MODEL-BASED, GOAL-ORIENTED, INDIVIDUALIZED DRUG-THERAPY - LINKAGE OF POPULATION MODELING NEW MULTIPLE MODEL DOSAGE DESIGN, BAYESIAN FEEDBACK AND INDIVIDUALIZED TARGET GOALS

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 compartmen t concentration. and, then to develop the dosage regimen individualise d to best hit that target in that patient. One must monitor the behavi our 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 fan one see ih : relationship between the dose and the absorption. distribution. effe ct 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 t he 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 d eveloped 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, specif ically to hit a desired target with the greatest possible precision fo r whatever past experience and present data are available on that drug - a new feature for this goal-oriented, model-based, individualised d rug therapy. As clinical versions of this new approach become availabl e from several centres. II should lead to further improvements in pati ent care, especially for bacterial and viral infections, cardiovascula r therapy, and cancer and transplant situations.