EXPANDING CLINICAL-APPLICATIONS OF POPULATION PHARMACODYNAMIC MODELING

Population pharmacokinetics or pharmacodynamics is the study of the va riability in drug concentration or pharmacological effect between indi viduals when standard dosage regimens are administered. We provide an overview of pharmacokinetic models, pharmacodynamic models, population models and residual error models. We outline how population modelling approaches seek to explain interpatient variability with covariate an alysis, and, in some approaches, to characterize the unexplained inter individual variability. The interpretation of the results of populatio n modelling approaches is facilitated by shifting the emphasis from th e perspective of the modeller to the perspective of the clinician. Bot h the explained and unexplained interpatient variability should be pre sented in terms of their impact on the dose-response relationship. Cli nically relevant questions relating to the explained and unexplained v ariability in the population can be posed to the model, and confidence intervals can be obtained for the fraction of the population that is estimated to fall within a specific therapeutic range given a certain dosing regimen. Such forecasting can be used to develop optimal initia l dosing guidelines. The development of population models (with random effects) permits the application of Bayes's formula to obtain improve d estimates of an individual's pharmacokinetic and pharmacodynamic par ameters in the light of observed responses. An important challenge to clinical pharmacology is to identify the drugs that might benefit from such adaptive-control-with-feedback dosing strategies. Drugs used for life threatening diseases with a proven pharmacokinetic-pharmacodynam ic relationship, a small therapeutic range, large interindividual vari ability, small interoccasion variability and severe adverse effects ar e likely to be good candidates. Rapidly evolving changes in health car e economics and consumer expectations make it unlikely that traditiona l drug development approaches will succeed in the future. A shift away from the narrow focus on rejecting the null hypothesis towards a broa der focus on seeking to understand the factors that influence the dose -response relationship-together with the development of the next gener ation of software based on population models-should permit a more effi cient and rational drug development programme.