Volume 49, Issue 5 p. 513-533

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

Natilie A. Hosea PhD

Corresponding Author

Natilie A. Hosea PhD

Department of Pharmacokinetics, Dynamics and Metabolism, Pfizer Global Research and Development, San Diego, California

Address for correspondence: Natilie A. Hosea, Pfizer, Inc, Department of Pharmacokinetics, Dynamics & Metabolism, 10724 Science Center Dr, San Diego, CA 92121; e-mail: [email protected].Search for more papers by this author
Wendy T. Collard PhD

Wendy T. Collard PhD

Department of Metabolism and Safety, Pfizer Animal Health, Kalamazoo, Michigan

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Susan Cole BSc

Susan Cole BSc

Department of Metabolism and Safety, Pfizer Animal Health, Sandwich, UK

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Tristan S. Maurer PharmD, PhD

Tristan S. Maurer PharmD, PhD

Department of Metabolism and Safety, Pfizer Animal Health, Groton, Connecticut

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Rick X. Fang PhD

Rick X. Fang PhD

Department of Drug and Biomaterial R&D, Genzyme Corporation, Waltham, Massachusetts

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Hannah Jones PhD

Hannah Jones PhD

Department of Metabolism and Safety, Pfizer Animal Health, Sandwich, UK

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Shefali M. Kakar PhD

Shefali M. Kakar PhD

Clinical Pharmacology, Novartis Pharmaceuticals Corporation, Florham Park, New Jersey

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Yasuhiro Nakai PhD

Yasuhiro Nakai PhD

Department of Pharmacokinetics and Metabolism, Taisho Pharmaceutical Co, Ltd, Japan

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Bill J. Smith PhD

Bill J. Smith PhD

Department of Pharmacokinetics, Dynamics and Metabolism, Pfizer Global Research and Development, San Diego, California

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Rob Webster BSc

Rob Webster BSc

Department of Metabolism and Safety, Pfizer Animal Health, Sandwich, UK

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Kevin Beaumont BSc

Kevin Beaumont BSc

Department of Metabolism and Safety, Pfizer Animal Health, Sandwich, UK

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First published: 07 March 2013
Citations: 257

Abstract

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.