Exploration of visual check approaches in clinical data management / 药学学报

Due to a great amount of data in clinical trials, the data cleansing needs to adopt a variety of measures, including the latest developed visual check approach. According to the different types of clinical data and the different stages in the course of clinical data management, this study reviews 8 types of visual graphics that show the relevance and trend among the data. The series of graphics can rapidly detect abnormal data, monitor clinical research in real-time, make the data management process much easier and improve the clinical trial efficiency and data quality.

Comparison of paper and electronic data management in clinical trials / 药学学报

Electronic case report forms (eCRFs) instead of the traditional paper case report forms (pCRFs) are increasingly used by investigators and sponsors of clinical research. We include a total of 14 phase III studies (8 pCRF, 6 eCRF) to compare paper and electronic data documentation both quantitatively and qualitatively in clinical studies. The result suggests that adaptions of electronic data capture (EDC) in clinical trials have the advantages in optimization of data capture process, improvement of data quality and earlier clinical decision compared to paper-based methods. Furthermore, the successful implementation of EDC requires accouplements with corresponding data management processes and reallocation of resources.

Quality control of clinical data management based on EDC / 药学学报

With the wide application of electronic data management (EDC), the data management is shifting to a new mode. In order to recognize the advantages of EDC, we choose 20 representative registered clinical trials, which involve 5 404 subjects and 321 sites. We found that EDC has many beneficial impacts on the course of clinical trial data management, including the process of data collection, data cleaning, data quality control and clinical trial decision-making. The result also provides a reference for the adoption of EDC in clinical trials.

Comparative pharmacokinetic analysis based on nonlinear mixed effect model / 药学学报

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.

In vitro pharmacodynamic interactions of antitumor effect of the combination of adriamycin and curcumin evaluated by the parameter method and the response surface / 药学学报

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.

Quantitative approach for calculation of pharmacodynamic interactions and simulation of combined response / 中国中药杂志

<p><b>OBJECTIVE</b>An approach is set up to calculate pharmacodynamic interaction and simulate the combined response.</p><p><b>METHOD</b>An orthogonal design with 1-level = high dose and 2-level = low dose was adopted. An example of the compound with four components was applied to evaluate this quantitative approach. The bias was evaluated by the both scatter plots.</p><p><b>RESULT</b>This approach can calculate the value of each component with different dose by its contribution to combined response, and the value is related to the importance of a compound. Drug interactions were evaluated among the combinations in each group. The prediction model performed well and simulated the combined response in the different of components in combination.</p><p><b>CONCLUSION</b>The approach can be used in the similar research, and it also provides predictions of component combinations from the other studies by simulation.</p>