Computer- aided biopharmaceutical characterization: gastrointestinal absorption simulation - Computer-Aided Applications in Pharmaceutical Technology - page 183

However, advances in the prediction of liver metabolism (Houston, 1994;
Howgate et al., 2006), tissue distribution (Poulin et al., 2001; Poulin and
Theil, 2002; Rodgers et al., 2005, 2006), and absorption (Agoram et al.,
2001; Willmann et al., 2004) from in vitro and in silico data have made
the PBPK model more attractive, leading to an increase in its use
(Jones et al., 2011; 2006a, 2012; De Buck et al., 2007a; Theil et al.,
2003; Lave et al., 2007).
GastroPlus™ ACAT modeling requires a number of input parameters,
which should adequately refl ect drug biopharmaceutical properties.
Default physiology parameters under fasted and fed states (e.g. transit
time, pH, volume, length, radii of the corresponding GI region) are
population mean values obtained from published data. The other input
parameters include drug physicochemical properties (i.e. solubility,
permeability, logP, pK a , diffusion coeffi cient) and PK parameters
(clearance (CL), volume of distribution (Yc), percentage of drug extracted
in the oral cavity, gut or liver, etc.), along with certain formulation
characteristics (e.g. particle size distribution and density, drug release
profi les for controlled-release formulations). Given a known solubility at
any single pH and drug pK a value(s), GastroPlus™ calculates regional
solubility based on the fraction of drug ionized at each compartmental
pH according to the Henderson-Hasselbalch relation. Recent versions of
the software have the ability to account for the bile salts effect on in vivo
drug solubility and dissolution (GastroPlus™, 2012). The program also
includes a mean precipitation time, to model possible precipitation of
poorly soluble weak bases when moving from stomach to the small
intestine. Effective permeability value (P eff ) refers to human jejunal
permeability. However, in the absence of the measured value, an estimated
value (derived from in silico prediction (ADMET Predictor), in vitro
measurements (e.g. CaCo −2 , PAMPA assay), or animal (rat, dog) studies)
can be used in the simulation. For this purpose, the program has provided
a permeability converter that transforms the selected input value to
human P eff , based on the correlation model generated on the basis of a
chosen training data set.
In general, modeling and simulation start from data collection, and
continue with parameter optimization (if needed) and model validation.
The generated drug-specifi c absorption model can further be utilized to
understand how formulation parameters or drug physicochemical
properties affect the drug PK profi le, to provide the target in vivo
dissolution profi le for in vitro-in vivo correlation (IVIVC) and
identifi cation of biorelevant dissolution specifi cation for the formulation
of interest, to simulate the effect of different dosing regiments, to predict

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