Literature DB >> 10801223

Quantitative prediction of in vivo drug-drug interactions from in vitro data based on physiological pharmacokinetics: use of maximum unbound concentration of inhibitor at the inlet to the liver.

S Kanamitsu1, K Ito, Y Sugiyama.   

Abstract

PURPOSE: To assess the degree to which the maximum unbound concentration of inhibitor at the inlet to the liver (I(inlet,u,max), used in the prediction of drug-drug interactions, overestimates the unbound concentration in the liver.
METHODS: The estimated value of I(inlet,u,max) was compared with the unbound concentrations in systemic blood, liver, and inlet to the liver, obtained in a simulation study based on a physiological flow model. As an example, a tolbutamide/sulfaphenazole interaction was predicted taking the plasma concentration profile of the inhibitor into consideration.
RESULTS: The value of I(inlet,u,max) differed from the concentration in each compartment, depending on the intrinsic metabolic clearance in the liver, first-order absorption rate constant, non-hepatic clearance and liver-to-blood concentration ratio (Kp) of the inhibitor. The AUC of tolbutamide was predicted to increase 4-fold when co-administered with sulfaphenazole, which agreed well with in vivo observations and was comparable with the predictions based on a fixed value of I(inlet,u,max). The blood concentration of tolbutamide was predicted to increase when it was co-administered with as little as 1/100 of the clinical dose of sulfaphenazole.
CONCLUSIONS: Although I(inlet,u,max) overestimated the unbound concentration in the liver, the tolbutamide/sulfaphenazole interaction could be successfully predicted by using a fixed value of I(inlet,u,max) indicating that the unbound concentration of sulfaphenazole in the liver after its clinical dose is by far larger than the concentration to inhibit CYP2C9-mediated metabolism and that care should be taken when it is co-administered with drugs that are substrates of CYP2C9.

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Year:  2000        PMID: 10801223     DOI: 10.1023/a:1007509324428

Source DB:  PubMed          Journal:  Pharm Res        ISSN: 0724-8741            Impact factor:   4.200


  18 in total

1.  SULPHAPHENAZOLE-INDUCED HYPOGLYCAEMIC ATTACKS IN TOLBUTAMIDE-TREATED DIABETICS.

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Review 4.  Genetic factors influencing the metabolism of tolbutamide.

Authors:  D J Back; M L Orme
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6.  Animal scale-up.

Authors:  R L Dedrick
Journal:  J Pharmacokinet Biopharm       Date:  1973-10

7.  Methotrexate pharmacokinetics.

Authors:  K B Bischoff; R L Dedrick; D S Zaharko; J A Longstreth
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Authors:  O Sugita; Y Sawada; Y Sugiyama; T Iga; M Hanano
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9.  The metabolic fate of tolbutamide in man and in the rat.

Authors:  R C Thomas; G J Ikeda
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10.  Lack of relationship between tolbutamide metabolism and debrisoquine oxidation phenotype.

Authors:  G F Peart; J Boutagy; G M Shenfield
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  15 in total

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