Biomedical Chemistry: Research and Methods, 2018, 1(3), e00064
The 40th Anniversary of the Institute of Physiologically Active Compounds of the Russian Academy of Sciences

QSAR Modeling of the NMDA Receptor Blockage by Polypharmacophoric Compounds Based on Carbazole and 1-aminoadamantane Derivatives

V.Yu. Grigorev*, O.A. Raevsky

Institute of Physiologically Active Compounds of the Russian Academy of Sciences, 1 Severny proezd, Moscow region, Chernogolovka, 142432 Russia;*e-mail: beng@ipac.ac.ru

Key words: QSAR; NMDAR; polypharmacophoric compounds

DOI: 10.18097/BMCRM00064

The whole version of this paper is available in Russian.

We investigated 14 compounds causing the NMDA receptor blockage. These polypharmacophoric compounds are conjugates of carbazole, tetrahydrocarbazole and 1-aminoadamantane derivatives. As a measure of biological activity of the compound tested, the IC50 (μM) value, reflecting 50% inhibition of [H3] MK-801 binding to the NMDA receptor, was used. The regression model with satisfactory statistical characteristics was obtained as a result of the QSAR modeling based on the Gaussian process.

Figure 1. Structural formulas of polypharmacophoric compounds based on carbazoles (type I), tetrahydrocarbazoles (type II) and 1-aminoadamantanes derivatives.

CLOSE
Table 1.

Type and substituents (Figure 1), biological activity (-lg(IC50), μM) and descriptors (D1 ÷ D7) of compounds.

CLOSE
Table 2. Statistical characteristics of QSAR models of the NMDA receptor inhibition.

ACKNOWLEDGEMENTS

The work was performed within the framework of the state task for 2018 (the topic number 0090-2017-0020).

REFERENCES

  1. Pangalos, M.N., Schechter, L.E., & Hurko O. (2007) Drug development for CNS disorders: strategies for balancing risk and reducing attrition. Nat. Rev. Drug. Discov., 6(7), 521-532. DOI
  2. Silva, T., Reis, J., Teixeira, J., & Borges F. (2014) Alzheimer’s disease, enzyme targets and drug discovery struggles: from natural products to drug prototypes. Ageing Res. Rev., 15, 116-145. DOI
  3. Gitto, R., Luca, L.D., Ferro, S.C., Sarro, G.D., Costa, L., Ciranna, L., & Chimirri, A. (2009) Development of 3-substituted-1H-indole derivatives as NR2B/NMDA receptor antagonists. Bioorg. Med. Chem., 17, 1640-1647. DOI
  4. Fang, J., Li, Y., Liu, R., Pang, X., Li, C., Yang, R., He, Y., Lian, W., Liu, A., & Du, G. (2015) Discovery of multi-target-directed ligands against Alzheimer’s disease through systematic prediction of chemical-protein interactions. J. Chem. Inf. Model., 55(1), 149-164. DOI
  5. Raevsky, O.A., Trepalin, S.V., Grigorev, V.Yu., Yarkov, A.V., Bachurin, S.O. (2017) Certificate of state registration of the database "Multitarget organic compounds with potential effects on the central nervous system" ¹ 2017620020.
  6. HYBOT. Retrieved August 24, 2018, from http://molpro.ipac.ac.ru/hybot.html
  7. DRAGON. Retrieved August 24, 2018, from http://www.talete.mi.it/products/dragon_projects.htm
  8. Forsythe, G.E., Malcolm, M.A., Moler, C.B. (1977) Computer Methods for Mathematical Computations, Englewood Cliffs, NJ, Prentice-Hall, 227-235.
  9. RF. Retrieved August 24, 2018, from http://www.stat.berkeley.edu/~breiman/RandomForests/cc_examples/prog.f
  10. Obrezanova, O., Cs?nyi, G., Gola, J.M.R., & Segall, M.D. (2007) Gaussian processes: a method for automatic QSAR modeling of ADME properties. J. Chem. Inf. Model., 47, 1847-1857. DOI
  11. Mitra, I., Saha, A., & Roy K. (2010) Exploring quantitative structure–activity relationship studies of antioxidant phenolic compounds obtained from traditional Chinese medicinal plants. Mol. Simul., 36(13), 1067-1079. DOI
  12. Tropsha, A., Gramatica, P., & Gombar, V.K. (2003) The importance of being earnest: validation is the absolute essential for successful application and interpretation of QSPR models. QSAR Comb. Sci., 21(1), 69–77. DOI