Generalized nonlinear models for pharmacokinetic data

Abstract

Phase I trials to study the pharmacokinetic properties of a new drug generally involve a re stricted number of healthy volunteers. Because of the nature of the group involved in such studies, the appropriate distributional assumptions are not always obvious. These model assumptions include the actual distribution but also the ways in which the dispersion of responses is allowed to vary over time and the fact that small concentrations of a substance are not easily detectable and hence are left censored. We propose that a reasonably wide class of generalized nonlinear models allowing for left censoring be considered now that this is feasible with current computer power and sophisticated statistical packages. These modelling strategies are applied to a Phase I study of the drug flosequinan and its metabolite. This drug was developed for the treatment of heart failure. Because the metabolite also exhibits an active pharmacologic effect, study of both the parent drug and the metabolite is of interest.