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each time step is calculated based on the relevant formulation parameters
and the conditions (pH, drug concentration, % fl uid, and bile salt
concentration) in the compartment at that time. Absorption rate constant
(k
a
) depends on drug effective permeability multiplied by an absorption
scale factor (ASF) for each compartment. The ASF corrects for changes in
permeability due to changes in physiological conditions along the GI
tract (e.g. surface area available for absorption, pH, expression of
transport/effl ux proteins). Default ASF values are estimated on the basis
of the so-called logD model, which considers the infl uence of logD of the
drug on the effective permeability. According to this model, as the ionized
fraction of a compound increases, the effective permeability decreases.
Besides passive absorption, including both transcellular and paracellular
routes, the ACAT model also accounts for infl ux and effl ux transport
processes, and presystemic metabolism in the gut wall. Lumenal
degradation rate constant (k
degrad
) is interpolated from the degradation
rate (or half-life) vs. pH, and the pH in the compartment. Finally, the
rates of absorption and exsorption depend on the concentration gradients
across the apical and basolateral enterocyte membranes. The total
amount of absorbed drug is summed over the integrated amounts being
absorbed/exsorbed from each absorption/transit compartment (Agoram
et al., 2001; SimulationPlus, Inc. GastroPlus™, 2012).
Once the drug passes through the basolateral membrane of enterocytes,
it reaches the portal vein and liver, where it can undergo fi rst pass
metabolism. From the liver, it goes into the systemic circulation from
where the ACAT model is connected to either a conventional PK
compartment model or a physiologically based PK (PBPK) disposition
model. PBPK is an additional feature included in more recent versions of
GastroPlus™. This model describes drug distribution in major tissues,
which can be treated as either perfusion limited or permeability limited.
Each tissue is represented by a single compartment, whereas different
compartments are linked together by blood circulation. By integrating
the key input parameters regarding drug absorption, distribution,
metabolism, and excretion (e.g. partition coeffi cients, metabolic rate
constants, elimination rate constants, permeability coeffi cients, diffusion
coeffi cients, protein binding constants), we can not only estimate drug
PK parameters and plasma and tissue concentration-time profi les, but
also gain a more mechanistic insight into the properties of a compound.
In addition, several authors reported an improved prediction accuracy of
human pharmacokinetics using such an approach (Jones et al., 2006a,
2012; De Buck et al., 2007b). One of the major obstacles for the wider
application of this model has been the vast number of input data required.
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