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However, in both studies, it was concluded that the model predicted
well the average
in vivo
observed PK profi le used as a reference. These
conclusions come from the fact that different
in vivo
observed plasma
profi les were used for model validation. The
in vivo
bioequivalence (BE)
data used in our study indicated fast CBZ absorption (mean t
max
= 7 h) in
comparison to the
in vivo
profi le rendered by Zhang et al. (2011)
(characterized by a plateau absorption phase, with a mean t
max
of 16 h).
Although seemingly diverse, the results of both studies could be
considered as reasonable estimates. Namely, considering CBZ variable
pharmacokinetics after oral administration (reported t
max
ranged between
2 and 24 h (Bauer et al., 2008)), it could be concluded that the PK
parameters predicted with both models were within the acceptable range.
The presented examples illustrate that the form of the generated
absorption model highly depends upon the PK profi le used as a reference.
This emphasizes the importance of considering the widest possible range
of literature reported and/or experimental values of drug PK parameters,
in order to fully perceive model predictability.
6.4 Parameter sensitivity analysis
The generated drug-specifi c absorption model can be used to further
explore within the model, such as understanding how the formulation
parameters and/or drug physicochemical properties affect the predicted
PK profi les. This kind of evaluation is performed by the Parameter
Sensitivity Analysis (PSA) feature in GastroPlus™. When performing
PSA, one parameter is changed gradually within a predetermined range,
which should be based on prior knowledge, while keeping all other
parameters at baseline levels. Another option is to use three-dimensional
PSA when two parameters are varied at a time, so the combined effect of
these parameters is assessed. In addition, an optimized design space can
be constructed as a function of the selected parameters. PSA can serve as
a useful tool when the input values for some of the physicochemical
properties of a compound are rough estimates (e.g. from
in silico
predictions), and when model predictions do not correlate well with
in
vivo
values. In these cases, the analyst can perform PSA to defi ne more
biorelevant input value(s), and in extension, to use them to generate a
drug-specifi c absorption model. Another useful application of this feature
concerns highly variable drugs, where PSA can predict the effect of inter-
individual variation in PK parameters on drug absorption. PSA can also
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