Ir al menú de navegación principal Ir al contenido principal Ir al pie de página del sitio

Vol. 16 Núm. 1 (2025)
Vol. 16 Núm. 1 (2025): Vol. 16 Núm. 1 (2025): Vol 16, Núm.1 (2025): Enero - Junio

Artículos de investigación / Research papers

COMPUTATIONAL STUDY OF ANANDAMIDE ANALOGUES AS LIGANDS FOR CB1 CANNABINOID RECEPTORS

https://doi.org/10.19053/uptc.01217488.v16.n1.2025.16794

Publicado 2025-04-20

  • Wilson Elias Rozo Nuñez
    • ,
  • Lyda Rosmary Rojas Bustamante
    • ,
  • Agobardo Cárdenas Chaparro
    • ,
  • Fabio Mayorga Niño
    • ,
  • Teodorico Castro Ramalho
    • ,
  • Karen Astrid Arias Soler

Wilson Elias Rozo Nuñez
Docente UPTC

Lyda Rosmary Rojas Bustamante


Agobardo Cárdenas Chaparro


Fabio Mayorga Niño


Teodorico Castro Ramalho


Karen Astrid Arias Soler


Resumen

La anandamida, también conocida como N-araquidonoiletanolamida (AEA), es un compuesto endocanabinoide sintetizado a partir de fosfolípidos presentes en las membranas celulares, incluyendo las del cerebro y el sistema nervioso periférico. El estudio in silico de este compuesto, no solo arroja luz sobre los intrincados mecanismos biológicos que rigen nuestra fisiología, sino que también promete desbloquear nuevas estrategias terapéuticas para mejorar la calidad de vida y tratar una amplia variedad de trastornos médicos. Este estudio se enfoca en la predicción de los procesos de absorción, distribución, metabolismo, excreción y toxicidad (ADMET) de la AEA y 30 nuevos análogos utilizando herramientas computacionales como SwissADME, ProTox-II y VenomPred. Adicionalmente, se evaluó el acoplamiento molecular de los análogos de AEA con el receptor endocannabinoide humano tipo 1 (CB1). Los resultados arrojaron que todos los compuestos exhibieron una biodisponibilidad oral aceptable y que solo dos compuestos permean la membrana hematoencefálica (11 y 12). Los datos de toxicidad indican que 26 ligandos se encuentran en clase 4. Por otro lado, el acoplamiento molecular identificó cinco análogos (10, 23, 24, 29 y 30) con valores óptimos de energía libre. Este estudio destaca a los análogos de AEA como compuestos con aplicaciones farmacéuticas.

Palabras clave

Anandamida, receptor CB1, docking molecular, predicción ADMET

Citas

  1. T. Behl et al., “Exploration of Multiverse Activities of Endocannabinoids in Biological Systems,” International Journal of Molecular Sciences, vol. 23, no. 10, 2022, doi: 10.3390/ijms23105734.
  2. B. Gagestein et al., “Chemical Proteomics Reveals Off-Targets of the Anandamide Reuptake Inhibitor WOBE437,” Cite This: ACS Chem. Biol, vol. 2022, pp. 1174–1183, 2022, doi: 10.1021/acschembio.2c00122.
  3. I. Fernández-Moncada, R. S. Rodrigues, U. B. Fundazuri, L. Bellocchio, and G. Marsicano, “Type-1 cannabinoid receptors and their ever-expanding roles in brain energy processes,” Journal of Neurochemistry, no. February, pp. 1–11, 2023, doi: 10.1111/jnc.15922.
  4. A. Sobiepanek, M. Milner-Krawczyk, P. Musolf, T. Starecki, and T. Kobiela, “Anandamide-Modulated Changes in Metabolism, Glycosylation Profile and Migration of Metastatic Melanoma Cells,” Cancers, vol. 14, no. 6, 2022, doi: 10.3390/cancers14061419.
  5. D. Melck et al., “Suppression of nerve growth factor Trk receptors and prolactin receptors by endocannabinoids leads to inhibition of human breast and prostate cancer cell proliferation,” Endocrinology, vol. 141, no. 1, pp. 118–126, 2000, doi: 10.1210/endo.141.1.7239.
  6. C. Ferreri, D. Anagnostopoulos, I. N. Lykakis, C. Chatgilialoglu, and A. Siafaka-Kapadai, “Synthesis of all-trans anandamide: A substrate for fatty acid amide hydrolase with dual effects on rabbit platelet activation,” Bioorganic and Medicinal Chemistry, vol. 16, no. 18, pp. 8359–8365, 2008, doi: 10.1016/j.bmc.2008.08.054.
  7. J. Ravi, A. Sneh, K. Shilo, M. W. Nasser, and R. K. Ganju, “FAAH inhibition enhances anandamide mediated anti-tumorigenic effects in non-small cell lung cancer by downregulating the EGF/EGFR pathway,” Oncotarget, vol. 5, no. 9, pp. 2475–2486, 2014, doi: 10.18632/oncotarget.1723.
  8. B. S. Tanturovska, R. Manaila, D. Fabbro, and A. Huwiler, “Lipids as Targets for Renal Cell Carcinoma Therapy,” 2023.
  9. K. Klatt-Schreiner et al., “High Glucosylceramides and Low Anandamide Contribute to Sensory Loss and Pain in Parkinson’s Disease,” Movement Disorders, vol. 35, no. 10, pp. 1822–1833, 2020, doi: 10.1002/mds.28186.
  10. A. Dlugos, E. Childs, K. L. Stuhr, C. J. Hillard, and H. De Wit, “Acute stress increases circulating anandamide and other n-acylethanolamines in healthy humans,” Neuropsychopharmacology, vol. 37, no. 11, pp. 2416–2427, 2012, doi: 10.1038/npp.2012.100.
  11. M. McKenna and J. J. McDougall, “Cannabinoid control of neurogenic inflammation,” British Journal of Pharmacology, vol. 177, no. 19, pp. 4386–4399, 2020, doi: 10.1111/bph.15208.
  12. C. Silvestri and V. Di Marzo, “The endocannabinoid system in energy homeostasis and the etiopathology of metabolic disorders,” Cell Metabolism, vol. 17, no. 4, pp. 475–490, 2013, doi: 10.1016/j.cmet.2013.03.001.
  13. J. P. M. Basto et al., “The bond between the endocannabinoid system and food addiction: A scoping review,” Revista Espanola de Nutricion Humana y Dietetica, vol. 25, no. 2, pp. 212–226, 2021, doi: 10.14306/RENHYD.25.2.1153.
  14. J. J. Manning, H. M. Green, M. Glass, and D. B. Finlay, “Pharmacological selection of cannabinoid receptor effectors: Signalling, allosteric modulation and bias,” Neuropharmacology, vol. 193, no. April, p. 108611, 2021, doi: 10.1016/j.neuropharm.2021.108611.
  15. F. Saldívar-González, F. D. Prieto-Martínez, and J. L. Medina-Franco, “Descubrimiento y desarrollo de fármacos: un enfoque computacional,” Educacion Quimica, vol. 28, no. 1, pp. 51–58, 2017, doi: 10.1016/j.eq.2016.06.002.
  16. X. Tian, J. Guo, F. Yao, D. P. Yang, and A. Makriyannis, “The conformation, location, and dynamic properties of the endocannabinoid ligand anandamide in a membrane bilayer,” Journal of Biological Chemistry, vol. 280, no. 33, pp. 29788–29795, 2005, doi: 10.1074/jbc.M502925200.
  17. P. Banerjee, A. O. Eckert, A. K. Schrey, and R. Preissner, “ProTox-II: A webserver for the prediction of toxicity of chemicals,” Nucleic Acids Research, vol. 46, no. W1, pp. W257–W263, 2018, doi: 10.1093/nar/gky318.
  18. C. A. Lipinski, F. Lombardo, B. W. Dominy, and P. J. Feeney, “Experimental and computational approaches to estimate solubility and permeability in drug discovery and development settings,” Advanced Drug Delivery Reviews, vol. 64, no. SUPPL., pp. 4–17, 2012, doi: 10.1016/j.addr.2012.09.019.
  19. A. Toughzaoui, “carboxamide derivatives as potent TRPV1 antagonists using 3D QSAR, ADMET and Molecular Docking,” pp. 1–13, 2023.
  20. E. A. Voight et al., “Research & Development, AbbVie Inc., 1 North Waukegan Road, North Chicago, Illinois 60064, United States,” vol. 1, 2014.
  21. L. W. Padgett, A. C. Howlett, and J. Y. Shim, “Binding mode prediction of conformationally restricted anandamide analogs within the CB1 receptor,” Journal of Molecular Signaling, vol. 3, pp. 1–19, 2008, doi: 10.1186/1750-2187-3-5.
  22. A. Daina, O. Michielin, and V. Zoete, “SwissADME: A free web tool to evaluate pharmacokinetics, drug-likeness and medicinal chemistry friendliness of small molecules,” Scientific Reports, vol. 7, no. March, pp. 1–13, 2017, doi: 10.1038/srep42717.
  23. M. J. F. J et al., “Gaussian 98, revision a. 7, Gaussian,” Inc., Pittsburgh, PA, vol. 12, 1998.
  24. K. B. Wiberg and P. R. Rablen, “Comparison of atomic charges derived via different procedures,” Journal of Computational Chemistry, vol. 14, no. 12, pp. 1504–1518, 1993, doi: 10.1002/jcc.540141213.
  25. T. Hua et al., “Crystal Structure of the Human Cannabinoid Receptor CB1,” Cell, vol. 167, no. 3, pp. 750-762.e14, 2016, doi: 10.1016/j.cell.2016.10.004.
  26. R. Thomsen and M. H. Christensen, “MolDock: A new technique for high-accuracy molecular docking,” JOURNAL OF MEDICINAL CHEMISTRY, vol. 49, no. 11, pp. 3315–3321, Jun. 2006, doi: 10.1021/jm051197e.
  27. E. F. F. Da Cunha, D. T. Mancini, and T. C. Ramalho, “Molecular modeling of the Toxoplasma gondii adenosine kinase inhibitors,” Medicinal Chemistry Research, vol. 21, no. 5, pp. 590–600, May 2012, doi: 10.1007/s00044-011-9554-z.
  28. J. M. Yang and C. C. Chen, “GEMDOCK: A Generic Evolutionary Method for Molecular Docking,” Proteins: Structure, Function and Genetics, vol. 55, no. 2, pp. 288–304, May 2004, doi: 10.1002/prot.20035.
  29. A. P. Guimarães et al., “Docking studies and effects of syn-anti isomery of oximes derived from pyridine imidazol bicycled systems as potential human acetylcholinesterase reactivators,” Journal of Applied Biomedicine, vol. 9, no. 3, pp. 163–171, Jan. 2011, doi: 10.2478/v10136-009-0037-1.
  30. J. A. Arnott and S. L. Planey, “The influence of lipophilicity in drug discovery and design,” Expert Opinion on Drug Discovery, vol. 7, no. 10, pp. 863–875, 2012, doi: 10.1517/17460441.2012.714363.
  31. J. S. Delaney, “ESOL: Estimating aqueous solubility directly from molecular structure,” Journal of Chemical Information and Computer Sciences, vol. 44, no. 3, pp. 1000–1005, 2004, doi: 10.1021/ci034243x.
  32. J. Ali, P. Camilleri, M. B. Brown, A. J. Hutt, and S. B. Kirton, “In silico prediction of aqueous solubility using simple QSPR models: The importance of phenol and phenol-like moieties,” Journal of Chemical Information and Modeling, vol. 52, no. 11, pp. 2950–2957, 2012, doi: 10.1021/ci300447c.
  33. J. Ali, P. Camilleri, M. B. Brown, A. J. Hutt, and S. B. Kirton, “Revisiting the general solubility equation: In silico prediction of aqueous solubility incorporating the effect of topographical polar surface area,” Journal of Chemical Information and Modeling, vol. 52, no. 2, pp. 420–428, 2012, doi: 10.1021/ci200387c.
  34. S. jun Cao et al., “Nanoparticles: Oral Delivery for Protein and Peptide Drugs,” AAPS PharmSciTech, vol. 20, no. 5, pp. 1–11, 2019, doi: 10.1208/s12249-019-1325-z.
  35. A. Daina and V. Zoete, “A BOILED-Egg to Predict Gastrointestinal Absorption and Brain Penetration of Small Molecules,” ChemMedChem, pp. 1117–1121, 2016, doi: 10.1002/cmdc.201600182.
  36. X. Liu, C. Chen, and B. J. Smith, “Progress in brain penetration evaluation in drug discovery and development,” Journal of Pharmacology and Experimental Therapeutics, vol. 325, no. 2, pp. 349–356, 2008, doi: 10.1124/jpet.107.130294.
  37. J. Kirchmair et al., “Predicting drug metabolism: Experiment and/or computation?” Nature Reviews Drug Discovery, vol. 14, no. 6, pp. 387–404, 2015, doi: 10.1038/nrd4581.
  38. R. A. B. van Waterschoot and A. H. Schinkel, “A Critical Analysis of the Interplay between Cytochrome P450 3A and P-Glycoprotein: Recent insights from knockout and transgenic mice,” Pharmacological Reviews, vol. 63, no. 2, pp. 390–410, 2011, doi: 10.1124/pr.110.002584.
  39. D. F. Veber, S. R. Johnson, H. Cheng, B. R. Smith, K. W. Ward, and K. D. Kopple, “Molecular Properties That Influence the Oral Bioavailability of Drug Candidates,” J. Med. Chem, vol. 45, pp. 2615–2623, 2002, doi: 10.1021/jm020017n.
  40. A. K. Ghose, V. N. Viswanadhan, and J. J. Wendoloski, “A Knowledge-Based Approach in Designing Combinatorial or Medicinal Chemistry Libraries for Drug Discovery. 1. A Qualitative and Quantitative Characterization of Known Drug Databases,” J. Comb. Chem, vol. 1, pp. 55–68, 1999, doi: 10.1021/cc9800071.
  41. I. Muegge, S. L. Heald, and D. Brittelli, “Simple selection criteria for drug-like chemical matter,” Journal of Medicinal Chemistry, vol. 44, no. 12, pp. 1841–1846, 2001, doi: 10.1021/jm015507e.
  42. W. J. Egan, K. M. Merz, and J. J. Baldwin, “Prediction of drug absorption using multivariate statistics,” Journal of Medicinal Chemistry, vol. 43, no. 21, pp. 3867–3877, 2000, doi: 10.1021/jm000292e.
  43. A. M. Rabasco Alvarez, “Biofarmacia y Farmacocinética Básica,” Avances en Farmacología y Farmacoterapia: Conceptos Básicos en Farmacología., pp. 32–56, 2002, [Online]. Available: http://dica.minec.gob.sv/inventa/attachments/article/3354/Biofarm-Farmacoc.pdf.

Descargas

Los datos de descargas todavía no están disponibles.

Artículos similares

También puede {advancedSearchLink} para este artículo.