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Due to dynamic interpretation of the processes a drug undergoes in the
GI tract, dynamic models are able to predict both the fraction of dose
absorbed and the rate of drug absorption, and can be related to PK
models to evaluate plasma concentration-time profi les (Yu et al., 1996).
Such models can be benefi cial at different stages of formulation
development. For example, taking into account all the relevant
biopharmaceutical properties of the compound of interest, the potential
advantage of various drug properties in terms of improving oral
bioavailability can be
in silico
assessed, before proceeding to
in vivo
studies. Also, by providing more mechanistic interpretation of PK
data, these models can be utilized to explore mechanistic hypotheses and
to help defi ne a formulation strategy. The effect of food on drug
absorption or possible impact of intestinal transporters and intestinal
metabolism can be explored, leading to a better understanding of the
observed pharmacokinetics, and guiding subsequent formulation attempts
to reduce these effects.
The decisive advantage of
in silico
simulation tools is that they require
less investment in resources and time in comparison to
in vivo
studies.
Also, they offer a potential to screen virtual compounds. As a consequence,
the number of experiments, and concomitant costs and time required for
compound selection and development, is considerably reduced. In
addition,
in silico
methods can be applied to predict oral drug absorption
when conventional PK analysis is limited, such as when intravenous
data are lacking due to poor drug solubility and/or if the drug
shows nonlinear kinetics. Many research articles have discussed and
explored the predictive properties of such mechanism-based models,
emphasizing both their advantages and possible drawbacks (Norris et al.,
2000; Parrott and Lave, 2002, Yokoe et al., 2003; Tubic et al., 2006;
Kovacevic et al., 2009; Parrott et al., 2009; Jones et al., 2011;
Reddy et al., 2011; Zhang et al., 2011; Abuasal et al., 2012). Several
reviews on this subject have been published (Agoram et al., 2009;
Grass and Sinko, 2002; Kesisoglou and Wu, 2008; Kuentz, 2008;
Huang et al., 2009).
In the following, selected studies concerning the employment of GI
simulation technology (GIST), in particular GastroPlus™ simulation
technology, will be reviewed. Basic principles of GIST will be presented,
along with the possibilities and limitations of using this mechanistic
approach to predict oral drug absorption, estimate the infl uence of drug
and/or formulation properties on the resulting absorption profi le, predict
the effects of food, assess the relationship between the
in vitro
and
in vivo
data, and aid justifi cation of biowaivers.
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