Format

Send to:

Choose Destination
See comment in PubMed Commons below
Curr Drug Metab. 2008 Nov;9(9):940-51.

Methods for predicting in vivo pharmacokinetics using data from in vitro assays.

Author information

  • 1School of Pharmacy and Pharmaceutical Sciences, University of Manchester, Manchester M13 9PT, UK. Brian.Houston@manchester.ac.uk

Abstract

Strategies for optimising in vivo predictions from in vitro data on metabolic stability and CYP inhibition are discussed. Potential pitfalls and areas of inaccuracy are highlighted together with recommendations for best practice. The use of both hepatic microsomes and isolated hepatocytes for the assessment of metabolic stability is discussed in terms of scaling from the in vitro system up to whole liver. The importance of integrating metabolic stability data together with other drug pharmacokinetic characteristics (e.g., protein binding and red blood cell uptake) as well as blood flow are presented within the context of different liver models. The assessment of CYP inhibition potential requires in vitro data on the inhibitor potency either in the form of Ki (for reversible inhibition) or KI and kinact (for time-dependent inhibition). The integration of these in vitro parameters together with other pharmacokinetic information is essential for the in vivo prediction. While a qualitative assessment may be made from the I/Ki ratio, a number of additional victim drug and enzyme-related parameters are required for quantitative prediction. Of particular importance is the parameter fmCYP (the fraction of the metabolic clearance of the victim drug that is catalyzed by the enzyme subject to the inhibition). Impact of other victim drug properties (e.g., fractional importance of the intestine) and enzyme properties (e.g., kdeg for time-dependent inhibition) on the drug-drug interaction prediction is discussed. In addition, mechanisms by which false negatives and false positives may result from in vitro strategies are summarized. Finally perspectives for future application and improvements in these predictions strategies are outlined.

PMID:
18991591
[PubMed - indexed for MEDLINE]
PubMed Commons home

PubMed Commons

0 comments
How to join PubMed Commons

    Supplemental Content

    Full text links

    Icon for Bentham Science Publishers Ltd.
    Loading ...
    Write to the Help Desk