ABSTRACT
This study was to determine the correlation between endothelial function and neuro-endocrine-immune (NEI) network through observing the changes of NEI network under the different endothelial dysfunction models. Three endothelial dysfunction models were established in male Wistar rats after exposure to homocysteine (Hcy), high fat diet (HFD) and Hcy+HFD. The results showed that there was endothelial dysfunction in all three models with varying degrees. However, the expression of NEI network was totally different. Interestingly, treatment with simvastatin was able to improve vascular endothelial function and restored the imbalance of the NEI network, observed in the Hcy+HFD group. The results indicated that NEI network may have a strong association with endothelial function, and this relationship can be used to distinguish different risk factors and evaluate drug effects.
Subject(s)
Animals , Humans , Male , Rats , Diet, High-Fat , Endocrine System , Homocysteine , Immune System , Nervous System , Rats, Wistar , Risk Factors , SimvastatinABSTRACT
Comparative pharmacokinetic (PK) analysis is often carried out throughout the entire period of drug development, the common approach for the assessment of pharmacokinetics between different treatments requires that the individual PK parameters, which employs estimation of 90% confidence intervals for the ratio of average parameters, such as AUC and Cmax, these 90% confidence intervals then need to be compared with the pre-specified equivalent interval, and last we determine whether the two treatments are equivalent. Unfortunately in many clinical circumstances, some or even all of the individuals can only be sparsely sampled, making the individual evaluation difficult by the conventional non-compartmental analysis. In such cases, nonlinear mixed effect model (NONMEM) could be applied to analyze the sparse data. In this article, we simulated a sparsely sampling design trial based on the dense sampling data from a truly comparative PK study. The sparse data were analyzed with NONMEM method, and the original dense data were analyzed with non-compartment analysis. Although the trial design and analysis methods are different, the 90% confidence intervals for the ratio of PK parameters based on 1000 Bootstrap are very similar, indicated that the analysis based on NONMEM is a reliable method to treat with the sparse data in the comparative pharmacokinetic study.
ABSTRACT
The paper aimed to find the optimal combination and evaluation of the interactions of antitumor effect of the curcumin (Cur) and adriamycin (ADM) in vitro. According to the factorial design and data characteristics, the parameter method combined with the response surface approach were used to analyze the pharmacodynamic interactions of in vitro antitumor effects of the combination of Cur and ADM at different dosages. The results showed that the dose-effect relationship of the combination with the ratio of ADM-Cur 1:3 showed significant differences in comparison with either used alone. The dose-effect curve was shift left in combination. The combination of adriamycin (ADM, 0.693-2.132 micromol L(-1)) and curcumin (Cur, 2.047-6.304 micromol L(-1)) with a fixed ratio (1:3) showed a synergism. With increasing doses of the combination, there is an additive effect. Computer simulation showed a trend of decreasing difference between the observed and expected effects with the dose increasing in Cur from 6.304 to 16.0 micromol L(-1) and ADM from 2.132 to 5.3 micromol L(-1). The response surface analysis showed the optimal combination to be Cur 18.50 micromol L(-1) and ADM 3.89 micromol L(-1) with a ratio of 5:1. This study suggests that the parameter method combined with the response surface analysis provides richer and more reasonable information, and is helpful for quantitative design of drug combination therapy and to describe the nature and degree of drug interaction.
ABSTRACT
To study the anti-inflammatory effects of the combinations of active components of Painong powder, a compound traditional Chinese herbal medicine, and the quantitative analysis of their interactions.