title: Interspecies allometric scaling: prediction of clearance in large animal species: part II: mathematical considerations.
abstract: Interspecies scaling is a useful tool for the prediction of pharmacokinetic parameters from animals to humans, and it is often used for estimating a first-time in human dose. However, it is important to appreciate the mathematical underpinnings of this scaling procedure when using it to predict pharmacokinetic parameter values across animal species. When cautiously applied, allometry can be a tool for estimating clearance in veterinary species for the purpose of dosage selection. It is particularly valuable during the selection of dosages in large zoo animal species, such as elephants, large cats and camels, for which pharmacokinetic data are scant. In Part I, allometric predictions of clearance in large animal species were found to pose substantially greater risks of inaccuracies when compared with that observed for humans. In this report, we examine the factors influencing the accuracy of our clearance estimates from the perspective of the relationship between prediction error and such variables as the distribution of body weight values used in the regression analysis, the influence of a particular observation on the clearance estimate, and the 'goodness of fit' (R(2)) of the regression line. Ultimately, these considerations are used to generate recommendations regarding the data to be included in the allometric prediction of clearance in large animal species.
authors.affiliation: Division of Therapeutic Drugs for Food Animals (HFV-130), Office of New Animal Drug Evaluation, Center for Veterinary Medicine, Food & Drug Administration, Rockville, MD 20855, USA. mmartin@cvm.fda.gov
authors.forename: M
authors.initials: M
authors.lastname: Martinez
authors.forename: I
authors.initials: I
authors.lastname: Mahmood
authors.forename: R P
authors.initials: RP
authors.lastname: Hunter
mesh_terms: D000818:Animals; D001835:Body Weight; D008657:Metabolic Clearance Rate; D009929:Organ Size; D012044:Regression Analysis; D013045:Species Specificity