Model-Based, Goal-Oriented, Individualised Drug Therapy

Abstract

This article examines the use of population pharmacokinetic models to store experiences about drugs in patients and to apply that experience to the care of new patients. Population models are the Bayesian prior. For truly individualised therapy, it is necessary first to select a specific target goal, such as a desired serum or peripheral compartment concentration, and then to develop the dosage regimen individualised to best hit that target in that patient. One must monitor the behaviour of the drug by measuring serum concentrations or other responses, hopefully obtained at optimally chosen times, not only to see the raw results, but to also make an individualised (Bayesian posterior) model of how the drug is behaving in that patient. Only then can one see the relationship between the dose and the absorption, distribution, effect and elimination of the drug, and the patient's clinical sensitivity to it; one must always look at the patient. Only by looking at both the patient and the model can it be judged whether the target goal was correct or needs to be changed. The adjusted dosage regimen is again developed to hit that target most precisely starting with the very next dose, not just for some future steady state. Nonparametric population models have discrete, not continuous, parameter distributions. These lead naturally into the multiple model method of dosage design, specifically to hit a desired target with the greatest possible precision for whatever past experience and present data are available on that drug--a new feature for this goal-oriented, model-based, individualised drug therapy. As clinical versions of this new approach become available from several centers, it should lead to further improvements in patient care, especially for bacterial and viral infections, cardiovascular therapy, and cancer and transplant situations.

Population Pharmacokinetics and Relationship Between Demographic and Clinical Variables and Pharmacokinetics of Gentamicin in Neonates

“…Recently an IT2B module has been added to the program. Population parameters were also calculated with a nonparametric expectation maximization algorithm (NPEM2, USCPACK 10.7, University of Southern California, USA) (13). We also analyzed the data with this program because a nonparametric method has the ability to discover subpopulations (13).…”

Development, validation and analytical error function of two chromatographic methods with fluorimetric detection for the determination of bisoprolol and metoprolol in human plasma

“…For example, in pharmacokinetic analysis of experimental data performed by weighted least squares nonlinear regression, the inverse of the concentration variance calculated through the analytical error function (1/variance) can be a correct weighting method since is not always adequate to use constant weighting such as 1/C or 1/C 2 (Jelliffe, 1989;Proost, 1995;Mariño et al, 1996;Jansat et al, 1998;Jelliffe et al, 1998).…”

A Retrospective Analysis of Vancomycin Pharmacokinetics in Japanese Cancer and Non-cancer Patients Based on Routine Trough Monitoring Data

“…[2][3][4] The pharmacokinetics of vancomycin in MRSA-infected patients has been studied, [5][6][7][8][9][10] and population pharmacokinetic parameters were reported in adult Japanese patients. 11) More recently, an empirical Bayesian method has been widely applied to TDM data, 12) and the predictability of the Bayesian forecasting methodology for vancomycin with the population pharmacokinetic parameters in Japanese patients was examined. 13,14) Vancomycin is mainly eliminated into urine, and most of the pharmacokinetic variability can be explained by the degree of renal function.…”