Approaches for predicting human pharmacokinetics using interspecies pharmacokinetic scaling

Reliably predicting pharmacokinetic behavior in humans from preclinical data is an important aspect of drug development. The most widely used technique in this regard is allometric scaling. In this review, various approaches developed for predicting pharmacokinetic parameters in humans using interspecies scaling are introduced and discussed. Methods to predict plasma concentration-time profiles in humans after intravenous and oral administration are also reviewed. The reliable prediction of human pharmacokinetics with regard to investigational drugs is aimed, ultimately, at selecting the first in-human dose with which to begin clinical studies. Approaches for the selection of the first in-human dose are also reviewed. Although there have been many trials to compare and optimize interspecies scaling methods, no firm conclusions have been reached. Because interspecies scaling methods are still highly empirical, further effort is needed to improve the reliability of predicting human pharmacokinetics by interspecies scaling.

Controversy in the allometric application of fixed- versus varying-exponent models: a statistical and mathematical perspective

This commentary is a reply to a recent article by Mahmood commenting on the authors' article on the use of fixed-exponent allometry in predicting human clearance. The commentary discusses eight issues that are related to criticisms made in Mahmood's article and examines the controversies (fixed-exponent vs. varying-exponent allometry) from the perspective of statistics and mathematics. The key conclusion is that any allometric method, which is to establish a power function based on a limited number of animal species and to extrapolate the resulting power function to human values (varying-exponent allometry), is infused with fundamental statistical errors.

Interspecies Prediction of Human Drug Clearance Based on Scaling Data from One or Two Animal Species

A data-driven approach was adopted to derive new one- and two-species-based methods for predicting human drug clearance (CL) using CL data from rat, dog, or monkey (n = 102). The new one-species methods were developed as CL(human)/kg = 0.152 x CL(rat)/kg, CL(human)/kg = 0.410 x CL(dog)/kg, and CL(human)/kg = 0.407 x CL(monkey)/kg, referred to as the rat, dog, and monkey methods, respectively. The coefficient of the monkey method (0.407) was similar to that of the monkey liver blood flow (LBF) method (0.467), whereas the coefficients of the rat method (0.152) and dog method (0.410) were considerably different from those of the LBF methods (rat, 0.247; dog, 0.700). The new rat and dog methods appeared to perform better than the corresponding LBF methods, whereas the monkey method and the monkey LBF method showed improved predictability compared with the rat and dog one-species-based methods and the allometrically based "rule of exponents" (ROE). The new two-species methods were developed as CL(human) = a(rat-dog) . W (human)(0.628) (referred to as rat-dog method) and CL(human) = a(rat-monkey) . W (human)(0.650) (referred to as rat-monkey method), where a(rat-dog) and a(rat-monkey) are the coefficients obtained allometrically from the corresponding two species. The predictive performance of the two-species methods was comparable with that of the three-species-based ROE. Twenty-six Wyeth compounds having data from mouse, rat, dog, monkey, and human were used to test these methods. The results showed that the rat, dog, monkey, rat-dog, and rat-monkey methods provided improved predictions for the majority of the compounds compared with those for the ROE, suggesting that the use of three or more species in an allometrically based approach may not be necessary for the prediction of human exposure.

A global examination of allometric scaling for predicting human drug clearance and the prediction of large vertical allometry**This work was presented at the American Association of Pharmaceutical Scientists Annual meeting, Salt Lake City, USA, Oct. 26, 2003

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 (CL(s); n = 102) is more predictable than apparent oral clearance (CL(po); n = 24), but caution needs to be applied because of potential CL(po) prediction error caused by differences in bioavailability across species. (2) CL(s) of proteins (n = 10) can be more accurately predicted than that of non-protein chemicals (n = 102). (3) CL(s) is more predictable for compounds eliminated by renal or biliary excretion (n = 33) than by metabolism (n = 57). (4) CL(s) 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 CL(s) of drugs having very low CL(s). 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 (f(u)) 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.