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.
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 I(u)/K(i) value was used for the prediction of DDIs between NE-100 and concomitant drugs. When the K(i) values (K(i-cal)) obtained from the microtiter plate (MTP) assay and the reported K(i) values (K(i-rep)) for these drugs were used to predict increases at levels of NE-100 AUC(oral) (AUC(oral) ratio), the AUC(oral) ratios from the I(u)/K(i-cal) correlated with those from the I(u)/K(i-rep). This result suggests that the K(i-cal) from the MTP assay can be used for prediction of DDIs instead of the K(i-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 AUC(oral) 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 I(u)/K(i). However, after co-administration of drugs such as quinidine, propafenone and thioridazine (potent inhibitors of CYP2D6), the NE-100 AUC(oral) 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 AUC(oral) by DDIs for high clearance drug, compared with the prediction method using I(u)/K(i) value.
Yamamoto
Takahito
T
Drug Metabolism Laboratory, Medicinal Development Research Laboratories, Taisho Pharmaceutical Co., Ltd., 403, Yoshino-cho 1-chome, Kita-ku, Saitama-shi, Saitama 331-9530, Japan. takahito.yamamoto@po.rd.taisho.co.jp
Suzuki
Akio
A
Kohno
Yoshiro
Y
Nagata
Kiyoshi
K
Yamazoe
Yasushi
Y
D000284:Administration, Oral; D000319:Adrenergic beta-Antagonists / Q000493:pharmacokinetics*; D000465:Algorithms; D000818:Animals; D000840:Anisoles / Q000493:pharmacokinetics*; D000889:Anti-Arrhythmia Agents / Q000493:pharmacokinetics; D000929:Antidepressive Agents, Tricyclic / Q000493:pharmacokinetics; D014150:Antipsychotic Agents / Q000493:pharmacokinetics*; D019540:Area Under Curve; D016367:Baculoviridae / Q000378:metabolism; D003891:Desipramine / Q000493:pharmacokinetics; D004347:Drug Interactions; D006801:Humans; D066298:In Vitro Techniques; D007313:Insecta / Q000378:metabolism; D008790:Metoprolol / Q000493:pharmacokinetics*; D008861:Microsomes / Q000378:metabolism; D008862:Microsomes, Liver / Q000378:metabolism; D015233:Models, Statistical; D011405:Propafenone / Q000493:pharmacokinetics; D011437:Propylamines / Q000493:pharmacokinetics*; D013881:Thioridazine / Q000493:pharmacokinetics
D000319:Adrenergic beta-Antagonists; D000840:Anisoles; D000889:Anti-Arrhythmia Agents; D000929:Antidepressive Agents, Tricyclic; D014150:Antipsychotic Agents; D011437:Propylamines; C083832:N,N-dipropyl-2-(4-methoxy-3-(2-phenylethoxy)phenyl)ethylamine monohydrochloride; D011405:Propafenone; D008790:Metoprolol; D013881:Thioridazine; D003891:Desipramine