Intravenous prediction of human pharmacokinetic parameters for ketorolac, a non-steroidal anti-inflammatory agent, using allometry approach

Abstract

The intravenous pharmacokinetics data of ketorolac in mice, rats, rabbits, dogs and monkeys were assembled from literature. The relationship between the main pharmacokinetic parameters [viz., volume of distribution (V (d)) and clearance (CL)] and body weight was studied across five mammalian species, using double-logarithmic plots to predict the human pharmacokinetic parameters of CL and V (d) using simple allometry or with correction factors [maximum life span potential (MLP), brain weight, CF1 (bile flow/liver weight) and CF2 (bile flow/body weight)]. The metabolism pattern, biotransformation pathways and the predominant urinary excretion of parent and the formed metabolites of ketorolac were found to be similar amongst mice, rats, rabbits, dogs, monkeys and humans, facilitating the scaling process. The human parameter value for V (d) was predicted by simple allometric equation: 0.2481W(1.0549) (r (2) = 0.9217). The predicted V (d) value (21.92 L) is close to the reported value (17.5 L), whereas the CL was predicted by simple allometric approach or with standard correction factors viz., MLP, brain weight, CF1 and CF2. Best proximity CL value was obtained with MLP having allometric equation: 0.7126W(1.3264) (r (2) = 0.9640). The outcome of this exercise suggests that allometric scaling with suitable correction factors could potentially be used to predict the human pharmacokinetic parameters of drugs belonging to non-steroidal anti-inflammatory drugs retrospectively.

Prediction of Human Pharmacokinetics of Bendamustine from Preclinical Species Pharmacokinetics Based on Normalizing Time Course Profiles

“…In addition, to scientific challenges and optimization of the experimental designs for better allometry predictions, Srinivas (2010) has provided insights on how to effectively use allometry for clinical candidate selection and answer some early drug development questions [15]. Previously, we have used these concepts in the prediction of pharmacokinetic parameters across diverse compounds representing various therapeutic areas [16][17][18][19][20][21][22][23].…”