Triagem in silico de compostos com atividade ansiolítica encontrados na espécie Magnolia obovata

Camila Moreira Caetano Vaz; GIOVANNA SIQUEIRA BOCCHI; LEONARDO LUIZ BORGES

doi:10.32712/2446-4775.2024.1661

Triagem in silico de compostos com atividade ansiolítica encontrados na espécie Magnolia obovata

v. 18 (2024)
24/01/2024
27/09/2024
https://doi.org/10.32712/2446-4775.2024.1661

Camila Moreira Caetano Vaz
GIOVANNA SIQUEIRA BOCCHI
LEONARDO LUIZ BORGES

Palavras-chave

Plantas Medicinais

Efeito Central

Modelagem Farmacofórica

in silico

Resumo

A Magnolia obovata, conhecida como “pepino japonês”, é uma árvore caducifólia de origem asiática, constituindo-se uma planta medicinal pelos seus efeitos anti-inflamatório, ansiolítico, antidepressivo, entre outros efeitos centrais, já demonstrados em literatura. O objetivo deste estudo foi sugerir os mecanismos de atuação para efeitos no sistema nervoso central dos compostos identificados na espécie M. obovata, sobretudo quanto ao efeito ansiolítico atualmente buscado com o uso da planta. Foram identificados 19 compostos presentes na M. obovata, sendo que apenas 2 moléculas (alfa-eudesmol e gama-eudesmol) apresentaram propriedades farmacocinéticas e toxicológicas in silico favoráveis à bioatividade ansiolítica. Tais moléculas inibem a hidrolase de acilcarnitina e aumentam a acilcarnitina livre, possivelmente gerando o efeito ansiolítico. A modelagens farmacofóricas dessas moléculas apresentaram 6 pontos de interação com os cinco ligantes mais potentes conhecidos da hidrolase de acilcarnitina e tal semelhança estrutural é promissora para atuação sobre esse alvo. Há vantagens do mecanismo de ação alternativo desse composto em relação aos ansiolíticos atuais, que poderá ser utilizado para formulação de novas terapias no tratamento do transtorno de ansiedade. Os resultados aqui obtidos abrem perspectivas para futuros testes em modelos in vitro e in vivo, visando confirmar os resultados das análises computacionais.

Referências

Sarmento CG, Pinto VMJ, Santos KKB, Maniçoba BBP, Borges PM. Estudo bibliográfico sobre o uso das plantas medicinais e fitoterápicos no Brasil. Revista Verde de Agroecologia e Desenvolvimento Sustentável. 2013;8(5):208–12. Disponível em: https://www.gvaa.com.br/revista/index.php/RVADS/article/view/3577
Carneiro FM, José M, Albernaz LC, Darc J, Costa P. Tendências Dos Estudos Com Plantas Medicinais No Brasil. Revista Sapiência: sociedade, saberes e práticas educacionais. 2014;3(2):44–75. Disponível em: https://www.revista.ueg.br/index.php/sapiencia/article/view/2954
Viegas C, Bolzani S, Química I De, Estadual U, Julio P, Filho DM, et al. Os produtos naturais e a Química Medicinal Moderna. Química Nova. 2006;29(2):326–37. DOI: https://doi.org/10.1590/S0100-40422006000200025
Ham HJ, Lee YS, Yun J, Han SB, Son DJ, Hong JT. Anxiolytic-like effects of the ethanol extract of Magnolia obovata leaves through its effects on GABA-benzodiazepine receptor and neuroinflammation. Behavioural Brain Research. 2020 abr;383:112518. DOI: https://doi.org/10.1016/j.bbr.2020.112518
Mangolini VI, Andrade LH, Wang YP. Epidemiologia dos transtornos de ansiedade em regiões do Brasil. Rev Med (Rio J). 2019 nov 27;98(6):415–22. DOI: https://doi.org/10.11606/issn.1679-9836.v98i6p415-422
Slee A, Nazareth I, Bondaronek P, Liu Y, Cheng Z, Freemantle N. Pharmacological treatments for generalised anxiety disorder: a systematic review and network meta-analysis. The Lancet. 2019 fev;393(10173):768–77. DOI: https://doi.org/10.1016/s0140-6736(18)31793-8
Shaker B, Ahmad S, Lee J, Jung C, Na D. In silico methods and tools for drug discovery. Comput Biol Med. 2021;137(September):104851. DOI: https://doi.org/10.1016/j.compbiomed.2021.104851
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. 2017;7(January):1–13. DOI: https://doi.org/10.1038/srep42717
Filimonov DA, Lagunin AA, Gloriozova TA, Rudik A V., Druzhilovskii DS, Pogodin P V., et al. Prediction of the biological activity spectra of organic compounds using the pass online web resource. Chem Heterocycl Compd (N Y). 2014;50(3):444–57. DOI: https://doi.org/10.1007/s10593-014-1496-1
Banerjee P, Eckert AO, Schrey AK, Preissner R. ProTox-II: A webserver for the prediction of toxicity of chemicals. Nucleic Acids Res. 2018;46(W1):W257–63. DOI: https://doi.org/10.1093/nar/gky318
Schneidman-Duhovny D, Dror O, Inbar Y, Nussinov R, Wolfson HJ. PharmaGist: a webserver for ligand-based pharmacophore detection. Nucleic Acids Res. 2008;36(Web Server issue):223–8. DOI: https://doi.org/10.1093%2Fnar%2Fgkn187
Lee YJ, Lee YM, Lee CK, Jung JK, Han SB, Hong JT. Therapeutic applications of compounds in the Magnolia family. Pharmacol Ther. 2011 maio;130(2):157–76. DOI: https://doi.org/10.1016/j.pharmthera.2011.01.010
Fuchs A, Baur R, Schoeder C, Sigel E, Müller CE. Structural analogues of the natural products magnolol and honokiol as potent allosteric potentiators of GABAA receptors. Bioorg Med Chem. 2014;22(24):6908–17. DOI: https://doi.org/10.1016/j.bmc.2014.10.027
Chagas CM, Moss S, Alisaraie L. Drug metabolites and their effects on the development of adverse reactions: Revisiting Lipinski’s Rule of Five. Int J Pharm. 2018;549(1–2):133–49. DOI: https://doi.org/10.1016/j.ijpharm.2018.07.046
Daina A, Michielin O, Zoete V. SwissTargetPrediction: updated data and new features for efficient prediction of protein targets of small molecules. Nucleic Acids Res. 2019;47(W1):W357–3664. DOI: https://doi.org/10.1093/nar/gkz382
Gallo K, Goede A, Preissner R, Gohlke BO. SuperPred 3.0: drug classification and target prediction - a machine learning approach. Nucleic Acids Res. 2022;50(W1):W726–31. DOI: https://doi.org/10.1093/nar/gkac297
Chen R, Liu X, Jin S, Lin J, Liu J. Machine Learning for Drug-Target Interaction Prediction. Molecules. 2018 ago 31;23(9):2208. DOI: https://doi.org/10.3390/molecules23092208
Ahmed AT, MahmoudianDehkordi S, Bhattacharyya S, Arnold M, Liu D, Neavin D, et al. Acylcarnitine metabolomic profiles inform clinically-defined major depressive phenotypes. J Affect Disord. 2020;264(May 2019):90–7. DOI: https://doi.org/10.1016%2Fj.jad.2019.11.122
Cuccurazzu B, Bortolotto V, Valente MM, Ubezio F, Koverech A, Canonico PL, et al. Upregulation of mGlu2 Receptors via NF-κB p65 Acetylation Is Involved in the Proneurogenic and Antidepressant Effects of Acetyl-L-Carnitine. Neuropsychopharmacology. 2013 out 14;38(11):2220–30. DOI: https://doi.org/10.1038/npp.2013.121
Bigio B, Mathé AA, Sousa VC, Zelli D, Svenningsson P, McEwen BS, et al. Epigenetics and energetics in ventral hippocampus mediate rapid antidepressant action: Implications for treatment resistance. Proceedings of the National Academy of Sciences. 2016 jul 12;113(28):7906–11. DOI: https://doi.org/10.1073%2Fpnas.1603111113
Tashiro K, Kaida Y, Yamagishi S ichi, Tanaka H, Yokoro M, Yano J, et al. L-carnitine Supplementation Improves Self-rating Depression Scale Scores in Uremic Male Patients Undergoing Hemodialysis. Lett Drug Des Discov. 2017;14(6):737–42. DOI: https://doi.org/10.2174%2F1570180814666170216102632
Sarzi-Puttini P, Giorgi V, di Lascio S, Fornasari D. Acetyl-L-carnitine in chronic pain: A narrative review. Pharmacol Res. 2021 nov;173:105874. DOI: https://doi.org/10.1016/j.phrs.2021.105874
Strawn JR, Geracioti L, Rajdev N, Clemenza K, Levine A. Pharmacotherapy for generalized anxiety disorder in adult and pediatric patients: an evidence-based treatment review. Expert Opin Pharmacother. 2018 jul 3;19(10):1057–70. DOI: https://doi.org/10.1080/14656566.2018.1491966
Delpino FM, da Silva CN, Jerônimo JS, Mulling ES, da Cunha LL, Weymar MK, et al. Prevalence of anxiety during the COVID-19 pandemic: A systematic review and meta-analysis of over 2 million people. J Affect Disord. 2022 dez;318:272–82. DOI: https://doi.org/10.1016/j.jad.2022.09.003
Giacobbe P, Flint A. Diagnosis and Management of Anxiety Disorders. CONTINUUM: Lifelong Learning in Neurology. 2018 jun;24(3):893–919. DOI: https://doi.org/10.1212/con.0000000000000607

PDF

HTML

Salvar em favoritos

Autor(es)

Camila Moreira Caetano Vaz
PONTIFÍCIA UNIVERSIDADE CATÓLICA DE GOIÁS
GIOVANNA SIQUEIRA BOCCHI
Pontifícia Universidade Católica de Goiás
https://orcid.org/0009-0004-6000-1889
LEONARDO LUIZ BORGES
Pontifícia Universidade Católica de Goiás
https://orcid.org/0000-0003-2183-3944

Categorias

Plantas Bioativas

Métricas

Artigo visto 62 vez(es)

Como Citar

Triagem in silico de compostos com atividade ansiolítica encontrados na espécie Magnolia obovata . Rev Fitos [Internet]. 27º de setembro de 2024 [citado 30º de outubro de 2024];18. Disponível em: https://teste.revistafitos.far.fiocruz.br/index.php/revista-fitos/article/view/1661

Este trabalho está licenciado sob uma licença Creative Commons Attribution 4.0 International License.

[1] Sarmento CG, Pinto VMJ, Santos KKB, Maniçoba BBP, Borges PM. Estudo bibliográfico sobre o uso das plantas medicinais e fitoterápicos no Brasil. Revista Verde de Agroecologia e Desenvolvimento Sustentável. 2013;8(5):208–12. Disponível em: https://www.gvaa.com.br/revista/index.php/RVADS/article/view/3577

[2] Carneiro FM, José M, Albernaz LC, Darc J, Costa P. Tendências Dos Estudos Com Plantas Medicinais No Brasil. Revista Sapiência: sociedade, saberes e práticas educacionais. 2014;3(2):44–75. Disponível em: https://www.revista.ueg.br/index.php/sapiencia/article/view/2954

[3] Viegas C, Bolzani S, Química I De, Estadual U, Julio P, Filho DM, et al. Os produtos naturais e a Química Medicinal Moderna. Química Nova. 2006;29(2):326–37. DOI: https://doi.org/10.1590/S0100-40422006000200025

[4] Ham HJ, Lee YS, Yun J, Han SB, Son DJ, Hong JT. Anxiolytic-like effects of the ethanol extract of Magnolia obovata leaves through its effects on GABA-benzodiazepine receptor and neuroinflammation. Behavioural Brain Research. 2020 abr;383:112518. DOI: https://doi.org/10.1016/j.bbr.2020.112518

[5] Mangolini VI, Andrade LH, Wang YP. Epidemiologia dos transtornos de ansiedade em regiões do Brasil. Rev Med (Rio J). 2019 nov 27;98(6):415–22. DOI: https://doi.org/10.11606/issn.1679-9836.v98i6p415-422

[6] Slee A, Nazareth I, Bondaronek P, Liu Y, Cheng Z, Freemantle N. Pharmacological treatments for generalised anxiety disorder: a systematic review and network meta-analysis. The Lancet. 2019 fev;393(10173):768–77. DOI: https://doi.org/10.1016/s0140-6736(18)31793-8

[7] Shaker B, Ahmad S, Lee J, Jung C, Na D. In silico methods and tools for drug discovery. Comput Biol Med. 2021;137(September):104851. DOI: https://doi.org/10.1016/j.compbiomed.2021.104851

[8] 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. 2017;7(January):1–13. DOI: https://doi.org/10.1038/srep42717

[9] Filimonov DA, Lagunin AA, Gloriozova TA, Rudik A V., Druzhilovskii DS, Pogodin P V., et al. Prediction of the biological activity spectra of organic compounds using the pass online web resource. Chem Heterocycl Compd (N Y). 2014;50(3):444–57. DOI: https://doi.org/10.1007/s10593-014-1496-1

[10] Banerjee P, Eckert AO, Schrey AK, Preissner R. ProTox-II: A webserver for the prediction of toxicity of chemicals. Nucleic Acids Res. 2018;46(W1):W257–63. DOI: https://doi.org/10.1093/nar/gky318

[11] Schneidman-Duhovny D, Dror O, Inbar Y, Nussinov R, Wolfson HJ. PharmaGist: a webserver for ligand-based pharmacophore detection. Nucleic Acids Res. 2008;36(Web Server issue):223–8. DOI: https://doi.org/10.1093%2Fnar%2Fgkn187

[12] Lee YJ, Lee YM, Lee CK, Jung JK, Han SB, Hong JT. Therapeutic applications of compounds in the Magnolia family. Pharmacol Ther. 2011 maio;130(2):157–76. DOI: https://doi.org/10.1016/j.pharmthera.2011.01.010

[13] Fuchs A, Baur R, Schoeder C, Sigel E, Müller CE. Structural analogues of the natural products magnolol and honokiol as potent allosteric potentiators of GABAA receptors. Bioorg Med Chem. 2014;22(24):6908–17. DOI: https://doi.org/10.1016/j.bmc.2014.10.027

[14] Chagas CM, Moss S, Alisaraie L. Drug metabolites and their effects on the development of adverse reactions: Revisiting Lipinski’s Rule of Five. Int J Pharm. 2018;549(1–2):133–49. DOI: https://doi.org/10.1016/j.ijpharm.2018.07.046

[15] Daina A, Michielin O, Zoete V. SwissTargetPrediction: updated data and new features for efficient prediction of protein targets of small molecules. Nucleic Acids Res. 2019;47(W1):W357–3664. DOI: https://doi.org/10.1093/nar/gkz382

[16] Gallo K, Goede A, Preissner R, Gohlke BO. SuperPred 3.0: drug classification and target prediction - a machine learning approach. Nucleic Acids Res. 2022;50(W1):W726–31. DOI: https://doi.org/10.1093/nar/gkac297

[17] Chen R, Liu X, Jin S, Lin J, Liu J. Machine Learning for Drug-Target Interaction Prediction. Molecules. 2018 ago 31;23(9):2208. DOI: https://doi.org/10.3390/molecules23092208

[18] Ahmed AT, MahmoudianDehkordi S, Bhattacharyya S, Arnold M, Liu D, Neavin D, et al. Acylcarnitine metabolomic profiles inform clinically-defined major depressive phenotypes. J Affect Disord. 2020;264(May 2019):90–7. DOI: https://doi.org/10.1016%2Fj.jad.2019.11.122

[19] Cuccurazzu B, Bortolotto V, Valente MM, Ubezio F, Koverech A, Canonico PL, et al. Upregulation of mGlu2 Receptors via NF-κB p65 Acetylation Is Involved in the Proneurogenic and Antidepressant Effects of Acetyl-L-Carnitine. Neuropsychopharmacology. 2013 out 14;38(11):2220–30. DOI: https://doi.org/10.1038/npp.2013.121

[20] Bigio B, Mathé AA, Sousa VC, Zelli D, Svenningsson P, McEwen BS, et al. Epigenetics and energetics in ventral hippocampus mediate rapid antidepressant action: Implications for treatment resistance. Proceedings of the National Academy of Sciences. 2016 jul 12;113(28):7906–11. DOI: https://doi.org/10.1073%2Fpnas.1603111113

[21] Tashiro K, Kaida Y, Yamagishi S ichi, Tanaka H, Yokoro M, Yano J, et al. L-carnitine Supplementation Improves Self-rating Depression Scale Scores in Uremic Male Patients Undergoing Hemodialysis. Lett Drug Des Discov. 2017;14(6):737–42. DOI: https://doi.org/10.2174%2F1570180814666170216102632

[22] Sarzi-Puttini P, Giorgi V, di Lascio S, Fornasari D. Acetyl-L-carnitine in chronic pain: A narrative review. Pharmacol Res. 2021 nov;173:105874. DOI: https://doi.org/10.1016/j.phrs.2021.105874

[23] Strawn JR, Geracioti L, Rajdev N, Clemenza K, Levine A. Pharmacotherapy for generalized anxiety disorder in adult and pediatric patients: an evidence-based treatment review. Expert Opin Pharmacother. 2018 jul 3;19(10):1057–70. DOI: https://doi.org/10.1080/14656566.2018.1491966

[24] Delpino FM, da Silva CN, Jerônimo JS, Mulling ES, da Cunha LL, Weymar MK, et al. Prevalence of anxiety during the COVID-19 pandemic: A systematic review and meta-analysis of over 2 million people. J Affect Disord. 2022 dez;318:272–82. DOI: https://doi.org/10.1016/j.jad.2022.09.003

[25] Giacobbe P, Flint A. Diagnosis and Management of Anxiety Disorders. CONTINUUM: Lifelong Learning in Neurology. 2018 jun;24(3):893–919. DOI: https://doi.org/10.1212/con.0000000000000607