Validation of a decision support system for use in drug development: pharmacokinetic data.

Abstract

Single dose pharmacokinetic data from several individuals can be used to predict the fraction of the population that is expected to be within a therapeutic range. Without having some measure of its reliability, however, that prediction is only likely to marginally influence critical drug development decision making. The system (Forecaster) described generates an approximate prediction interval that contains the original prediction and where, for example, the probability is approximately 85% that a similar prediction from a new set of data will also be within the range. The goal is to validate that the system functions as designed. The strategy requires having a Surrogate Population (SP), which is a large number (> or = 1500) of hypothetical individuals each represented by set of model parameter values having unique attributes. The SP is generated so that a sample taken from it will give data that is statistically indistinguishable from the available experimental data. The automated method for building the SP is described. Validation studies using 300 independent samples document that for this example the SP can be used to make useful predictions, and that the approximate prediction interval functions as designed. For the boundary conditions and assumptions specified, the Forecaster can make valid predictions of pharmacokinetic-based population targets that without a SP would not be possible. Finally, the approximate prediction interval does provide a useful measure of prediction reliability.