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Maruyama Takumi
Department Other, Other Position |
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| Article types | Original article |
| Language | English |
| Peer review | Peer reviewed |
| Title | Comparison of the predictive accuracy of the physiologically based pharmacokinetic (PBPK) model and population pharmacokinetic (PPK) model of vancomycin in Japanese patients with MRSA infection. |
| Journal | Formal name：Journal of infection and chemotherapy : official journal of the Japan Society of Chemotherapy Abbreviation：J Infect Chemother ISSN code：14377780/1341321X |
| Domestic / Foregin | Foregin |
| Volume, Issue, Page | pp.10.1016/j.jiac.2023.08.017 |
| Author and coauthor | Maruyama Takumi, Kimura Toshimi, Ebihara Fumiya, Kasai Hidefumi, Matsunaga Nobuaki, Hamada Yukihiro |
| Authorship | Lead author |
| Publication date | 2023/09 |
| Summary | INTRODUCTION:The latest therapeutic drug monitoring guidelines for vancomycin (VCM) recommend that area under the concentration-time curve is estimated based on model-informed precision dosing and used to evaluate efficacy and safety. Therefore, we predicted VCM concentrations in individual methicillin-resistant Staphylococcus aureus-infected patients using existing a physiologically based pharmacokinetic (PBPK) model and 1- and 2-compartment population pharmacokinetic (PPK) models and confirmed and verified the accuracy of the PBPK model in estimating VCM concentrations with the PPK model.METHODS:The subjects of the study are 20 patients, and the predicted concentrations were evaluated by comparing the observed and predicted trough and peak values of VCM concentrations for individual patients.RESULTS:The results showed good correlation between the observed and predicted trough and peak concentrations of VCM was observed generally in the PBPK model, R2 values of 0.72, 0.62, and 0.40 with trough values of 0.49, 0.40, and 0.34 with peak values for PBPK model, 1-compartment, and 2-compartment model, respectively.CONCLUSIONS:Although the performance of the PBPK model is not as predictive as the PPK model, generally similar predictive trends were obtained, suggesting that it may be a valuable tool for rapid and accurate prediction of AUC for VCM. |
| DOI | 10.1016/j.jiac.2023.08.017 |
| PMID | 37673298 |