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Drug Metab Rev. 1999 May;31(2):545-60.

Extrapolating in vitro data on drug metabolism to in vivo pharmacokinetics: evaluation of the pharmacokinetic interaction between amitriptyline and fluoxetine.

Author information

1
Department of Clinical Pharmacology and Experimental Therapeutics, Tufts University School of Medicine, Boston, Massachusetts, USA. schmider@rz.charite.hu-berlin.de

Abstract

Recently, models have been proposed to extrapolate in vitro data on the influence of inhibitors on drug metabolism to in vivo decrement in drug clearance. Many factors influence drug clearance such as age, gender, habits, diet, environment, liver disease, heredity, and other drugs. In vitro investigation of hepatic cytochrome P450 activity has generally centered on genetic influences and interactions with other drugs. This group of enzymes is involved in many, although not all, drug interactions. The interaction of amitriptyline and fluoxetine is an example. Of the different in vitro paradigms, interaction studies utilizing human liver microsomal preparations have proved to be the most generally applicable for in vitro scaling models. Assuming Michaelis-Menten conditions and applying nonlinear regression, a hybrid inhibition constant (Ki) can be generated that allows classification of the inhibitory potency of an inhibitor toward a specific reaction. This constant is largely independent of the substrate concentration, but in vivo relevance is critically dependent on the inhibitor concentration in the site of metabolic activity, the liver cell cytosol. Many lipophilic drugs are extensively bound to plasma protein but, nonetheless, demonstrate extensive partitioning into liver tissue. This is not compatible with diffusion only of the unbound drug fraction into liver cells. The introduction of a partition factor, based on data from a number of possible sources, provided a reasonable basis for the scaling of in vitro data to in vivo conditions. Many interactions could be reconstructed or predicted with greater accuracy and clinical relevance for interactions such as terfenadine or midazolam and ketoconazole. Even for less marked interactions such as amitriptyline and fluoxetine, this model provides a forecast consistent with the clinically observed range of 22-45% reduction in oral clearance, although this interaction is complicated by the presence of two inhibitors, fluoxetine and norfluoxetine. The concept of in vitro-in vivo scaling is promising and might ultimately yield a fast and more cost-effective screening for drug interactions with reduced human drug exposure and risk.

PMID:
10335452
DOI:
10.1081/DMR-100101935
[Indexed for MEDLINE]

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