title: PhRMA CPCDC initiative on predictive models of human pharmacokinetics, part 3: comparative assessement of prediction methods of human clearance.
abstract: The objective of this study was to evaluate the performance of various allometric and in vitro-in vivo extrapolation (IVIVE) methodologies with and without plasma protein binding corrections for the prediction of human intravenous (i.v.) clearance (CL). The objective was also to evaluate the IVIVE prediction methods with animal data. Methodologies were selected from the literature. Pharmaceutical Research and Manufacturers of America member companies contributed blinded datasets from preclinical and clinical studies for 108 compounds, among which 19 drugs had i.v. clinical pharmacokinetics data and were used in the analysis. In vivo and in vitro preclinical data were used to predict CL by 29 different methods. For many compounds, in vivo data from only two species (generally rat and dog) were available and/or the required in vitro data were missing, which meant some methods could not be properly evaluated. In addition, 66 methods of predicting oral (p.o.) area under the curve (AUCp.o. ) were evaluated for 107 compounds using rational combinations of i.v. CL and bioavailability (F), and direct scaling of observed p.o. CL from preclinical species. Various statistical and outlier techniques were employed to assess the predictability of each method. Across methods, the maximum success rate in predicting human CL for the 19 drugs was 100%, 94%, and 78% of the compounds with predictions falling within 10-fold, threefold, and twofold error, respectively, of the observed CL. In general, in vivo methods performed slightly better than IVIVE methods (at least in terms of measures of correlation and global concordance), with the fu intercept method and two-species-based allometry (rat-dog) being the best performing methods. IVIVE methods using microsomes (incorporating both plasma and microsomal binding) and hepatocytes (not incorporating binding) resulted in 75% and 78%, respectively, of the predictions falling within twofold error. IVIVE methods using other combinations of binding assumptions were much less accurate. The results for prediction of AUCp.o. were consistent with i.v. CL. However, the greatest challenge to successful prediction of human p.o. CL is the estimate of F in human. Overall, the results of this initiative confirmed predictive performance of common methodologies used to predict human CL.
authors.affiliation: Drug Disposition, Lilly Research Laboratories, Eli Lilly and Company, Indianapolis, Indiana 46285.
authors.forename: Barbara J
authors.initials: BJ
authors.lastname: Ring
authors.forename: Jenny Y
authors.initials: JY
authors.lastname: Chien
authors.forename: Kimberly K
authors.initials: KK
authors.lastname: Adkison
authors.forename: Hannah M
authors.initials: HM
authors.lastname: Jones
authors.forename: Malcolm
authors.initials: M
authors.lastname: Rowland
authors.forename: Rhys Do
authors.initials: RD
authors.lastname: Jones
authors.forename: James W T
authors.initials: JW
authors.lastname: Yates
authors.forename: M Sherry
authors.initials: MS
authors.lastname: Ku
authors.forename: Christopher R
authors.initials: CR
authors.lastname: Gibson
authors.forename: Handan
authors.initials: H
authors.lastname: He
authors.forename: Ragini
authors.initials: R
authors.lastname: Vuppugalla
authors.forename: Punit
authors.initials: P
authors.lastname: Marathe
authors.forename: Volker
authors.initials: V
authors.lastname: Fischer
authors.forename: Sandeep
authors.initials: S
authors.lastname: Dutta
authors.forename: Vikash K
authors.initials: VK
authors.lastname: Sinha
authors.forename: Thorir
authors.initials: T
authors.lastname: Björnsson
authors.forename: Thierry
authors.initials: T
authors.lastname: Lavé
authors.forename: Patrick
authors.initials: P
authors.lastname: Poulin
mesh_terms: D022126:Access to Information; D061605:Administration, Intravenous; D000818:Animals; D019540:Area Under Curve; D003198:Computer Simulation; D003299:Cooperative Behavior; D062313:Databases, Pharmaceutical; D004285:Dogs; D055808:Drug Discovery / Q000379:methods*; D004353:Drug Evaluation, Preclinical; D006801:Humans; D033183:Interdisciplinary Communication; D008657:Metabolic Clearance Rate; D008954:Models, Biological; D015233:Models, Statistical; D004364:Pharmaceutical Preparations / Q000008:administration & dosage / Q000097:blood / Q000378:metabolism*; D010599:Pharmacokinetics; D016730:Program Development; D015397:Program Evaluation; D011485:Protein Binding; D051381:Rats; D015203:Reproducibility of Results; D013045:Species Specificity
chemical_list: D004364:Pharmaceutical Preparations