Bayesian approaches in pharmacokinetic decision making

The theory of Bayesian analysis and its application to therapeutic and pharmacokinetic decision making are discussed. Diagnostic and therapeutic decisions are commonly based on institution, experience, and laboratory information; these decisions reflect varying degrees of uncertainty. Bayesian analysis quantifies the decision process by attaching probabilities to the likelihood of accuracy of each of these decision-making factors to achieve an overall estimate of decision quality. Using Bayesian principles to quantify the probability of efficacy and toxicity associated with serum drug concentrations represents one application of Bayesian theory to enhance therapeutic decisions. The Bayesian approach in pharmacokinetics involves the prediction of pharmacokinetic values, dosage regimens, and serum concentrations for drugs. Beginning with mean population pharmacokinetic parameters, one uses observed serum concentrations in individual patients to modify these parameters through Bayesian analysis to improve the accuracy of future serum concentration predictions. As more clinical pharmacokinetic laboratories and consultation services become familiar with the procedure, Bayesian forecasting promises to expand markedly the sophistication of therapeutic drug monitoring.