Gender based Dosing of Metoprolol in the Elderly using Population Pharmacokinetic Modeling and Simulations

Abstract

Introduction: This article seeks to clarify if gender-based differences occur in the pharmacokinetics of metoprolol in the elderly patients. There are a series of physiologic changes that occur in the elderly ranging from decreased hepatic blood flow to increased adiposity causing higher plasma concentrations at therapeutic doses as compared to the healthy young population.

Methods: Population pharmacokinetic modeling were performed using MONOLIX and Monte-Carlo simulations were conducted using MATLAB. The data was based from a previously published dataset where elderly patients, having multiple comorbidities, were administered a 50mg dose of metoprolol.

Results: Metoprolol was modeled using a one-compartment model and resulted in the following population pharmacokinetic parameters: volume of distribution, V=38L (CV=155%), clearance rates, CL-Men=105L/hour and CL-Women=59.1L/hour (38%), time lag, Tlag=0.469 hour (CV=17%), and the absorption rate constant, Ka=0.235 hr^-1 (CV=23%).

Conclusion: Gender stratified doses resulting in an equivalent systemic metoprolol exposure in geriatric patients have been identified. Metoprolol doses resulting a similar AUC in a healthy young male administered 50mg tablet were 15mg for geriatric women and 25mg for geriatric men. Further, Metoprolol doses of 25mg for geriatric women and 50mg for geriatric men resulted in an equivalent AUC to a healthy young males dosed with a 100mg tablet. A 15mg Metoprolol tablet may need to be compounded to account for the gender differences in Metoprolol pharmacokinetics.

Keywords: dose; gender; geriatrics; metoprolol; monolix; population pharmacokinetics.

Figure 1

Goodness-of-fit plots for the observed versus predicted model diagnostics of the population (left) fit and the individual (right) fit of metoprolol plasma concentrations in geriatric patients.

Figure 2

Goodness-of-Fit plots illustrating the individual weighted residuals (IWRES) and the normalized prediction distribution errors (NPDE) over time (top) and the IWRES and NPDE over the predicted metoprolol concentration. The bottom two graphs illustrate the probability density function distributions for the IWRES and NPDE.

Figure 3

The Visual Predictive Check for the final population pharmacokinetic model for metoprolol in geriatric patients illustrating percentile distribution bands. The black solid line represents the population median while the blue dots represent the observed data points.

Figure 4

Monte-Carlo simulation results illustrating Metoprolol pharmacokinetic profiles for 5000 virtual patients and dose-finding simulation results identifying equivalent systemic drug exposures measured as area under the concentration-time curve.