Prediction of oral pharmacokinetics of cMet kinase inhibitors in humans: physiologically based pharmacokinetic model versus traditional one-compartment model.

Drug Metab Dispos 39(3):383-93 (2011) PMID 21098644

The objective of this study was to assess the physiologically based pharmacokinetic (PBPK) model for predicting plasma concentration-time profiles of orally available cMet kinase inhibitors, (R)-3-[1-(2,6-dichloro-3-fluoro-phenyl)-ethoxy]-5-(1-piperidin-4-yl-1H-pyrazol-4-yl)-pyridin-2-ylamine (PF02341066) and 2-[4-(3-quinolin-6-ylmethyl-3H-[1,2,3]triazolo[4,5-b]pyrazin-5-yl)-pyrazol-1-yl]-ethanol (PF04217903), in humans. The prediction accuracy of pharmacokinetics (PK) by PBPK modeling was compared with that of a traditional one-compartment PK model based on allometric scaling. The predicted clearance values from allometric scaling with the correction for the interspecies differences in protein binding were used as a representative comparison, which showed more accurate PK prediction in humans than the other methods. Overall PBPK modeling provided better prediction of the area under the plasma concentration-time curves for both PF02341066 (1.2-fold error) and PF04217903 (1.3-fold error) compared with the one-compartment PK model (1.8- and 1.9-fold errors, respectively). Of more importance, the simulated plasma concentration-time profiles of PF02341066 and PF04217903 by PBPK modeling seemed to be consistent with the observed profiles showing multiexponential declines, resulting in more accurate prediction of the apparent half-lives (t(1/2)): the observed and predicted t(1/2) values were, respectively, 10 and 12 h for PF02341066 and 6.6 and 6.3 h for PF04217903. The predicted t(1/2) values by the one-compartment PK model were 17 h for PF02341066 and 1.9 h for PF04217903. Therefore, PBPK modeling has the potential to be more useful and reliable for the PK prediction of PF02341066 and PF04217903 in humans than the traditional one-compartment PK model. In summary, the present study has shown examples to indicate that the PBPK model can be used to predict PK profiles in humans.