Pharmaceutical Research

, Volume 25, Issue 8, pp 1891–1901

The Quantitative Prediction of CYP-mediated Drug Interaction by Physiologically Based Pharmacokinetic Modeling

  • Motohiro Kato
  • Yoshihisa Shitara
  • Hitoshi Sato
  • Kunihiro Yoshisue
  • Masaru Hirano
  • Toshihiko Ikeda
  • Yuichi Sugiyama
Research Paper

Abstract

Purpose

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.

Method

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.

Results

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.

Conclusion

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 WORDS

CYP drug interaction enzyme inhibition physiologically based pharmacokinetics 

Abbreviations

AUC

area under the curve

CYP

cytochrome P450

F

bioavailability

Fa

fraction absorbed

Fg

intestinal availability

Fh

hepatic availability

Ip,max,u

maximum unbound concentration in the circulating blood

Iu,max

maximum unbound concentration at the inlet to the liver

Ki

inhibition constant

PBPK

physiologically based pharmacokinetic

Qh

hepatic blood flow rate

References

  1. 1.
    K. Ito, T. Iwatsubo, S. Kanamitsu, K. Ueda, H. Suzuki, and Y. Sugiyama. Prediction of pharmacokinetic alterations caused by drug–drug interactions: metabolic interaction in the liver. Pharmacol. Rev. 50:387–412 (1998).PubMedGoogle Scholar
  2. 2.
    J. H. Lin, and A. Y. Lu. Inhibition and induction of cytochrome P450 and the clinical implications. Clin. Pharmacokinet. 35:361–390 (1998).PubMedCrossRefGoogle Scholar
  3. 3.
    G. T. Tucker, J. B. Houston, and S. M. Huang. Optimizing drug development: strategies to assess drug metabolism/transporter interaction potential—toward a consensus. Pharm. Res. 18:1071–1080 (2001).PubMedCrossRefGoogle Scholar
  4. 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. 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
  6. 6.
    M. Kato, T. Tachibana, K. Ito, and Y. Sugiyama. Evaluation of methods for predicting drug–drug interactions by Monte Carlo simulation. Drug Metab. Pharmacokinet. 18:121–127 (2003).PubMedCrossRefGoogle Scholar
  7. 7.
    K. Ito, H. S. Brown, and J. B. Houston. Database analyses for the prediction of in vivo drug–drug interactions from in vitro data. Br. J. Clin. Pharmacol. 57:473–486 (2004).PubMedCrossRefGoogle Scholar
  8. 8.
    R. S. Obach, R. L. Walsky, K. Venkatakrishnan, E. A. Gaman, J. B. Houston, and L. M. Tremaine. The utility of in vitro cytochrome P450 inhibition data in the prediction of drug–drug interactions. J. Pharmacol. Exp. Ther. 316:336–348 (2005).PubMedCrossRefGoogle Scholar
  9. 9.
    P. Poulin, and F. P. Theil. Prediction of pharmacokinetics prior to in vivo studies. II. Generic physiologically based pharmacokinetic models of drug disposition. J. Pharm. Sci. 91:1358–1370 (2002).PubMedCrossRefGoogle Scholar
  10. 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
  11. 11.
    D. M. Stresser, A. P. Blanchard, S. D. Turner, J. C. Erve, A. A. Dandeneau, V. P. Miller, and C. L. Crespi. Substrate-dependent modulation of CYP3A4 catalytic activity: analysis of 27 test compounds with four fluorometric substrates. Drug Metab. Dispos. 28:1440–1448 (2000).PubMedGoogle Scholar
  12. 12.
    Methods of Drug interaction studies: Notification No.813 of the Pharmaceutical Affair Bureau, the Ministry of Health, Labour, Welfare, Japan (2001)Google Scholar
  13. 13.
    L. L. von Moltke, A. L. Durol, S. X. Duan, and D. J. Greenblatt. Potent mechanism-based inhibition of human CYP3A in vitro by amprenavir and ritonavir: comparison with ketoconazole. Eur. J. Clin. Pharmacol. 56:259–261 (2000).CrossRefGoogle Scholar
  14. 14.
    K. L. Kunze, and W. F. Trager. Isoform-selective mechanism-based inhibition of human cytochrome P450 1A2 by furafylline. Chem. Res. Toxicol. 6:649–656 (1993).PubMedCrossRefGoogle Scholar
  15. 15.
    W. K. Chan, and A. B. Delucchi. Resveratrol, a red wine constituent, is a mechanism-based inactivator of cytochrome P450 3A4. Life Sci. 67:3103–3112 (2000).PubMedCrossRefGoogle Scholar
  16. 16.
    K. M. Bertelsen, K. Venkatakrishnan, L. L. Von Moltke, R. S. Obach, and D. J. Greenblatt. Apparent mechanism-based inhibition of human CYP2D6 in vitro by paroxetine: comparison with fluoxetine and quinidine. Drug Metab. Dispos. 31:289–293 (2003).PubMedCrossRefGoogle Scholar
  17. 17.
    D. R. Jones, J. C. Gorski, M. A. Hamman, B. S. Mayhew, S. Rider, and S. D. Hall. Diltiazem inhibition of cytochrome P-450 3A activity is due to metabolite intermediate complex formation. J. Pharmacol. Exp. Ther. 290:1116–1125 (1999).PubMedGoogle Scholar
  18. 18.
    C. S. Ernest 2nd, S. D. Hall, and D. R. Jones. Mechanism-based inactivation of CYP3A by HIV protease inhibitors. J. Pharmacol. Exp. Ther. 312:583–591 (2004).PubMedCrossRefGoogle Scholar
  19. 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. 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
  21. 21.
    M. Ishigam, M. Uchiyama, T. Kondo, H. Iwabuchi, S. Inoue, W. Takasaki, T. Ikeda, T. Komai, K. Ito, and Y. Sugiyama. Inhibition of in vitro metabolism of simvastatin by itraconazole in humans and prediction of in vivo drug–drug interactions. Pharm. Res. 18:622–631 (2001).PubMedCrossRefGoogle Scholar
  22. 22.
    N. Isoherranen, K. L. Kunze, K. E. Allen, W. L. Nelson, and K. E. Thummel. Role of itraconazole metabolites in CYP3A4 inhibition. Drug Metab. Dispos. 32:1121–1131 (2004).PubMedCrossRefGoogle Scholar
  23. 23.
    R. P. Austin, P. Barton, S. L. Cockroft, M. C. Wenlock, and R. J. Riley. The influence of nonspecific microsomal binding on apparent intrinsic clearance, and its prediction from physicochemical properties. Drug Metab. Dispos. 30:1497–1503 (2002).PubMedCrossRefGoogle Scholar
  24. 24.
    K. Ito, K. Ogihara, S. Kanamitsu, and T. Itoh. Prediction of the in vivo interaction between midazolam and macrolides based on in vitro studies using human liver microsomes. Drug Metab. Dispos. 31:945–954 (2003).PubMedCrossRefGoogle Scholar
  25. 25.
    S. Kanamitsu, K. Ito, C. E. Green, C. A. Tyson, N. Shimada, and Y. Sugiyama. Prediction of in vivo interaction between triazolam and erythromycin based on in vitro studies using human liver microsomes and recombinant human CYP3A4. Pharm. Res. 17:419–426 (2000).PubMedCrossRefGoogle Scholar

Copyright information

© Springer Science+Business Media, LLC 2008

Authors and Affiliations

  • Motohiro Kato
    • 1
  • Yoshihisa Shitara
    • 2
  • Hitoshi Sato
    • 3
  • Kunihiro Yoshisue
    • 4
  • Masaru Hirano
    • 4
  • Toshihiko Ikeda
    • 5
  • Yuichi Sugiyama
    • 4
  1. 1.Pre-clinical Research DepartmentChugai Pharmaceutical Co. Ltd.ShizuokaJapan
  2. 2.Chiba UniversityChibaJapan
  3. 3.Showa UniversityTokyoJapan
  4. 4.The University of TokyoTokyoJapan
  5. 5.Association for Promoting Drug DevelopmentTokyoJapan

Personalised recommendations