In vivo and in silico evaluation of the lipid-lowering properties of the phospholipids constituents of LECISAN™
Keywords:
soy lecithin, preclinical experimentation, lipid profile, molecular dockingAbstract
Introduction: Nutritional supplements such as soy lecithin can have a lipid-lowering effect, and they are susceptible of being evaluated by in vivo and in silico methods.
Objective: To evaluate the atherogenic risk of soy lecithin in rats based on atherogenicity parameters and indexes and a predictive study of molecular coupling of its constituents with target proteins.
Methods: An experimental study of preclinical pharmacology was carried out in 2022. Soy lecithin was administered to two experimental groups of Wistar rats in doses considered maximum and minimum. It was compared with a control group that received habitual feeding. Biochemical variables of the lipid profile were estimated and risk indices were calculated. Docking was carried out using the AutoDock 4.2 program and with target proteins. Free energy, dissociation constant and ligand efficiency were estimated.
Results: The supplement reduced the atherogenicity indexes associated with the increase in serum lipid levels, as the dose increased. Positive correlations were observed between risk indexes and lipid profile parameters. The in silico predictive study revealed potential as a possible inhibitor of the lipoprotein lipase enzyme.
Conclusions: Soy lecithin supposes an apparently favorable effect as a possible inhibitor of the lipoprotein lipase enzyme, taking into consideration the analysis carried out when modifying the risk indexes.Downloads
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References
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2. Díaz-Ortega JL, Quispe-Tácunan A, Gallo-Ancajima M, et al. Indicadores de aterogenicidad en la predicción del síndrome metabólico en adultos, Trujillo-Perú. Rev chil nutr [Internet]. 2021 [citado 24/06/2023];48(4):586-94. Disponible en: http://www.scielo.cl/scielo.php?script=sci_arttext&pid=S0717-75182021000400586&lng=es
3. De la Torre-Cisneros K, Acosta-Rodríguez Z, Aragundi-Intriago V. Utilidad clínica de los índices aterogénicos para valoración de riesgo cardiovascular: un enfoque desde el laboratorio clínico. Dom Cien [Internet]. 2019 [citado 24/06/2023];5(3):57-70. Disponible en: https://dominiodelasciencias.com/ojs/index.php/es/article/view/924
4. El-Bacha T, Torres AG. Phospholipids: Physiology. En: Caballero B, Finglas P, Toldrá F. Encyclopedia of Food and Health [Internet]. Cambridge: Academic Press; 2016 [citado 24/06/2023]. Disponible en: https://shop.elsevier.com/books/encyclopedia-of-food-and-health/caballero/978-0-12-384947-2
5. McMaster CR. From yeast to humans - roles of the Kennedy pathway for phosphatidylcholine synthesis. FEBS lett. 2018;592(8):1256-72. DOI: 10.1002/1873-3468.12919.
6. Deol P, Kozlova E, Valdez M, et al. Dysregulation of Hypothalamic Gene Expression and the Oxytocinergic System by Soybean Oil Diets in Male Mice. Endocrinology. 2020;161(2):bqz044. DOI: 10.1210/endocr/bqz044.
7. Fernández I, Giacomino MS, Condori AI, et al. Efecto de la suplementación con ácidos grasos n-3 sobre el perfil de lípidos séricos de ratas. Rev Chil Nutr [Internet]. 2021 [citado 24/06/2023];48(2):170-8. Disponible en: http://www.scielo.cl/scielo.php?script=sci_arttext&pid=S0717-75182021000200170&lng=es
8. Li D, Ikaga R, Yamazaki T. Soya protein β-conglycinin ameliorates fatty liver and obesity in diet-induced obese mice through the down-regulation of PPARγ. Br J Nutr. 2018;119(11):1220-32. DOI: 10.1017/S0007114518000739.
9. Mu Y, Kou T, Wei B, et al. Soy Products Ameliorate Obesity-Related Anthropometric Indicators in Overweight or Obese Asian and Non-Menopausal Women: A Meta-Analysis of Randomized Controlled Trials. Nutrients. 2019;11(11):2790. DOI: 10.3390/nu11112790.
10. Barchuk M, Miksztowicz VJ, López G, et al. Actividad de lipoproteína lipasa en el tejido adiposo epicárdico de pacientes con enfermedad coronaria: ¿Es determinante de la composición de las lipoproteínas de muy baja densidad circulantes? Revista Argentina de lípidos [Internet]. 2018 [citado 24/06/2023];2. Disponible en: https://ri.conicet.gov.ar/handle/11336/100046
11. Newman CB, Tobert JA. Targeting PCSK9 with Antibodies and Gene Silencing to Reduce LDL-cholesterol. J Clin Endocrinol Metab. 2022;108(4):784-90. DOI: 10.1210/clinem/dgac708.
12. De las Heras-Polo B. Productos naturales: De la medicina tradicional a cabezas de serie para el desarrollo de nuevos fármacos del siglo XXI. An Real Acad Farm [Internet]. 2020 [citado 24/06/2023];87(1):97-104. Disponible en: https://analesranf.com/articulo/8701_05/
13. Dos Prazeres-Almeida E, Sabino-Pinho CP, Dornelas-Leão AP, et al. Razón entre grasa visceral y subcutánea como predictor de alteraciones cardiometabólicas. Rev Chil Nutr. 2018;45(1):28-36. DOI: 10.4067/s0717-75182018000100028.
14. Rudiño-Piñera E, Quintero-Hernández V, Juárez-González VR. El Protein Data Bank (PDB) y su impacto en la investigación científica. Alianzas y Tendencias BUAP. 2022;7(25):21-35. DOI: 10.5281/zenodo.5885737.
15. Bitencourt-Ferreira G, Pintro VO, de Azevedo WF. Docking with AutoDock4. En: Methods in molecular biology. New York, NY: Springer; 2019. p. 125-48. DOI: 10.1007/978-1-4939-9752-7_9.
16. Meneses L, Cuesta S. Determinación Computacional de la Afinidad y Eficiencia de Enlace de Antinflamatorios No Esteroideos Inhibidores de la Ciclooxigenasa-2. Revista Ecuatoriana de Medicina y Ciencias Biológicas. 2015;36(1-2):17-25. DOI: 10.26807/remcb.v36i1-2.260.
17. Hopkins AL, Keserű GM, Leeson PD, et al. The Role of Ligand Efficiency Measures in Drug Discovery. Nat Rev Drug Discov. 2014;13(2):105-21. DOI: 10.1038/nrd4163.
18. Atkovska K, Samsonov SA, Paszkowski-Rogacz M, et al. Multipose binding in molecular docking. Int J Mol Sci. 2014;15(2):2622-45. DOI: DOI: 10.3390/ijms15022622.
19. Picinin-Antunes A, Lopes-Cristiny G. Nutracêuticos no manejo das dislipidemias: terapia baseada em evidência. UNINGÁ Rev [Internet]. 2017 [citado 24/06/2023];29(1). Disponible en: https://revista.uninga.br/uningareviews/article/view/1919
20. Nascimento E, Muniz GdS, Silva AAM, et al. Western-style diet changes murinometric and metabolic parameters of rat offspring in time-specific windows. Braz J Dev [Internet]. 2020 [citado 24/06/2023];6(7):48355-72. Disponible en: https://ojs.brazilianjournals.com.br/ojs/index.php/BRJD/article/view/13477
21. Mohammed AS, Waleed KL. Protective role of soybean lecithin in reducing hypercholesterolemia and DNA fragmentation inducing by high cholesterol in adult male rats. Kufa Jou Vete Med Sci [Internet]. 2018 [citado 24/06/2023];9(1):35-45. Disponible en: https://journal.uokufa.edu.iq/index.php/kjvs/article/view/4093/3749
22. Zhao Q, Liu F, Wang YH, et al. LDL-C: HDL-C ratio and common carotid plaque in Xinjiang Uygur obese adults: a cross-sectional study. BMJ Open. 2018;8(10):e022757. DOI: 10.1136/bmjopen-2018-022757.
23. Dreier DA, Bowden JA, Aristizabal-Henao JJ, et al. Ecotoxico-lipidomics: An emerging concept to understand chemical-metabolic relationships in comparative fish models. Comp Biochem Physiol Part D Genomics Proteomics. 2020;36:100742. DOI: 10.1016/j.cbd.2020.100742.
24. de Lima-Barros A, de Lima EJSP, Faria JV, et al. Cytotoxicity and Lipase Inhibition of Essential Oils from Amazon Annonaceae Species. Chemistry. 2022;4(4):1208-25. DOI: 10.3390/chemistry4040081.
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Published
2024-08-13
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1.
Cala-Calviño L, Bordelois-López CL, Cruz Vadell H, Rojas-Vargas JA, Morris-Quevedo HJ. In vivo and in silico evaluation of the lipid-lowering properties of the phospholipids constituents of LECISAN™. Rev Méd Electrón [Internet]. 2024 Aug. 13 [cited 2025 Jan. 30];46:e5451. Available from: https://revmedicaelectronica.sld.cu/index.php/rme/article/view/5451
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