Article ID | Journal | Published Year | Pages | File Type |
---|---|---|---|---|
10162818 | Journal of Pharmaceutical Sciences | 2012 | 8 Pages |
Abstract
In clinical settings, decrease of renal function is frequently observed in patients treated with vancomycin (VCM). In this study, mutable covariates models (MCMs) were constructed to analyze the pharmacokinetics (PK) of VCM with Bayesian estimation, considering time-dependent decreases in creatinine clearance in 23 patients with decreasing renal function. The predicted mean percentage error (MPE) of VCM concentrations analyzed with a conventional fixed covariates model (FCM) was â19.1%, whereas the MPE was improved to 2.5% by applying MCM. Furthermore, a probable lag time between fluctuations in VCM clearance (CLVCM) and serum creatinine (Scr) was analyzed by MCM, MCMLag1d, and MCMLag2d, which considered lag times of 0, 1, and 2Â days, respectively. Compared with FCM, all MCMs improved fitness with the significantly decreased root mean square percentage error (RMSPE) and MPE. However, RMSPE and MPE analyzed with MCM were not significantly different from those with MCMLag1d and MCMLag2d, indicating that lag times between alterations in CLVCM and Scr were obscure in these patients. Collectively, these results suggest that PK parameters of VCM were more accurately calculated by MCMs than by conventional FCM, and that VCM dosages calculated by FCM would be overestimated by approximately 20% in patients with decreasing renal function.
Keywords
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Drug Discovery
Authors
Takehito Yamamoto, Hirokazu Terakawa, Akihiro Hisaka, Hiroshi Suzuki,