The Quantitative Prediction of CYP-mediated Drug Interaction by Physiologically Based Pharmacokinetic Modeling
- First Online:
- 988 Downloads
The objective is to confirm if the prediction of the drug–drug interaction using a physiologically based pharmacokinetic (PBPK) model is more accurate. In vivo Ki values were estimated using PBPK model to confirm whether in vitro Ki values are suitable.
The plasma concentration–time profiles for the substrate with coadministration of an inhibitor were collected from the literature and were fitted to the PBPK model to estimate the in vivo Ki values. The AUC ratios predicted by the PBPK model using in vivo Ki values were compared with those by the conventional method assuming constant inhibitor concentration.
The in vivo Ki values of 11 inhibitors were estimated. When the in vivo Ki values became relatively lower, the in vitro Ki values were overestimated. This discrepancy between in vitro and in vivo Ki values became larger with an increase in lipophilicity. The prediction from the PBPK model involving the time profile of the inhibitor concentration was more accurate than the prediction by the conventional methods.
A discrepancy between the in vivo and in vitro Ki values was observed. The prediction using in vivo Ki values and the PBPK model was more accurate than the conventional methods.
KEY WORDSCYP drug interaction enzyme inhibition physiologically based pharmacokinetics
area under the curve
maximum unbound concentration in the circulating blood
maximum unbound concentration at the inlet to the liver
physiologically based pharmacokinetic
hepatic blood flow rate
- 4.Food and Drug Administration. Guidance for industry: in vivo drug metabolism/drug interaction studies—study design, data analysis, and recommendations for dosing and labeling, (1999).Google Scholar
- 5.T. D. Bjornsson, J. T. Callaghan, H. J. Einolf, V. Fischer, L. Gan, S. Grimm, J. Kao, S. P. King, G. Miwa, L. Ni, G. Kumar, J. McLeod, R. S. Obach, S. Roberts, A. Roe, A. Shah, F. Snikeris, J. T. Sullivan, D. Tweedie, J. M. Vega, J. Walsh, and S. A. Wrighton. The conduct of in vitro and in vivo drug–drug interaction studies: a Pharmaceutical Research and Manufacturers of America (PhRMA) perspective. Drug Metab. Dispos. 31:815–832 (2003).PubMedCrossRefGoogle Scholar
- 10.K. Ito, K. Chiba, M. Horikawa, M. Ishigami, N. Mizuno, J. Aoki, Y. Gotoh, T. Iwatsubo, S. Kanamitsu, M. Kato, I. Kawahara, K. Niinuma, A. Nishino, N. Sato, Y. Tsukamoto, K. Ueda, T. Itoh, and Y. Sugiyama. Which concentration of the inhibitor should be used to predict in vivo drug interactions from in vitro data? AAPS PharmSci. 4:E25 (2002).PubMedCrossRefGoogle Scholar
- 12.Methods of Drug interaction studies: Notification No.813 of the Pharmaceutical Affair Bureau, the Ministry of Health, Labour, Welfare, Japan (2001)Google Scholar
- 19.J. H. Lillibridge, B. H. Liang, B. M. Kerr, S. Webber, B. Quart, B. V. Shetty, and C. A. Lee. Characterization of the selectivity and mechanism of human cytochrome P450 inhibition by the human immunodeficiency virus-protease inhibitor nelfinavir mesylate. Drug Metab. Dispos. 26:609–616 (1998).PubMedGoogle Scholar
- 20.M. Kato, K. Chiba, A. Hisaka, M. Ishigami, M. Kayama, N. Mizuno, Y. Nagata, S. Takakuwa, Y. Tsukamoto, K. Ueda, H. Kusuhara, K. Ito, and Y. Sugiyama. The intestinal first-pass metabolism of substrates of CYP3A4 and P-glycoprotein-quantitative analysis based on information from the literature. Drug Metab. Pharmacokinet. 18:365–372 (2003).PubMedCrossRefGoogle Scholar