1 research outputs found
A Physiologically Based Modeling Strategy during Preclinical CNS Drug Development
Physiologically
based pharmacokinetic (PBPK) modeling of the central
nervous system (CNS) provides the opportunity to predict the relevant
drug concentrations at the therapeutic target site during preclinical
and clinical development. In order to successfully interpret model
results, and to provide confidence in the subsequent human predictions,
it is essential that an appropriate model structure is chosen at the
preclinical stage which takes into account both physiological and
drug-specific knowledge. However, the models published to date in
the literature show significant variation in the approaches applied
by different authors, which can lead to difficulties in the interpretation
of model parameter estimates. We aimed to develop a coherent PBPK
modeling approach in the rat, which would also be adaptable depending
on the quantity and quality of <i>in vivo</i> data obtained
during drug development. Based on a sensitivity analysis of the model
parameters, and using three CNS drugs as case studies (atomoxetine,
acetaminophen, and S 18986), we proposed a decision tree to aid in
the appropriate parametrization and structure of the model according
to the data available. We compared our parameter estimates to those
originally published, and considered the impact of the respective
approaches on the mechanistic interpretation of the parameter values.
Since the measurement of brain extracellular fluid (ECF) concentrations
using microdialysis is not routinely performed in the industrial environment,
we also evaluated the bottom-up scaling of <i>in vitro</i> permeability data from the Caco-2 cell line to predict BBB passive
permeability in the absence of measured ECF concentrations. Our strategy
demonstrates the value of PBPK as a prediction tool throughout the
development process of CNS-targeting drugs