AAPS PharmSci

, Volume 4, Issue 4, pp 53–60 | Cite as

Which concentration of the inhibitor should be used to predict in vivo drug interactions from in vitro data?

  • Kiyomi Ito
  • Koji Chiba
  • Masato Horikawa
  • Michi Ishigami
  • Naomi Mizuno
  • Jun Aoki
  • Yasumasa Gotoh
  • Takafumi Iwatsubo
  • Shin-ichi Kanamitsu
  • Motohiro Kato
  • Iichiro Kawahara
  • Kayoko Niinuma
  • Akiko Nishino
  • Norihito Sato
  • Yuko Tsukamoto
  • Kaoru Ueda
  • Tomoo Itoh
  • Yuichi Sugiyama
Article

Abstract

When the metabolism of a drug is competitively or noncompetitively inhibited by another drug, the degree of in vivo interaction can be evaluated from the [I]u/Ki ratio, where [I]u is the unbound concentration around the enzyme and Ki is the inhibition constant of the inhibitor. In the present study, we evaluated the metabolic inhibition potential of drugs known to be inhibitors or substrates of cytochrome P450 by estimating their [I]u/Ki ratio using literature data.

The maximum concentration of the inhibitor in the circulating blood ([I]max), its maximum unbound concentration in the circulating blood ([I]max,u), and its maximum unbound concentration at the inlet to the liver ([I]in,max,u) were used as [I]u, and the results were compared with each other. In order to calculate the [I]u/Ki ratios, the pharmacokinetic parameters of each drug were obtained from the literature, together with their reported Ki values determined in in vitro studies using human liver microsomes.

For most of the drugs with a calculated [I]in,max,u/Ki ratio less than 0.25, which applied to about half of the drugs investigated, no in vivo interactions had been reported or “no interaction” was reported in clinical studies. In contrast, the [I]max,u/Ki and [I]max/Ki ratio was calculated to be less than 0.25 for about 90% and 65% of the drugs, respectively, and more than a 1.25-fold increase was reported in the area under the concentration-time curve of the co-administered drug for about 30% of such drugs. These findings indicate that the possibility of underestimation of in vivo interactions (possibility of false-negative prediction) is greater when [I]max,u or [I]max values are used compared with using [I]in,max,u values.

Keywords

Drug interaction metabolism quantitative prediction 

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Copyright information

© American Association of Pharmaceutical Scientists 2002

Authors and Affiliations

  • Kiyomi Ito
    • 1
  • Koji Chiba
    • 2
  • Masato Horikawa
    • 3
  • Michi Ishigami
    • 4
  • Naomi Mizuno
    • 5
  • Jun Aoki
    • 6
  • Yasumasa Gotoh
    • 7
  • Takafumi Iwatsubo
    • 8
  • Shin-ichi Kanamitsu
    • 9
  • Motohiro Kato
    • 10
  • Iichiro Kawahara
    • 11
  • Kayoko Niinuma
    • 12
  • Akiko Nishino
    • 13
  • Norihito Sato
    • 14
  • Yuko Tsukamoto
    • 15
  • Kaoru Ueda
    • 16
  • Tomoo Itoh
    • 1
  • Yuichi Sugiyama
    • 17
  1. 1.School of Pharmaceutical SciencesKitasato UniversityTokyo
  2. 2.PharmaciaThe University of TokyoTokyoJapan
  3. 3.Nissan Chemical Industries, LtdThe University of TokyoTokyoJapan
  4. 4.Sankyo Co, LtdThe University of TokyoTokyoJapan
  5. 5.Mitsubishi-Tokyo Pharmaceuticals, IncThe University of TokyoTokyoJapan
  6. 6.Mitsui Pharmaceuticals IncThe University of TokyoTokyoJapan
  7. 7.Kissei Pharmaceutical Co, LtdThe University of TokyoTokyoJapan
  8. 8.Yamanouchi Pharmaceutical Co, LtdThe University of TokyoTokyoJapan
  9. 9.Otsuka Pharmaceutical Factory, IncThe University of TokyoTokyoJapan
  10. 10.Chugai Pharmaceutical Co, LtdThe University of TokyoTokyoJapan
  11. 11.Bayer Yakuhin LtdThe University of TokyoTokyoJapan
  12. 12.Daiichi Pharmaceutical Co, LtdThe University of TokyoTokyoJapan
  13. 13.Nippon Boehringer Ingelheim Co, LtdThe University of TokyoTokyoJapan
  14. 14.Shionogi & Co, LtdThe University of TokyoTokyoJapan
  15. 15.Nippon Roche KKThe University of TokyoTokyoJapan
  16. 16.Teikoku Hormone Mfg Co, LtdThe University of TokyoTokyoJapan
  17. 17.Graduate School of Pharmaceutical SciencesThe University of TokyoTokyoJapan

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