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Alteration of Amino Acid Profiling Influenced by the Active Ingredients of DanHong Injection After Prescription Optimization

INTRODUCTION: The aim of this work was to optimize the formulation composition of DanHong injection and to study the disturbance of microscopic components of cerebral ischemia in amino acid metabolites and metabolic pathways. The subtle relationship among these three substances and the influence of...

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Detalles Bibliográficos
Autores principales: Guo, Zhili, Zhu, Yan, Xu, Wenjuan, Luo, Kaitao, Xiao, Hongbin, Wang, Zhong
Formato: Online Artículo Texto
Lenguaje:English
Publicado: Dove 2019
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC6876559/
https://www.ncbi.nlm.nih.gov/pubmed/31819368
http://dx.doi.org/10.2147/DDDT.S220314
Descripción
Sumario:INTRODUCTION: The aim of this work was to optimize the formulation composition of DanHong injection and to study the disturbance of microscopic components of cerebral ischemia in amino acid metabolites and metabolic pathways. The subtle relationship among these three substances and the influence of metabolic pathways were also studied. METHODS: In this study, the central composite design (CCD) matrix and response surface methodology (RSM) were used to design the experiments and to evaluate the interactive effects of three substances. Targeted metabolomics was used to detect the amino acid variation in CCD sets. RESULTS: Response surfaces were generated, and the formulation was optimized by superimposing the contour plots. It was found that the optimum values of the responses could be obtained at an SAB concentration (x1) of 8–9 mg/kg, a TSN concentration (x2) of 14–16 mg/kg, and an HSYA yellow A concentration (x3) of 6 mg/kg. Statistical analysis showed that the three independent variables had significant effects (p < 0.05) on the responses. A total of 22 experimental runs were performed, and the kinetic data were analyzed using a second-order polynomial. Model algorithm calculation indicated that glutamic acid, serine, leucine, glycine, and valine had a very close correlation with the active ingredients. Methionine, aspartic acid, asparagine, glutamic acid, and valine were important for distinguishing different groups, and they were identified as potential biomarkers. Cluster analysis and pathway analysis indicated that the valine, leucine, and isoleucine degradation (VLI degradation) pathway was the major metabolic pathway. Arginine and proline metabolites were most frequently detected, and they were closely associated with other networks according to the network analysis results. VLI degradation pathway and arginine and proline metabolism pathway had a significant influence on cerebral ischemia. DISCUSSION: The integration of CCD and metabolomics may be an effective strategy for optimizing the formulation composition and identifying the mechanism of action of traditional chinese medicine.