Vancomycin (VCM) is widely used for the treatment of infectious diseases including bacteremia, endocarditis, pneumonia, and meningitis, and it is the first-line agent for the treatment of methicillin-resistant strains of Staphylococcus
]. After intravenous infusion, VCM is rarely metabolized and is primarily excreted unchanged in urine by glomerular filtration. Thus, renal function is one of the most important factors influencing patient exposure to VCM [4
]. The elimination half-life with normal renal function is reported to be from 3 to 9 h [5
When patients receive VCM treatment, therapeutic drug management (TDM) is generally recommended because VCM exhibits clear exposure–response relationships and has exposure-related nephrotoxicity [6
]. Although the ranges of desired concentration differ depending on the strains and site of infections, the general target trough concentrations are 10 to 20 mg/L [2
]. Several papers have reported that trough concentrations greater than 15 mg/L [8
] or 12 mg/L [9
] increase the risk of nephrotoxicity. Therefore, dose adjustment is needed depending on the patient’s renal function and concomitant nephrotoxic medications. In other studies, the area under the plasma concentration–time curve up to 24 h (AUC24
)/minimum inhibitory concentration (MIC) or AUC24
≥ 400 mg h/L was suggested as the target exposure for VCM to optimize the response to therapy [6
In clinical settings, Bayesian feedback TDM is widely applied, and peak plasma concentration (Cpeak
) and trough plasma concentration (Ctrough
) are usually monitored for target attainment. However, it has been reported that the concentrations of VCM predicted by commercially available software tended to be higher than the observed concentrations in patients with low renal function, low body weight, or old age [12
]. The population pharmacokinetic (PK) parameters implemented in TDM software were generally obtained in Western populations and might be inappropriate for PK prediction in a Korean population (who has different demographic characteristics in comparison to the Western population). Moreover, patients who received hemodialysis (HD) or continuous renal replacement therapy (CRRT) had significantly higher VCM exposures, and to our knowledge, no software that is currently available can consider the patient’s HD or CRRT treatment status.
Our aims in this study were (1) to develop a population PK model for VCM using Korean data, and (2) to establish open-source TDM software for Korean patients using our final PK model. In this study, we have accounted for renal function in the clearance (CL) parameters and incorporated changes to CL according to HD and CRRT treatment as well as the patient’s estimated creatinine clearance (CLCR).
The purposes of this study were (1) to build the population PK model of VCM for Korean patients, and (2) to develop open-source TDM software for VCM. It is well known that not only the toxicity but also the efficacy of VCM is related to the exposure of the drug, and thus the need for TDM of VCM has been emphasized through numerous articles. Nevertheless, for the following reasons, this study is different from the previous research. Firstly, this PK model was constructed based on a large amount of data from patients with severe and moderate renal function. In the patient data used to construct the model, the median and the 25th percentile (Q1) of CLCR
calculated by the Cockcroft–Gault formula were 72.4 mL/min and 40 mL/min, respectively. With covariate analysis, body weight for V2
, and CLCR
were significant and these findings were consistent with the results from previous studies [2
]. In addition, HD and CRRT were included as significant covariates of CL
in this model, allowing more accurate CL
s for patients with renal dysfunction. The final estimates of CL
in each group were 2.82 L/h for normal renal function, whereas those for HD therapy and CRRT therapy were 0.716 L/h and 0.334 L/h, respectively. The assessment of the predictive performance of the final PK model was shown as the basic goodness-of-fit plots in Figure 2
. For validation of the model, the median parameter estimates obtained from the 1000 bootstrap data were very similar to the estimates from the final PK model.
With the final PK model, simulation studies reflecting different renal functions and different body weights were conducted. All groups received the same dosage regimen—daily 1 g VCM for 7 days—and according to the results, for patients with CLCR > 100 mL/min (i.e., patients with normal renal function) dose adjustments, such as increasing the dose or dosing intervals, would be required to achieve target concentrations that could be changed by infection type and site. On the other hand, dose reduction of the patients who received HD or CRRT should be carefully considered because the increased concentrations of VCM can induce concentration-related toxicity such as nephrotoxicity.
Based on these results, TDM software for Koreans was constructed with R Shiny, an open-source program. For TDM, it is essential to predict individual PK profiles by reflecting individual concentration, and Bayesian maximum a posteriori (MAP) estimation is widely used for this. In NONMEM, the objective function (Equation (5)) was used for estimating the post hoc η
of patients, and this is easily obtained using the POSTHOC option [14
]. In this study, to reproduce the estimation method for post hoc η
, the objective function was incorporated into the code, and some parts of the code were taken from Kim’s study [16
] to convert a vector notation to a full matrix. In the R script, the “optim” function was used to optimize the post hoc η
s of CL
for a patient. In the R shiny app, not only patient information on CLCR
(which can be directly calculated in the app if the age, sex, weight, and serum creatinine level of the patient are available) and weight, but also observation data of VCM should be entered for PK simulation. Based on the simulation results, an adaptive dosage regimen including infusion duration time, dosing amount, and dosing interval could be applied to simulate further PK profiles. In addition to dosage regimen, the target therapeutic range may be adjusted by the user according to the indication. The R shiny app for VCM TDM can be easily accessed online without a local installation.
Most of the commercially available TDM software for VCM have incorporated population PK parameters obtained from Caucasian patients, and no software based on Korean data can be found so far. The development of TDM software using Korean PK parameters has a great significance itself and furthermore, this interface and the code used for VCM TDM could be applied to many other drugs that require concentration monitoring. This is web-based software which is easy to access, and anyone can use it anywhere, anytime, free of charge. However, this study has several limitations to consider. Because the study was not a strictly controlled clinical trial but a retrospective investigation with data produced in clinical settings, the explained (wide ranges of weight, and renal functions) and unexplained variability (expressed as the value of ωCL) between subjects was relatively large. Even though the influences of some patient factors were well reflected in the PK model, to avoid an abnormally large or small EBE estimated by the system, the plasma concentration values and the actual sampling time data should be accurately given. In addition, due to the insufficiency of the source data, the credibility of the system in the extended dosing period (e.g. later than 20 days after the initiation of treatment) is not clear. In order to improve the robustness and accuracy of the model, further study should be performed to acquire more data especially on patients with CRRT and HD therapy.