ABSTRACT
With the increasing cost of drug development and clinical trials, it is of great value to make full use of all kinds of data to improve the efficiency of drug development and to provide valid information for medication guidelines. Model-based meta-analysis (MBMA) combines mathematical models with meta-analysis to integrate information from multiple sources (preclinical and clinical data, etc.) and multiple dimensions (targets/mechanisms, pharmacokinetics/pharmacodynamics, diseases/indications, populations, regimens, biomarkers/efficacy/safety, etc.), which not only provides decision-making for all key points of drug development, but also provides effective information for rational drug use and cost-effectiveness analysis. The classical meta-analysis requires high homogeneity of the data, while MBMA can combine and analyze the heterogeneous data of different doses, different time courses, and different populations through modeling, so as to quantify the dose-effect relationship, time-effect relationship, and the relevant impact factors, and thus the efficacy or safety features at the level of dose, time and covariable that have not been involved in previous studies. Although the modeling and simulation methods of MBMA are similar to population pharmacokinetics/pharmacodynamics (Pop PK/PD), compared with Pop PK/PD, the advantage of MBMA is that it can make full use of literature data, which not only improves the strength of evidence, but also can answer the questions that have not been proved or can not be answered by a single study. At present, MBMA has become one of the important methods in the strategy of model-informed drug development (MIDD). This paper will focus on the application value, data analysis plan, data acquisition and processing, data analysis and reporting of MBMA, in order to provide reference for the application of MBMA in drug development and clinical practice.
ABSTRACT
This study aims to save cost of sampling for estimating the area under the amlodipine plasma concentration versus time curve in 24 hours (AUC(0-24 h)). Limited sampling strategy (LSS) models was developed and validated by mutiple 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 Jackknife validation showed that 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, separately. Limited sampling strategies have been developed and validated for estimating AUC(0-24 h) of amlodipine. The present study indicated that the implemention of both 5 mg and 10 mg dosage could enable accurate predictions of AUC(0-24 h) by the same LSS model. This study shows that 12, 4, 24, 2 h after administration are key sampling time points. The combination of (12, 4), (12, 4, 24) or (12, 4, 24, 2 h) might be chosen as sampling hours for predicting AUC(0-24 h) in practical application according to requirement.