HOME HELP FEEDBACK SUBSCRIPTIONS ARCHIVE SEARCH TABLE OF CONTENTS		QUICK SEARCH:   [advanced] Author: Keyword(s): Year:  Vol:  Page:
Sign In to gain access to subscriptions and/or personal tools.

This Article Full Text Full Text (PDF) All Versions of this Article: 0091270009333209v1 49/5/513    most recent Alert me when this article is cited Alert me if a correction is posted Citation Map Services Email this article to a friend Similar articles in this journal Similar articles in PubMed Alert me to new issues of the journal Download to citation manager Request Permissions Request Reprints Citing Articles Citing Articles via HighWire Citing Articles via Google Scholar Google Scholar Articles by Hosea, N. A. Articles by Beaumont, K. Search for Related Content PubMed PubMed Citation Articles by Hosea, N. A. Articles by Beaumont, K. Social Bookmarking                         What's this?

Prediction of Human Pharmacokinetics From Preclinical Information: Comparative Accuracy of Quantitative Prediction Approaches

From the Department of Pharmacokinetics, Dynamics and Metabolism, Pfizer Global Research and Development, San Diego, California (N. A. Hosea, B. J. Smith); Department of Metabolism and Safety, Pfizer Animal Health, Kalamazoo, Michigan (W. T. Collard); Sandwich, UK (S. Cole, H. Jones, R. Webster, K. Beaumont); Groton, Connecticut (T. S. Maurer); Clinical Pharmacology, Novartis Pharmaceuticals Corporation, Florham Park, New Jersey (S. M. Kakar); Department of Drug and Biomaterial R&D, Genzyme Corporation, Waltham, Massachusetts (R. X. Fang); and Department of Pharmacokinetics and Metabolism, Taisho Pharmaceutical Co, Ltd, Japan (Y. Nakai).

Quantitative prediction of human pharmacokinetics is critical in assessing the viability of drug candidates and in determining first-in-human dosing. Numerous prediction methodologies, incorporating both in vitro and preclinical in vivo data, have been developed in recent years, each with advantages and disadvantages. However, the lack of a comprehensive data set, both preclinical and clinical, has limited efforts to evaluate the optimal strategy (or strategies) that results in quantitative predictions of human pharmacokinetics. To address this issue, the authors conducted a retrospective analysis using 50 proprietary compounds for which in vitro, preclinical pharmacokinetic data and oral single-dose human pharmacokinetic data were available. Five predictive strategies, involving either allometry or use of unbound intrinsic clearance from microsomes or hepatocytes, were then compared for their ability to predict human oral clearance, half-life through predictions of systemic clearance, volume of distribution, and bioavailability. Use of a single-species scaling approach with rat, dog, or monkey was as accurate as or more accurate than using multiple-species allometry. For those compounds cleared almost exclusively by P450-mediated pathways, scaling from human liver microsomes was as predictive as single-species scaling of clearance based on data from rat, dog, or monkey. These data suggest that use of predictive methods involving either single-species in vivo data or in vitro human liver microsomes can quantitatively predict human in vivo pharmacokinetics and suggest the possibility of streamlining the predictive methodology through use of a single species or use only of human in vitro microsomal preparations.

Key Words: human pharmacokinetics • prediction methodologies • drug discovery

Address for reprints: Natilie A. Hosea, Pfizer, Inc, Department of Pharmacokinetics, Dynamics & Metabolism, 10724 Science Center Dr, San Diego, CA 92121; e-mail: natilie.hosea{at}pfizer.com.

CiteULike    Complore    Connotea    Del.icio.us    Digg    Reddit    Technorati    Twitter    What's this?

This article has been cited by other articles:

				T. Hay, R. Jones, K. Beaumont, and M. Kemp Modulation of the Partition Coefficient between Octanol and Buffer at pH 7.4 and pKa to Achieve the Optimum Balance of Blood Clearance and Volume of Distribution for a Series of Tetrahydropyran Histamine Type 3 Receptor Antagonists Drug Metab. Dispos., September 1, 2009; 37(9): 1864 - 1870. [Abstract] [Full Text] [PDF]