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Current Drug Metabolism

Editor-in-Chief

ISSN (Print): 1389-2002
ISSN (Online): 1875-5453

Prediction of Drug-Drug Interactions for AUCoral of High Clearance Drug from In Vitro Data: Utilization of a Microtiter Plate Assay and a Dispersion Model

Author(s): Takahito Yamamoto, Akio Suzuki, Yoshiro Kohno, Kiyoshi Nagata and Yasushi Yamazoe

Volume 7, Issue 2, 2006

Page: [135 - 146] Pages: 12

DOI: 10.2174/138920006775541570

Price: $65

Abstract

The purpose of this study was to propose a new method to predict in vivo drug-drug interactions (DDIs) for a high clearance drug from in vitro data. As the high clearance drug, NE-100 (N, N-dipropyl-2-[4-methoxy-3-(2- phenylethoxy)phenyl]ethylamine monohydrochloride) was used. First, approach based on Iu/Ki value was used for the prediction of DDIs between NE-100 and concomitant drugs. When the Ki values (Ki-cal) obtained from the microtiter plate (MTP) assay and the reported Ki values (Ki-rep) for these drugs were used to predict increases at levels of NE-100 AUCoral (AUCoral ratio), the AUCoral ratios from the Iu /Ki-cal correlated with those from the Iu/Ki-rep. This result suggests that the Kical from the MTP assay can be used for prediction of DDIs instead of the Ki-rep value. Second, a new approach combining the inhibition rate (R) calculated from the MTP assay and two physiological models was used to predict DDIs. When the AUCoral ratios of NE-100 by various drugs were predicted using the R value and the well-stirred model, the ratios were similar to those predicted using the Iu/Ki. However, after co-administration of drugs such as quinidine, propafenone and thioridazine (potent inhibitors of CYP2D6), the NE-100 AUCoral ratios predicted from the dispersion model was much greater than those from well-stirred model. This result shows that application of the dispersion model to the prediction method using the R value might sensitively and precisely predict the increased levels of AUCoral by DDIs for high clearance drug, compared with the prediction method using Iu/Ki value.

Keywords: Drug-drug interactions, Prediction, High throughput screening, Microtiter plate assay, Dispersion model, Cytochrome P450, CYP2D6


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