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Practical use of the regression offset approach for the prediction of in vivo intrinsic clearance from hepatocytes
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2012 (English)In: Xenobiotica, ISSN 0049-8254, E-ISSN 1366-5928, Vol. 42, no 9, 841-853 p.Article in journal (Refereed) Published
Abstract [en]

1. Systematic under-prediction of clearance is frequently associated with in vitro kinetic data when extrapolated using physiological scaling factors, appropriate binding parameters and the well-stirred model. The present study describes a method of removing this systematic bias through application of empirical correction factors derived from regression analyses applied to the in vitro and in vivo data for a defined set of reference compounds.

2. Linear regression lines were established with in vivo intrinsic clearance (CLint), derived from in vivo clearance data and scaled in vitro intrinsic clearance from isolated hepatocyte incubations. The scaled CLint was empirically corrected to a predicted in vivo CLint using the slope and intercept from a uniform weighted linear regression applied to the in vitro to in vivo extrapolation.

3. Cross validation of human data demonstrated that 66% of the reference compounds had a predicted in vivo CLint within two-fold of the observed value. The average absolute fold error (AAFE) for the in vivo CLint predictions was 1.90. For rat, 54% of the compounds had a predicted value within two-fold of the observed and the AAFE was 1.98.

4. Three AstraZeneca projects are used to exemplify how a two-sided prediction interval, applied to the rat regression corrected reference data, can form the basis for assessing the likelihood that, for a given chemical series, the in vitro kinetic data is predictive of in vivo clearance and is therefore appropriate to guide optimisation of compound metabolic stability.

Place, publisher, year, edition, pages
2012. Vol. 42, no 9, 841-853 p.
Keyword [en]
Liver, metabolism, hepatocytes, CLint, in vitro to in vivo scaling, regression offset, well-stirred model
National Category
Medical and Health Sciences
URN: urn:nbn:se:uu:diva-181402DOI: 10.3109/00498254.2012.669080ISI: 000307301900003OAI: oai:DiVA.org:uu-181402DiVA: diva2:557680
Available from: 2012-09-28 Created: 2012-09-24 Last updated: 2012-09-28Bibliographically approved

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