Comparative performance of different stochastic methods to simulate drug exposure and variability in a population.
Monte Carlo simulations (MCSs) are increasingly being used to predict the pharmacokinetic variability of antimicrobials in a population. However, various MCS approaches may differ in the accuracy of the predictions. We compared the performance of 3 different MCS approaches using a data set with known parameter values and dispersion. Ten concentration-time profiles were randomly generated and used to determine the best-fit parameter estimates. Three MCS methods were subsequently used to simulate the AUC(0-infinity) of the population, using the central tendency and dispersion of the following in the subject sample: 1) K and V; 2) clearance and V; 3) AUC(0-infinity). In each scenario, 10000 subject simulations were performed. Compared to true AUC(0-infinity) of the population, mean biases by various methods were 1) 58.4, 2) 380.7, and 3) 12.5 mg h L(-1), respectively. Our results suggest that the most realistic MCS approach appeared to be based on the variability of AUC(0-infinity) in the subject sample.
Tam
Vincent H
VH
Department of Clinical Sciences and Administration, University of Houston College of Pharmacy, Houston, TX 77030, USA.
Kabbara
Samer
S
D019540:Area Under Curve; D008954:Models, Biological; D015233:Models, Statistical; D009010:Monte Carlo Method; D004364:Pharmaceutical Preparations / Q000097:blood / Q000378:metabolism; D010599:Pharmacokinetics; D013269:Stochastic Processes; D014018:Tissue Distribution
D004364:Pharmaceutical Preparations