Evaluación in silico de flavonoides como potenciales inhibidores del fentanilo

La creciente preocupación por el uso del fentanilo, debido a su abuso y efectos adversos, subraya la necesidad urgente de encontrar alternativas terapéuticas más seguras. El objetivo general de esta investigación evaluar la interacción de metabolitos secundarios con el receptor opioide mu (Mor) y su...

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Autores:
Giraldo Muñoz, Ariany Yoaly
Tipo de recurso:
Trabajo de grado de pregrado
Fecha de publicación:
2024
Institución:
Universidad Distrital Francisco José de Caldas
Repositorio:
RIUD: repositorio U. Distrital
Idioma:
spa
OAI Identifier:
oai:repository.udistrital.edu.co:11349/41913
Acceso en línea:
http://hdl.handle.net/11349/41913
Palabra clave:
Fentanilo
Receptor opioide mu
Metabolitos secundarios
Acoplamiento molecular
Compuestos naturales.
Licenciatura en Biología -- Tesis y disertaciones académicas
Inhibidores naturales del fentanilo
Acoplamiento molecular y análisis ADMET
Desarrollo de alternativas terapéuticas
Crisis de opioides y seguridad farmacológica
Fentanyl
Mu opioid receptor
Secondary metabolites
Molecular docking
Natural compounds.
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id UDISTRITA2_9d0580d41dc899cbb496901a7496528e
oai_identifier_str oai:repository.udistrital.edu.co:11349/41913
network_acronym_str UDISTRITA2
network_name_str RIUD: repositorio U. Distrital
repository_id_str
dc.title.none.fl_str_mv Evaluación in silico de flavonoides como potenciales inhibidores del fentanilo
dc.title.titleenglish.none.fl_str_mv In silico evaluation of flavonoids as potential fentanyl inhibitors
title Evaluación in silico de flavonoides como potenciales inhibidores del fentanilo
spellingShingle Evaluación in silico de flavonoides como potenciales inhibidores del fentanilo
Fentanilo
Receptor opioide mu
Metabolitos secundarios
Acoplamiento molecular
Compuestos naturales.
Licenciatura en Biología -- Tesis y disertaciones académicas
Inhibidores naturales del fentanilo
Acoplamiento molecular y análisis ADMET
Desarrollo de alternativas terapéuticas
Crisis de opioides y seguridad farmacológica
Fentanyl
Mu opioid receptor
Secondary metabolites
Molecular docking
Natural compounds.
title_short Evaluación in silico de flavonoides como potenciales inhibidores del fentanilo
title_full Evaluación in silico de flavonoides como potenciales inhibidores del fentanilo
title_fullStr Evaluación in silico de flavonoides como potenciales inhibidores del fentanilo
title_full_unstemmed Evaluación in silico de flavonoides como potenciales inhibidores del fentanilo
title_sort Evaluación in silico de flavonoides como potenciales inhibidores del fentanilo
dc.creator.fl_str_mv Giraldo Muñoz, Ariany Yoaly
dc.contributor.advisor.none.fl_str_mv Mahecha Jiménez, Oscar Javier
Rodríguez Lopez, Edwin Alexander
dc.contributor.author.none.fl_str_mv Giraldo Muñoz, Ariany Yoaly
dc.contributor.orcid.none.fl_str_mv Mahecha, Oscar Javier [0000-0002-8682-0020]
dc.subject.none.fl_str_mv Fentanilo
Receptor opioide mu
Metabolitos secundarios
Acoplamiento molecular
Compuestos naturales.
topic Fentanilo
Receptor opioide mu
Metabolitos secundarios
Acoplamiento molecular
Compuestos naturales.
Licenciatura en Biología -- Tesis y disertaciones académicas
Inhibidores naturales del fentanilo
Acoplamiento molecular y análisis ADMET
Desarrollo de alternativas terapéuticas
Crisis de opioides y seguridad farmacológica
Fentanyl
Mu opioid receptor
Secondary metabolites
Molecular docking
Natural compounds.
dc.subject.lemb.none.fl_str_mv Licenciatura en Biología -- Tesis y disertaciones académicas
Inhibidores naturales del fentanilo
Acoplamiento molecular y análisis ADMET
Desarrollo de alternativas terapéuticas
Crisis de opioides y seguridad farmacológica
dc.subject.keyword.none.fl_str_mv Fentanyl
Mu opioid receptor
Secondary metabolites
Molecular docking
Natural compounds.
description La creciente preocupación por el uso del fentanilo, debido a su abuso y efectos adversos, subraya la necesidad urgente de encontrar alternativas terapéuticas más seguras. El objetivo general de esta investigación evaluar la interacción de metabolitos secundarios con el receptor opioide mu (Mor) y su uso como inhibidores de fentanilo a través de un enfoque in silico. El proceso de investigación se dividió en varias etapas clave: inicialmente, se realizó un acoplamiento molecular detallado para evaluar la interacción inicial de los compuestos con el receptor. Posteriormente, se llevó a cabo un acoplamiento masivo utilizando una amplia base de datos de compuestos naturales, seguido por un análisis ADMET para evaluar la absorción, distribución, metabolismo, excreción y toxicidad de los compuestos seleccionados. Los resultados del acoplamiento mostraron consistencia con la estructura cristalina del receptor, validando la metodología empleada. Los compuestos más prometedores fueron ZINC_1297, ZINC_287, ZINC_1299, ZINC_1474, ZINC_1793, ZINC_2014, ZINC_819, ZINC_2302, ZINC_1605, ZINC_2050, ZINC_2179 y ZINC_2513, aquellos que no violaban más de tres reglas de Lipinski, asegurando su viabilidad como fármacos orales efectivos. Además, se exploró la posible relación entre los puentes de hidrógeno y la permeabilidad de la barrera hematoencefálica, sugiriendo que estas interacciones pueden facilitar el paso de los compuestos al cerebro. En conclusión, esta investigación no solo avanza en la identificación de posibles inhibidores naturales del fentanilo, sino que también establece un marco metodológico robusto para futuras exploraciones de compuestos naturales en el tratamiento de adicciones y manejo del dolor, contribuyendo significativamente a la mitigación de la crisis de los opioides y al desarrollo de terapias más seguras y efectivas.
publishDate 2024
dc.date.accessioned.none.fl_str_mv 2024-10-22T20:52:44Z
dc.date.available.none.fl_str_mv 2024-10-22T20:52:44Z
dc.date.created.none.fl_str_mv 2024-08-05
dc.type.none.fl_str_mv bachelorThesis
dc.type.degree.none.fl_str_mv Investigación-Innovación
dc.type.driver.none.fl_str_mv info:eu-repo/semantics/bachelorThesis
dc.type.coar.none.fl_str_mv http://purl.org/coar/resource_type/c_7a1f
format http://purl.org/coar/resource_type/c_7a1f
dc.identifier.uri.none.fl_str_mv http://hdl.handle.net/11349/41913
url http://hdl.handle.net/11349/41913
dc.language.iso.none.fl_str_mv spa
language spa
dc.relation.references.none.fl_str_mv 1. Synthetic opioids: a review and clinical update - Abu Shafi, Alex J. Berry, Harry Sumnall, David M. Wood, Derek K. Tracy, 2022. Disponible en: https://journals.sagepub.com/doi/full/10.1177/20451253221139616
2. Maclean JC, Mallatt J, Ruhm CJ, Simon K. Economic Studies on the Opioid Crisis: A Review . National Bureau of Economic Research; 2020 . (Working Paper Series). Disponible en: https://www.nber.org/papers/w28067
3. Fentanyls: Are we missing the signs? Highly potent and on the rise in Europe. Int J Drug Policy . 1 de julio de 2015 ;26(7):626-31. Disponible en: https://www.sciencedirect.com/science/article/abs/pii/S0955395915000973
4. Harborne JB, Williams CA. Advances in flavonoid research since 1992. Phytochemistry . noviembre de 2000 ;55(6):481-504. Disponible en: https://linkinghub.elsevier.com/retrieve/pii/S0031942200002351
5. Natural Products as Sources of New Drugs over the Nearly Four Decades from 01/1981 to 09/2019 | Journal of Natural Products . . Disponible en: https://pubs.acs.org/doi/full/10.1021/acs.jnatprod.9b01285
6. Zhuang Y, Wang Y, He B, He X, Zhou XE, Guo S, et al. Molecular recognition of morphine and fentanyl by the human μ-opioid receptor. Cell . 10 de noviembre de 2022 ;185(23):4361-4375.e19. Disponible en: https://www.cell.com/cell/abstract/S0092- 8674(22)01260-0
7. Manglik A, Kim TH, Masureel M, Altenbach C, Yang Z, Hilger D, et al. Structural Insights into the Dynamic Process of β2-Adrenergic Receptor Signaling. Cell . 21 de mayo de 2015 ;161(5):1101-11. Disponible en: https://www.cell.com/cell/abstract/S0092-8674(15)00499-7
8. Hashemi ZS, Zarei M, Fath MK, Ganji M, Farahani MS, Afsharnouri F, et al. In silico Approaches for the Design and Optimization of Interfering Peptides Against Protein– 56 Protein Interactions. Front Mol Biosci . 28 de abril de 2021 ;8. Disponible en: https://www.frontiersin.org/journals/molecular biosciences/articles/10.3389/fmolb.2021.669431/full
9. Lipinski CA, Lombardo F, Dominy BW, Feeney PJ. Experimental and computational approaches to estimate solubility and permeability in drug discovery and development settings. Adv Drug Deliv Rev . diciembre de 2012 ;64:4-17. Disponible en: https://linkinghub.elsevier.com/retrieve/pii/S0169409X12002797
10. Comer SD, Cahill CM. Fentanyl: Receptor pharmacology, abuse potential, and implications for treatment. Neurosci Biobehav Rev . noviembre de 2019 ;106:49-57. Disponible en: https://linkinghub.elsevier.com/retrieve/pii/S0149763418302070
11. Full Opioid Agonists and Tramadol: Pharmacological and Clinical Considerations - PMC . . Disponible en: https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8520671/
12. Han Y, Yan W, Zheng Y, Khan MZ, Yuan K, Lu L. The rising crisis of illicit fentanyl use, overdose, and potential therapeutic strategies. Transl Psychiatry . 11 de noviembre de 2019 ;9(1):1-9. Disponible en: https://www.nature.com/articles/s41398-019-0625-0
13. Bhatti MZ, Ismail H, Kayani WK, Bhatti MZ, Ismail H, Kayani WK. Plant Secondary Metabolites: Therapeutic Potential and Pharmacological Properties. En: Secondary Metabolites - Trends and Reviews . IntechOpen; 2022 . Disponible en: https://www.intechopen.com/chapters/81728
14. Mishra P, Sohrab S, Mishra SK. A review on the phytochemical and pharmacological properties of Hyptis suaveolens (L.) Poit. Future J Pharm Sci . 12 de marzo de 2021 ;7(1):65. Disponible en: https://doi.org/10.1186/s43094-021-00219-1
15. Zhong HA. ADMET Properties: Overview and Current Topics. En: Grover A, editor. Drug Design: Principles and Applications . Singapore: Springer Singapore; 2017 . p. 113-33. Disponible en: http://link.springer.com/10.1007/978-981-10-5187-6_8
16. Poduri R, editor. Drug Discovery and Development: From Targets and Molecules to Medicines . Singapore: Springer Singapore; 2021 . Disponible en: https://link.springer.com/10.1007/978-981-15-5534-3
17. Hyland SJ, Brockhaus KK, Vincent WR, Spence NZ, Lucki MM, Howkins MJ, et al. Perioperative Pain Management and Opioid Stewardship: A Practical Guide. Healthcare . marzo de 2021 ;9(3):333. Disponible en: https://www.mdpi.com/2227- 9032/9/3/333
18. Hilal B, Khan MM, Fariduddin Q. Recent advancements in deciphering the therapeutic properties of plant secondary metabolites: phenolics, terpenes, and alkaloids. Plant Physiol Biochem . 1 de junio de 2024 ;211:108674. Disponible en: https://www.sciencedirect.com/science/article/pii/S0981942824003425
19. Steglitz J, Buscemi J, Ferguson MJ. The future of pain research, education, and treatment: a summary of the IOM report “Relieving pain in America: a blueprint for transforming prevention, care, education, and research”. Transl Behav Med . marzo de 2012 ;2(1):6-8. Disponible en: https://www.ncbi.nlm.nih.gov/pmc/articles/PMC3717812/
20. Katzung BG, editor. Basic & clinical pharmacology. Fourteenth edition. New York Chicago San Francisco Athens London Madrid Mexico City Milan New Delhi Singapore Sydney Toronto: McGraw-Hill Education; 2018. 1250 p. (A Lange medical book).
21. Malik KM, Imani F, Beckerly R, Chovatiya R. Risk of Opioid Use Disorder from Exposure to Opioids in the Perioperative Period: A Systematic Review. Anesthesiol Pain Med . 19 de febrero de 2020 ;10(1):e101339. Disponible en: https://www.ncbi.nlm.nih.gov/pmc/articles/PMC7158240/
22. CDC Warns of Surge in Drug Overdose Deaths During COVID-19 | Health Policy | JAMA Health Forum | JAMA Network . . Disponible en: https://jamanetwork.com/journals/jama-health-forum/fullarticle/2774898
23. Vo QN, Mahinthichaichan P, Shen J, Ellis CR. How μ-opioid receptor recognizes fentanyl. Nat Commun . 12 de febrero de 2021 ;12(1):984. Disponible en: https://www.nature.com/articles/s41467-021-21262-9
24. Hill R, Santhakumar R, Dewey W, Kelly E, Henderson G. Fentanyl depression of respiration: Comparison with heroin and morphine. Br J Pharmacol . 2020 ;177(2):254-65. Disponible en: https://onlinelibrary.wiley.com/doi/abs/10.1111/bph.14860
25. Darcq E, Kieffer BL. Opioid receptors: drivers to addiction? Nat Rev Neurosci . agosto de 2018 ;19(8):499-514. Disponible en: https://www.nature.com/articles/s41583-018-0028-x
26. Mahinthichaichan P, Vo QN, Ellis CR, Shen J. Kinetics and Mechanism of Fentanyl Dissociation from the μ-Opioid Receptor. JACS Au . 27 de diciembre de 2021 ;1(12):2208-15. Disponible en: https://doi.org/10.1021/jacsau.1c00341
27. Kelly E, Sutcliffe K, Cavallo D, Ramos-Gonzalez N, Alhosan N, Henderson G. The anomalous pharmacology of fentanyl. Br J Pharmacol . 2023 ;180(7):797-812. Disponible en: https://onlinelibrary.wiley.com/doi/abs/10.1111/bph.15573
28. Ehrlich AT, Kieffer BL, Darcq E. Current strategies toward safer mu opioid receptor drugs for pain management. Expert Opin Ther Targets . 3 de abril de 2019 ;23(4):315- 26. Disponible en: https://www.tandfonline.com/doi/full/10.1080/14728222.2019.1586882
29. Guryanov I, Fiorucci S, Tennikova T. Receptor-ligand interactions: Advanced biomedical applications. Mater Sci Eng C . noviembre de 2016 ;68:890-903. Disponible en: https://linkinghub.elsevier.com/retrieve/pii/S092849311630746
30. Vizurraga A, Adhikari R, Yeung J, Yu M, Tall GG. Mechanisms of adhesion G protein–coupled receptor activation. J Biol Chem . 9 de octubre de 2020 ;295(41):14065-83. Disponible en: https://www.jbc.org/article/S0021-9258(17)49804- 8/abstract
31. Williams JT, Ingram SL, Henderson G, Chavkin C, Von Zastrow M, Schulz S, et al. Regulation of µ -Opioid Receptors: Desensitization, Phosphorylation, Internalization, and Tolerance. Dolphin AC, editor. Pharmacol Rev . enero de 2013 ;65(1):223-54. Disponible en: http://pharmrev.aspetjournals.org/lookup/doi/10.1124/pr.112.005942
32. Senese NB, Kandasamy R, Kochan KE, Traynor JR. Regulator of G-Protein Signaling (RGS) Protein Modulation of Opioid Receptor Signaling as a Potential Target for Pain Management. Front Mol Neurosci . 24 de enero de 2020 ;13. Disponible en: https://www.frontiersin.org/journals/molecular neuroscience/articles/10.3389/fnmol.2020.00005/full
33. Koehl A, Hu H, Maeda S, Zhang Y, Qu Q, Paggi JM, et al. Structure of the µ-opioid receptor–Gi protein complex. Nature . junio de 2018 ;558(7711):547-52. Disponible en: https://www.nature.com/articles/s41586-018-0219-7
34. Sutcliffe KJ, Corey RA, Charlton SJ, Sessions RB, Henderson G, Kelly E. Fentanyl binds to the μ-opioid receptor via the lipid membrane and transmembrane helices . bioRxiv; 2021 . p. 2021.02.04.429703. Disponible en: https://www.biorxiv.org/content/10.1101/2021.02.04.429703v1
35. Chopra B, Dhingra AK. Natural products: A lead for drug discovery and development. Phytother Res . septiembre de 2021 ;35(9):4660-702. Disponible en: https://onlinelibrary.wiley.com/doi/10.1002/ptr.7099
36. Singh VK, Kumar A. Secondary metabolites from endophytic fungi: Production, methods of analysis, and diverse pharmaceutical potential. Symbiosis . 1 de junio de 2023 ;90(2):111-25. Disponible en: https://doi.org/10.1007/s13199-023-00925-9
37. Kainhofer R. Screening of plant-derived compounds as potential new ligands at the µ opioid receptor.
38. McCurdy CR, Scully SS. Analgesic substances derived from natural products (natureceuticals). Life Sci . diciembre de 2005 ;78(5):476-84. Disponible en: https://linkinghub.elsevier.com/retrieve/pii/S0024320505008830
39. Lim EY, Kim YT. Food-Derived Natural Compounds for Pain Relief in Neuropathic Pain. BioMed Res Int . 2016 ;2016(1):7917528. Disponible en: https://onlinelibrary.wiley.com/doi/abs/10.1155/2016/7917528
40. Katavic PL, Lamb K, Navarro H, Prisinzano TE. Flavonoids as Opioid Receptor Ligands: Identification and Preliminary Structure−Activity Relationships. J Nat Prod . 1 de agosto de 2007 ;70(8):1278-82. Disponible en: https://pubs.acs.org/doi/10.1021/np070194x
41. Fudin, J. (2018) . Opioid Agonists, Partial Agonists, Antagonists: Oh My!. Pharmacy Times, 1-4 Disponible en: https://www.pharmacytimes.com/view/opioid agonists-partial-agonists-antagonists-oh-my
42. Pasternak GW, Childers SR, Pan YX. Emerging Insights into Mu Opioid Pharmacology. En: Nader MA, Hurd YL, editores. Substance Use Disorders . Cham: Springer International Publishing; 2019 . p. 89-125. (Handbook of Experimental Pharmacology; vol. 258). Disponible en: http://link.springer.com/10.1007/164_2019_270
43. Wold EA, Chen J, Cunningham KA, Zhou J. Allosteric Modulation of Class A GPCRs: Targets, Agents, and Emerging Concepts. J Med Chem . 10 de enero de 2019 ;62(1):88-127. Disponible en: https://pubs.acs.org/doi/10.1021/acs.jmedchem.8b00875
44. Fan J, Fu A, Zhang L. Progress in molecular docking. Quant Biol . junio de 2019 ;7(2):83-9. Disponible en: https://onlinelibrary.wiley.com/doi/10.1007/s40484-019-0172- y
45. Ellis CR, Kruhlak NL, Kim MT, Hawkins EG, Stavitskaya L. Predicting opioid receptor binding affinity of pharmacologically unclassified designer substances using molecular docking. PLOS ONE . 24 de mayo de 2018 ;13(5):e0197734. Disponible en: https://journals.plos.org/plosone/article?id=10.1371/journal.pone.0197734
46. Pinzi L, Rastelli G. Molecular Docking: Shifting Paradigms in Drug Discovery. Int J Mol Sci . 4 de septiembre de 2019 ;20(18):4331. Disponible en: https://www.mdpi.com/1422-0067/20/18/4331
47. Murail S, de Vries SJ, Rey J, Moroy G, Tufféry P. SeamDock: An Interactive and Collaborative Online Docking Resource to Assist Small Compound Molecular Docking. Front Mol Biosci . 17 de septiembre de 2021 ;8. Disponible en: https://www.frontiersin.org/journals/molecular biosciences/articles/10.3389/fmolb.2021.716466/full
48. Wang Q. Protein-ligand Docking Application and Comparison using Discovery Studio and AutoDock [Thesis]. North Dakota State University; 2017 . Disponible en: https://library.ndsu.edu/ir/handle/10365/28365
49. Dhorajiwala T, Halder S, Samant L. Comparative In Silico Molecular Docking Analysis of L-Threonine-3-Dehydrogenase, a Protein Target Against African Trypanosomiasis Using Selected Phytochemicals. J Appl Biotechnol Rep . 11 de septiembre de 2019 ;6(3):101-8. Disponible en: http://www.biotechrep.ir/article_93040.html
50. Irwin JJ, Sterling T, Mysinger MM, Bolstad ES, Coleman RG. ZINC: A Free Tool to Discover Chemistry for Biology. J Chem Inf Model . 23 de julio de 2012 ;52(7):1757- 68. Disponible en: https://doi.org/10.1021/ci3001277
51. Tingle BI, Tang KG, Castanon M, Gutierrez JJ, Khurelbaatar M, Dandarchuluun C, et al. ZINC-22─A Free Multi-Billion-Scale Database of Tangible Compounds for Ligand Discovery. J Chem Inf Model . 27 de febrero de 2023 ;63(4):1166-76. Disponible en: https://doi.org/10.1021/acs.jcim.2c01253
52. Sterling T, Irwin JJ. ZINC 15 – Ligand Discovery for Everyone. J Chem Inf Model . 23 de noviembre de 2015 ;55(11):2324-37. Disponible en: https://doi.org/10.1021/acs.jcim.5b00559
53. Cheng F, Li W, Zhou Y, Shen J, Wu Z, Liu G, et al. admetSAR: A Comprehensive Source and Free Tool for Assessment of Chemical ADMET Properties. J Chem Inf Model . 26 de noviembre de 2012 ;52(11):3099-105. Disponible en: https://pubs.acs.org/doi/10.1021/ci300367a
54. Wu F, Zhou Y, Li L, Shen X, Chen G, Wang X, et al. Computational Approaches in Preclinical Studies on Drug Discovery and Development. Front Chem . 11 de septiembre de 2020 ;8. Disponible en: https://www.frontiersin.org/journals/chemistry/articles/10.3389/fchem.2020.00726/full
55. Daina A, Michielin O, Zoete V. SwissADME: a free web tool to evaluate pharmacokinetics, drug-likeness and medicinal chemistry friendliness of small molecules. Sci Rep . 3 de marzo de 2017 ;7(1):42717. Disponible en: https://www.nature.com/articles/srep42717
56. Kerns EH, Di L. Drug-like properties: concepts, structure design and methods: from ADME to toxicity optimization. Amsterdam ; Boston: Academic Press; 2008. 526 p.
57. Smith DA. Metabolism, Pharmacokinetics and Toxicity of Functional Groups: Impact of Chemical Building Blocks on ADMET. Royal Society of Chemistry; 2010. 545 p.
58. Pang KS. Modeling of intestinal drug absorption: roles of transporters and metabolic enzymes (for the gillette review series). Drug Metab Dispos . diciembre de 2003 ;31(12):1507-19. Disponible en: http://dmd.aspetjournals.org/lookup/doi/10.1124/dmd.31.12.1507
59. Guengerich FP. Cytochrome P450s and other enzymes in drug metabolism and toxicity. AAPS J . marzo de 2006 ;8(1):E101-11. Disponible en: http://link.springer.com/10.1208/aapsj080112
60. Yang W, Lipert M, Nofsinger R. Current screening, design, and delivery approaches to address low permeability of chemically synthesized modalities in drug discovery and early clinical development. Drug Discov Today . 1 de septiembre de 2023 ;28(9):103685. Disponible en: https://www.sciencedirect.com/science/article/pii/S1359644623002015
61. Lipinski CA, Lombardo F, Dominy BW, Feeney PJ. Experimental and computational approaches to estimate solubility and permeability in drug discovery and 63 development settings. Adv Drug Deliv Rev . enero de 1997 ;23(1-3):3-25. Disponible en: https://linkinghub.elsevier.com/retrieve/pii/S0169409X96004231
62. Meng EC, Goddard TD, Pettersen EF, Couch GS, Pearson ZJ, Morris JH, et al. UCSF ChimeraX: Tools for structure building and analysis. Protein Sci . 2023 ;32(11):e4792. Disponible en: https://onlinelibrary.wiley.com/doi/abs/10.1002/pro.4792
63. Bosquez-Berger T.A. (2023). Impact of µor-negative allosteric modulators on cellular, behavioral, and molecular aspects of fentanyl signaling.Indiana University
64. Uehara S, Tanaka S. AutoDock-GIST: Incorporating Thermodynamics of Active Site Water into Scoring Function for Accurate Protein-Ligand Docking. Molecules . 23 de noviembre de 2016 ;21(11):1604. Disponible en: https://www.mdpi.com/1420- 3049/21/11/1604
65. Madhavi Sastry G, Adzhigirey M, Day T, Annabhimoju R, Sherman W. Protein and ligand preparation: parameters, protocols, and influence on virtual screening enrichments. J Comput Aided Mol Des . marzo de 2013 ;27(3):221-34. Disponible en: http://link.springer.com/10.1007/s10822-013-9644-8
66. El-Hachem N, Haibe-Kains B, Khalil A, Kobeissy FH, Nemer G. AutoDock and AutoDockTools for Protein-Ligand Docking: Beta-Site Amyloid Precursor Protein Cleaving Enzyme 1(BACE1) as a Case Study. En: Kobeissy FH, Stevens, SM, editores. Neuroproteomics . New York, NY: Springer New York; 2017 . p. 391-403. (Methods in Molecular Biology; vol. 1598). Disponible en: http://link.springer.com/10.1007/978-1- 4939-6952-4_20
67. Arifian H, Maharani R, Megantara S, Ikram NKK, Muchtaridi M. Glycine Conjugated α-Mangostins as Potential Estrogen Receptor Alpha (ERα) Antagonists through Pharmacophore Modeling, Docking Analysis, and Molecular Dynamics Simulations. Appl Sci . enero de 2024 ;14(13):5549. Disponible en: https://www.mdpi.com/2076-3417/14/13/5549
68. Manjunathan R, Periyaswami V, Mitra K, Rosita AS, Pandya M, Selvaraj J, et al. Molecular docking analysis reveals the functional inhibitory effect of Genistein and Quercetin on TMPRSS2: SARS-COV-2 cell entry facilitator spike protein. BMC Bioinformatics . 16 de mayo de 2022 ;23(1):180. Disponible en: https://doi.org/10.1186/s12859-022-04724-9
69. Sorokina M, Steinbeck C. Review on natural products databases: where to find data in 2020. J Cheminformatics . 3 de abril de 2020 ;12(1):20.
70. Fanelli A, Sullivan ML. Chapter Three - Tools for protein structure prediction and for molecular docking applied to enzyme active site analysis: A case study using a BAHD hydroxycinnamoyltransferase. En: Jez J, editor. Methods in Enzymology . Academic Press; 2023 . p. 41-79. (Biochemical Pathways and Environmental Responses in Plants: Part C; vol. 683). Disponible en: https://www.sciencedirect.com/science/article/pii/S0076687922004219
71. Jairajpuri DS, Hussain A, Nasreen K, Mohammad T, Anjum F, Tabish Rehman Md, et al. Identification of natural compounds as potent inhibitors of SARS-CoV-2 main protease using combined docking and molecular dynamics simulations. Saudi J Biol Sci . 1 de abril de 2021 ;28(4):2423-31. Disponible en: https://www.sciencedirect.com/science/article/pii/S1319562X21000401
72. de Araújo ACJ, Freitas PR, Araújo IM, Siqueira GM, de Oliveira Borges JA, Alves DS, et al. Potentiating-antibiotic activity and absorption, distribution, metabolism, excretion and toxicity properties (ADMET) analysis of synthetic thiadiazines against multi-drug resistant (MDR) strains. Fundam Clin Pharmacol . 2024 ;38(1):84-98. Disponible en: https://onlinelibrary.wiley.com/doi/abs/10.1111/fcp.12950
73. Patil VS, Patil PA. Molecular Docking: A useful approach of Drug Discovery on the Basis of their Structure. Asian J Pharm Res . 7 de septiembre de 2023 ;13(3):191-5. Disponible en: https://asianjpr.com/AbstractView.aspx?PID=2023-13-3-10
74. Bottegoni G, Kufareva I, Totrov M, Abagyan R. Four-Dimensional Docking: A Fast and Accurate Account of Discrete Receptor Flexibility in Ligand Docking. J Med Chem . 22 de enero de 2009 ;52(2):397-406. Disponible en: https://pubs.acs.org/doi/10.1021/jm8009958
75. Bell EW, Zhang Y. DockRMSD: an open-source tool for atom mapping and RMSD calculation of symmetric molecules through graph isomorphism. J Cheminformatics . 7 de junio de 2019 ;11(1):40. Disponible en: https://doi.org/10.1186/s13321-019-0362-7
76. Janjua TI, Rewatkar P, Ahmed-Cox A, Saeed I, Mansfeld FM, Kulshreshtha R, et al. Frontiers in the treatment of glioblastoma: Past, present and emerging. Adv Drug Deliv Rev . 1 de abril de 2021 ;171:108-38. Disponible en: https://www.sciencedirect.com/science/article/pii/S0169409X21000223
77. Du X, Li Y, Xia YL, Ai SM, Liang J, Sang P, et al. Insights into Protein–Ligand Interactions: Mechanisms, Models, and Methods. Int J Mol Sci . febrero de 2016 ;17(2):144. Disponible en: https://www.mdpi.com/1422-0067/17/2/144
78. Jeffrey P, Summerfield SG. Challenges for blood–brain barrier (BBB) screening. Xenobiotica . noviembre de 2007 ;37(10-11):1135-51. Disponible en: http://www.tandfonline.com/doi/full/10.1080/00498250701570285
79. Liu S, Wen X, Zhang X, Mao S. Oral delivery of biomacromolecules by overcoming biological barriers in the gastrointestinal tract: an update. Expert Opin Drug Deliv. 2023;20(10):1333-47.
80. Hong Y, Ha J, Lim CJ, Oh KS, Chandrasekaran R, Kim B, et al. Accurate Prediction of Protein-Ligand Interactions by Combining Physical Energy Functions and Graph Neural Networks . 2024 . Disponible en: https://www.researchsquare.com/article/rs 3887850/v1
81. Aljuaid A, Salam A, Almehmadi M, Baammi S, Alshabrmi FM, Allahyani M, et al. Structural Homology-Based Drug Repurposing Approach for Targeting NSP12 SARS- 66 CoV-2. Molecules . enero de 2022 ;27(22):7732. Disponible en: https://www.mdpi.com/1420-3049/27/22/7732
82. Klebe G. Protein–Ligand Interactions as the Basis for Drug Action. En: Klebe G, editor. Drug Design . Berlin, Heidelberg: Springer Berlin Heidelberg; 2013 . p. 61-88. Disponible en: http://link.springer.com/10.1007/978-3-642-17907-5_4
83. Arthur DE, Akoji JN, Sahnoun R, Okafor GC, Abdullahi KL, Abdullahi SA, et al. A theoretical insight in interactions of some chemical compounds as mTOR inhibitors. Bull Natl Res Cent . 20 de marzo de 2021 ;45(1):67. Disponible en: https://doi.org/10.1186/s42269-021-00525-x
84. Bulusu G, Desiraju GR. Strong and Weak Hydrogen Bonds in Protein–Ligand Recognition. J Indian Inst Sci . enero de 2020 ;100(1):31-41. Disponible en: http://link.springer.com/10.1007/s41745-019-00141-9
85. Madushanka A, Moura RT, Verma N, Kraka E. Quantum Mechanical Assessment of Protein–Ligand Hydrogen Bond Strength Patterns: Insights from Semiempirical Tight Binding and Local Vibrational Mode Theory. Int J Mol Sci . enero de 2023 ;24(7):6311. Disponible en: https://www.mdpi.com/1422-0067/24/7/6311
86. Liu W, Liu R, Qin Q, Wang H, Wu H, Ren J, et al. Interaction mechanisms of ACE inhibitory peptides: molecular docking and molecular dynamics simulation studies on five wheat gluten derived peptides. Eur Food Res Technol. 5 de abril de 2024;1-14.
87. Amin N, Singh VK, Kannaujiya VK. Mycosporine-Like Amino Acids as a Potential Inhibitor of Tyrosinase-Related Protein 1: Computational Screening, Pharmacokinetics, and Molecular Dynamics Simulation. Mol Biotechnol. 23 de abril de 2024;
88. Vesga LC, Ruiz-Hernández CA, Alvarez-Jacome JJ, Duque JE, Rincon-Orozco B, Mendez-Sanchez SC. Repurposing of Four Drugs as Anti-SARS-CoV-2 Agents and Their Interactions with Protein Targets. Sci Pharm . junio de 2022 ;90(2):24. Disponible en: https://www.mdpi.com/2218-0532/90/2/24
89. Chu YC, Yang CS, Cheng MJ, Fu SL, Chen JJ. Comparison of Various Solvent Extracts and Major Bioactive Components from Unsalt-Fried and Salt-Fried Rhizomes of Anemarrhena asphodeloides for Antioxidant, Anti-α-Glucosidase, and Anti Acetylcholinesterase Activities. Antioxidants . febrero de 2022 ;11(2):385. Disponible en: https://www.mdpi.com/2076-3921/11/2/385
90. Yang H, Lou C, Sun L, Li J, Cai Y, Wang Z, et al. admetSAR 2.0: web-service for prediction and optimization of chemical ADMET properties. Bioinformatics . 15 de marzo de 2019 ;35(6):1067-9. Disponible en: https://doi.org/10.1093/bioinformatics/bty707
91. Perkin VO, Antonyan GV, Radchenko EV, Palyulin VA. Web Services for the Prediction of ADMET Parameters Relevant to the Design of Neuroprotective Drugs. En: Roy K, editor. Computational Modeling of Drugs Against Alzheimer’s Disease . New York, NY: Springer US; 2023 . p. 465-85. Disponible en: https://doi.org/10.1007/978-1- 0716-3311-3_16
92. Gupta M, Lee HJ, Barden CJ, Weaver DF. The Blood–Brain Barrier (BBB) Score. J Med Chem . 14 de noviembre de 2019 ;62(21):9824-36. Disponible en: https://pubs.acs.org/doi/10.1021/acs.jmedchem.9b01220
93. Gao Z, Chen Y, Cai X, Xu R. Predict drug permeability to blood–brain-barrier from clinical phenotypes: drug side effects and drug indications. Bioinformatics . 15 de marzo de 2017 ;33(6):901-8. Disponible en: https://doi.org/10.1093/bioinformatics/btw713
94. Kosinska GP, Ognichenko LM, Shyrykalova AO, Burdina YaF, Artemenko AG, Kuz’min VE. Influence of Chemical Structure of Molecules on Blood–Brain Barrier Permeability on the Pampa Model. Theor Exp Chem . 1 de marzo de 2022 ;58(1):29- 33. Disponible en: https://doi.org/10.1007/s11237-022-09718-5
95. Suenderhauf C, Hammann F, Huwyler J. Computational Prediction of Blood-Brain Barrier Permeability Using Decision Tree Induction. Molecules . septiembre de 2012 ;17(9):10429-45. Disponible en: https://www.mdpi.com/1420-3049/17/9/10429
96. S. Coimbra JT, Feghali R, P. Ribeiro R, J. Ramos M, A. Fernandes P. The importance of intramolecular hydrogen bonds on the translocation of the small drug piracetam through a lipid bilayer. RSC Adv . 2021 ;11(2):899-908. Disponible en: https://pubs.rsc.org/en/content/articlelanding/2021/ra/d0ra09995c
97. León F, Gao J, Dale O, Wu Y, Habib E, Husni A, et al. Secondary Metabolites from Eupenicillium parvum and Their in Vitro Binding Affinity for Human Opioid and Cannabinoid Receptors. Planta Med . 28 de noviembre de 2013 ;79(18):1756-61. Disponible en: http://www.thieme-connect.de/DOI/DOI?10.1055/s-0033-1351099
98. Joseph TT, Bu W, Lin W, Zoubak L, Yeliseev A, Liu R, et al. Ketamine Metabolite (2R,6R)-Hydroxynorketamine Interacts with μ and κ Opioid Receptors. ACS Chem Neurosci . 5 de mayo de 2021 ;12(9):1487-97. Disponible en: https://doi.org/10.1021/acschemneuro.0c00741
99. Sharma VK, Srivedavyasasri R, Ali Z, Zjawiony JK, Ross SA, Ferreira D, et al. Rotenoids and Other Specialized Metabolites from the Roots of Mirabilis multiflora : Opioid and Cannabinoid Receptor Radioligand Binding Affinities. J Nat Prod . 23 de abril de 2021 ;84(4):1392-6. Disponible en: https://pubs.acs.org/doi/10.1021/acs.jnatprod.0c00939
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spelling Mahecha Jiménez, Oscar JavierRodríguez Lopez, Edwin AlexanderGiraldo Muñoz, Ariany YoalyMahecha, Oscar Javier [0000-0002-8682-0020]2024-10-22T20:52:44Z2024-10-22T20:52:44Z2024-08-05http://hdl.handle.net/11349/41913La creciente preocupación por el uso del fentanilo, debido a su abuso y efectos adversos, subraya la necesidad urgente de encontrar alternativas terapéuticas más seguras. El objetivo general de esta investigación evaluar la interacción de metabolitos secundarios con el receptor opioide mu (Mor) y su uso como inhibidores de fentanilo a través de un enfoque in silico. El proceso de investigación se dividió en varias etapas clave: inicialmente, se realizó un acoplamiento molecular detallado para evaluar la interacción inicial de los compuestos con el receptor. Posteriormente, se llevó a cabo un acoplamiento masivo utilizando una amplia base de datos de compuestos naturales, seguido por un análisis ADMET para evaluar la absorción, distribución, metabolismo, excreción y toxicidad de los compuestos seleccionados. Los resultados del acoplamiento mostraron consistencia con la estructura cristalina del receptor, validando la metodología empleada. Los compuestos más prometedores fueron ZINC_1297, ZINC_287, ZINC_1299, ZINC_1474, ZINC_1793, ZINC_2014, ZINC_819, ZINC_2302, ZINC_1605, ZINC_2050, ZINC_2179 y ZINC_2513, aquellos que no violaban más de tres reglas de Lipinski, asegurando su viabilidad como fármacos orales efectivos. Además, se exploró la posible relación entre los puentes de hidrógeno y la permeabilidad de la barrera hematoencefálica, sugiriendo que estas interacciones pueden facilitar el paso de los compuestos al cerebro. En conclusión, esta investigación no solo avanza en la identificación de posibles inhibidores naturales del fentanilo, sino que también establece un marco metodológico robusto para futuras exploraciones de compuestos naturales en el tratamiento de adicciones y manejo del dolor, contribuyendo significativamente a la mitigación de la crisis de los opioides y al desarrollo de terapias más seguras y efectivas.The growing concern over the use of fentanyl, due to its abuse and adverse effects, underscores the urgent need to find safer therapeutic alternatives. The general objective of this research is to evaluate the interaction of secondary metabolites with the mu opioid receptor (MOR) and their use as fentanyl inhibitors through an in silico approach. The research process was divided into several key stages: initially, detailed molecular docking was performed to evaluate the initial interaction of the compounds with the receptor. Subsequently, massive docking was carried out using a broad database of natural compounds, followed by an ADMET analysis to evaluate the absorption, distribution, metabolism, excretion, and toxicity of the selected compounds. The docking results showed consistency with the receptor's crystal structure, validating the methodology used. The most promising compounds were ZINC_1297, ZINC_287, ZINC_1299, ZINC_1474, ZINC_1793, ZINC_2014, ZINC_819, ZINC_2302, ZINC_1605, ZINC_2050, ZINC_2179, and ZINC_2513, those that did not violate more than three of Lipinski's rules, ensuring their viability as effective oral drugs. Additionally, the possible relationship between hydrogen bonds and the permeability of the blood-brain barrier was explored, suggesting that these interactions may facilitate the passage of the compounds into the brain. In conclusion, this research not only advances the identification of potential natural inhibitors of fentanyl but also establishes a robust methodological framework for future explorations of natural compounds in addiction treatment and pain management, significantly contributing to mitigating the opioid crisis and developing safer and more effective therapies.pdfspaUniversidad Distrital Francisco José de CaldasFentaniloReceptor opioide muMetabolitos secundariosAcoplamiento molecularCompuestos naturales.Licenciatura en Biología -- Tesis y disertaciones académicasInhibidores naturales del fentaniloAcoplamiento molecular y análisis ADMETDesarrollo de alternativas terapéuticasCrisis de opioides y seguridad farmacológicaFentanylMu opioid receptorSecondary metabolitesMolecular dockingNatural compounds.Evaluación in silico de flavonoides como potenciales inhibidores del fentaniloIn silico evaluation of flavonoids as potential fentanyl inhibitorsbachelorThesisInvestigación-Innovacióninfo:eu-repo/semantics/bachelorThesishttp://purl.org/coar/resource_type/c_7a1fAbierto (Texto Completo)http://purl.org/coar/access_right/c_abf21. Synthetic opioids: a review and clinical update - Abu Shafi, Alex J. Berry, Harry Sumnall, David M. Wood, Derek K. Tracy, 2022. Disponible en: https://journals.sagepub.com/doi/full/10.1177/204512532211396162. Maclean JC, Mallatt J, Ruhm CJ, Simon K. Economic Studies on the Opioid Crisis: A Review . National Bureau of Economic Research; 2020 . (Working Paper Series). Disponible en: https://www.nber.org/papers/w280673. Fentanyls: Are we missing the signs? Highly potent and on the rise in Europe. Int J Drug Policy . 1 de julio de 2015 ;26(7):626-31. Disponible en: https://www.sciencedirect.com/science/article/abs/pii/S09553959150009734. Harborne JB, Williams CA. Advances in flavonoid research since 1992. Phytochemistry . noviembre de 2000 ;55(6):481-504. Disponible en: https://linkinghub.elsevier.com/retrieve/pii/S00319422000023515. Natural Products as Sources of New Drugs over the Nearly Four Decades from 01/1981 to 09/2019 | Journal of Natural Products . . Disponible en: https://pubs.acs.org/doi/full/10.1021/acs.jnatprod.9b012856. Zhuang Y, Wang Y, He B, He X, Zhou XE, Guo S, et al. Molecular recognition of morphine and fentanyl by the human μ-opioid receptor. Cell . 10 de noviembre de 2022 ;185(23):4361-4375.e19. Disponible en: https://www.cell.com/cell/abstract/S0092- 8674(22)01260-07. Manglik A, Kim TH, Masureel M, Altenbach C, Yang Z, Hilger D, et al. Structural Insights into the Dynamic Process of β2-Adrenergic Receptor Signaling. Cell . 21 de mayo de 2015 ;161(5):1101-11. Disponible en: https://www.cell.com/cell/abstract/S0092-8674(15)00499-78. Hashemi ZS, Zarei M, Fath MK, Ganji M, Farahani MS, Afsharnouri F, et al. In silico Approaches for the Design and Optimization of Interfering Peptides Against Protein– 56 Protein Interactions. Front Mol Biosci . 28 de abril de 2021 ;8. Disponible en: https://www.frontiersin.org/journals/molecular biosciences/articles/10.3389/fmolb.2021.669431/full9. Lipinski CA, Lombardo F, Dominy BW, Feeney PJ. Experimental and computational approaches to estimate solubility and permeability in drug discovery and development settings. Adv Drug Deliv Rev . diciembre de 2012 ;64:4-17. Disponible en: https://linkinghub.elsevier.com/retrieve/pii/S0169409X1200279710. Comer SD, Cahill CM. Fentanyl: Receptor pharmacology, abuse potential, and implications for treatment. Neurosci Biobehav Rev . noviembre de 2019 ;106:49-57. Disponible en: https://linkinghub.elsevier.com/retrieve/pii/S014976341830207011. Full Opioid Agonists and Tramadol: Pharmacological and Clinical Considerations - PMC . . Disponible en: https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8520671/12. Han Y, Yan W, Zheng Y, Khan MZ, Yuan K, Lu L. The rising crisis of illicit fentanyl use, overdose, and potential therapeutic strategies. Transl Psychiatry . 11 de noviembre de 2019 ;9(1):1-9. Disponible en: https://www.nature.com/articles/s41398-019-0625-013. Bhatti MZ, Ismail H, Kayani WK, Bhatti MZ, Ismail H, Kayani WK. Plant Secondary Metabolites: Therapeutic Potential and Pharmacological Properties. En: Secondary Metabolites - Trends and Reviews . IntechOpen; 2022 . Disponible en: https://www.intechopen.com/chapters/8172814. Mishra P, Sohrab S, Mishra SK. A review on the phytochemical and pharmacological properties of Hyptis suaveolens (L.) Poit. Future J Pharm Sci . 12 de marzo de 2021 ;7(1):65. Disponible en: https://doi.org/10.1186/s43094-021-00219-115. Zhong HA. ADMET Properties: Overview and Current Topics. En: Grover A, editor. Drug Design: Principles and Applications . Singapore: Springer Singapore; 2017 . p. 113-33. Disponible en: http://link.springer.com/10.1007/978-981-10-5187-6_816. Poduri R, editor. Drug Discovery and Development: From Targets and Molecules to Medicines . Singapore: Springer Singapore; 2021 . Disponible en: https://link.springer.com/10.1007/978-981-15-5534-317. Hyland SJ, Brockhaus KK, Vincent WR, Spence NZ, Lucki MM, Howkins MJ, et al. Perioperative Pain Management and Opioid Stewardship: A Practical Guide. Healthcare . marzo de 2021 ;9(3):333. Disponible en: https://www.mdpi.com/2227- 9032/9/3/33318. Hilal B, Khan MM, Fariduddin Q. Recent advancements in deciphering the therapeutic properties of plant secondary metabolites: phenolics, terpenes, and alkaloids. Plant Physiol Biochem . 1 de junio de 2024 ;211:108674. Disponible en: https://www.sciencedirect.com/science/article/pii/S098194282400342519. Steglitz J, Buscemi J, Ferguson MJ. The future of pain research, education, and treatment: a summary of the IOM report “Relieving pain in America: a blueprint for transforming prevention, care, education, and research”. Transl Behav Med . marzo de 2012 ;2(1):6-8. Disponible en: https://www.ncbi.nlm.nih.gov/pmc/articles/PMC3717812/20. Katzung BG, editor. Basic & clinical pharmacology. Fourteenth edition. New York Chicago San Francisco Athens London Madrid Mexico City Milan New Delhi Singapore Sydney Toronto: McGraw-Hill Education; 2018. 1250 p. (A Lange medical book).21. Malik KM, Imani F, Beckerly R, Chovatiya R. Risk of Opioid Use Disorder from Exposure to Opioids in the Perioperative Period: A Systematic Review. Anesthesiol Pain Med . 19 de febrero de 2020 ;10(1):e101339. Disponible en: https://www.ncbi.nlm.nih.gov/pmc/articles/PMC7158240/22. CDC Warns of Surge in Drug Overdose Deaths During COVID-19 | Health Policy | JAMA Health Forum | JAMA Network . . Disponible en: https://jamanetwork.com/journals/jama-health-forum/fullarticle/277489823. Vo QN, Mahinthichaichan P, Shen J, Ellis CR. How μ-opioid receptor recognizes fentanyl. Nat Commun . 12 de febrero de 2021 ;12(1):984. Disponible en: https://www.nature.com/articles/s41467-021-21262-924. Hill R, Santhakumar R, Dewey W, Kelly E, Henderson G. Fentanyl depression of respiration: Comparison with heroin and morphine. Br J Pharmacol . 2020 ;177(2):254-65. Disponible en: https://onlinelibrary.wiley.com/doi/abs/10.1111/bph.1486025. Darcq E, Kieffer BL. Opioid receptors: drivers to addiction? Nat Rev Neurosci . agosto de 2018 ;19(8):499-514. Disponible en: https://www.nature.com/articles/s41583-018-0028-x26. Mahinthichaichan P, Vo QN, Ellis CR, Shen J. Kinetics and Mechanism of Fentanyl Dissociation from the μ-Opioid Receptor. JACS Au . 27 de diciembre de 2021 ;1(12):2208-15. Disponible en: https://doi.org/10.1021/jacsau.1c0034127. Kelly E, Sutcliffe K, Cavallo D, Ramos-Gonzalez N, Alhosan N, Henderson G. The anomalous pharmacology of fentanyl. Br J Pharmacol . 2023 ;180(7):797-812. Disponible en: https://onlinelibrary.wiley.com/doi/abs/10.1111/bph.1557328. Ehrlich AT, Kieffer BL, Darcq E. Current strategies toward safer mu opioid receptor drugs for pain management. Expert Opin Ther Targets . 3 de abril de 2019 ;23(4):315- 26. Disponible en: https://www.tandfonline.com/doi/full/10.1080/14728222.2019.158688229. Guryanov I, Fiorucci S, Tennikova T. Receptor-ligand interactions: Advanced biomedical applications. Mater Sci Eng C . noviembre de 2016 ;68:890-903. Disponible en: https://linkinghub.elsevier.com/retrieve/pii/S09284931163074630. Vizurraga A, Adhikari R, Yeung J, Yu M, Tall GG. Mechanisms of adhesion G protein–coupled receptor activation. J Biol Chem . 9 de octubre de 2020 ;295(41):14065-83. Disponible en: https://www.jbc.org/article/S0021-9258(17)49804- 8/abstract31. Williams JT, Ingram SL, Henderson G, Chavkin C, Von Zastrow M, Schulz S, et al. Regulation of µ -Opioid Receptors: Desensitization, Phosphorylation, Internalization, and Tolerance. Dolphin AC, editor. Pharmacol Rev . enero de 2013 ;65(1):223-54. Disponible en: http://pharmrev.aspetjournals.org/lookup/doi/10.1124/pr.112.00594232. Senese NB, Kandasamy R, Kochan KE, Traynor JR. Regulator of G-Protein Signaling (RGS) Protein Modulation of Opioid Receptor Signaling as a Potential Target for Pain Management. Front Mol Neurosci . 24 de enero de 2020 ;13. Disponible en: https://www.frontiersin.org/journals/molecular neuroscience/articles/10.3389/fnmol.2020.00005/full33. Koehl A, Hu H, Maeda S, Zhang Y, Qu Q, Paggi JM, et al. Structure of the µ-opioid receptor–Gi protein complex. Nature . junio de 2018 ;558(7711):547-52. Disponible en: https://www.nature.com/articles/s41586-018-0219-734. Sutcliffe KJ, Corey RA, Charlton SJ, Sessions RB, Henderson G, Kelly E. Fentanyl binds to the μ-opioid receptor via the lipid membrane and transmembrane helices . bioRxiv; 2021 . p. 2021.02.04.429703. Disponible en: https://www.biorxiv.org/content/10.1101/2021.02.04.429703v135. Chopra B, Dhingra AK. Natural products: A lead for drug discovery and development. Phytother Res . septiembre de 2021 ;35(9):4660-702. Disponible en: https://onlinelibrary.wiley.com/doi/10.1002/ptr.709936. Singh VK, Kumar A. Secondary metabolites from endophytic fungi: Production, methods of analysis, and diverse pharmaceutical potential. Symbiosis . 1 de junio de 2023 ;90(2):111-25. Disponible en: https://doi.org/10.1007/s13199-023-00925-937. Kainhofer R. Screening of plant-derived compounds as potential new ligands at the µ opioid receptor.38. McCurdy CR, Scully SS. Analgesic substances derived from natural products (natureceuticals). Life Sci . diciembre de 2005 ;78(5):476-84. Disponible en: https://linkinghub.elsevier.com/retrieve/pii/S002432050500883039. Lim EY, Kim YT. Food-Derived Natural Compounds for Pain Relief in Neuropathic Pain. BioMed Res Int . 2016 ;2016(1):7917528. Disponible en: https://onlinelibrary.wiley.com/doi/abs/10.1155/2016/791752840. Katavic PL, Lamb K, Navarro H, Prisinzano TE. Flavonoids as Opioid Receptor Ligands: Identification and Preliminary Structure−Activity Relationships. J Nat Prod . 1 de agosto de 2007 ;70(8):1278-82. Disponible en: https://pubs.acs.org/doi/10.1021/np070194x41. Fudin, J. (2018) . Opioid Agonists, Partial Agonists, Antagonists: Oh My!. Pharmacy Times, 1-4 Disponible en: https://www.pharmacytimes.com/view/opioid agonists-partial-agonists-antagonists-oh-my42. Pasternak GW, Childers SR, Pan YX. Emerging Insights into Mu Opioid Pharmacology. En: Nader MA, Hurd YL, editores. Substance Use Disorders . Cham: Springer International Publishing; 2019 . p. 89-125. (Handbook of Experimental Pharmacology; vol. 258). Disponible en: http://link.springer.com/10.1007/164_2019_27043. Wold EA, Chen J, Cunningham KA, Zhou J. Allosteric Modulation of Class A GPCRs: Targets, Agents, and Emerging Concepts. J Med Chem . 10 de enero de 2019 ;62(1):88-127. Disponible en: https://pubs.acs.org/doi/10.1021/acs.jmedchem.8b0087544. Fan J, Fu A, Zhang L. Progress in molecular docking. Quant Biol . junio de 2019 ;7(2):83-9. Disponible en: https://onlinelibrary.wiley.com/doi/10.1007/s40484-019-0172- y45. Ellis CR, Kruhlak NL, Kim MT, Hawkins EG, Stavitskaya L. Predicting opioid receptor binding affinity of pharmacologically unclassified designer substances using molecular docking. PLOS ONE . 24 de mayo de 2018 ;13(5):e0197734. Disponible en: https://journals.plos.org/plosone/article?id=10.1371/journal.pone.019773446. Pinzi L, Rastelli G. Molecular Docking: Shifting Paradigms in Drug Discovery. Int J Mol Sci . 4 de septiembre de 2019 ;20(18):4331. Disponible en: https://www.mdpi.com/1422-0067/20/18/433147. Murail S, de Vries SJ, Rey J, Moroy G, Tufféry P. SeamDock: An Interactive and Collaborative Online Docking Resource to Assist Small Compound Molecular Docking. Front Mol Biosci . 17 de septiembre de 2021 ;8. Disponible en: https://www.frontiersin.org/journals/molecular biosciences/articles/10.3389/fmolb.2021.716466/full48. Wang Q. Protein-ligand Docking Application and Comparison using Discovery Studio and AutoDock [Thesis]. North Dakota State University; 2017 . Disponible en: https://library.ndsu.edu/ir/handle/10365/2836549. Dhorajiwala T, Halder S, Samant L. Comparative In Silico Molecular Docking Analysis of L-Threonine-3-Dehydrogenase, a Protein Target Against African Trypanosomiasis Using Selected Phytochemicals. J Appl Biotechnol Rep . 11 de septiembre de 2019 ;6(3):101-8. Disponible en: http://www.biotechrep.ir/article_93040.html50. Irwin JJ, Sterling T, Mysinger MM, Bolstad ES, Coleman RG. ZINC: A Free Tool to Discover Chemistry for Biology. J Chem Inf Model . 23 de julio de 2012 ;52(7):1757- 68. Disponible en: https://doi.org/10.1021/ci300127751. Tingle BI, Tang KG, Castanon M, Gutierrez JJ, Khurelbaatar M, Dandarchuluun C, et al. ZINC-22─A Free Multi-Billion-Scale Database of Tangible Compounds for Ligand Discovery. J Chem Inf Model . 27 de febrero de 2023 ;63(4):1166-76. Disponible en: https://doi.org/10.1021/acs.jcim.2c0125352. Sterling T, Irwin JJ. ZINC 15 – Ligand Discovery for Everyone. J Chem Inf Model . 23 de noviembre de 2015 ;55(11):2324-37. Disponible en: https://doi.org/10.1021/acs.jcim.5b0055953. Cheng F, Li W, Zhou Y, Shen J, Wu Z, Liu G, et al. admetSAR: A Comprehensive Source and Free Tool for Assessment of Chemical ADMET Properties. J Chem Inf Model . 26 de noviembre de 2012 ;52(11):3099-105. Disponible en: https://pubs.acs.org/doi/10.1021/ci300367a54. Wu F, Zhou Y, Li L, Shen X, Chen G, Wang X, et al. Computational Approaches in Preclinical Studies on Drug Discovery and Development. Front Chem . 11 de septiembre de 2020 ;8. Disponible en: https://www.frontiersin.org/journals/chemistry/articles/10.3389/fchem.2020.00726/full55. Daina A, Michielin O, Zoete V. SwissADME: a free web tool to evaluate pharmacokinetics, drug-likeness and medicinal chemistry friendliness of small molecules. Sci Rep . 3 de marzo de 2017 ;7(1):42717. Disponible en: https://www.nature.com/articles/srep4271756. Kerns EH, Di L. Drug-like properties: concepts, structure design and methods: from ADME to toxicity optimization. Amsterdam ; Boston: Academic Press; 2008. 526 p.57. Smith DA. Metabolism, Pharmacokinetics and Toxicity of Functional Groups: Impact of Chemical Building Blocks on ADMET. Royal Society of Chemistry; 2010. 545 p.58. Pang KS. Modeling of intestinal drug absorption: roles of transporters and metabolic enzymes (for the gillette review series). Drug Metab Dispos . diciembre de 2003 ;31(12):1507-19. Disponible en: http://dmd.aspetjournals.org/lookup/doi/10.1124/dmd.31.12.150759. Guengerich FP. Cytochrome P450s and other enzymes in drug metabolism and toxicity. AAPS J . marzo de 2006 ;8(1):E101-11. Disponible en: http://link.springer.com/10.1208/aapsj08011260. Yang W, Lipert M, Nofsinger R. Current screening, design, and delivery approaches to address low permeability of chemically synthesized modalities in drug discovery and early clinical development. Drug Discov Today . 1 de septiembre de 2023 ;28(9):103685. Disponible en: https://www.sciencedirect.com/science/article/pii/S135964462300201561. Lipinski CA, Lombardo F, Dominy BW, Feeney PJ. Experimental and computational approaches to estimate solubility and permeability in drug discovery and 63 development settings. Adv Drug Deliv Rev . enero de 1997 ;23(1-3):3-25. Disponible en: https://linkinghub.elsevier.com/retrieve/pii/S0169409X9600423162. Meng EC, Goddard TD, Pettersen EF, Couch GS, Pearson ZJ, Morris JH, et al. UCSF ChimeraX: Tools for structure building and analysis. Protein Sci . 2023 ;32(11):e4792. Disponible en: https://onlinelibrary.wiley.com/doi/abs/10.1002/pro.479263. Bosquez-Berger T.A. (2023). Impact of µor-negative allosteric modulators on cellular, behavioral, and molecular aspects of fentanyl signaling.Indiana University64. Uehara S, Tanaka S. AutoDock-GIST: Incorporating Thermodynamics of Active Site Water into Scoring Function for Accurate Protein-Ligand Docking. Molecules . 23 de noviembre de 2016 ;21(11):1604. Disponible en: https://www.mdpi.com/1420- 3049/21/11/160465. Madhavi Sastry G, Adzhigirey M, Day T, Annabhimoju R, Sherman W. Protein and ligand preparation: parameters, protocols, and influence on virtual screening enrichments. J Comput Aided Mol Des . marzo de 2013 ;27(3):221-34. Disponible en: http://link.springer.com/10.1007/s10822-013-9644-866. El-Hachem N, Haibe-Kains B, Khalil A, Kobeissy FH, Nemer G. AutoDock and AutoDockTools for Protein-Ligand Docking: Beta-Site Amyloid Precursor Protein Cleaving Enzyme 1(BACE1) as a Case Study. En: Kobeissy FH, Stevens, SM, editores. Neuroproteomics . New York, NY: Springer New York; 2017 . p. 391-403. (Methods in Molecular Biology; vol. 1598). Disponible en: http://link.springer.com/10.1007/978-1- 4939-6952-4_2067. Arifian H, Maharani R, Megantara S, Ikram NKK, Muchtaridi M. Glycine Conjugated α-Mangostins as Potential Estrogen Receptor Alpha (ERα) Antagonists through Pharmacophore Modeling, Docking Analysis, and Molecular Dynamics Simulations. Appl Sci . enero de 2024 ;14(13):5549. Disponible en: https://www.mdpi.com/2076-3417/14/13/554968. Manjunathan R, Periyaswami V, Mitra K, Rosita AS, Pandya M, Selvaraj J, et al. Molecular docking analysis reveals the functional inhibitory effect of Genistein and Quercetin on TMPRSS2: SARS-COV-2 cell entry facilitator spike protein. BMC Bioinformatics . 16 de mayo de 2022 ;23(1):180. Disponible en: https://doi.org/10.1186/s12859-022-04724-969. Sorokina M, Steinbeck C. Review on natural products databases: where to find data in 2020. J Cheminformatics . 3 de abril de 2020 ;12(1):20.70. Fanelli A, Sullivan ML. Chapter Three - Tools for protein structure prediction and for molecular docking applied to enzyme active site analysis: A case study using a BAHD hydroxycinnamoyltransferase. En: Jez J, editor. Methods in Enzymology . Academic Press; 2023 . p. 41-79. (Biochemical Pathways and Environmental Responses in Plants: Part C; vol. 683). Disponible en: https://www.sciencedirect.com/science/article/pii/S007668792200421971. Jairajpuri DS, Hussain A, Nasreen K, Mohammad T, Anjum F, Tabish Rehman Md, et al. Identification of natural compounds as potent inhibitors of SARS-CoV-2 main protease using combined docking and molecular dynamics simulations. Saudi J Biol Sci . 1 de abril de 2021 ;28(4):2423-31. Disponible en: https://www.sciencedirect.com/science/article/pii/S1319562X2100040172. de Araújo ACJ, Freitas PR, Araújo IM, Siqueira GM, de Oliveira Borges JA, Alves DS, et al. Potentiating-antibiotic activity and absorption, distribution, metabolism, excretion and toxicity properties (ADMET) analysis of synthetic thiadiazines against multi-drug resistant (MDR) strains. Fundam Clin Pharmacol . 2024 ;38(1):84-98. Disponible en: https://onlinelibrary.wiley.com/doi/abs/10.1111/fcp.1295073. Patil VS, Patil PA. Molecular Docking: A useful approach of Drug Discovery on the Basis of their Structure. Asian J Pharm Res . 7 de septiembre de 2023 ;13(3):191-5. Disponible en: https://asianjpr.com/AbstractView.aspx?PID=2023-13-3-1074. Bottegoni G, Kufareva I, Totrov M, Abagyan R. Four-Dimensional Docking: A Fast and Accurate Account of Discrete Receptor Flexibility in Ligand Docking. J Med Chem . 22 de enero de 2009 ;52(2):397-406. Disponible en: https://pubs.acs.org/doi/10.1021/jm800995875. Bell EW, Zhang Y. DockRMSD: an open-source tool for atom mapping and RMSD calculation of symmetric molecules through graph isomorphism. J Cheminformatics . 7 de junio de 2019 ;11(1):40. Disponible en: https://doi.org/10.1186/s13321-019-0362-776. Janjua TI, Rewatkar P, Ahmed-Cox A, Saeed I, Mansfeld FM, Kulshreshtha R, et al. Frontiers in the treatment of glioblastoma: Past, present and emerging. Adv Drug Deliv Rev . 1 de abril de 2021 ;171:108-38. Disponible en: https://www.sciencedirect.com/science/article/pii/S0169409X2100022377. Du X, Li Y, Xia YL, Ai SM, Liang J, Sang P, et al. Insights into Protein–Ligand Interactions: Mechanisms, Models, and Methods. Int J Mol Sci . febrero de 2016 ;17(2):144. Disponible en: https://www.mdpi.com/1422-0067/17/2/14478. Jeffrey P, Summerfield SG. Challenges for blood–brain barrier (BBB) screening. Xenobiotica . noviembre de 2007 ;37(10-11):1135-51. Disponible en: http://www.tandfonline.com/doi/full/10.1080/0049825070157028579. Liu S, Wen X, Zhang X, Mao S. Oral delivery of biomacromolecules by overcoming biological barriers in the gastrointestinal tract: an update. Expert Opin Drug Deliv. 2023;20(10):1333-47.80. Hong Y, Ha J, Lim CJ, Oh KS, Chandrasekaran R, Kim B, et al. Accurate Prediction of Protein-Ligand Interactions by Combining Physical Energy Functions and Graph Neural Networks . 2024 . Disponible en: https://www.researchsquare.com/article/rs 3887850/v181. Aljuaid A, Salam A, Almehmadi M, Baammi S, Alshabrmi FM, Allahyani M, et al. Structural Homology-Based Drug Repurposing Approach for Targeting NSP12 SARS- 66 CoV-2. Molecules . enero de 2022 ;27(22):7732. Disponible en: https://www.mdpi.com/1420-3049/27/22/773282. Klebe G. Protein–Ligand Interactions as the Basis for Drug Action. En: Klebe G, editor. Drug Design . Berlin, Heidelberg: Springer Berlin Heidelberg; 2013 . p. 61-88. Disponible en: http://link.springer.com/10.1007/978-3-642-17907-5_483. Arthur DE, Akoji JN, Sahnoun R, Okafor GC, Abdullahi KL, Abdullahi SA, et al. A theoretical insight in interactions of some chemical compounds as mTOR inhibitors. Bull Natl Res Cent . 20 de marzo de 2021 ;45(1):67. Disponible en: https://doi.org/10.1186/s42269-021-00525-x84. Bulusu G, Desiraju GR. Strong and Weak Hydrogen Bonds in Protein–Ligand Recognition. J Indian Inst Sci . enero de 2020 ;100(1):31-41. Disponible en: http://link.springer.com/10.1007/s41745-019-00141-985. Madushanka A, Moura RT, Verma N, Kraka E. Quantum Mechanical Assessment of Protein–Ligand Hydrogen Bond Strength Patterns: Insights from Semiempirical Tight Binding and Local Vibrational Mode Theory. Int J Mol Sci . enero de 2023 ;24(7):6311. Disponible en: https://www.mdpi.com/1422-0067/24/7/631186. Liu W, Liu R, Qin Q, Wang H, Wu H, Ren J, et al. Interaction mechanisms of ACE inhibitory peptides: molecular docking and molecular dynamics simulation studies on five wheat gluten derived peptides. Eur Food Res Technol. 5 de abril de 2024;1-14.87. Amin N, Singh VK, Kannaujiya VK. Mycosporine-Like Amino Acids as a Potential Inhibitor of Tyrosinase-Related Protein 1: Computational Screening, Pharmacokinetics, and Molecular Dynamics Simulation. Mol Biotechnol. 23 de abril de 2024;88. Vesga LC, Ruiz-Hernández CA, Alvarez-Jacome JJ, Duque JE, Rincon-Orozco B, Mendez-Sanchez SC. Repurposing of Four Drugs as Anti-SARS-CoV-2 Agents and Their Interactions with Protein Targets. Sci Pharm . junio de 2022 ;90(2):24. Disponible en: https://www.mdpi.com/2218-0532/90/2/2489. Chu YC, Yang CS, Cheng MJ, Fu SL, Chen JJ. Comparison of Various Solvent Extracts and Major Bioactive Components from Unsalt-Fried and Salt-Fried Rhizomes of Anemarrhena asphodeloides for Antioxidant, Anti-α-Glucosidase, and Anti Acetylcholinesterase Activities. Antioxidants . febrero de 2022 ;11(2):385. Disponible en: https://www.mdpi.com/2076-3921/11/2/38590. Yang H, Lou C, Sun L, Li J, Cai Y, Wang Z, et al. admetSAR 2.0: web-service for prediction and optimization of chemical ADMET properties. Bioinformatics . 15 de marzo de 2019 ;35(6):1067-9. Disponible en: https://doi.org/10.1093/bioinformatics/bty70791. Perkin VO, Antonyan GV, Radchenko EV, Palyulin VA. Web Services for the Prediction of ADMET Parameters Relevant to the Design of Neuroprotective Drugs. En: Roy K, editor. Computational Modeling of Drugs Against Alzheimer’s Disease . New York, NY: Springer US; 2023 . p. 465-85. Disponible en: https://doi.org/10.1007/978-1- 0716-3311-3_1692. Gupta M, Lee HJ, Barden CJ, Weaver DF. The Blood–Brain Barrier (BBB) Score. J Med Chem . 14 de noviembre de 2019 ;62(21):9824-36. Disponible en: https://pubs.acs.org/doi/10.1021/acs.jmedchem.9b0122093. Gao Z, Chen Y, Cai X, Xu R. Predict drug permeability to blood–brain-barrier from clinical phenotypes: drug side effects and drug indications. Bioinformatics . 15 de marzo de 2017 ;33(6):901-8. Disponible en: https://doi.org/10.1093/bioinformatics/btw71394. Kosinska GP, Ognichenko LM, Shyrykalova AO, Burdina YaF, Artemenko AG, Kuz’min VE. Influence of Chemical Structure of Molecules on Blood–Brain Barrier Permeability on the Pampa Model. Theor Exp Chem . 1 de marzo de 2022 ;58(1):29- 33. Disponible en: https://doi.org/10.1007/s11237-022-09718-595. Suenderhauf C, Hammann F, Huwyler J. Computational Prediction of Blood-Brain Barrier Permeability Using Decision Tree Induction. Molecules . septiembre de 2012 ;17(9):10429-45. Disponible en: https://www.mdpi.com/1420-3049/17/9/1042996. S. Coimbra JT, Feghali R, P. Ribeiro R, J. Ramos M, A. Fernandes P. The importance of intramolecular hydrogen bonds on the translocation of the small drug piracetam through a lipid bilayer. RSC Adv . 2021 ;11(2):899-908. Disponible en: https://pubs.rsc.org/en/content/articlelanding/2021/ra/d0ra09995c97. León F, Gao J, Dale O, Wu Y, Habib E, Husni A, et al. Secondary Metabolites from Eupenicillium parvum and Their in Vitro Binding Affinity for Human Opioid and Cannabinoid Receptors. Planta Med . 28 de noviembre de 2013 ;79(18):1756-61. Disponible en: http://www.thieme-connect.de/DOI/DOI?10.1055/s-0033-135109998. Joseph TT, Bu W, Lin W, Zoubak L, Yeliseev A, Liu R, et al. Ketamine Metabolite (2R,6R)-Hydroxynorketamine Interacts with μ and κ Opioid Receptors. ACS Chem Neurosci . 5 de mayo de 2021 ;12(9):1487-97. Disponible en: https://doi.org/10.1021/acschemneuro.0c0074199. Sharma VK, Srivedavyasasri R, Ali Z, Zjawiony JK, Ross SA, Ferreira D, et al. Rotenoids and Other Specialized Metabolites from the Roots of Mirabilis multiflora : Opioid and Cannabinoid Receptor Radioligand Binding Affinities. J Nat Prod . 23 de abril de 2021 ;84(4):1392-6. 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