QSAR analysis and molecular docking study of pyrrolo- and pyridoquinolinecarboxamides with diuretic activity





molecular descriptors, quantitative structure-activity relationship (QSAR), molecular docking, diuretic activity, quinolones, carboxamides, tricyclic heterocycles


The aim. The aim of the study was to reveal QSAR and ascertain the possible mechanism of action via docking study in the row of tricyclic quinoline derivatives with diuretic activity.

Materials and methods. Pyrrolo- and pyridoquinolinecarboxamides with proven diuretic activity were involved in the study. Molecular descriptors were calculated using HyperChem and GRAGON software, and QSAR models were built using BuildQSAR software. For receptor-oriented flexible docking, the Autodock 4.2 software package was used.

Results. Multivariate linear QSAR models were built on two datasets of quinolinecarboxamides: Vol = a∙X1 + b∙X2 + c∙X3 + d, where Vol – volume of the daily produced urine in rats, Xi – molecular descriptor. QSAR analysis showed that the diuretic activity is determined by the geometric and spatial structure of molecules, logP, the energy values, RDF- and 3D-MoRSE-descriptors. Based upon internal and external validation of the models, the most informative two-parameter linear QSAR model was proposed. Docking data showed the high affinity of two lead compounds to the carbonic anhydrase II.

Conclusions. QSAR analysis of tricyclic quinoline derivatives revealed that the diuretic activity increases with the increase of value of logP, refractivity, and dipole moment and with the decrease of volume, surface area, and polarization of the molecules. Increase of values of such energy descriptors as bonds energy, core-core interaction, and energy of the highest occupied molecular orbital results in higher diuresis; decrease in hydration energy leads to higher diuretic activity. Based upon molecular docking calculation, the mechanism of diuretic action is proposed to be carbonic anhydrase inhibition.

QSAR models and docking data are useful for in-depth study of diuretic activity of tricyclic quinolines and could be a theoretical basis for de novo-design of new diuretics

Author Biographies

Mykola Golik, National University of Pharmacy

PhD, Associate Professor Department of Inorganic and Physical Chemistry

Tetiana Titko, National University of Pharmacy

PhD, Associate Professor Department of Medicinal Chemistry

Angelina Shaposhnyk, V. N. Karazin Kharkiv National University

PhD, Associate Professor

Department of Natural Disciplines

International Education Institute for Study and Research 

Marharyta Suleiman, National University of Pharmacy

PhD, Assistant

Department of Medicinal Chemistry

Iryna Drapak, Danylo Halytsky Lviv National Medical University

Doctor of Pharmaceutical Sciences, Professor

Department of General, Bioinorganic, Physical and Colloidal Chemistry

Irina Sych, National University of Pharmacy

PhD, Associate Professor

Department of Medicinal Chemistry

Lina Perekhoda, National University of Pharmacy

Doctor of Pharmaceutical Sciences, Professor

Department of Medicinal Chemistry


  1. Cherkasov, A., Muratov, E. N., Fourches, D., Varnek, A., Baskin, I. I., Cronin, M. et. al. (2014). QSAR Modeling: Where Have You Been? Where Are You Going To? Journal of Medicinal Chemistry, 57 (12), 4977–5010. doi: http://doi.org/10.1021/jm4004285
  2. Neves, B. J., Braga, R. C., Melo-Filho, C. C., Moreira-Filho, J. T., Muratov, E. N., Andrade, C. H. (2018). QSAR-Based Virtual Screening: Advances and Applications in Drug Discovery. Frontiers in Pharmacology, 9. doi: http://doi.org/10.3389/fphar.2018.01275
  3. Wang, T., Wu, M.-B., Lin, J.-P., Yang, L.-R. (2015). Quantitative structure–activity relationship: promising advances in drug discovery platforms. Expert Opinion on Drug Discovery, 10(12), 1283–1300. doi: http://doi.org/10.1517/17460441.2015.1083006
  4. Tandon, H., Chakraborty, T., Suhag, V. (2019). A Concise Review on the Significance of QSAR in Drug Design. Chemical and Biomolecular Engineering, 4 (4), 45–51. doi: http://doi.org/10.11648/j.cbe.20190404.11
  5. Perekhoda, L. A. (2013). Quantitative Analysis of the Structure – Anticonvulsant Activity Relationship in Series of 1,2,3-Triazole(1H), 1,2,4-Triazole(4H), 1,3,4-Oxadiazole(1H), and 1,3,4-Thiadiazole(1H) Derivatives. Pharmaceutical Chemistry Journal, 47 (11), 42–44.
  6. Perekhoda, L., Drapak, I., Sych, І., Tsapko, Т. (2016). (2016). In silico approaches for rational design of potential anticonvulsants among 5-substituted 2-(R-amino)-1,3,4-thiadiazoles. ScienceRise, 2 (4 (19)), 44–50. doi: http://doi.org/10.15587/2313-8416.2016.61078
  7. Huang, H.-J., Chetyrkina, M., Wong, C.-W., Kraevaya, O. A., Zhilenkov, A. V., Voronov, I. I. et. al. (2021). Identification of potential descriptors of water-soluble fullerene derivatives responsible for antitumor effects on lung cancer cells via QSAR analysis. Computational and Structural Biotechnology Journal, 19, 812–825. doi: http://doi.org/10.1016/j.csbj.2021.01.012
  8. Tejera, E., Munteanu, C. R., López-Cortés, A., Cabrera-Andrade, A., Pérez-Castillo, Y. (2020). Drugs Repurposing Using QSAR, Docking and Molecular Dynamics for Possible Inhibitors of the SARS-CoV-2 Mpro Protease. Molecules, 25 (21), 5172. doi: http://doi.org/10.3390/molecules25215172
  9. Hadavand Mirzaei, H., Jassbi, A. R., Pirhadi, S., Firuzi, O. (2020). Study of the mechanism of action, molecular docking, and dynamics of anticancer terpenoids from Salvia lachnocalyx. Journal of Receptors and Signal Transduction, 40 1), 24–33. doi: http://doi.org/10.1080/10799893.2019.1710847
  10. Vilar, S., Costanzi, S. (2012). Predicting the biological activities through QSAR analysis and docking-based scoring. Methods in molecular biology, 914, 271–284. doi: http://doi.org/10.1007/978-1-62703-023-6_16
  11. Roush, G. C., Sica, D. A. (2016). Diuretics for Hypertension: A Review and Update. American Journal of Hypertension, 29 (10), 1130–1137. doi: http://doi.org/10.1093/ajh/hpw030
  12. Li, X., Liao, J., Jiang, Z., Liu, X., Chen, S., He, X. et. al. (2020). A concise review of recent advances in anti-heart failure targets and its small molecules inhibitors in recent years. European Journal of Medicinal Chemistry, 186, 111852. doi: http://doi.org/10.1016/j.ejmech.2019.111852
  13. Sica, D. A. (2011). Diuretic use in renal disease. Nature Reviews Nephrology, 8 (2), 100–109. doi: http://doi.org/10.1038/nrneph.2011.175
  14. Burnier, M., Bakris, G., Williams, B. (2019). Redefining diuretics use in hypertension: why select a thiazide-like diuretic? Journal of Hypertension, 37 (8), 1574–1586. doi: http://doi.org/10.1097/hjh.0000000000002088
  15. Alzghari, S. K., Rambaran, K. A., Ray, S. D. (2020). Diuretics. Side Effects of Drugs Annual, 42, 227–237. doi: http://doi.org/10.1016/bs.seda.2020.07.005
  16. Bowman, B. N., Nawarskas, J. J., Anderson, J. R. (2016). Treating Diuretic Resistance. Cardiology in Review, 24 (5), 256–260. doi: http://doi.org/10.1097/crd.0000000000000116
  17. Titko, T., Perekhoda, L., Drapak, I., Tsapko, Y. (2020). Modern trends in diuretics development. European Journal of Medicinal Chemistry, 208, 112855. doi: http://doi.org/10.1016/j.ejmech.2020.112855
  18. Honndorf, V. S., Heine, A., Klebe, G., Supuran, C. T. (2006). carbonic anhydrase II in complex with furosemide as sulfonamide inhibitor. doi: http://doi.org/10.2210/pdb1z9y/pdb
  19. Supuran, C. T., De Simone, G. (Ed.) (2015). Carbonic Anhydrases as Biocatalysts. Elsevier, 398. doi: http://doi.org/10.1016/c2012-0-13548-1
  20. Ukrainets, I., Golik, M., Sidorenko, L., Korniyenko, V., Grinevich, L., Sim, G., Kryvanych, O. (2018). The Study of the Structure—Diuretic Activity Relationship in a Series of New N-(Arylalkyl)-6-hydroxy-2-methyl-4-oxo-2,4-dihydro-1H-pyrrolo-[3,2,1-ij]quinoline-5-carboxamides. Scientia Pharmaceutica, 86 (3), 31. doi: http://doi.org/10.3390/scipharm86030031
  21. Ukrainets, I., Sidorenko, L., Golik, M., Chernenok, I., Grinevich, L., Davidenko, A. (2018). N-Aryl-7-hydroxy-5-oxo-2,3-dihydro-1H,5H-pyrido-[3,2,1-ij]quinoline-6-carboxamides. The Synthesis and Effects on Urinary Output. Scientia Pharmaceutica, 86 (2), 12. doi: http://doi.org/10.3390/scipharm86020012
  22. de Oliveira, D. B., Gaudio, A. C. (2000). BuildQSAR: A New Computer Program for QSAR Analysis. Quantitative Structure-Activity Relationships, 19(6), 599–601. doi: http://doi.org/10.1002/1521-3838(200012)19:6<599::aid-qsar599>3.0.co;2-b
  23. Semenets, A., Suleiman, M., Georgiyants, V., Kovalenko, S., Kobzar, N., Grinevich, L. et. al. (2020). Theoretical justification of a purposeful search of potential neurotropic drugs. ScienceRise: Pharmaceutical Science, 4 (26), 4–17. doi: http://doi.org/10.15587/2519-4852.2020.210042
  24. Hehre, W. J. (2003). A Guide to Molecular Mechanics and Quantum Chemical Calculations. Irvine: Wavefunction, 796.
  25. Chemistry Software, HyperChem, Molecular Modeling. Available at: http://www.hyper.com/
  26. Todeschini, R., Consonni, V. (2009). Molecular Descriptors for Chemoinformatics. Molecular Descriptors for Chemoinformatics. doi: http://doi.org/10.1002/9783527628766
  27. Patel, S. R., Gangwal, R., Sangamwar, A. T., Jain, R. (2015). Synthesis, biological evaluation and 3D QSAR study of 2,4-disubstituted quinolines as anti-tuberculosis agents. European Journal of Medicinal Chemistry, 93, 511–522. doi: http://doi.org/10.1016/j.ejmech.2015.02.034
  28. Wang, J., Zhao, C., Tu, J., Yang, H., Zhang, X., Lv, W., Zhai, H. (2018). Design of novel quinoline-aminopiperidine derivatives as Mycobacterium tuberculosis (MTB) GyrB inhibitors: an in silico study. Journal of Biomolecular Structure and Dynamics, 37 (11), 2913–2925. doi: http://doi.org/10.1080/07391102.2018.1498806
  29. Jiménez Villalobos, T. P., Gaitán Ibarra, R., Montalvo Acosta, J. J. (2013). 2D, 3D-QSAR and molecular docking of 4(1H)-quinolones analogues with antimalarial activities. Journal of Molecular Graphics and Modelling, 46, 105–124. doi: http://doi.org/10.1016/j.jmgm.2013.10.002
  30. Karnik, K. S., Sarkate, A. P., Tiwari, S. V., Azad, R., Burra, P. V. L. S., Wakte, P. S. (2021). Computational and Synthetic approach with Biological Evaluation of Substituted Quinoline derivatives as small molecule L858R/T790M/C797S triple mutant EGFR inhibitors targeting resistance in Non-Small Cell Lung Cancer (NSCLC). Bioorganic Chemistry, 107, 104612. doi: http://doi.org/10.1016/j.bioorg.2020.104612
  31. Metelytsia, L., Hodyna, D., Dobrodub, I., Semenyuta, I., Zavhorodnii, M., Blagodatny, V. et. al. (2020). Design of (quinolin-4-ylthio)carboxylic acids as new Escherichia coli DNA gyrase B inhibitors: machine learning studies, molecular docking, synthesis and biological testing. Computational Biology and Chemistry, 85, 107224. doi: http://doi.org/10.1016/j.compbiolchem.2020.107224




How to Cite

Golik, M., Titko, T., Shaposhnyk, A., Suleiman, M., Drapak, I., Sych, I., & Perekhoda, L. (2021). QSAR analysis and molecular docking study of pyrrolo- and pyridoquinolinecarboxamides with diuretic activity. ScienceRise: Pharmaceutical Science, (3(31), 19–27. https://doi.org/10.15587/2519-4852.2021.234493



Pharmaceutical Science