Interspecies Population Modeling Of Pharmacokinetic Data Available At The End Of Drug Discovery
Jeroen Schaap
PK-PD/M&S, Clinical Pharmacology and Kinetics, Organon N.V., The Netherlands
Objectives: One of the challenges in pharmacokinetics is to predict pharmacokinetic profiles of new chemical entities in the transition from preclinical to clinical research. It is widely accepted that current allometric techniques provide insufficient quality in predictions of human PK. An extension of standard methods is non-linear modeling of all available preclinical PK data [1]. Application of this method in the pharmaceutical industry has been reported with extensive datasets, sometimes including human data [2,3,4]. We developed a interspecies nonlinear mixed-effects model for the preclinical pharmacokinetics of an (anonymous) compound as a test-case for feasibility of such an approach in an early stage of drug development.
Methods: The nonlinear population model was developed in NONMEM V. Model selection was performed on the basis of goodness-of-fit plots including Dedrick graphs, standard diagnostics in NONMEM output files and simulations sampled from the variance-covariance matrix. No covariates other than species and body weight were included in the dataset. The dataset contained 4 species (mouse, rat, dog, monkey), 2 routes of dosing (iv and po) and 2-3 richly sampled animals per route or n=3 samples per time point in mouse (one sample per animal).
Results: An interspecies population model was developed with reasonable success. Predictions were close to observed data but more importantly the model successfully facilitated project team discussions around hypotheses applied in the prediction of human pharmacokinetics and its uncertainty. The final model featured some extra parameters to capture the profile of one species (monkey); predictions were therefore augmented with scenarios for in/exclusion of these parameters. Limitations of the model were its instable bootstrap results and the amount of manpower spent, approximately 0.1 FTE. Such investment of resources is relatively large for an early stage of drug development. Of technical interest is that the assumption of multivariate normal distribution of uncertainty in structural model parameters seemed to hold only after lognormal specification of parameters.
Conclusion: Interspecies mixed-effect modeling was possible also with a pharmacokinetic dataset such as typically available at the end of drug discovery. The amount of manpower currently required however seems to limit routine application of this technique in the transition of preclinical to clinical research.
References:
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[2] V. F. Cosson, E. Fuseau, C. Efthymiopoulos, and A. Bye. Mixed effect modeling of sumatriptan pharmacokinetics during drug development. I: Interspecies allometric scaling. J.Pharmacokinet.Biopharm. 25 (2):149-167, 1997.
[3] K. Jolling, J. J. Perez Ruixo, A. Hemeryck, A. Vermeulen, and T. Greway. Mixed-effects modelling of the interspecies pharmacokinetic scaling of pegylated human erythropoietin. Eur.J.Pharm.Sci. 24 (5):465-475, 2005.
[4] T. Martin-Jimenez and J. E. Riviere. Mixed-effects modeling of the interspecies pharmacokinetic scaling of oxytetracycline. J.Pharm.Sci. 91 (2):331-341, 2002.
