Predicting Antibiotic Effect of Vancomycin Using Pharmacokinetic/Pharmacodynamic Modeling and Simulation: Dense Sampling versus Sparse Sampling

Abstract

This study aimed to investigate the effect of a structural pharmacokinetic (PK) model with fewer compartments developed following sparse sampling on the PK parameter estimation and the probability of target attainment (PTA) prediction of vancomycin. Two- and three-compartment PK models of vancomycin were used for the virtual concentration–time profile simulation. Datasets with reduced blood sampling times were generated to support a model with a lesser number of compartments. Monte Carlo simulation was conducted to evaluate the PTA. For the two-compartment PK profile, the total clearance (CL) of the reduced one-compartment model showed a relative bias (RBias) and relative root mean square error (RRMSE) over 90%. For the three-compartment PK profile, the CL of the reduced one-compartment model represented the largest RBias and RRMSE, while the steady-state volume of distribution of the reduced two-compartment model represented the largest absolute RBias and RRMSE. A lesser number of compartments corresponded to a lower predicted area under the concentration–time curve of vancomycin. The estimated PK parameters and predicted PK/PD index from models built with sparse sampling designs that cannot support the PK profile can be significantly inaccurate and unprecise. This might lead to the misprediction of the PTA and selection of improper dosage regimens when clinicians prescribe antibiotics.

Beyond One-Size-Fits-All: Tailoring Teicoplanin Regimens for Normal Renal Function Patients Using Population Pharmacokinetics and Monte Carlo Simulation

“…Such models are characterized by three distinct slopes when plotted on a graph with the y-axis representing a logarithmic scale, corresponding to the composition of three exponential equations on a normal scale. Because of the potential risk for significant inaccuracies and imprecision in estimated PK parameters and predicted PK/PD indices with structural PK models that use fewer compartments based on sparse sampling [ 21 , 22 ], our version of the three-compartment model was effective at mitigating these potential risks. This model more accurately captures the PK profile, leading to more reliable and precise characterizations of teicoplanin behavior in the body.…”

Concentrations of Co-Administered Meropenem and Vancomycin in Spinal Tissues Relevant for the Treatment of Pyogenic Spondylodiscitis—An Experimental Microdialysis Study

“…Further, the present setup does not permit direct comparisons to established steady-state treatment targets. On the other hand, our dataset is strengthened by dense sampling, which has been demonstrated to improve the ability to predict target attainment [33]. For future investigations, performing population pharmacokinetic simulations based on spinal tissue concentrations would provide important knowledge regarding the effects of higher doses and other modes of administration.…”

Editorial: Antibiotics Special Issue on Pharmacokinetic/Pharmacodynamic Models of Antibiotics

“…Structural PK models are useful tools to forecast the probability of target attainment, but post-deployment validation and sensitivity testing are inconsistently performed. In this collection, Kim et al [ 9 ] use Monte Carlo simulations to assess the relative bias and relative root mean square error of popular one-compartment and two-compartment models of vancomycin pharmacokinetics. The authors demonstrate that models with fewer compartments and more sparse sampling result in inaccurate and imprecise vancomycin PK profile estimates.…”