VALIDATION OF A DECISION-SUPPORT SYSTEM FOR USE IN DRUG DEVELOPMENT -PHARMACOKINETIC DATA

Purpose. 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 rel iability, however, that prediction is only likely to marginally influe nce critical drug development decision making. The system (Fore easter ) described generates an approximate prediction interval that contains the original prediction and where, for example, the probability is ap proximately 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 func tions as designed. Methods. The strategy requires having a Surrogate P opulation (SP), which is a large number (equal to or greater than 1500 ) of hypothetical individuals each represented by set of model paramet er values having unique attributes. The SP is generated so that a samp le taken from it will give data that is statistically indistinguishabl e from the available experimental data. The automated method for build ing the SP is described. Results. Validation studies using 300 indepen dent samples document that for this example the SP can be used to make useful predictions, and that the approximate prediction interval func tions as designed. Conclusions. For the boundary conditions and assump tions specified, the Forecaster can make valid predictions of pharmaco kinetic-based population targets that without a SP would not be possib le. Finally, the approximate prediction interval does provide a useful measure of prediction reliability.