Pharmacokinetics, Pharmacodynamics and Drug Transport and MetabolismToward a new paradigm for the efficient in vitro–in vivo extrapolation of metabolic clearance in humans from hepatocyte data
Section snippets
Abbreviations used:
AAG, alpha1‐acid glycoprotein; AFE, average‐fold error; AL, albumin; CCC, concordance correlation coefficient global; CL, clearance; CLint, intrinsic clearance; fuinc, unbound fraction in incubation; fuliver, unbound fraction in liver; fub, unbound fraction in blood; fub‐app, apparent unbound fraction in blood; IVIVE, in vitro–in vivo extrapolation; Km, Michaelis–Menten constant; LBF, blood flow rate to liver; SF, scaling factor; PhRMA, Pharmaceutical Manufacturers of America; RMSE, root
INTRODUCTION
Hepatic metabolic clearance (CL) is one of the more essential pharmacokinetics (PK) parameters to estimate in drug discovery and development. It is common practice to use in vitro–in vivo extrapolation (IVIVE) methods to scale up the intrinsic clearance (CLint) determined in vitro in the human liver preparations for predicting CL in vivo of drugs that are mainly eliminated by metabolism.1,2 Recently, a comparative analysis using a dataset of 139 drugs obtained in preclinical species and human
METHOD
The methodology used to attain our goal consisted of comparing predicted CL derived from in vitro hepatocyte data and in vivo CL values observed in humans for several drugs. Five IVIVE methods that have undergone previous comparative assessments1, 2, 3 were the focus of further evaluation in this study. In addition, the recently published “regression method” of Sohlenius‐Sternbeck et al.4 was also included in the current comparative analysis. Therefore, a total of six IVIVE methods were
Comparative Assessment for Various IVIVE Methods for Predicting CL
Six IVIVE calculation methods of CL were compared using the same drug dataset, and the comparative assessment was made on the basis of several statistical parameters. The overall statistical summary in terms of accuracy, precision, and correlation is listed in Tables 3 and 4 for the different scenarios of prediction. The plots of predicted versus observed blood CL values for each method are shown in Figures 1a–1e, whereas Figure 2 compares the precision bias across several IVIVE methods.
Predictivity of Human Dataset
On the
DISCUSSION
The main focus of this study was to provide a comparative analysis between recently published IVIVE methods by predicting blood CL in vivo in humans from hepatocyte data for several drugs. The findings of the current comparative analysis made from hepatocyte data are similar to three other assessments made previously from microsomal data1, 2, 3; the novel IVIVE method of Poulin et al.1,2 offers a significant improvement for the prediction of blood CL over other IVIVE methods. This is
CONCLUSIONS
This study demonstrated the prediction performance of six IVIVE methods for the prediction of blood CL in vivo of drugs in human from hepatocyte data. The results of the current comparative analysis performed from hepatocyte data confirm the findings of a previous analysis made from microsomal data; therefore, the novel IVIVE method proposed in two previous manuscripts1,2 was the most successful in various prediction scenarios over other methods either based on microsomal or hepatocyte data.
Acknowledgements
This work represents an initiative undertaken in collaboration as a part of the research program of Dr. Poulin, and of Dr. Haddad's program, which is supported by a Discovery Grant from the National Sciences and Engineering Research Council of Canada (NSERC). The authors wish to thanks Dr. Cornelis Hop and Dr. Jane Kenny, at Genentech Inc., as precursors of fruitful discussions that have strongly contributed to the conduct of this work.
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