A global examination of allometric scaling for predicting human drug clearance and the prediction of large vertical allometry*

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Abstract

Allometrically scaled data sets (138 compounds) used for predicting human clearance were obtained from the literature. Our analyses of these data have led to four observations. (1) The current data do not provide strong evidence that systemic clearance (CLs; n = 102) is more predictable than apparent oral clearance (CLpo; n = 24), but caution needs to be applied because of potential CLpo prediction error caused by differences in bioavailability across species. (2) CLs of proteins (n = 10) can be more accurately predicted than that of non-protein chemicals (n = 102). (3) CLs is more predictable for compounds eliminated by renal or biliary excretion (n = 33) than by metabolism (n = 57). (4) CLs predictability for hepatically eliminated compounds followed the order: high CL (n = 11) > intermediate CL (n = 17) > low CL (n = 29). All examples of large vertical allometry (% error of prediction greater than 1000%) occurred only when predicting human CLs of drugs having very low CLs. A qualitative analysis revealed the application of two potential rules for predicting the occurrence of large vertical allometry: (1) ratio of unbound fraction of drug in plasma (fu) between rats and humans greater than 5; (2) C logP greater than 2. Metabolic elimination could also serve as an additional indicator for expecting 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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      For this reason, a direct translation of xenobiotic metabolism from in vivo experiments in animal species to humans may be challenging. To predict pharmacokinetic profiles in other species allometric scaling is frequently applied in pharmaceutical development programs [4]. Basically, allometric scaling assumes that plasma clearance and volume of distribution scale exponentially with the body-weight of an organism.

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    *

    This work was presented at the American Association of Pharmaceutical Scientists Annual meeting, Salt Lake City, USA, Oct. 26, 2003.

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