Development and Application of an Individualized Dosing-assisted Decision Platform for Vancomycin Based on Population Pharmacokinetic Model
10.13748/j.cnki.issn1007-7693.20224113
- VernacularTitle:基于群体药动学模型的万古霉素个体化给药辅助决策平台的研制及应用
- Author:
Jing WANG
1
,
2
;
Huaijun ZHU
1
,
2
;
Zejun WU
1
;
Yue SHEN
3
;
Bin LI
4
;
Huakai JING
5
;
Shengpeng YANG
5
;
Weihong GE
1
Author Information
1. Department of Pharmacy, The Affiliated Nanjing Drum Tower Hospital of Nanjing University Medical School, Nanjing 210008, China
2. Nanjing Medical Center for Clinical Pharmacy, Nanjing 210008, China
3. China Pharmaceutical University Nanjing Drum Tower Hospital, Nanjing 210008, China
4. Department of Computer Science and Engineering, Southeast University, Nanjing 211189, China
5. Hangzhou Ruijie Information Technology Co., Ltd., Hangzhou 310051, China
- Publication Type:Clinical Trial
- Keywords:
vancomycin , population pharmacokinetics , individualized drug delivery , decision support platform , maximum a posteriori Bayesian
- From:
Chinese Journal of Modern Applied Pharmacy
2023;40(24):3423-3430
- CountryChina
- Language:Chinese
-
Abstract:
Abstract:OBJECTIVE To develop a vancomycin individualized dosing-assisted decision platform suitable for practical clinical application scenarios and provide individualized dosing recommendations for the rational use of vancomycin. METHODS Based on the vancomycin population pharmacokinetic model that had been constructed and verified to be feasible, the vancomycin individualized assisted decision-making platform was developed by using Idea2019, JDK1.8, ETL and other software tools. The platform development had gone through three main stages, included ①requirement analysis; ②design stage; ③software testing and optimization. RESULTS The vancomycin individualized assisted decision-making platform, which was successfully developed and applied, had the advantages of simple page, perfect function and convenient operation, and was divided into four main modules according to functions, namely retrieval module, information module, concentration prediction module and reporting module. The platform could connect to the hospital intranet platform to automatically obtain patient information, medication information and blood concentration test results, and calculate individual pharmacokinetic parameters for subsequent concentration prediction based on the embedded population pharmacokinetic model, combined with individual parameters. The concentration prediction module incorporated the Bayesian feedback method with patient medication information, drug concentration measurement results and relevant covariate parameter values, took the guideline-recommended trough concentration and AUC range as the target value, calculated the individualized drug administration scheme that met the target concentration range, and set up custom simulation functions considering the actual clinical application scenarios, which was of more popularization and application value. CONCLUSION Based on the vancomycin population pharmacokinetic model that has been successfully constructed in the previous stage, with the assistance of Idea2019, JDK1.8, ETL and other software tools, a vancomycin individualized dosing-assisted decision platform has successfully constructed, which can more efficiently and conveniently assist monitoring pharmacists to provide individualized dosing advice for clinical use of vancomycin.
