ADAPTIVE-CONTROL OF DRUG-DOSAGE REGIMENS USING MAXIMUM A-POSTERIORI PROBABILITY BAYESIAN FITTING

Optimal drug therapy can only be achieved if a drug is given in the ri ght dosage regimen. Therefore the dosage regimen needs to be optimized , using the available information of the drug, the patient, and his di sease. The optimization of drug therapy comprises two major steps: Fir st, the clinician should define explicit therapeutic goals for each pa tient individually. Second, a strategy to achieve these goals with the greatest possible precision should be chosen. An overview of the opti mization of drug therapy is presented, with special reference to maxim um a posteriori probability (MAP) Bayesian fitting. Drug dosage optimi zation requires 1. measurement of a performance index related to the t herapeutic goal, generally one or more plasma concentration measuremen ts, 2. population pharmacokinetic parameters, including mean values, s tandard deviations, covariances and information on the statistical dis tribution, and 3. reliable software for adaptive control strategy and optimal dosage regimen calculation. The benefit of optimal drug therap y by adaptive control using MAP Bayesian fitting has been proven, resu lting 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.