Application of CYP3A4 in vitro data to predict clinical drug-drug interactions; predictions of compounds as objects of interaction.
BACKGROUND
Numerous retrospective analyses have shown the utility of in vitro systems for predicting potential drug-drug interactions (DDIs). Prediction of DDIs from in vitro data is commonly obtained using estimates of enzyme K(i), inhibitor and substrate concentrations and absorption rate for substrate and inhibitor.


CONCLUSIONS
Using a generic approach for all test compounds, the findings from the current study showed the use of recombinant P450s provide a more robust in vitro measure of P450 contribution (fraction metabolized, f(m)) than that achieved when using chemical inhibitors in combination with human liver microsomes, for the prediction of potential CYP3A4 drug-drug interactions prior to clinical investigation. The current study supported the use of SIMCYP(R), a modelling and simulation software in utilizing the in vitro measures in the prediction of potential drug-drug interactions.


OBJECTIVE
The aim of this study was to explore and optimize the in vitro and in silico approaches used for predicting clinical DDIs. A data set containing clinical information on the interaction of 20 Pfizer compounds with ketoconazole was used to assess the success of the techniques.


METHODS
The study calculated the fraction and the rate of metabolism of 20 Pfizer compounds via each cytochrome P450. Two approaches were used to determine fraction metabolized (f(m)); 1) by measuring substrate loss in human liver microsomes (HLM) in the presence and absence of specific chemical inhibitors and 2) by measuring substrate loss in individual cDNA expressed P450s (also referred to as recombinant P450s (rhCYP)) The fractions metabolized via each CYP were used to predict the drug-drug interaction due to CYP3A4 inhibition by ketoconazole using the modelling and simulation software SIMCYP.


RESULTS
When in vitro data were generated using Gentest supersomes, 85% of predictions were within two-fold of the observed clinical interaction. Using PanVera baculosomes, 70% of predictions were predicted within two-fold. In contrast using chemical inhibitors the accuracy was lower, predicting only 37% of compounds within two-fold of the clinical value. Poorly predicted compounds were found to either be metabolically stable and/or have high microsomal protein binding. The use of equilibrium dialysis to generate accurate protein binding measurements was especially important for highly bound drugs.


CONCLUSIONS
The current study demonstrated that the use of rhCYPs with SIMCYP provides a robust in vitro system for predicting the likelihood and magnitude of changes in clinical exposure of compounds as a consequence of CYP3A4 inhibition by a concomitantly administered drug.
Youdim
Kuresh A
KA
Pfizer Global Research and Development, Department of Pharmacokinetics, Dynamics and Metabolism, Sandwich, Kent, UK. kuresh.youdim@pfizer.com
Zayed
Aref
A
Dickins
Maurice
M
Phipps
Alex
A
Griffiths
Michelle
M
Darekar
Amanda
A
Hyland
Ruth
R
Fahmi
Odette
O
Hurst
Susan
S
Plowchalk
David R
DR
Cook
Jack
J
Guo
Feng
F
Obach
R Scott
RS
D019540:Area Under Curve; D051544:Cytochrome P-450 CYP3A / Q000494:pharmacology*; D065607:Cytochrome P-450 Enzyme Inhibitors; D003577:Cytochrome P-450 Enzyme System / Q000378:metabolism*; D004347:Drug Interactions; D006801:Humans; D007654:Ketoconazole / Q000037:antagonists & inhibitors / Q000378:metabolism*; D011237:Predictive Value of Tests; D011485:Protein Binding / Q000502:physiology
D065607:Cytochrome P-450 Enzyme Inhibitors; D003577:Cytochrome P-450 Enzyme System; D051544:Cytochrome P-450 CYP3A; C510163:CYP3A4 protein, human; D007654:Ketoconazole