Practical therapeutic drug management in HIV-infected patients: use of population pharmacokinetic models supplemented by individualized Bayesian dose optimization

Abstract

Individualized, model-based, target-oriented optimal concentration-controlled dosing of HIV medications can be beneficial to patients for whom there are limited dosing guidelines, such as children, adolescents, or patients with altered physiologic function. Barriers to this approach include lack of training, expertise, and access to appropriate software to assist the clinician. The authors present 4 illustrative clinical cases of HIV-infected patients whose therapy was optimized using population pharmacokinetic models (here generated from published studies) and supplemented by individualized Bayesian adaptive control of dosage regimens as implemented in the MM-USCPACK software. These 4 cases illustrate how clinicians can maximize therapeutic success in (1) patients with reduced drug clearance, (2) young adolescents transitioning to adult physiology, (3) patients with dose-dependent toxicity, and (4) adolescents with limited therapeutic options.

Figure 1

Barriers to the use of therapeutic drug management.

Figure 2

Nonparametric population model for atazanavir with clearance and absorption on the horizontal plane and the associated probability of each set of estimates on the vertical axis. There is at most 1 point per simulated individual in the population, although individuals with similar parameter estimates within the context of model error will not be resolvable as separate points. The population model (the Bayesian prior) is shown in (A), and the individualized Bayesian-posterior model for patient 4 is shown in (B) fitted to that patient’s measured atazanavir concentrations. Some points in the model have risen to higher probabilities (note the changed vertical axis scale), other points are lower, and half are so unlikely as to not be plotted. The more probable sets of parameter estimates are now accorded more weight in selecting the dose that minimizes the least-squared error between the predicted and observed concentrations for the patient.

Figure 3

MM-USC*PACK output for each patient. Black lines are weighted average Bayesian-posterior predicted concentrations. Dots are measured serum concentrations. Vertical lines at the bottom are dose events. Intervals between measured serum concentrations have been compressed for clarity. Target concentrations have been added as a reference. (A) Patient 1, efavirenz. (B) Patient 2, nelfinavir. (C) Patient 3, amprenavir given as fos-amprenavir. (D) Patient 4, atazanavir.