title: Prediction of human pharmacokinetics from preclinical information: comparative accuracy of quantitative prediction approaches.
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.
authors.affiliation: Pfizer Inc, Department of Pharmacokinetics, Dynamics & Metabolism, San Diego, CA 92121, USA. natilie.hosea@pfizer.com
authors.forename: Natilie A
authors.initials: NA
authors.lastname: Hosea
authors.forename: Wendy T
authors.initials: WT
authors.lastname: Collard
authors.forename: Susan
authors.initials: S
authors.lastname: Cole
authors.forename: Tristan S
authors.initials: TS
authors.lastname: Maurer
authors.forename: Rick X
authors.initials: RX
authors.lastname: Fang
authors.forename: Hannah
authors.initials: H
authors.lastname: Jones
authors.forename: Shefali M
authors.initials: SM
authors.lastname: Kakar
authors.forename: Yasuhiro
authors.initials: Y
authors.lastname: Nakai
authors.forename: Bill J
authors.initials: BJ
authors.lastname: Smith
authors.forename: Rob
authors.initials: R
authors.lastname: Webster
authors.forename: Kevin
authors.initials: K
authors.lastname: Beaumont
mesh_terms: D000284:Administration, Oral; D000818:Animals; D019540:Area Under Curve; D001682:Biological Availability; D001699:Biometry; D004353:Drug Evaluation, Preclinical / Q000379:methods*; D006207:Half-Life; D022781:Hepatocytes / Q000378:metabolism; D006801:Humans; D008657:Metabolic Clearance Rate; D008862:Microsomes, Liver / Q000378:metabolism; D008954:Models, Biological; D010599:Pharmacokinetics; D015203:Reproducibility of Results; D012189:Retrospective Studies; D013045:Species Specificity; D015262:Xenobiotics / Q000493:pharmacokinetics
chemical_list: D015262:Xenobiotics