Database analyses for the prediction of in vivo drug-drug interactions from in vitro data.
OBJECTIVE
In theory, the magnitude of an in vivo drug-drug interaction arising from the inhibition of metabolic clearance can be predicted using the ratio of inhibitor concentration ([I]) to inhibition constant (K(i)). The aim of this study was to construct a database for the prediction of drug-drug interactions from in vitro data and to evaluate the use of the various estimates for the inhibitor concentrations in the term [I]/K(i).


METHODS
One hundred and ninety-three in vivo drug-drug interaction studies involving inhibition of CYP3A4, CYP2D6 or CYP2C9 were collated from the literature together with in vitro K(i) values and pharmacokinetic parameters for inhibitors, to allow calculation of average/maximum systemic plasma concentration during the dosing interval and maximum hepatic input plasma concentration (both total and unbound concentration). The observed increase in AUC (decreased clearance) was plotted against the estimated [I]/K(i) ratio for qualitative zoning of the predictions.


RESULTS
The incidence of false negative predictions (AUC ratio > 2, [I]/K(i) < 1) was largest using the average unbound plasma concentration and smallest using the hepatic input total plasma concentration of inhibitor for each of the CYP enzymes. Excluding mechanism-based inhibition, the use of total hepatic input concentration resulted in essentially no false negative predictions, though several false positive predictions (AUC ratio < 2, [I]/K(i) > 1) were found. The incidence of true positive predictions (AUC ratio > 2, [I]/K(i) > 1) was also highest using the total hepatic input concentration.


CONCLUSIONS
The use of the total hepatic input concentration of inhibitor together with in vitro K(i) values was the most successful method for the categorization of putative CYP inhibitors and for identifying negative drug-drug interactions. However this approach should be considered as an initial discriminating screen, as it is empirical and requires subsequent mechanistic studies to provide a comprehensive evaluation of a positive result.
Ito
Kiyomi
K
School of Pharmacy and Pharmaceutical Sciences, University of Manchester, Manchester M13 9PL, UK.
Brown
Hayley S
HS
Houston
J Brian
JB
D001189:Aryl Hydrocarbon Hydroxylases / Q000037:antagonists & inhibitors*; D065729:Cytochrome P-450 CYP2C9; D065690:Cytochrome P-450 CYP2D6 Inhibitors; D051544:Cytochrome P-450 CYP3A; D065607:Cytochrome P-450 Enzyme Inhibitors; D003625:Data Collection; D004347:Drug Interactions; D006801:Humans; D011237:Predictive Value of Tests
D065690:Cytochrome P-450 CYP2D6 Inhibitors; D065607:Cytochrome P-450 Enzyme Inhibitors; C450260:CYP2C9 protein, human; D065729:Cytochrome P-450 CYP2C9; D001189:Aryl Hydrocarbon Hydroxylases; C104464:CYP3A protein, human; D051544:Cytochrome P-450 CYP3A; C510163:CYP3A4 protein, human