Christine M Bowman1, Leslie Z Benet2. 1. Department of Bioengineering and Therapeutic Sciences, Schools of Pharmacy and Medicine, University of California San Francisco, San Francisco, California, 94143-0912, USA. 2. Department of Bioengineering and Therapeutic Sciences, Schools of Pharmacy and Medicine, University of California San Francisco, San Francisco, California, 94143-0912, USA. Leslie.Benet@ucsf.edu.
Abstract
PURPOSE: To examine the interlaboratory variability in CLint values generated with human hepatocytes and determine trends in variability and clearance prediction accuracy using physicochemical and pharmacokinetic parameters. METHODS: Data for 50 compounds from 14 papers were compiled with physicochemical and pharmacokinetic parameter values taken from various sources. RESULTS: Coefficients of variation were as high as 99.8% for individual compounds and variation was not dependent on the number of prediction values included in the analysis. When examining median values, it appeared that compounds with a lower number of rotatable bonds had more variability. When examining prediction uniformity, those compounds with uniform in vivo underpredictions had higher CLint, in vivo values, while those with non-uniform predictions typically had lower CLint, in vivo values. Of the compounds with uniform predictions, only a small number were uniformly predicted accurately. Based on this limited dataset, less lipophilic, lower intrinsic clearance, and lower protein binding compounds yield more accurate clearance predictions. CONCLUSIONS: Caution should be taken when compiling in vitro CLint values from different laboratories as variations in experimental procedures (such as extent of shaking during incubation) may yield different predictions for the same compound. The majority of compounds with uniform in vitro values had predictions that were inaccurate, emphasizing the need for a better mechanistic understanding of IVIVE. The non-uniform predictions, often with low turnover compounds, reaffirmed the experimental challenges for drugs in this clearance range. Separating new chemical entities by lipophilicity, intrinsic clearance, and protein binding may help instill more confidence in IVIVE predictions.
PURPOSE: To examine the interlaboratory variability in CLint values generated with human hepatocytes and determine trends in variability and clearance prediction accuracy using physicochemical and pharmacokinetic parameters. METHODS: Data for 50 compounds from 14 papers were compiled with physicochemical and pharmacokinetic parameter values taken from various sources. RESULTS: Coefficients of variation were as high as 99.8% for individual compounds and variation was not dependent on the number of prediction values included in the analysis. When examining median values, it appeared that compounds with a lower number of rotatable bonds had more variability. When examining prediction uniformity, those compounds with uniform in vivo underpredictions had higher CLint, in vivo values, while those with non-uniform predictions typically had lower CLint, in vivo values. Of the compounds with uniform predictions, only a small number were uniformly predicted accurately. Based on this limited dataset, less lipophilic, lower intrinsic clearance, and lower protein binding compounds yield more accurate clearance predictions. CONCLUSIONS: Caution should be taken when compiling in vitro CLint values from different laboratories as variations in experimental procedures (such as extent of shaking during incubation) may yield different predictions for the same compound. The majority of compounds with uniform in vitro values had predictions that were inaccurate, emphasizing the need for a better mechanistic understanding of IVIVE. The non-uniform predictions, often with low turnover compounds, reaffirmed the experimental challenges for drugs in this clearance range. Separating new chemical entities by lipophilicity, intrinsic clearance, and protein binding may help instill more confidence in IVIVE predictions.
Entities:
Keywords:
hepatocytes; in vitro-in vivo extrapolation; intrinsic clearance; variability
Authors: Anna-Karin Sohlenius-Sternbeck; Christopher Jones; Douglas Ferguson; Brian J Middleton; Denis Projean; Eva Floby; Johan Bylund; Lovisa Afzelius Journal: Xenobiotica Date: 2012-04-18 Impact factor: 1.908
Authors: Daniel F Veber; Stephen R Johnson; Hung-Yuan Cheng; Brian R Smith; Keith W Ward; Kenneth D Kopple Journal: J Med Chem Date: 2002-06-06 Impact factor: 7.446
Authors: R S Obach; J G Baxter; T E Liston; B M Silber; B C Jones; F MacIntyre; D J Rance; P Wastall Journal: J Pharmacol Exp Ther Date: 1997-10 Impact factor: 4.030
Authors: Courtney Sakolish; Yu-Syuan Luo; Alan Valdiviezo; Lawrence A Vernetti; Ivan Rusyn; Weihsueh A Chiu Journal: Toxicology Date: 2021-09-17 Impact factor: 4.221