Predição in silico do metabolismo de fase 1 da 6b,7-dihidro-5H-ciclopenta[b]nafto[2,1-d]furano-5,6 (9aH)-diona
DOI:
https://doi.org/10.53660/539.prw2005Palavras-chave:
Triagem virtual, Ancoragem molecular, Citocromo P450Resumo
O desenvolvimento de um medicamento é um processo desafiador e caro que requer a avaliação das propriedades farmacocinéticas, toxicológicas e moleculares dos candidatos a medicamentos para obter sucesso. Nesse sentido, modelos de metabolismo de drogas in silico são utilizados como ferramentas para avaliar os mecanismos envolvidos no metabolismo de candidatos a fármacos, como 6b,7-dihidro-5H-ciclopenta[b]nafto[2,1-d]furan-5,6(9aH)-diona (CNFD), uma naftoquinona semissintética com propriedades antitumorais promissoras contra células de câncer de mama e melanoma. Este estudo explora os parâmetros farmacocinéticos e as vias de metabolização da CNFD usando métodos in silico. O software de previsão in silico é usado para avaliar a especificidade do citocromo P450 e os locais de metabolismo, bem como modelos de docking molecular via autodock vina. Os resultados revelam que o CNFD tem uma alta taxa de absorção intestinal (97%) e forte ligação às proteínas plasmáticas (93%) sem inibir a glicoproteína-P. Além disso, exibiu especificidade e complementaridade na inibição de CYP1A2, 2C19 e 2C9, e é provável que sofra reações de metabolismo de fase 1, particularmente no carbono 13.
Downloads
Referências
ALMEIDA, P. D. O. et al. A new synthetic antitumor naphthoquinone induces ROS-mediated apoptosis with activation of the JNK and p38 signaling pathways. Chemico-Biological Interactions, v. 30, n. 343, 2021. https://doi.org/10.1016/j.cbi.2021.109444
ANDRADE, E. L. et al. Non-clinical studies in the process of new drug development - Part II: Good laboratory practice, metabolism, pharmacokinetics, safety and dose translation to clinical studies. Brazilian Journal of Medical and Biological Research,
v. 49, n. 12, 12 dez. 2016. https://doi.org/10.1590/1414-431X20165646
BANERJEE, P. et al. SuperCYPsPred-a web server for the prediction of cytochrome activity. Nucleic acids research, v. 48, n. W1, p. W580–W585, 2020. https://doi.org/10.1093/nar/gkaa166
CHAGAS, F. P. DE A. Computational analysis of eugenol inhibitory activity in lipoxygenase and cyclooxygenase pathways. Scientific Reports, v. 10, n. 1, p. 16204, 2020. https://doi.org/10.1038/s41598-020-73203-z
CHEN, J. et al. Drug discovery and drug marketing with the critical roles of modern administration. American Journal of Translational Research, v. 10, n. 12, p. 4302–4312, 2018. https://pubmed.ncbi.nlm.nih.gov/30662672/
CHENG, F. et al. admetSAR: a comprehensive source and free tool for assessment of chemical ADMET properties. Journal of Chemical Information and Modeling, v. 52, n. 11, p. 3099-3105, 2012. https://doi.org/10.1021/ci300367a
COSTA, E. M. A. et al. CYP450 Metabolism of a Semisynthetic Naphthoquinone, an Anticancer Drug Candidate, by Human Liver Microsomes. Journal of the Brazilian Chemical Society, v. 33, n. 10, 2022. https://doi.org/10.21577/0103-5053.20220034
DAINA, A.; MICHIELIN, O.; ZOETE, V. SwissADME: a free web tool to evaluate pharmacokinetics, drug-likeness and medicinal chemistry friendliness of small molecules. Scienific Reports, v. 3, n.7, p. 42717, 2017. https://doi.org/10.1038/srep42717
DOLABELLA, M. F. et al. Estudo in silico das atividades de triterpenos e iridoides isolados de Himatanthus articulatus (Vahl) Woodson. Revista Fitos, v. 12, n. 3, 2018. http://revistafitos.far.fiocruz.br/index.php/revista-fitos/article/view/602
DU, X. et al. Insights into Protein–Ligand Interactions: Mechanisms, Models, and Methods. International Journal of Molecular Sciences, v. 17, n. 2, 2016. https://doi.org/10.3390/ijms17020144
FILIMONOV, D. A. et al. Computer-aided estimation of biological activity profiles of drug-like compounds taking into account their metabolism in human body. International Journal of Molecular Sciences, v. 21, n. 20, 2020. https://doi.org/10.3390/ijms21207492
FREIRE, C. P. V. et al. Synthesis and biological evaluation of substituted α- and β-2,3-dihydrofuran naphthoquinones as potent anticandidal agents. MedChemComm, v. 1, n. 3, p. 229, 2010. https://doi.org/10.1039/C0MD00074D
GUAN, L. et al. ADMET-score – a comprehensive scoring function for evaluation of chemical drug-likeness. Medchemcomm, v. 10, n. 1, p. 148-157, 2019. https://doi.org/10.1039%2Fc8md00472b
HENNEMANN, M. et al. CypScore: Quantitative prediction of reactivity for cytochromes P450 based on semi-empirical molecular orbital theory. ChemMedChem, v.4, n. 4, p. 657-669, 2009. https://doi.org/10.1002/cmdc.200800384
ISSA, N. T. et al. Drug Metabolism in Preclinical Drug Development: A Survey of the Discovery Process, Toxicology, and Computational Tools. Current Drug Metabolism,
v. 18, n. 6, p. 556, 2017. https://doi.org/10.2174%2F1389200218666170316093301
JONES, J. P. et al. Computational models for cytochrome P450: a predictive electronic model for aromatic oxidation and hydrogen atom abstraction. Drug Metabolism & Disposition, v. 30, n. 1, p. 7-12, 2002. https://doi.org/10.1124/dmd.30.1.7
JUVONEN, R. O. et al. Substrate Selectivity of Coumarin Derivatives by Human CYP1 Enzymes: In Vitro Enzyme Kinetics and In Silico Modeling. ACS Omega, v. 6, n. 17, p. 11286-11296, 2021. https://doi.org/10.1021/acsomega.1c00123
MAIA, W. M. N. et al. Antidepressant activity of rose oxide essential oil: possible involvement of serotonergic transmission. Heliyon, v. 7, n. 4, 2021. https://doi.org/10.1016%2Fj.heliyon.2021.e06620
MOHS, R. C.; GREIG, N. H. Drug discovery and development: Role of basic biological research. Alzheimer’s and Dementia: Translational Research and Clinical Interventions, v. 3, n. 4, p. 651-657, 2017. https://doi.org/10.1016%2Fj.trci.2017.10.005
NEMBRI, S. et al. In silico prediction of cytochrome P450-drug interaction: QSARs for CYP3A4 and CYP2C9. International Journal of Molecular Sciences, v. 6, n. 17, p. 914, 2016. https://doi.org/10.3390/ijms17060914
OLSEN, L. et al. SMARTCyp 3.0: Enhanced Cytochrome P450 Metabolism Site Prediction Server. Bioinformatics, v. 35, n. 17, p. 3174-3175, 2019. https://doi.org/10.1093/bioinformatics/btz037
PANTALEÃO, S. Q. et al. Recent Advances in the Prediction of Pharmacokinetics Properties in Drug Design Studies: A Review. ChemMedChem, v. 17, n. 1, 2022. https://doi.org/10.1002/cmdc.202100542
PANTSAR, T.; POSO, A. Binding affinity via docking: fact and fiction. Molecules, v. 23, n. 8, 2018. https://doi.org/10.3390/molecules23081899
PARIKH, S. J. et al. Structure of Cytochrome P450 2C9*2 in Complex with Losartan: Insights into the Effect of Genetic Polymorphism. Molecular Pharmacology, v. 98, n. 5, p. 529-539, 2020. https://doi.org/10.1124/molpharm.120.000042
PINZI, L.; RASTELLI, G. Molecular docking: shifting paradigms in drug discovery. International Journal of Molecular Sciences, v. 20, n. 18, p. 4331, 2019. https://doi.org/10.3390/ijms20184331
PIRES, C. L. et al. Re-Use of Caco-2 Monolayers in Permeability Assays-Validation Regarding Cell Monolayer Integrity. Pharmaceutics, v. 13, n. 10, p. 1563, 2021. https://doi.org/10.3390%2Fpharmaceutics13101563
POTEGA, A. Glutathione-Mediated Conjugation of Anticancer Drugs: An Overview of Reaction Mechanisms and Biological Significance for Drug Detoxification and Bioactivation. Molecules, v. 27, n. 16, 2022. https://doi.org/10.3390/molecules27165252
RYDBERG, P. et al. SMARTCyp: A 2D Method for prediction of Cytochrome P450-mediated drug metabolism. ACS Medicinal Chemistry Letters, v. 1, n. 3, p. 96-100, 2010. https://doi.org/10.1021%2Fml100016x
RUDIK, A. et al. SOMP: Web server for in silico prediction of sites of metabolism for
drug-like compounds. Bioinformatics, v. 31, n. 12, p. 2046–2048, 2015. https://doi.org/10.1093/bioinformatics/btv087
SANSEN, S. et al. Adaptations for the Oxidation of Polycyclic Aromatic Hydrocarbons Exhibited by the Structure of Human P450 1A2. Journal of Biological Chemistry, v. 282, n. 19, p. 14348-14355, 2007. https://doi.org/10.1074/jbc.M611692200
SHIVANIKA, C. et al. Molecular docking, validation, dynamics simulations, and pharmacokinetic prediction of natural compounds against the SARS-CoV-2 main-protease. Journal of Biomolecular Structure and Dynamics, p. 1-27, 2020. https://doi.org/10.1080%2F07391102.2020.1815584
SUN, D. et al. Why 90% of clinical drug development fails and how to improve it? Acta Pharmaceutica Sinica B, v. 12, n. 7, p. 3049-3062, 2022. https://doi.org/10.1016/j.apsb.2022.02.002
TESSEROMATIS, C.; ALEVIZOU, A. The role of the protein-binding on the mode of drug action as well the interactions with other drugs. European Journal of Drug Metabolism and Pharmacokinetics, v. 33, n. 4, p. 225-230, 2008. https://doi.org/10.1007/bf03190876
TROTT, O.; OLSON, A. J. AutoDock Vina: improving the speed and accuracy of docking with a new scoring function, efficient optimization, and multithreading. Journal of computational chemistry, v. 31, n. 2, p. 455–461, 2010. https://doi.org/10.1002%2Fjcc.21334
VAVOUGIOS, G. D. et al. Novel candidate genes of the PARK7 interactome as mediators of apoptosis and acetylation in multiple sclerosis: An in silico analysis. Multiple Sclerosis and Related Disorders, v. 18, p. 8-14, 2018. https://doi.org/10.1016/j.msard.2017.10.013
YEE, S. In vitro permeability across Caco-2 cells (colonic) can predict in vivo (small intestinal) absorption of man-fact or myth. Pharmaceutical Research, v. 14, n. 6, p. 763-766, 1997. https://doi.org/10.1023/a:1012102522787
WÓJCIKOWSKI, J. et al. In vitro inhibition of human cytochrome P450 enzymes by the novel atypical antipsychotic drug asenapine: a prediction of possible drug–drug interactions. Pharmacological Reports, v. 72, n. 3, p. 612–621, 2020. https://doi.org/10.1007/s43440-020-00089-z
ZHAO, Y. H. et al. Evaluation of human intestinal absorption data and subsequent derivation of a quantitative structure-activity relationship (QSAR) with the Abraham descriptors. Journal of Pharmaceutical Sciences, v. 90, n. 6, p. 749-784, 2001. https://doi.org/10.1002/jps.1031
ZHANG, B. et al. Molecular docking-based computational platform for high-throughput virtual screening. CCF Transactions on High Performance Computing, v. 4, p. 63-74, 2022. https://doi.org/10.1007/s42514-021-00086-5
