Implementation and Comparison of Two Pharmacometric Tools for Model-Based Therapeutic Drug Monitoring and Precision Dosing of Daptomycin

Abstract

Daptomycin is a candidate for therapeutic drug monitoring (TDM). The objectives of this work were to implement and compare two pharmacometric tools for daptomycin TDM and precision dosing. A nonparametric population PK model developed from patients with bone and joint infection was implemented into the BestDose software. A published parametric model was imported into Tucuxi. We compared the performance of the two models in a validation dataset based on mean error (ME) and mean absolute percent error (MAPE) of individual predictions, estimated exposure and predicted doses necessary to achieve daptomycin efficacy and safety PK/PD targets. The BestDose model described the data very well in the learning dataset. In the validation dataset (94 patients, 264 concentrations), 21.3% of patients were underexposed (AUC_24h < 666 mg.h/L) and 31.9% of patients were overexposed (C_min > 24.3 mg/L) on the first TDM occasion. The BestDose model performed slightly better than the model in Tucuxi (ME = -0.13 ± 5.16 vs. -1.90 ± 6.99 mg/L, p < 0.001), but overall results were in agreement between the two models. A significant proportion of patients exhibited underexposure or overexposure to daptomycin after the initial dosage, which supports TDM. The two models may be useful for model-informed precision dosing.

Keywords: bone and joint infection; daptomycin; model-informed precision dosing; pharmacokinetics; therapeutic drug monitoring.

Figure 1

Observed versus model-based predicted concentrations of daptomycin in the learning dataset. Open circles and dashed lines, population predictions; black dots and dotted lines, individual predictions. Abbreviations: Ind., individual; Pop., population.

Figure 2

Observed plasma concentrations of daptomycin versus individual predictions from BestDose and Tucuxi in the validation dataset. Blue circles and dashed lines, BestDose predictions; red circles and solid line, Tucuxi predictions.

Figure 3

Bland–Altman plot assessing the agreement between estimates of daptomycin concentrations, AUC_24h, D_min and D_max from BestDose and Tucuxi. The difference was calculated as BestDose estimate minus Tucuxi estimate in all subplots. Units of daptomycin concentrations, AUC_24h, D_min and D_max are mg/L, mg.h/L, mg/kg and mg/kg, respectively. The dotted line is the regression line; the dashed lines are the limits of agreement. Regression-based limits of agreement were used for plots of daptomycin AUC_24h and Dmin because the slope of the regression was significantly different from zero, suggesting proportional bias.