A Meta-Analytical Framework to Include Historical Data in Allometric Scaling

Abstract

To predict human pharmacokinetics such as the clearance and the plasma concentration profile of a new compound, many animal-based methods have been used in the past. They are typically based on animal information on the compound of interest. This translational step is crucial in pharmaceutical development since it is used to estimate the human pharmacokinetic (PK) parameters and the starting dose for the first-in-human study. Among the currently used methods, allometric scaling is probably one of the oldest and simplest, because it uses essentially body weight and brain weight to correct for species in the prediction of the human PK measures. The assumption that body weight can be used as a surrogate for an animal species is key in the current methods. It also assumes that there is a general biological process that holds in mammal species such as mice, rats, rabbits, monkeys, dogs, and man. Brain weight, lifespan, and a number of other corrections are often successfully used to fine-tune the relationship between clearance and body weight. This research project investigates the variability that goes along with current practice and suggests a meta-analytical approach to control for the variability of human unbound clearance. The new approach also establishes a model linking the animal data to human data using historical data in a way that has not been done before.