Prediction of Human Hepatic Clearance Using an In Vitro Plated Hepatocyte Clearance Model

Author(s): Piyun Chao, Jeffrey Barminko, Eric Novik, Yi Han, Timothy Maguire, K.-C. Cheng.

Journal Name: Drug Metabolism Letters

Volume 3 , Issue 4 , 2009

Become EABM
Become Reviewer


Previously we have used human hepatocytes in suspension by measuring the parent loss for prediction of metabolic clearance according to a 1st-order kinetic model. In this study, we evaluated a novel integrative approach using plated human hepatocytes to include both uptake processes and metabolism in a single assay. Test articles were added in the medium, and the intrinsic clearance was determined based on the disappearance of the parent compound from the medium. Three different methods: direct, well-stirred, and parallel tube were tested for scaling purpose. With 30 randomly selected compounds with clinical clearance data, the scaled clearance showed reasonable linear correlation with r2 values of 0.67, 0.72, and 0.70 for direct, well-stirred and parallel tube models, respectively. When human serum albumin (HSA) was added to the incubation medium a shift to lower in vitro clearance was observed for most of the compounds, suggesting that protein binding may have an effect on the metabolic clearance. In the presence of 4% of HSA, which is equivalent to the albumin concentration in the human plasma, the in vitro clearance data have the best prediction of human clearance when using the well-stirred method, followed by the parallel tube method and direct method. This study demonstrates the utility of using plated human hepatocyte as an integrated system for the prediction of human metabolic clearance. In addition, evaluation of the protein binding shift in the clearance showed that a significant number of compounds may not follow the equilibrium assumption according to the well-stirred model.

Keywords: Human Hepatic, metabolic clearance, Hepatocyte

Rights & PermissionsPrintExport Cite as

Article Details

Year: 2009
Page: [296 - 307]
Pages: 12
DOI: 10.2174/187231209790218073
Price: $58

Article Metrics

PDF: 24