RT Journal Article
SR Electronic
T1 Critique of the two-fold measure of prediction success for ratios: application for the assessment of drug-drug interactions
JF Drug Metabolism and Disposition
JO Drug Metab Dispos
FD American Society for Pharmacology and Experimental Therapeutics
SP dmd.110.036103
DO 10.1124/dmd.110.036103
A1 Eleanor J. Guest
A1 Leon Aarons
A1 J. Brian Houston
A1 Amin Rostami-Hodjegan
A1 Aleksandra Galetin
YR 2010
UL http://dmd.aspetjournals.org/content/early/2010/10/29/dmd.110.036103.abstract
AB Current assessment of drug-drug interaction (DDI) prediction success is based on whether predictions fall within a two-fold range of the observed data. This results in a potential bias towards successful prediction at lower interaction levels (ratio of the area under the concentration-time profile (AUC) in the presence of inhibitor/inducer compared to control is <2). This scenario can bias any assessment of different DDI prediction algorithms if databases contain large proportion of interactions in this lower range. Therefore, the current study proposes an alternative method to assess prediction success with a variable prediction margin dependent on the particular AUC ratio. The method is applicable for assessment of both induction and inhibition related algorithms. The inclusion of variability into this predictive measure is also considered using midazolam as a case study. Comparison of the traditional two-fold and the new predictive method was performed on a subset of midazolam DDIs collated from previous databases; in each case DDIs were predicted using dynamic model in Simcyp Simulator®. A 21% reduction in prediction accuracy was evident using the new predictive measure, in particular at the level of no/weak interaction (AUC ratio<2). However, inclusion of variability increased the prediction success at these levels by 2-fold. The trend of lower prediction accuracy at higher potency of DDIs reported in previous studies is no longer apparent when predictions are assessed via the new predictive measure. Thus, the study proposes a more logical method for the assessment of prediction success and its application for induction and inhibition DDIs.