Skip to main content

Advertisement

Log in

Monte Carlo-based QSAR modeling of dimeric pyridinium compounds and drug design of new potent acetylcholine esterase inhibitors for potential therapy of myasthenia gravis

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

Abstract

The Monte Carlo method was used for QSAR modeling of dimeric pyridinium compounds as acetylcholine esterase inhibitors. QSAR model was developed for a series of 39 dimeric pyridinium compounds. QSAR models were calculated with the representation of the molecular structure by the simplified molecular-input line-entry system. One split into the training and test set have been examined. The statistical quality of the developed model is very good. The calculated model for dimeric pyridinium derivatives had following statistical parameters: r 2 = 0.9477 for the training set and r 2 = 0.9332 the test set. Structural indicators considered as molecular fragments responsible for the increase and decrease in the inhibition activity have been defined. The computer-aided design of new dimeric pyridinium compounds potential acetylcholine esterase inhibitors with the application of defined structural alerts has been presented.

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

Access this article

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

Instant access to the full article PDF.

Fig. 1
Fig. 2
Fig. 3
Fig. 4
Fig. 5

Similar content being viewed by others

References

  1. Giacobini E (2008) Cholinesterase inhibitors: new roles and therapeutic alternatives. Pharmacol Res 50:433–440

    Article  Google Scholar 

  2. Millard CB, Broomfield CA (1995) Anticholinesterases: medical applications of neurochemical principles. J Neurochem 64:1909–1918

    Article  CAS  Google Scholar 

  3. Bevan DR, Donati F, Kopman AF (1992) Reversal of neuromuscular blockade. Anesthesiology 77:785–805

    Article  CAS  Google Scholar 

  4. Drachman DB (1994) Myasthenia gravis. N Engl J Med 330:1797–1810

    Article  CAS  Google Scholar 

  5. Richman DP, Agius MA (2003) Treatment of autoimmune myasthenia gravis. Neurology 61:1652–1661

    Article  CAS  Google Scholar 

  6. Lindstrom JM (2000) Acetylcholine receptors and myasthenia. Muscle Nerve 23:453–477

    Article  CAS  Google Scholar 

  7. Vincent A, Palace J, Hilton-Jones D (2001) Myasthenia gravis. Lancet 357:2122–2128

    Article  CAS  Google Scholar 

  8. Conti-Fine BM, Milani M, Kaminski HJ (2006) Myasthenia gravis: past, present, and future. J Clin Invest 116:2843–2854

    Article  CAS  Google Scholar 

  9. Rubin DI, Hentschel K (2007) Is exercise necessary with repetitive nerve stimulation in evaluating patients with suspected myasthenia gravis? Muscle Nerve 35:103–106

    Article  Google Scholar 

  10. Lucia A, Maté-Muñoz JL, Pérez M, Foster C, Gutiérrez-Rivas E, Arenas J (2007) Double trouble (McArdle’s disease and myasthenia gravis): How can exercise help? Muscle Nerve 35:125–128

    Article  CAS  Google Scholar 

  11. Leigh P, Abrahams S, Al-Chalabi A, Ampong M, Goldstein L, Johnson J, Lyall R, Moxham J, Mustfa N, Rio A, Shaw C, Willey E (2003) The management of motor neurone disease. J Neurol Neurosurg Psychiatry 74(Suppl 4):iv32–iv47

    Google Scholar 

  12. Juel VC, Massey JM (2005) Autoimmune Myasthenia Gravis: recommendations for treatment and immunologic modulation. Curr Treat Options Neurol 7:3–14

    Article  Google Scholar 

  13. Froelich J, Eagle CJ (1996) Anaesthetic management of a patient with myasthenia gravis and tracheal stenosis. Can J Anaesth 43:84–89

    Article  CAS  Google Scholar 

  14. Juel VC, Massey JM (2007) Myasthenia gravis. Orphanet J Rare Dis 2:44

    Article  Google Scholar 

  15. Hansch C, Hoekman D, Gao H (1996) Comparative QSAR: toward a deeper understanding of chemicobiological interactions. Chem Rev 96:1045–1076

    Article  CAS  Google Scholar 

  16. Cherkasov A, Muratov EN, Fourches D, Varnek A, Baskin II, Cronin M, Dearden J, Gramatica P, Martin YC, Todeschini R, Consonni V, Kuz’Min VE, Cramer R, Benigni R, Yang C, Rathman J, Terfloth L, Gasteiger J, Richard A, Tropsha A (2014) QSAR modeling: Where have you been? Where are you going to? J Med Chem 57:4977–5010

    Article  CAS  Google Scholar 

  17. Tropsha A, Golbraikh A (2007) Predictive QSAR modeling workflow, model applicability domains, and virtual screening. Curr Pharm Des 13:3494–3504

    Article  CAS  Google Scholar 

  18. Cronin MTD, Schultz TW (2003) Pitfalls in QSAR. J Mol Struct-THEOCHEM 622:39–51

    Article  CAS  Google Scholar 

  19. Duchowicz PR, Comelli NC, Ortiz EV, Castro EA (2012) QSAR study for carcinogenicity in a large set of organic compounds. Curr Drug Saf 7:282–288

    Article  CAS  Google Scholar 

  20. Talevi A, Bellera CL, Ianni MD, Duchowicz PR, Bruno-Blanch LE, Castro EA (2012) An integrated drug development approach applying topological descriptors. Curr Comput Aided Drug Des 8:172–181

    Article  CAS  Google Scholar 

  21. Randic M, Basak SC (2010) New descriptor for structure-property and structure-activity correlations. J Chem Inf Comput Sci 41:650–656

    Article  Google Scholar 

  22. da Silva Junkes B, Arruda ACS, Yunes RA, Porto LC, Heinzen VEF (2005) Semi-empirical topological index: a tool for QSPR/QSAR studies. J Mol Model 11:128–134

    Article  Google Scholar 

  23. Ivanciuc O (2013) Chemical graphs, molecular matrices and topological indices in chemoinformatics and quantitative structure–activity relationships. Curr Comput Aided Drug Des 9:153–163

    Article  CAS  Google Scholar 

  24. Weininger D (1988) SMILES, a chemical language and information system. 1. Introduction to methodology and encoding rules. J Chem Inf Comput Sci 28:31–36

    Article  CAS  Google Scholar 

  25. Weininger D, Weininger A, Weininger JL (1989) SMILES. 2. Algorithm for generation of unique SMILES notation. J Chem Inf Comput Sci 29:97–101

    Article  CAS  Google Scholar 

  26. Weininger D (1990) SMILES. 3. Depict. Graphical depiction of chemical structures. J Chem Inf Comput Sci 30:237–243

    Article  CAS  Google Scholar 

  27. Toropov AA, Benfenati E (2007) SMILES in QSPR/QSAR modeling: results and perspectives. Curr Drug Discov Technol 4:77–116

    Article  CAS  Google Scholar 

  28. Toropov AA, Benfenati E (2007) SMILES as an alternative to the graph in QSAR modelling of bee toxicity. Comput Biol Chem 31:57–60

    Article  CAS  Google Scholar 

  29. Veselinović AM, Veselinović JB, Živković JV, Nikolić GM (2015) Application of SMILES notation based optimal descriptors in drug discovery and design. Curr Top Med Chem 15:1768–1779

    Article  Google Scholar 

  30. Conejo-García A, Pisani L, Del Carmen Núñez M, Catto M, Nicolotti O, Leonetti F, Campos JM, Gallo MA, Espinosa A, Carotti A (2011) Homodimeric bis-quaternary heterocyclic ammonium salts as potent acetyl- and butyrylcholinesterase inhibitors: a systematic investigation of the influence of linker and cationic heads over affinity and selectivity. J Med Chem 54:2627–2645

    Article  Google Scholar 

  31. Ojha PK, Roy K (2011) Comparative QSARs for antimalarial endochins: importance of descriptor-thinning and noise reduction prior to feature selection. Chemometr Intell Lab Syst 109:146–161

    Article  CAS  Google Scholar 

  32. Toropova AP, Toropov AA, Benfenati E, Gini G, Leszczynska D, Leszczynski J (2011) CORAL: quantitative structure-activity relationship models for estimating toxicity of organic compounds in rats. J Comput Chem 32:2727–2733

    Article  CAS  Google Scholar 

  33. Toropov AA, Toropova AP, Puzyn T, Benfenati E, Gini G, Leszczynska D, Leszczynski J (2013) QSAR as a random event: modeling of nanoparticles uptake in PaCa2 cancer cells. Chemosphere 92:31–37

    Article  CAS  Google Scholar 

  34. Roy K (2007) On some aspects of validation of predictive quantitative structure activity relationship models. Expert Opin Drug Dis 2:1567–1577

    Article  CAS  Google Scholar 

  35. Roy PP, Leonard JT, Roy K (2008) Exploring the impact of the size of training sets for the development of predictive QSAR models. Chemometr Intell Lab Syst 90:31–42

    Article  CAS  Google Scholar 

  36. Gramatica P (2007) Principles of QSAR models validation: internal and external. QSAR Comb Sci 26:694–701

    Article  CAS  Google Scholar 

  37. Toropov AA, Toropova AP, Lombardo A, Roncaglioni A, Benfenati E, Gini G (2011) CORAL: building up the model for bioconcentration factor and defining it’s applicability domain. Eur J Med Chem 46:1400–1403

    Article  CAS  Google Scholar 

  38. Roy K, Mitra I, Kar S, Ojha PK, Das RN, Kabir H (2012) Comparative studies on some metrics for external validation of QSPR models. J Chem Inf Model 52:396–408

    Article  CAS  Google Scholar 

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

    Article  CAS  Google Scholar 

  40. Ojha PK, Roy K (2011) Comparative QSARs for antimalarial endochins: importance of descriptor-thinning and noise reduction prior to feature selection. Chemometr Intell Lab 109:146–161

    Article  CAS  Google Scholar 

  41. Toropova AP, Toropov AA, Veselinović JB, Miljković FN, Veselinović AM (2014) QSAR models for HEPT derivates as NNRTI inhibitors based on Monte Carlo method. Eur J Med Chem 77:298–305

    Article  CAS  Google Scholar 

  42. Veselinović AM, Milosavljević JB, Toropov AA, Nikolić GM (2013) SMILES-based QSAR model for arylpiperazines as high-affinity 5-HT1A receptor ligands using CORAL. Eur J Pharm Sci 48:532–541

    Article  Google Scholar 

Download references

Acknowledgments

We would like to thank reviewers whose suggestions have improved our manuscript. This work has been supported by the Ministry of Education and Science, the Republic of Serbia, under Project Number 43012.

Author information

Authors and Affiliations

Authors

Corresponding author

Correspondence to Aleksandar M. Veselinović.

Ethics declarations

Conflict of interest

All authors declare that they have no conflict of interest.

Electronic supplementary material

Below is the link to the electronic supplementary material.

Supplementary material 1 (DOCX 75 kb)

Rights and permissions

Reprints and permissions

About this article

Check for updates. Verify currency and authenticity via CrossMark

Cite this article

Sokolović, D., Stanković, V., Toskić, D. et al. Monte Carlo-based QSAR modeling of dimeric pyridinium compounds and drug design of new potent acetylcholine esterase inhibitors for potential therapy of myasthenia gravis . Struct Chem 27, 1511–1519 (2016). https://doi.org/10.1007/s11224-016-0776-z

Download citation

  • Received:

  • Accepted:

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.1007/s11224-016-0776-z

Keywords

Navigation