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This article introduces the mechanism including antigen presentation, adjuvant, lymphatic system and the characteristics of vaccine, and then summarizes the key applications of core pharmacometrics approaches including QSP, PK/PD, dose response analysis, MBMA, in dose-response, preclinical and clinical translation, and correlation between biomarkers and efficacy of vaccines. It is expected that the successful application of model informed drug development can promote model informed vaccine development so that pharmacometrics makes its due contributions to the development of safer, more effective and more controllable vaccine products.
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AIM: To build a meropenem population pharmacokinetic model for Chinese elderly through model-based meta-analysis. METHODS: Informations including dosing regimen, sampling times, concentrations, sample size, age, gender, body weight (BW) and creatinine clearance were extracted after the literature were retrieved. The model was built by NONMEM. Stepwise covariate modeling strategy was used for covariates analysis. RESULTS: A two-compartment model was applied to describe meropenem pharmacokinetics. After stepwise covariate modeling, covariates that remained significant in the final model were creatinine clearance (CLcr) on CL and the BW on V
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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.
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Physiologically based pharmacokinetics (PBPK) is one of the main research fields of pharmacometrics, and it plays an important role at all the stages of drug development and clinical practice. In early drug discovery and development, human pharmacokinetics (PK) could be predicted by PBPK modeling using in silico, in vitro and preclinical in vivo data. During clinical studies, PBPK model could be used to investigate the effects of various physiological and pathological factors on PK, such as age, gender, liver/kidney impairment, and to guide dose adjustment of special population (pregnant women, children, etc.). Furthermore, PBPK modeling is now becoming more appealing with the ability to predict drug-drug interaction (DDI) in the case of co-administration of multiple drugs. In recent years, the application of PBPK modeling in industry has increased widely. Also, regulatory agencies have recognized the potential of PBPK and its impact on labeling recommendations. As the popularity of model-informed drug development, the combination of PBPK modeling with other commonly used modeling methods, such as population pharmacokinetics (PopPK), pharmacokinetic/pharmacodynamic (PK/PD) modeling and model-based meta-analysis (MBMA), has shown attractive advantages. In this paper, the origin and development, as well as the application status of PBPK are introduced briefly, and the application of PBPK modeling merged with PopPK, PK/PD and MBMA is reviewed.
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OBJECTIVE: To establish a quantitative method to evaluate the relationship on the PK parameters of paclitaxel among different species by using the model-based Meta-analysis, and provide a reference for species extrapolation and dose determination of new drug research and development. METHODS: Relevant literatures were searched in Pub-Med, CNKI, WanFang and other databases, and all search results were filtrated with the criteria and classified according to species. Using NONMEM to construct the model for human, rats and mice, respectively. Evaluating the performance of the model with normalized prediction distribution errors (NPDE), and calculating the correlation coefficient of species with allometric scaling method. RESULTS: The nonlinear mixed-effects model (NONMEM) was developed to describe the paclitaxel PK profiles for mice, rats and humans. A two-compartment pharmacokinetic model fitted the data well, and consistent with the reported results. The models were evaluated by NDPE, the final model was accurate and reliable. The allometric scaling of CL and Vtotal among three different species for paclitaxel was r2=0.9974 and r2=0.9372, respectively. CONCLUSION: Take paclitaxel for example, established the model-based meta-analysis, successfully, and evaluated the correlation on the PK parameters of paclitaxel among different species quantitatively.