title: Adaptive control of drug dosage regimens using maximum a posteriori probability Bayesian fitting.
abstract: Optimal drug therapy can only be achieved if a drug is given in the right dosage regimen. Therefore the dosage regimen needs to be optimized, using the available information of the drug, the patient, and his disease. The optimization of drug therapy comprises two major steps: First, the clinician should define explicit therapeutic goals for each patient individually. Second, a strategy to achieve these goals with the greatest possible precision should be chosen. An overview of the optimization of drug therapy is presented, with special reference to maximum a posteriori probability (MAP) Bayesian fitting. Drug dosage optimization requires 1. measurement of a performance index related to the therapeutic goal, generally one or more plasma concentration measurements, 2. population pharmacokinetic parameters, including mean values, standard deviations, covariances and information on the statistical distribution, and 3. reliable software for adaptive control strategy and optimal dosage regimen calculation. The benefit of optimal drug therapy by adaptive control using MAP Bayesian fitting has been proven, resulting in improved patient outcome by improved efficacy of therapy and a reduction of adverse reactions, and in reduced costs, mainly due to a reduction of hospitalization. Newer strategies might replace the MAP Bayesian fitting procedure, if their advantage has been demonstrated convincingly, and if reliable and user-friendly software is available.
authors.affiliation: Department of Pharmacokinetics and Drug Delivery, University Centre for Pharmacy, Groningen, The Netherlands.
authors.forename: J H
authors.initials: JH
authors.lastname: Proost
mesh_terms: D001499:Bayes Theorem; D003362:Cost-Benefit Analysis; D004334:Drug Administration Schedule; D016903:Drug Monitoring / Q000191:economics / Q000706:statistics & numerical data / Q000639:trends; D006801:Humans; D015233:Models, Statistical