ABSTRACT
OBJECTIVE: To develop a therapeutic drug monitoring software of vancomycin for adult patients to help physician for personalized use of vancomycin. METHODS: A system literature retrieval search was conducted in the PubMed database to collect the population pharmacokinetic studies published before September 2012. A set of therapeutic drug monitoring models for vancomycin was assembled by the models included in the above literatures and the model developed by the authors. Based on the Bayesian principle, a therapeutic drug monitoring software built upon these population pharmacokinetic models was developed with C++ in the integrated development environment (Microsoft Visual Studio 2005). RESULTS: The set of therapeutic drug monitoring models included 14 population pharmacokinetic analyses. The software developed on these pharmacokinetic models could be applied to conduct population, subpopulation and individual predictions, thus help clinician in their individualized treatments. CONCLUSION: This study developed software covers the current reported vancomycin pharmacokinetic model. Clinician can choose the appropriate model according to the actual situation of the patients.
ABSTRACT
This study is to develop a therapeutic drug monitoring (TDM) network server of tacrolimus for Chinese renal transplant patients, which can facilitate doctor to manage patients' information and provide three levels of predictions. Database management system MySQL was employed to build and manage the database of patients and doctors' information, and hypertext mark-up language (HTML) and Java server pages (JSP) technology were employed to construct network server for database management. Based on the population pharmacokinetic model of tacrolimus for Chinese renal transplant patients, above program languages were used to construct the population prediction and subpopulation prediction modules. Based on Bayesian principle and maximization of the posterior probability function, an objective function was established, and minimized by an optimization algorithm to estimate patient's individual pharmacokinetic parameters. It is proved that the network server has the basic functions for database management and three levels of prediction to aid doctor to optimize the regimen of tacrolimus for Chinese renal transplant patients.
Subject(s)
Humans , Algorithms , Bayes Theorem , Database Management Systems , Drug Monitoring , Methods , Immunosuppressive Agents , Pharmacokinetics , Kidney Transplantation , Models, Biological , Tacrolimus , PharmacokineticsABSTRACT
The objective of this study is to compare the normalized prediction distribution errors (NPDE) and the visual predictive check (VPC) on model evaluation under different study designs. In this study, simulation method was utilized to investigate the capability of NPDE and VPC to evaluate the models. Data from the false models were generated by biased parameter typical value or inaccurate parameter inter-individual variability after single or multiple doses with the same sampling time or multiple doses with varied sampling time, respectively. The results showed that there was no clear statistic test for VPC and it was difficult to make sense of VPC under the multiple doses with varied sampling time. However, there were corresponding statistic tests for NPDE and the factor of study design did not affect NPDE significantly. It suggested that the clinical data and model which VPC was not fit for could be evaluated by NPDE.