sometimes Sometimes, people do something you don't understand, but I believe that they might have a good reason for it.

writting practise 1 disease progression model development in current quantitative research Abstract: As a new trend in model-based drug development, disease progress model is a description of the dynamic changing within disease status over time. It is rapidly becoming an important method to study how the drug action on disease. To make the treatment appropriate to a specific patient, the trend of the individual disease changing information can be predicted by the research of disease progress model. We summarized some disease progress models and drug action mode which have been successfully used in recent years and discussed the prospect of modeling and simulation of disease progression model.

The 2nd abstract of my paper, I'll submit it tomorrow. Thanks! Abstract To save cost of sampling for estimating the area under the amlodipine plasma concentration versus time curve in 24 hours (AUC0-24). Limited-sampling strategy models (LSS) was developed and validated by mutilple regression model within 4 or fewer amlodipine concentration values. Absolute prediction error(APE), root of mean square error(RMSE) and visual predict check were used as criterion. The results of Jackkinife validation shows there are fifteen (9.4%) of the 160 LSS based on regression analysis were not within an APE of 15% by using one concentration-time point. 156 (97.5%), 159 (99.4%) and 160 (100%) of the 160 LSS model were capable of predicting within an APE 15% by using 2, 3, 4 points, respectively. Limited sampling strategies were developed and validated for estimating AUC0-24 of amlodipine. The present study indicated that the implemention of both 5 mg and 10 mg dosage were enabled accurate predictions of AUC0-24 by the same LSS model. This study shows 12, 4, 24, 2h after administration are key sampling time. The combination of (12, 4), (12, 4, 24) or (12, 4, 24, 2h) might be choice as sampling hours for predicting AUC0-24 in practical application according to requirement.

an abstract ABSTRACT AIM: To find the optimal combination and evaluate the interaction between the curcumin (Cur) and adriamycin (ADM) in vitro antitumor effects. METHODS: According to the experimental design and data characteristic, the parameter method and the response surface approach combined with a simulation technique were used to analyze the pharmacodynamic interactions of in vitro antitumor effects in the combination of Cur and ADM at different dose ranges. RESULT: The dose-effect relationship of combination in the ratio of ADM:Cur = 1:3 showed significant change in comparison with used alone. in combination of adriamycin (ADM) (0.7～1.38 μmol•L-1) and curcumin (Cur) (2.04～4.07 μmol•L-1) with a fixed ratio (1:3)，the interaction nature indicated a synergism. With the doses of combination increasing, the combination showed an addition. Computer simulation showed the difference between the observed and expected effect decreasing trend with the dose of Cur increasing from 2.04 to 16.29 μmol • L-1. The response surface analysis shows an optimal combination is the combination of Cur 18.50 μmol • L-1 and ADM 3.89μmol • L-1 with the ratio of 1:0.2. CONCLUSION: The parameter method combined with the response surface analysis can provide richer and more reasonable information, and it is helpful to quantitative design for a drug combination therapy and to describe the natures and degree of drug interaction.