A global examination of allometric scaling for predicting human drug clearance and the prediction of large vertical allometry*
Section snippets
INTRODUCTION
Allometric scaling is a widely used approach for predicting human clearance (CL) based on animal data. The observed power function relationship between clearance and species body weight is empirical, although there is some possible underlying physiological rationale.1,2 Recently, the ¾ power law of metabolic rate was theoretically derived based on the fractal geometry of nutrition-supply network of organisms.3 However, numerous prediction errors, mostly overprediction (also referred to as
Data Collection
One hundred and thirty-eight (138) sets of CL data, each set having investigated at least three animal species, were obtained from the literature. The majority of the allometric analyses included the rat and the dog. The data were categorized according to the following criteria: oral clearance (CLpo) or systemic clearance (CLs); elimination routes (by metabolism or excretion or both); protein or nonprotein chemicals; low, intermediate, or high metabolic CL (Fig. 1).
Complicated CLpo data were
RESULTS
One hundred and thirty-eight (138) sets of CL data as well as other useful information are listed in Table 1. The data are arranged such that CLs of chemicals, CLpo of chemicals, CLs of proteins and CLs of peptides could be distinguished clearly. The scheme for data categorization is further illustrated in Figure 1.
DISCUSSION
The scaling of CLpo is compromised by the potential differences in bioavailability across species. Animal bioavailability has been shown to bear a weak correlation with human bioavailability. For example, the correlation coefficient (r) between oral bioavailability in humans and dogs was shown to be only about 0.3 (Tang et al., unpublished data). Thus, CLpo prediction may deviate for compounds with significantly different bioavailability among species. Therefore, we distinguished the prediction
Acknowledgements
We are grateful to Dr. Iftekhar Mahmood (Food and Drug Administration, Rockville, MD) and Dr. Stacey Tannenbaum (Novartis Pharmaceutical Co., East Hanover, NJ) for providing part of the data.
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This work was presented at the American Association of Pharmaceutical Scientists Annual meeting, Salt Lake City, USA, Oct. 26, 2003.