In silico screening of brazilian semiarid compounds to identify potential drugs with glucocorticoid receptor interaction




Virtual Screening; Molecular Docking; Anti-inflammatory Potential; Glucocorticoid Receptor.


The glucocorticoid receptor regulates the anti-inflammatory response, and prevents transcription of anti-inflammatory substances such as nuclear factor kB and lipocortin-1, IL-2, IL-6, TNF and prostaglandins. Thus, a search for new molecules with potential interaction with the glucocorticoid receptor is an interesting strategy for the treatment of inflammatory diseases. Virtual screening has proven to be a viable tool for discovering new products at lower cost and practicality. Thus, the aim of this study is to identify and evaluate brazilian semiarid compounds with anti-inflammatory potential with glucocorticoid receptor action through molecular coupling. Protein selection was performed by searching the 3D structure database, Protein Data Bank. A total of 382 semi-arid molecules available in the ZINC database of State University of Feira de Santana (UEFS) were used. Molecular docking was performed using Autodock Vina and as interaction clouds analyzed by the Discovery Studio Visualizer program. Mometasone furoate shows a binding energy of -12.7 Kcal.mol-1. A ZINC 69481862 molecule fits Lipinski and Veber rules, however, the best interaction was the ZINC 69482012 molecule, evidenced by the binding energy -11.2 Kcal.mol-1. Analyses of intermolecular interactions have shown that Van der Waals interactions and electrostatic bonds are crucial for the binding of the molecule at the receptor's active site. It is necessary to test in vitro to verify the viability and toxicity of the potential drug.


Barreiro, E. J. & Bolzani, V. S. (2009). Biodiversidade: fonte potencial para a descoberta de fármacos. Química Nova Sociedade Brasileira de Química, 32 (3), 679-688.

Retrieved from

Berman, H. M., Westbrook., J, Feng Z., Gilliland G., Bhat, T. N., Weissig. H, Shindyalov, I.N & Bourne, P.E. (2000). The Protein Data Bank Nucleic Acids Research, 28, 235-242.

doi: 10.1093/nar/28.1.235

BIOVIA., Dassault Systèmes. (2016). Discovery Studio Modeling Environment, Release 2017, San Diego: Dassault Systèmes.

Retrieved from

Chen X., Carillo M., Haltiwanger, R.C. & Bradley, P. (2005). Solid state characterization of mometasone furoate anhydrous and monohydrate forms. Journal of Pharmaceutical Sciences, 94 (11), 2496–2509.

doi: 10.1002/jps.20470

Costa, J. F. O., David, J. P. L., David, J. M., Giulietti, A. M., Queiroz, L. P. & Santos, R. R. (2008). Immunomodulatory activity of extracts from Cordia superba Cham. and Cordia rufescens A. DC. (Boraginaceae), plant species native from Brazilian Semi-arid. Rev. bras. Farmacogn, 18 (1): 11-15.

Retrieved from

Costa, J. F. O., Juiz, P., São Pedro, A., David, J. P. L., David, J. M. & Giulietti, A.M. (2010) Immunomodulatory and antibacterial activities of extracts from Rutaceae species. Rev. bras. Farmacogn, 20 (4): 502-505.

Retrieved from

Derendorf, H. & Meltzer, E. O (2008). Molecular and clinical pharmacology of intranasal corticosteroids: clinical and therapeutic implications. Allergy, 63 (10): 1292–1300.

doi: 10.1111/j.1398-9995.2008.01750.x

Dey, R. & Bishayi, B. (2019). Dexamethasone exhibits its anti-inflammatory effects in S. aureus induced microglial inflammation via modulating TLR-2 and glucocorticoid receptor expression. International Immunopharmacology, (75): 105806.

doi: 10.1016/j.intimp.2019.105806

Drwal, M. N. & Griffith, R. (2013). Combination of ligand- and structure-based methods in virtual screening. Drug Discov Today Technol, 10 (3): 395-401.

doi: 10.1016/j.ddtec.2013.02.002

Faria, C. D. C. & Longui, C. A. (2006). Aspectos Moleculares da Sensibilidade aos Glicocorticoides. Arq Bras Endocricol Metab, 50 (6): 983-995.

Retrieved from

Ferreira, L. G., Santos, R. N., Oliva, G. & Andricopulo, A. D. (2005) Molecular Docking and Structure-Based Drug Design Strategies. Molecules, 20 (7):13384-13421.

doi: 10.3390/molecules200713384

Fullerton, J. N. & Gilroy, D. W. (2016). Resolution of inflammation: a new therapeutic frontier. Nature Reviews Drug Discovery, 15 (8): 551–567.

doi: 10.1038/nrd.2016.39

Irwin, J. J. & Shoichet, B. K. (2005) ZINC-a free database of commercially available compounds for virtual screening. J Chem Inf Model, 45(1):177-182.

doi: 10.1021/ci049714+

Kadmiel, M. & Cidlowski, J. A. (2013). Glucocorticoid receptor signaling in health and disease. Trends in Pharmacological Sciences, 34 (9): 518–530.

doi: 10.1016/

Kazmi, S. R., Jun, R., Yu, M. S., Jung, C. & Na, D. (2019). In silico approaches and tools for the prediction of drug metabolism and fate: A review. Computers in Biology and Medicine, 106, 54–64.

Retrieved from

Kim, S., Chen, J., Cheng, T., Gindulyte, A., He, J., He, S. & Li, Q., Shoemaker, B. A., Thiessen, P. A., Yu, B., Zaslavsky, L., Zhang, J. & Bolton, E. E. (2019). PubChem 2019 update: improved access to chemical data. Nucleic Acids Res, 8 (47): 1102-1109.

doi: 10.1093/nar/gky1033

Kumar, V. (2019). Inflammation research sails through the sea of immunology to reach immunometabolism. Int Immunopharmacol, 73:128-145.

doi: 10.1016/j.intimp.2019.05.002

Lipinski, C. A., Lombardo, F., Dominy, B. W. & Feeney, P. J. (2001). Experimental and computational approaches to estimate solubility and permeability in drug discovery and development settings. Adv Drug Deliv Rev, 46 (1-3): 3-26.

doi: 10.1016/s0169-409x(00)00129-0

Martins, A. C. V., Neto, P.L., Silva, M. G. V. & Freire, V. N. (2014). Estudo in sílico de potenciais compostos anticâncer derivados de biflorina. 37ª Reunião Anual da Sociedade Brasileira de Química, 64.

Retrieved from

Ministry of the Environment. (2020). Caatinga.

Retrieved from

Ozawa, S., Takahashi, M., Yamaotsu, N. & Hirono, S. (2019). Structure-based virtual screening for novel chymase inhibitors by in silico fragment mapping. Journal of Molecular Graphics and Modelling, 89: 102-108.

doi: 10.1016/j.jmgm.2019.03.011

Rodrigues, R. P., Mantoani, S. P., De Almeida, J. R., Pinsetta, F. R., Semighini, E. P, Da Silva, V. B. & Da Silva, C. H. P. (2012). Estratégias de Triagem Virtual no Planejamento de Fármacos. Revista Virtual de Química, 4 (6), 739-736.

doi: 10.5935/1984-6835.20120055

Sanner M. F. (1999). Python: a programming language for software integration and development. Journal of molecular graphics & modelling, 17(1), 57–61.

Retrieved from

Shityakov, S. & Foerster, C. (2014). In silico structure-based screening of versatile P-glycoprotein inhibitors using polynomial empirical scoring functions. Advances and Applications in Bioinformatics and Chemistry, 7, 1-9.

doi: 10.2147/AABC.S56046

Silva, J. D. de S., Leite, S. da C., Silva, M. T. S. da, Meirelles, L. M. A., & Andrade, A. W. L. (2020). In silico evaluation of the inhibitory effect of antiretrovirals Atazanavir and Darunavir on the main protease of SARS-CoV-2: docking studies and molecular dynamics. Research, Society and Development, 9(8), e826986562.

Sivakumar, K. C., Sajeevan, T. P. & Bright Singh, I. S. (2016). Marine derived compounds as binders of the White spot syndrome virus VP28 envelope protein: In silico insights from molecular dynamics and binding free energy calculations. Computational Biology and Chemistry, 64: 359–367.

Retrieved from

Smith, C. L. & Kreutner, W. (1998). In vitro glucocorticoid receptor binding and transcriptional activation by topically active glucocorticoids. Arzneimittelforschung, 48 (9):956-60.

Retrieved from

Torres, R. C, Insuela, D. B. R., Carvalho, V. F. (2012). Mecanismos celulares e moleculares da ação antiinflamatória dos glicocorticoides. Corpus et Scientia, 8(2):36-51.

Retrieved from

Trott, O. & Olson, A. J. (2010). AutoDock Vina: improving the speed and accuracy of docking with a new scoring function, efficient optimization and multithreading. J Comput Chem, 31:455-461.

doi: 10.1002/jcc.21334

Veber, D. F., Johnson, S. R., Cheng, H. Y., Smith, B. R., Ward, K. W. & Kopple, K. D. (2002). Molecular properties that influence the oral bioavailability of drug candidates. J Med Chem, 45 (12): 2615 – 2623.

doi: 10.1021/jm020017n

Wang, H., Aslanian, R. & Madison, V. S. (2008). Induced-fit docking of mometasone furoate and further evidence for glucocorticoid receptor 17α pocket flexibility. Journal of Molecular Graphics and Modelling, 27 (4), 512–521.

Retrieved from

Zheng, M., Liu, X., Xu, Y., Li, H., Luo, C. & Jiang, H. (2013). Computational methods for drug design and discovery: focus on China. Trends Pharmacol Sci, 34 (10):549-559.

doi: 10.1016/

Zianna, A. G. D., Geromichalos, A. P. (2019). A palladium(II) complex with the Schiff base 4-chloro-2-(N-ethyliminomethyl)-phenol: Synthesis, structural characterization, and in vitro and in silico biological activity studies, Journal of Inorganic Biochemistry, 199:110792.

doi: 10.1016/j.jinorgbio.2019.110792




How to Cite

CÔRTES FILHO, A. B.; LIMA, D. M. .; CEDRO, P. Évelin P.; MENDES, T. P. S. .; MIRANDA, A. C. dos A. .; BARRETO, M. M.; LIMA, E. R. .; NASCIMENTO JUNIOR, B. B. do .; VALASQUES JUNIOR, G. L. In silico screening of brazilian semiarid compounds to identify potential drugs with glucocorticoid receptor interaction. Research, Society and Development, [S. l.], v. 9, n. 9, p. e734997865, 2020. DOI: 10.33448/rsd-v9i9.7865. Disponível em: Acesso em: 20 may. 2024.



Exact and Earth Sciences