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method offers a distinctive opportunity to mechanistically interpret the
infl uence of the underlying processes on the resulting PK profi le. Namely,
by understanding the complex interplay between drug physicochemical
and PK properties, formulation factors, and human physiology
characteristics, we might gain an insight into the infl uence of a particular
factor or set of factors on drug absorption profi le, and understand possible
reasons for poor oral bioavailability. In this context, PSA is particularly
useful, since it allows identifi cation of critical factors affecting the rate and
extent of drug absorption prior to formulation development. In addition,
PSA can be used to optimize parameter values for which accurate data are
not available. Other features, such as the Virtual Trials and PBPK modeling,
enable even more advanced predictions of, for example, inter-individual
variability or factors contributing to variability in disposition, thus further
enhancing the reliability of
in silico
absorption modeling.
The examples also demonstrate that the
in vitro-in silico
approach can
be successfully used to identify biorelevant dissolution specifi cations for
the
in vitro
assessment of the drug product of interest, and facilitate the
choice of the relevant
in vitro
test conditions for the prediction of the
drug release process
in vivo
. Finding the
in vitro
dissolution test conditions
that best predict drug
in vivo
performance is a substantial part of
product development and quality testing strategy, thus implying that
mechanistically based absorption modeling might facilitate the QbD
approach in drug development. In addition, it was illustrated that GI
simulation, in conjunction with IVIVC, might contrive identifi cation of
biowaiver candidate drugs.
In view of the complexity of the described GastroPlus™ model and a
number of data required for simulation, it is evident that the reliability of
the modeling results is dependent on both the model and the selected data
set. Therefore, the necessary input data have to be carefully selected and/
or experimentally verifi ed. However, with the right selection of input
data, well-validated absorption model, and correct interpretation of
modeling results, GI simulation shows great promise in assessing
biorelevant features of formulated drugs.
In summary, computational absorption modeling offers an effi cient
and cost-effective way to assess drug bioperformance in a relatively
short time frame, thus becoming an indispensable tool that facilitates
formulation development process. However, certain gaps still exist,
mostly concerning the lack of relevant information on drug and dosage
form properties required for accurate prediction of drug PK profi le. Also,
lack of confi dence in
in silico
predictions is one of the reasons why these
methods have not yet been adequately exploited by the industry. With the
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