2010 - Berlin - Germany

PAGE 2010: Applications- CNS
Gailing  Li

Towards Quantitative Prediction Of In Vivo Brain Penetration Using A Physiology Based CNS Disposition Model

Gai Ling Li, Don Nichols

Pfizer Ltd, Clinical Pharmacology, Sandwich CT13 9NJ, UK.

Background: Drug discovery for CNS disorders has been challenged with markedly high attrition rate. This has driven extensive preclinical and clinical CNS pharmacokinetic (PK) and pharmacodynamic evaluation recently. In clinical programs, human cerebrospinal fluid (CSF) sampling and positron emission tomography (PET) studies have become "routine" but at high cost. In contrast to the data collection efforts, predicting brain penetration in vivo in animals and humans has remained a largely untouched area.

The aim of this report is to share our recent efforts in developing a mathematical model for predicting CNS disposition in vivo and to demonstrate the integral role of multiple drug and system parameters on brain penetration through simulations.

Methods: A physiology based CNS disposition model in rats (only passive mechanisms) was developed. Simulations were performed using Berkeley Madonna Version 8.0.1 to explore (1) the interrelationships between drug concentrations in brain parenchyma, CSF, and plasma; and to (2) identify critical influencing factors for CNS penetration. In addition, evaluations of the model prediction versus experimental observation in vivo were conducted.

Results: Drug concentration in brain is determined by the plasma PK, permeability cross BBB and non-specific binding, but not quantitatively affected by the distribution between brain interstitial fluid (ISF) and CSF. Plasma kinetics can predict brain kinetics reasonably well only if there is a large proportion of free drug with a rapid transport cross BBB. CSF kinetics does not always follow the time course of unbound brain concentration. The level of resemblance of CSF kinetics to unbound brain kinetics is largely dependant upon the distribution rate between ISF and CSF domain, and CSF turnover rate.

Conclusion: The physiology based CNS disposition model has provided a valuable framework for quantitative understanding of time course of CNS disposition.

Reference: PAGE 19 (2010) Abstr 1691 [www.page-meeting.org/?abstract=1691]
Poster: Applications- CNS