 

  45. Kubinyi H (1994) Variable selection in QSAR studies. I. An evolutionary algorithm. Quant Struct Act Relat 13:285–294. doi:10.1002/qsar.19940130306

    CAS  Google Scholar 

  46. Bowers KJ, Chow E, Xu H, Dror RO, Eastwood MP, Gregersen BA, Klepeis JL, Kolossváry I, Moraes MA, Sacerdoti FD, Salmon JK, Shan Y, Shaw DE (2006) Scalable algorithms for molecular dynamics simulations on commodity clusters. In: Proceedings of the ACM/IEEE conference on supercomputing (SC06), Tampa, FL, 11–17 Nov 2006

  47. Pradhan D, Priyadarshini V, Munikumar M, Swargam S, Umamaheswari A, Bitla A (2014) Para-(benzoyl)-phenylalanine as a potential inhibitor against LpxC of \(Leptospira\) spp.: homology modeling, docking, and molecular dynamic study. J Biomol Struct Dyn 32:171–185. doi: 10.1080/07391102.2012.758056

    Article  CAS  PubMed  Google Scholar 

  48. Mohmak W, Chunsrivirot S, Assawamakin A, Choowongkomon K, Tongsima S (2013) Molecular dynamics simulations reveal structural instability of human trypsin inhibitor upon D50E and Y54H mutations. J Mol Model 19:521–528. doi:10.1007/s00894-012-1565-2

    Article  Google Scholar 

  49. Gonzalez MP, Teran C, Teijeira M, Gonzalez-Moa MJ (2005) GETAWAY descriptors to predicting A2A adenosine receptors agonists. Eur J Med Chem 40:1080–1086. doi:10.1016/j.ejmech.2005.04.014

    Article  CAS  PubMed  Google Scholar 

  50. Hall L, Kier L (2000) The E-state as the basis for molecular structure space definition and structure similarity. J Chem Inf Comput Sci 40:784–791. doi:10.1021/ci990140w

    Article  CAS  PubMed  Google Scholar 

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Acknowledgments

Authors are thankful to the All India Council for Technical Education (AICTE), New Delhi, Council for Scientific and Industrial Research (CSIR), New Delhi and University Grants Commission (UGC), New Delhi for providing financial support. One of the authors (CM) thanks University Grant Commission (UGC) for providing Rajiv Gandhi Fellowship. Two authors (AKH and NA) thank Council for Scientific and Industrial Research (CSIR), New Delhi for providing a Senior Research Fellowship (SRF). We are also thankful to the authority of Jadavpur University for providing us the facility required for the work.

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  1. Natural Science Laboratory, Division of Medicinal and Pharmaceutical Chemistry, Department of Pharmaceutical Technology, Jadavpur University, P.O. Box 17020, Kolkata , 700032, India

    Chanchal Mondal, Amit Kumar Halder, Nilanjan Adhikari & Tarun Jha

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  1. Chanchal Mondal
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Correspondence to Tarun Jha.

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Mondal, C., Halder, A.K., Adhikari, N. et al. Structural findings of cinnolines as anti-schizophrenic PDE10A inhibitors through comparative chemometric modeling. Mol Divers 18, 655–671 (2014). https://doi.org/10.1007/s11030-014-9523-9

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