Computer- aided biopharmaceutical characterization: gastrointestinal absorption simulation PDF

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Computer- aided biopharmaceutical
characterization: gastrointestinal

absorption simulation
Sandra Grbic, Jelena Parojcic, and Zorica Djuric,
Department of Pharmaceutical Technology and
Cosmetology, Faculty of Pharmacy, University of


Abstract: T his chapter introduces the concept of gastrointestinal
absorption simulation using i n silico methodology. Parameters used
for model construction and the sensitivity predicted pharmacokinetic
responses to various input parameters are described. Virtual trials
for in silico modeling of drug absorption are presented. The infl uence
of food on drug absorption, as well as correlation between the
in vitro and i n vivo results, are also addressed, followed by biowaiver
considerations. Numerous examples are provided throughout the

Key words: G astrointestinal absorption, in silico modeling,
parameter sensitivity analysis, virtual trials, food effects, in vitro-i n
vivo correlation, biowaiver.

6.1 Introduction
Biopharmaceutical assessment of drugs is of crucial importance in
different phases of drug discovery and development. In early phases,
pharmaceutical profi ling can help to fi nd an appropriate ‘drug- like’
molecule for preclinical and clinical development, and in later stages,

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extended biopharmaceutical evaluation can be used to guide formulation
strategy or to predict the effect of food on drug absorption. A growing
concern for biopharmaceutical characterization of drugs/pharmaceutical
products increased the interest in development and evaluation of i n silico
tools capable of identifying critical factors (i.e. drug physicochemical
properties, dosage form factors) infl uencing drug in vivo performance,
and predicting drug absorption based on the selected data set(s) of input

Although an i n silico pharmacokinetic (PK) model can confi rm different
drug administration routes (Gonda and Gipps, 1990; Grass and Vee,
1993; Mahar Doan and Boje, 2000), the main focus has been on prediction
of pharmacokinetics of orally administered drugs (Yu et al., 1996; Grass,
1997; Grass and Sinko, 2002; Norris et al., 2000; Agoram et al., 2001;
Boobil et al., 2002). Drug absorption from the gastrointestinal (GI) tract
is a complex interplay between a large number of factors (i.e. drug
physicochemical properties, physiological factors, and formulation related
factors), and its correct representation in the i n silico models has been a
major challenge. Various qualitative/quantitative approaches have been
proposed, starting from the pH-partition hypothesis (Shore et al., 1957),
and later moving to the more complex models, such as the Compartmental
Absorption and Transit (CAT) model (Yu and Amidon, 1999). Yu et al.
gave a good review of these models, classifying them into quasi-
equilibrium, steady-s tate, and dynamic models categories (Yu et al., 1996).

In recent years, substantial effort has been allocated to develop and
promote dynamic models that represent GI tract physiology in view of
drug transit, dissolution, and absorption. Among these are the Advanced
Dissolution, Absorption and Metabolism (ADAM) model, the Grass
model, the GI-Transit-Absorption (GITA) model, the CAT model, and
the Advanced CAT (ACAT) model (Huang et al., 2009). Some of them
have been integrated in commercial software packages, such as
GastroPlus™, SimCYP, PK-Sim ® , IDEA™ (no longer available), Cloe ®
PK, Cloe® HIA, and INTELLIPHARM® PKCR (Norris et al., 2000; ; w ; Willmann et al., 2003; ; PKCR. One of the fi rst
overviews of the available software intended for in silico prediction of
absorption, distribution, metabolism, and excretion (ADME) properties
was given in the report of Boobis et al. (2002). Cross- evaluation of the
presented software packages was interpreted in terms of software purpose
and function, scientifi c basis, nature of the software, required data to run
the simulations, performance, predictive power, user friendliness,
fl exibility, and evolution possibilities.

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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-t ime 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 i n silico simulation tools is that they require
less investment in resources and time in comparison to i n 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-b ased 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 i n vitro and in vivo
data, and aid justifi cation of biowaivers.

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6.2 Theoretical background
Simulation software packages, such as GastroPlus™, are advanced
technology computer programs designed to predict PK, and optionally,
pharmacodynamic effects of drugs in humans and certain animals.

The underlying model in GastroPlus™ is the ACAT model (Agoram
et al., 2001), an improved version of the original CAT model described
by Yu and Amidon (1999). This semi- physiological absorption model is
based on the concept of the Biopharmaceutics Classifi cation System
(BCS) (Amidon et al., 1995) and prior knowledge of GI physiology, and
is modeled by a system of coupled linear and nonlinear rate equations
used to simulate the effect of physiological conditions on drug absorption
as it transits through successive GI compartments.

The ACAT model of the human GI tract ( Figure 6.1 ) consists of nine
compartments linked in series, each of them representing a different
segment of the GI tract (stomach, duodenum, two jejunum compartments,
three ileum compartments, caecum, and ascending colon). These
compartments are further subdivided to comprise the drug that is
unreleased, undissolved, dissolved, and absorbed (entered into the
enterocytes). Movement of the drug between each sub- compartment is
described by a series of differential equations. In general, the rate of
change of dissolved drug concentration in each GI compartment depends
on ten processes:

I. transit of drug into the compartment;
II. transit of drug out of the compartment;
III. release of drug from the formulation into the compartment;
IV. dissolution of drug particles;
V. precipitation of drug;
VI. lumenal degradation of drug;
VII. absorption of drug into the enterocytes;
VIII. e xsorption of drug from the enterocytes back into the lumen;
IX. absorption of drug into portal vein via paracellular pathway; and
X. exsorption of drug from portal vein via paracellular pathway.

The time scale associated with each of these processes is set by an
adequate rate constant. Transfer rate constant (k t ), associated with
lumenal transit, is determined from the mean transit time within each
compartment. The dissolution rate constant (kd ) for each compartment at

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Figure 6.1 ACAT model interpretation of in vivo drug behavior (according to SimulationPlus, Inc. GastroPlus™
version 8.0 manual)


Computer-aided applications in pharmaceutical technology

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
(ka ) 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-l ife) 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-t ime 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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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 pKa 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 i n 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 i n silico prediction (ADMET Predictor), i n 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 Pe ff , 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-i n 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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Figure 6.2 GI simulation: general modeling and simulation

food effects on drug pharmacokinetics, or to perform stochastic
simulations on a group of virtual subjects ( Figure 6.2 ).

6.3 Model construction
Modeling and simulation start from data collection. Mechanistic
absorption models require a number of input parameters, which can
either be experimentally determined or i n silico predicted. The common
approach is to use literature reported values as initial inputs.

There is a number of examples in the literature describing the use of
GastroPlus™ to predict the drug PK profi le after oral administration
(Tubic et al., 2006; Wei and Löbenberg, 2006; De Buck et al., 2007a;
Aburub et al., 2008; Okumu et al., 2008, 2009; Tubic-Grozdanis et al.,
2008; Wei et al., 2008; Kovacevic et al., 2009; Parrott et al., 2009; Grbic
et al., 2011; Jones et al., 2011; Paroj č i ć et al., 2011; Reddy et al., 2011;

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Zhang et al., 2011; Abuasal et al., 2012; Crison et al., 2012; Kocic
et al., 2012). The reported studies involved different dosage forms,
including solutions, suspensions, immediate and controlled release (CR)
formulations, and all four BCS classes of drugs. Depending on the
objective of the study, human or animal physiologies under fasted or fed
conditions were selected for simulations. The required input parameters
were taken from the literature, in silico predicted, or experimentally
determined, highlighting diversity in the approaches to build a drug
specifi c absorption model. The feasibility of using either Single Simulation
or Virtual Trial mode (enables incorporation of inter-s ubject variability
in the model) has also been explored.

A recently published study on GI simulation of nimesulide oral
absorption is an interesting example on how selection of input data might
infl uence model accuracy to predict a drug PK profi le (Grbic et al., 2012).
Drug specifi c absorption models were constructed by two independent
analysts, using the same set of i n vivo data, but with different presumptions
regarding the key factors that govern nimesulide absorption. A summary
of the input parameters concerning nimesulide physicochemical and PK
data is given in Table 6.1 .

Table 6.1 Summary of nimesulide input parameters employed
for GI simulation

Parameter Model 1 Model 2
Molecular weight (g/mol) 308.31
logD (pH 7.4) 1.8a 1 .48 b
pKa 6 .4 b
Human jejunal permeability (cm/s) 2.225 × 10− 4 c 2 .002 × 10 −4 d
Dose (mg) 100
Dose volume (mL) 200 e

Solubility at pH 4.5 (mg/mL) 0.007f 0.030d
Mean precipitation time (s) 900g
Diffusion coeffi cient (cm2 /s) 0.757 × 10− 5 c
Drug particle density (g/mL) 1.2 g
Effective particle radius ( μ m) 5d 2 5 g
Body weight (kg) 88e
First pass extraction (FPE) in liver (%) 0.1h /


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Table 6.1 Summary of nimesulide input parameters employed
for GI simulation (continued)

Parameter Model 1 Model 2
Blood/plasma conc. ratio 0.668c 1 g
Unbound percent in plasma (%) 4.513c 3 a
CL (L/h/kg) 0.039h 0.028a
Vc (L/kg) 0.226h 0.14a
Elimination half- life ta/a (h) 4.02 3.42
Simulation time (h) 15
Dosage form IR tablet IR suspension/

IR tablet

a literature values taken from Rainsford, 2005; b literature values taken from Dellis et al.,
2007; c in silico predicted (ADMETPredictor™ module); d optimized values; e literature
values taken from Jovanovic et al., 2005; f experimental value (Grbic et al., 2009);
g default GastroPlus™ values; h literature values taken from Bernareggi, 1998.

Model 1 was constructed, assuming that nimesulide might be a
substrate for infl ux transporters in the intestine. Therefore, the ASFs
were adjusted to best match the resultant profi le to the in vivo observed
data ( Table 6.2 ). Experimentally determined intrinsic solubility was used
as the input value, and human jejunal permeability was i n silico predicted.
Drug particle radius was assumed to be 5 microns. All other parameters
were fi xed at default values that represent human fasted physiology.

The approach used to construct and validate Model 2 was based on the
comparative study of two dosage forms of nimesulide (immediate-r elease
(IR) suspension and IR tablet). The absorption model was initially
constructed for IR suspension, and was afterwards validated for IR tablet
formulation. The main premise in Model 2 was that nimesulide is well
absorbed after oral administration mainly due to the pH-surfactant
induced increase in solubility in the GI milieu. Therefore, the ASFs were
kept on default GastroPlus™ values (T able 6.2 ), and input solubility and
permeability values were optimized to best match the in vivo data.

The simulation results were compared with actual clinical data
(Jovanovic et al., 2005), in order to identify the model yielding the best

The simulation results (nimesulide plasma concentration-t ime profi les,
absorption and dissolution profi les, and the predicted and in vivo
observed PK parameters) obtained using the Model 1 and 2 input data
sets, are presented in Figure 6.3 and Table 6.3 .

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Table 6.2 ASF values employed

Compartment M odel 1 Model 2
(GastroPlus™ default*)

Stomach 0 0
Duodenum 1 000 2.687
Jejunum 1 500 2.668
Jejunum 2 2.600 2.633
Ileum 1 0.500 2.588
Ileum 2 0.500 2.551
Ileum 3 5.547 2.460
Caecum 6 .098 1.328
Asc colon 12.240 1.995

* Opt logD Model SA/V 6.1

Figure 6.3 GastroPlus™ Model 1 and Model 2 predicted and in
vivo observed mean NIM plasma profi les following
administration of a single 100 mg nimesulide IR tablet
(a); predicted dissolution and absorption profi les (b)

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Table 6.3 Comparison of PK parameters between Model 1 and
Model 2 predicted and in vivo observed data

Parameter M odel 1 Model 2
Observed Simulated % PE* S imulated % PE*

Cm ax ( μ g/mL) 3 .19 3.21 −0.63 3 .39 − 6.16
t max (h) 4.00 3.15 21.25 3 .40 1 5.00
AUC 0→t ( μ g h/mL) 25.78 25.96 −0.70 2 5.69 0 .35
AUC 0→∞ ( μ g h/mL) 30.96 29.10 6.01 2 7.92 9 .82

* % PE – percent prediction error

According to the obtained data, both Models 1 and 2 gave accurate
predictions of nimesulide average plasma profi le after oral administration.
In both cases, the percentage prediction errors for Cm ax and area under
the curve (AUC) values were less than 10%, indicating that the models
have predicted these parameters well. The largest deviation was observed
for tm ax (PE of 21.25a/a and 15% in Model 1 and Model 2, respectively).
Nevertheless, the predicted values of 3.15 h (Model 1) and 3.4 h (Model
2) were considered as reasonable estimates, since the reported tm ax values
after oral administration of nimesulide IR tablets varied between 1 and
4 h (Jovanovic et al., 2005; Rainsford, 2006).

However, according to Model 1, the resultant ASF values in the
duodenum and jejunum were much higher than the default GastroPlus™
values, refl ecting fast absorption of NIM in the proximal parts of the
intestine. There were two distinct interpretations: Model 1 outcomes
indicated involvement of infl ux transporters in nimesulide absorption,
while according to the Model 2 outcomes, the pH-surfactant induced
increase in drug solubility was a predominant factor leading to relatively
rapid absorption in the proximal intestine. It should be noted that the
Model 2 assumption was supported by the concept of Biopharmaceutics
Drug Disposition Classifi cation System (BDCCS), according to which
BCS class II drugs are not expected to be substrates for infl ux transporters
(Wu and Benet, 2005). In addition, parameters for which accurate data
were not available (i.e. i n vivo solubility and human jejunal permeability)
were optimized in Model 2. Also, Model 2 was developed using the set of
in vivo data for two dosage forms (oral suspension and IR tablet), and
revealed incomplete drug absorption from the IR tablet (∼70% of the
administered dose, as compared to almost 100% drug absorbed estimated
for the same set of in vivo data when Model 1 was applied). This fi nding

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indicated that nimesulide dissolution from IR tablets is expected to be the
limiting factor for drug absorption.

Overall, the described independent procedures to build a nimesulide
specifi c absorption model illustrated the importance of understanding
complex interplay between drug physicochemical and PK properties,
formulation factors, and human physiology characteristics, in order to
predict drug PK profi le in vivo . Interpretation of the obtained data
indicated that the approach applied in Model 2 might be considered as
more realistic, signifying that the related absorption model more likely
refl ects nimesulide i n vivo absorption. It was also stressed that, in order
to obtain meaningful i n silico modeling, the necessary input data have to
be carefully selected and/or experimentally verifi ed.

In the next example, gliclazide (GLK) was used as the model drug to
illustrate general steps of mechanistic modeling and simulation using
GastroPlus™ to predict oral drug absorption. GLK is an ampholyte with
pH-dependent solubility in the GI pH range (Grbic et al., 2011). According
to the BCS, GLK meets the criteria of a low solubility drug. Reports from
the in vivo studies show that, after oral administration, GLK is almost
completely absorbed (Delrat et al., 2002; Najib et al., 2002), although its
absorption rate appears to be slow and variable (Kobayashi et al., 1981;
Hong et al., 1998; Davis et al., 2000). A summary of the input parameters
employed for GI simulation is given in Table 6.4.

In the initial attempt to construct a GLK-specifi c absorption model, Opt
logD Model SA/V 6.1, considering default values for the absorption
gradient coeffi cients C1–C4 (used to calculate the ASFs), was used to
estimate changes in permeability as the drug travels along the GI tract. The
resultant GLK absorption profi le, based on the selected input parameters
( Table 6.4) and default C1–C4 values, diverged from the mean in vivo
observed Cp -time data (Najib et al., 2002) (F igure 6.4) . Therefore, the
absorption gradient coeffi cients, and consequently, the ASF values, were
adjusted (using the Optimization module) to best match the resultant model
to the i n vivo data. Default and adjusted ASF values are given in T able 6.5.

The resultant ASF values in the small intestine, adjusted to best fi t the
observed plasma concentration-t ime data for GLK IR tablets, were lower
than GastroPlus™ generated values, indicating the possible infl uence of
effl ux transporters on GLK absorption through this part of the intestine.
This assumption was supported by the results of Al-Salami and associates,
who revealed that GLK is a substrate of the ileal effl ux drug transporters
Mrp2 and Mrp3 (Al-Salami et al., 2008, 2009). The generated plasma
concentration- time profi le, based on the selected input parameters along
with the adjusted ASF values, is presented in Figure 6.4.

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Table 6.4 Summary of the GLK input parameters employed for
GI simulation

Parameter Value
Molecular weight (g/mol) 323.4
log P 1 .448 a
pKa 2.9; 5.8; 9.6b
Human jejunal permeability (cm/s) 3.683 × 10− 4 c
Dose (mg) 80
Dose volume (mL) 250
Solubility at pH 4.37 (mg/mL) 0.025d
Mean precipitation time (s) 900e
Diffusion coeffi cient (cm2 /s) 0 .782 × 10 −5 a
Drug particle density (g/mL) 1.2e
Effective particle radius ( μ m) 2 5e
Body weight (kg) 74
FPE (liver) (%) 30f
Blood/plasma conc. ratio 1 e
Unbound percent in plasma (%) 4.7f
CL (L/h/kg) 0.012 f
Vc (L/kg) 0.23f
t1 /2 (h) 13.29
Simulation time (h) 48
Dosage form IR tablet

a in silico predicted (ADMETPredictor™ module); b estimated by GastroPlus™ on the basis
of experimentally determined pH-solubility profi le; c value calculated on the basis of i n
vitro measured permeability (CaCo− 2 cell line) (Stetinova et al., 2008) using permeability
converter integrated in GastroPlus™ software: d experimental value; e default
GastroPlus™; f literature value taken from Davis et al., 2000.

The predicted fraction of drug absorbed (Fa ) was 99.94%, which is in
accordance with the literature reporting almost 100% bioavailability of
GLK after oral administration (Delrat et al., 2002; Najib et al., 2002).
The predicted and i n vivo observed PK parameters rendered percentage
prediction errors of less than 10% for C max and AUC values, indicating
that the model has predicted these parameters well. The largest deviation
was observed for tm ax (PE = 18.22%). However, considering variable
GLK i n vivo kinetics (reported mean t max values after oral administration

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Figure 6.4 GastroPlus™ predicted and observed (Najib et al.,
2002) mean GLK plasma Cp –time profi les following
administration of a single 80-mg GLK IR tablet

Table 6.5 Default and adjusted ASF values

Compartment Default ASFs O ptimized ASFs
Stomach 0 0
Duodenum 2.760 1.289
Jejunum 1 2.699 1.262
Jejunum 2 2.683 1.256
Ileum 1 2.632 1.234
Ileum 2 2.589 1.216
Ileum 3 2.512 1.181
Caecum 0.339 1.782
Asc colon 0.549 2.417

of IR tablets varied between 2.3 and 4.5 h (Kobayashi et al., 1981;
Glowka et al., 1998; Najib et al., 2002)), the simulated value of 3.68 h
was considered a reasonable estimate.

GastroPlus™ generated regional absorption distribution demonstrated
that the majority of GLK, formulated in IR dosage form, is absorbed in
the duodenum and jejunum (69.9%), while the rest of the dose is absorbed
in the mid- and distal GI regions ( Figure 6.5 ).

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Figure 6.5 Compartmental absorption of GLK

Several other examples from the literature summarize values of the
input parameters employed to design GI absorption models for the
selected drugs. One of the most detailed descriptions of modeling and
simulation strategy using GastroPlus™ was given by Zhang et al. (2011),
who used carbamazepine (CBZ), a BCS class II compound, as an example
to illustrate the general steps of applying mechanistic modeling and
simulation to identify important factors in formulation design and discuss
important aspects of modeling and simulation. Four oral dosage forms of
CBZ, namely IR suspension, IR tablet, extended-r elease (XR) tablet, and
XR capsule, under both fasted and fed state were modeled. The required
input parameters were collected from the literature, New Drug
Applications (NDAs), Abbreviated NDAs (ANDAs), or i n silico predicted,
except the particle density for the IR tablet, which was a GastroPlus™
optimized value. A summary of the CBZ input parameters employed for
ACAT model simulation is presented in Table 6.6 .

The PK parameters and ASFs were obtained by two methods. The fi rst
method included dec onvolution of the PK data for IR suspension under
fasted conditions, to obtain systemic CL, Vc , distribution constants
between central and peripheral compartments (K 12 , K 21 ), and absorption
rate constant (Ka ), and tl ag . These values were then fi xed and the ASF
values were optimized to obtain the physiology model. The optimized
ASFs were about 10 times higher than the default Opt logD Model
values, indicating rapid absorption of CBZ in the small intestine. The

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Table 6.6 Summary of the CBZ input parameters employed for ACAT model simulation (data from Zhang et al.,

Parameter S uspension I R tablet X R tablet X R capsule
Molecular weight (g/mol) 236.3
log P 2.38
pKa 12.01 (acidic)

0.26 (basic)
Permeability (cm/s) 4.3 × 10 −4
Dose (mg) 200 400 400 3 00
Dose volume (mL) 240
Solubility at pH 6.8 (mg/mL) 0.12 (fasted)

0.32 (fed)
Precipitation time (s) 900
Diffusion coeffi cient (cm2 /s) 9.72 × 10 −4
Particle density (g/mL) 1.2 1.5 1.2 1 .2
Mean particle radius ( μ m) 5 75 100 5 0
Particle radius standard deviation 0 20 10 2 0
Particle radius bin # 1 5 3 3
Body weight (kg) 75 (fasted) 75 (fasted) 70 (fasted) 75 (fasted)

80 (fed) 70 (fed) 70 (fed) 75 (fed)


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Table 6.6 Summary of the CBZ input parameters employed for ACAT model simulation (data from Zhang et al.,
2011) (continued)

Parameter S uspension I R tablet X R tablet X R capsule
CL (L/h) 1.127
V c (L) 63.06
t1 /2 (h) 44.65
Lag time, t lag (h) 0.309
Distribution rate constant, k1 2 (1/h) 0.034
Distribution rate constant, k 21 (1/h) 0.241
Peripheral volume, V 2 (L) 8.939
Simulation time (h) 168 168 240 1 92
Dosage form IR suspension IR tablet CR: Integral CR:

tablet Dispersed


Computer-aided biopharmaceutical characterization

other approach considered fi tting nine parameters in the ACAT model
(Vc , CL, K 12 , K 21 , K a , mean particle radius, drug particle density, solubility,
and C1 and C2 constants used in calculation of ASFs), using the
Optimization module. Coeffi cients C3 and C4, used to calculate the ASFs
of the colon, were kept as default values. The optimized PK values
revealed no signifi cant differences in comparison to the PK parameters
obtained by the fi rst method; therefore PK parameter values obtained by
fi tting the conventional PK model were used for further simulations.
Stomach transit times of 0.1 and 0.25 h were used for the IR suspension,
and tablet and capsule under the fasted state, respectively, while a
stomach transit time of 1 h was used for all dosage forms under fed
conditions. A colon transit time of 36 h was used for all dosage forms
under both fasted and fed conditions. All other parameters were
GastroPlus™ default values. In the case of XR products, Weibull
controlled- release functions were used as inputs for GI simulation
(Weibull parameters were obtained by deconvoluting mean PK profi les
after p.o. administration of XR tablets and capsules under fasted and fed

Predicted CBZ PK profi les were close to the observed mean PK profi les
for all tested CBZ products under both fasted and fed conditions, as
indicated by correlation coeffi cients, which ranged between 0.876 and
0.991. The model was also able to capture the absorption plateau that
exists after oral administration of the investigated CBZ IR tablet under
fasted conditions (the observed peak occupancy time (POT 20 , time span
over which the concentration was within 20% of C max ) ranged from
3.7 to 41 h under fasted conditions, while the predicted POT2 0 ranged
from 2.9 to 40 h).

Regional absorption distribution revealed that CBZ was mainly
absorbed in the small intestine for IR formulation, but in caecum and
colon for XR formulation, under both fasted and fed conditions,
indicating formulation may have signifi cant impact on CBZ regional
absorption ( Figure 6.6 ). Comparing the percentage of drug absorbed in
different GI regions under fasted and fed conditions revealed that food
had the greatest effects on the rate of absorption from the IR suspension
and tablet, and increased CBZ absorption in duodenum.

Another study of CBZ oral absorption simulation using GastroPlus™
was conducted by our group (Kovacevic et al. (2009). The prime objective
of this study was to use GIST, in conjunction with IVIVC, to investigate
a possible extension of biowaiver criteria to CBZ IR tablets. In this
context, GIST was used to predict the fraction of CBZ dose absorbed
under fasted state, and the drug disposition based on its physicochemical

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Figure 6.6 Effects of dosage forms on CBZ regional absorption:
(a) fasted; (b) fed (reprinted from Zhang et al., 2011;
with permission from Springer)

and PK parameters. T able 6.7 shows that some of the input parameters
selected for simulation differed from the values used by Zhang et al.
(2011). For example, drug particle radius was three times larger in the
study of Zhang et al. (2011), which inevitably led to slower in vivo
dissolution, and consequently, drug absorption. Another notable
difference referred to PK parameters employed for the simulations.
Opposite to Zhang et al. (2011), who used a two-c ompartment model to
describe CBZ pharmacokinetics following administration of an IR
formulation, in our study, a one-c ompartment model was employed, and
the corresponding PK parameters were used as inputs. Consequently, the
generated absorption models differed, and the simulated PK profi les
diverged, as illustrated by the predicted plasma PK parameters (T able 6.8) .

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Table 6.7 Summary of the CBZ input parameters employed for
GI simulation (data from Kovacevic et al., 2009)

Parameter V alue
Molecular weight (g/mol) 238.29
log P 2 .45
pKa 1 1.83
Human jejunal permeability (cm/s) 4.3 × 10− 4
Dose (mg) 400
Dose volume (mL) 250
Solubility in water (mg/mL) 0.12
Mean precipitation time (s) 900
Diffusion coeffi cient (cm2 /s) 0.869 × 10− 5
Drug particle density (g/mL) 1.2
Effective particle radius ( μ m) 2 5
Body weight (kg) 72
Unbound percent in plasma (%) 30
CL (L/h/kg) 0.024
Vc (L/kg) 1.26
t1 /2 (h) 36.39
Simulation time (h) 120
Dosage form IR tablet

Table 6.8 Comparison of PK parameters between simulated
and in vivo observed data for CBZ following oral
administration of a single 400 mg dose from IR tablet
in fasted state

Parameter Zhang et al. (2011) K ovacevic et al. (2009)
Observed Predicted % PE O bserved Predicted % PE

Cm ax ( μ g/mL) 3.61 3 .71 − 2.77 3.78 3.76 0 .53
t max (h) 24.00 1 6.00 3 3.33 6.00 7.00 −16.67
AUC 0→∞ ( μ g h/mL) 298.60 3 30.00 − 10.52 229.10 2 26.90 0 .96
AUC0 →t ( μ g h/mL) 279.80 3 01.60 − 7.79 224.60 2 01.20 1 0.42

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Computer-aided applications in pharmaceutical technology

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 i n vivo observed plasma
profi les were used for model validation. The i n vivo bioequivalence (BE)
data used in our study indicated fast CBZ absorption (mean tm ax = 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-s pecifi 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-d imensional
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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be used to guide formulation design. For example, if a compound has a
poor predicted percentage of drug absorbed, PSA can aid identifi cation of
critical parameters limiting the absorption or bioavailability of a drug.
Once the limiting factors are known, it may be possible to devise methods
to overcome these limitations (e.g. reduction of drug particle size,
addition of solubilizers, co-s olvents, permeability enhancers, use of
different salt forms). In this way, researchers can save a great deal of time
and effort, and minimize loss of resources in (pre)formulation processes.

In the previously described case of GLK, PSA was performed to assess
the effect of the selected formulation parameters (i.e. effective particle
radius, drug particle density), and certain drug physicochemical properties
(i.e. solubility and permeability) on the predicted rate and extent of GLK
absorption. The selected parameters were varied in the range covering
one- tenth to ten- fold actual input parameter value, except for the human
effective permeability, which was varied from one-h alf to two-f old input
value. The results are presented in Figure 6.7 .

According to the PSA outcomes, the percentage of GLK absorbed (Fa )
would not be signifi cantly infl uenced by variations in drug particle density
and effective particle radius. The PSA for solubility showed that even a
10-fold decrease in solubility would not cause bioavailability problems
(F a >85%) (F igure 6.7a) . However, it was demonstrated that larger
particles, higher density and/or lower solubility values than the ones
used for simulation would decrease the rate of GLK absorption
( Figure 6.7c) . The results also indicated that variations in the input
effective permeability did not signifi cantly affect the drug absorption
profi le.

Other examples describe the use of PSA to investigate the effects
of different input parameters on GastroPlus™ predicted drug PK
performance. In our CBZ study (Kovacevic et al., 2009), PSA was used to
assess the importance of the selected input parameters (i.e. drug solubility,
dose, effective particle radius, and drug particle density) in predicting the
percentage of CBZ absorbed. The selected parameters were varied in the
range from one-t enth to ten-f old actual input parameter value. According
to the results, the extent of drug absorption was rather insensitive to the
variation in the input parameters tested. PSA for drug solubility indicated
that complete absorption (F a >85%) could be achieved with CBZ
solubility 2.5 times lower than the initially used input value (0.05 mg/mL
in comparison to 0.12 mg/mL), signifying that eventual CBZ
transformation to less soluble polymorph would not cause bioavailability
problems. PSA for particle radius revealed that high bioavailability would
be achieved with CBZ particle sizes up to 90 µ m (25 µ m was used as the

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Computer-aided applications in pharmaceutical technology

Figure 6.7 Parameter sensitivity analysis: dependence of the
percentage of drug absorbed (a), Cm ax (b), and tm ax
(c) on different input parameters (the center of the
x -axis for each of the parameters tested represents the
value that was used in the simulations; horizontal
dotted line represents complete absorption (>85%
drug absorbed) (a))

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Parameter sensitivity analysis: dependence of fraction
Figure 6.8

CBZ absorbed on different input parameters (the
center of the x -axis for each of the parameters tested
represents the value that was used in the simulations)

input value), and PSA for drug dose indicated that single doses up to
1200 mg would not impair the extent of CBZ absorption ( Figure 6.8 ).

In another case where CBZ was used as the model drug (Zhang et al.,
2011), PSA was performed for parameters for which accurate data were
not available and the selected formulation parameters, including mean
particle radius, particle radius standard deviation, drug particle density,
diffusion coeffi cient, dose volume, drug permeability, drug solubility,
precipitation time, and four Weibull parameters were used to describe
release profi le of the XR formulations. Four dosage forms of CBZ (IR
suspension, IR tablet, XR tablet, and XR capsule), under both fasted and
fed conditions, were studied. PSA results for solubility indicated that
drug in vivo solubility had a signifi cant impact on PK profi les when it was

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Computer-aided applications in pharmaceutical technology

less than 0.2 mg/mL under the fasted state. However, since this border
value was within the reported range of aqueous solubility of CBZ, the
authors speculated that CBZ absorption is dissolution rate-l imited rather
than solubility-l imited. This assumption coincides well with our fi ndings
(Kovacevic et al., 2009) that CBZ in vivo solubility would not cause
bioavailability problems. PSA also denoted that permeability had
less effect on the predicted PK parameters (C max , tm ax , AUC0 -t ) when CBZ
was formulated as a suspension. As for the formulation factors, it was
shown that drug particle size and density had a signifi cant effect on CBZ
PK from IR formulations, being more pronounced in the case of IR
tablet in comparison to the IR suspension, but having no effect on drug
PK from XR formulations. However, the authors elucidated that this
occurred because in XR formulations the particle size effect was integrated
in the dissolution profi les, which were translated into Weibull functions
for input into the ACAT model. Another phenomenon observed was that
CBZ absorption profi les showed different sensitivity to the same factors,
depending on whether the PSA was performed for fasted state or fed
state. In general, it was shown that CBZ absorption profi les were more
sensitive to variations in input parameters tested in fasted state than in
fed state.

The work of Kuentz et al. (2006) is a good example of how PSA can be
used as an integral part of a strategy for preclinical formulation
development. In order to obtain detailed biopharmaceutical data on the
selected model drug, initially profi led to have poor solubility and high
permeability, GastroPlus™ simulations, together with the statistically
designed study in dogs, were conducted. In the fi rst step, the software
was used to simulate the absorption process based on pre-f ormulation
data. Then PSA was performed where drug particle size and solubility
values were varied (>100-fold range) and their impact on the oral
drug bioavailability was assessed. PK experiments in beagle dogs
were run according to the factorial design set-u p to examine the effect of
the formulation in parallel with a potential food effect in a clinically
foreseen dose range. The obtained PSA results revealed that changes in
particle size and reference solubility in the investigated range would not
signifi cantly affect drug bioavailability (F igure 6.9 ), and the beagle
dogs study indicated that different dosage forms (solution and capsules
fi lled with micronized drug) were not expected to be signifi cantly
different in terms of AUC0 -inf . Based on the fi ndings that particle size
reduction and/or solubility enhancement would not lead to increased
absorption, it was decided that there was no need to develop a
sophisticated drug delivery system; instead, capsule formulation was

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Parameter sensitivity analysis: oral bioavailability
Figure 6.9

(%) as a function of reference solubility at pH 6.5
(mg/mL) (dark squares), and effective particle radius
( μ m) (light squares) at a dose of 160 mg R1315
(reprinted from Kuentz et al., 2006; with permission
from Elsevier)

selected for phase I clinical studies, leading to considerable resources
being saved.

Dannenfelser et al. (2004) reported a case where PSA analysis revealed
that drug solubility and particle size clearly infl uenced oral absorption of
a poorly soluble drug. Additional PK studies in dogs revealed that
solid dispersion containing water soluble polymer with a surface
active agent showed comparable bioavailability with the cosolvent-
surfactant solution (considered to be 100% bioavailable), both of
which showed 10-fold higher bioavailability than the dry blend of
micronized drug with microcrystalline cellulose. Thus, a capsule
containing solid dispersion formulation was selected for clinical

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6.5 Virtual trial
In the later stages of formulation development, it is especially valuable to
anticipate inter-s ubject variability that may infl uence oral drug
bioavailability. In this way, the formulator might gain a better insight on
what can be achieved by means of the formulation.

In order to i n silico simulate the infl uence of population variability and/
or the combined effect of formulation variables that are not precise values,
but for which distributions of values can be estimated, the Virtual Trial
feature in GastroPlus™ can be used. This feature allows the user to perform
stochastic simulations on a number of virtual subjects, wherein the values
of the selected variables are randomly sampled from predetermined
distributions (defi ned as means with coeffi cients of variation (CV%) in
absolute or log space). CV% values are usually estimated on the basis of
previous knowledge or analysis of literature data. The results of the
simulations are expressed as means and coeffi cients of variation for fraction
of drug absorbed, bioavailability, t max , Cm ax , and AUC values, as well as
absolute minimum and maximum values for each of these parameters
reached during the trials. Also, the average Cp -time curve, 90% confi dence
intervals, probability contours (10, 25, 50, 75, 90, 95, and 100%), and
experimental data with possible BE limits (if available), are displayed.

An illustration of the use of virtual trials for in silico modeling of oral
drug absorption can be seen in the paper of Tubic et al. (2006). Although
the prime objective of this study was to demonstrate how an in silico
approach can be used to predict nonlinear dose-d ependent absorption
properties of talinolol, this section will focus solely on the results of
virtual trial simulations. The reason why the authors performed
simulations in a virtual trial mode was to include the effects of
physiological variables, such as transit times in various GI compartments,
GI pH, lengths and radii, PK parameters, plasma protein binding, and
renal CL on talinolol absorption. Stochastic variables were randomly
selected within the range defi ned by the means with predetermined
coeffi cients of variation in log normal space, and used for the simulation.
Virtual trials were performed with 12 subjects (equal to the number of
subjects used in the clinical study), and the results were presented as
mean C p vs. time profi le with 90% confi dence intervals around the mean,
along with Cp vs. time curves for 25, 75, and 100% probability of
simulated patient data. The simulation results revealed that all of the
observed clinical data lay within the minimal and maximal individual
patient simulations, suggesting that the CV% values used for the log
normal distributions of the stochastic variables produced variability that

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encompasses the observed clinical results. Thus, it was deduced that
virtual trial simulations based on the presumed distribution of the selected
variables were able to predict variability associated with the observed
clinical data.

The Virtual Trial mode can also be used to conduct virtual BE studies,
as demonstrated in the work of Tsume and Amidon (2010) (Section 6.8:
Biowaiver Considerations) and Zhang et al. (2011). In the latter example,
virtual BE studies on 25 subjects were performed for a hypothetical XR
CBZ tablet under fasted and fed conditions, using a conventional 2×2
crossover design. Stochastic variables included physiological and PK
parameters, which were randomly sampled from the predefi ned range in
log-n ormal scale. Along with the reference product, two virtual test
formulations were examined: Test 1 having similar dissolution profi le to
the reference formulation (f2 = 67.4), and Test 2 that differed in i n vitro
dissolution compared to the reference product (f2 = 38.2) (F igure 6.10a) .
Drug PK profi les were predicted from the corresponding i n vitro
dissolution profi les described by the Weibull function. A random sequence
was assigned to the test formulations for 90% confi dence intervals (CI)
calculation of C max , AUC 0-t , and AUC 0-inf . The simulation results showed
that, in spite of the difference in in vitro dissolution, Test 2 was
bioequivalent to the reference formulation using the 80 to 125% criteria
( Figures 6.10b,c) , indicating that the in vitro dissolution test was more
sensitive to formulation differences than an i n vivo study. Also, it was
perceived that the confi dence intervals calculated for the test/reference
ratios from virtual BE studies were narrower than the observed ones. This
was attributed to the fact that physiological and PK parameters of the
same subjects were equal when the subjects were administered with
the test vs. reference formulations. Therefore, the authors speculated that
the Test 2 formulation might not be bioequivalent to the reference
formulation if intra- subject variability was included in the simulations.

6.6 Fed vs. fasted state
The presence of food may affect drug absorption via a variety of
mechanisms; by impacting GI tract physiology (e.g. food-i nduced changes
in gastric emptying time, gastric pH, intestinal fl uid composition, hepatic
blood fl ow), drug solubility and dissolution, and drug permeation
(Welling, 1996). For example, lipophilic drugs often show increased
systemic exposure with food, and this phenomenon is attributable to

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Figure 6.10 Virtual BE study: (a) Weibull controlled release profi les; (b) fasted state; (c) fed state (reprinted from
Zhang et al., 2011; with permission from Springer)


Computer-aided biopharmaceutical characterization

improved solubilization due to higher bile salt and lipid concentrations.
Negative food effects are mostly seen for hydrophilic drugs, where food
impedes permeation (Gu et al., 2007). One of the frequently used
approaches to assess the effect of food on oral drug absorption involves
animal studies (Humberstone et al., 1996; Paulson et al., 2001; Wu et al.,
2004; Xu et al., 2012). However, due to the fact that physiological factors
are species dependent, the magnitude of food effect for a given compound
across species is usually different, thus complicating the prediction of
food effects in humans (Jones et al., 2006b). One alternative to animal
experiments is to simulate food effects in humans using physiologically
based absorption models. Considering that these models are built based
on a prior knowledge of GI physiology in the fasted and fed states, they
are able to describe the kinetics of drug transit, dissolution, and absorption
on the basis of drug-s pecifi c features such as permeability, biorelevant
solubility, ionization constant(s), dose, metabolism and distribution data,
etc. Gastroplus™ default physiology parameters, which differ between
fasted and fed states, are given in Table 6.9.

Several studies have confi rmed the usefulness of the in silico modeling
approach to assess food effects on oral drug absorption. For example,
Jones et al. (2006b) incorporated biorelevant solubility and degradation
data into the GastroPlus™ absorption model to predict plasma profi les in
fed, fasted, and/or high- fat conditions for six model compounds.
Biorelevant solubilities were measured in different media: simulated
human gastric fl uid for the fasted and fed state, simulated human
intestinal fl uid for the fasted, fed, and high-f at state, and simulated human
colonic fl uid for the upper and the lower colon. The effect of food was

Table 6.9 GastroPlus™ (version 8.0) interpretation of changes in
human physiology between fasted and fed states

Physiological parameter Fasted Fed
Stomach pH 1.3 4.9
Stomach transit time (h) 0.25 1.00
Stomach volume (mL) 50 1000
Duodenum pH 6.0 5.4
Jejunum 1 pH 6.2 5.4
Jejunum 2 pH 6.4 6.0
Hepatic blood fl ow (L/min) 1.5 2.0

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simulated by changing physiological parameters and inserting the relevant
solubility data into the appropriate ACAT compartments (stomach,
intestine, and colon). The food effect for each drug was estimated by
comparing AUC or Cm ax between fasted, fed, and/or high-f at conditions.
Predicted and observed plasma concentration-t ime profi les and food
effects were compared for a range of doses to assess the accuracy of the
simulations. The obtained results demonstrated that GI simulation using
GastroPlus™ was able to correctly predict the observed plasma exposure
in fasted, fed, and high-f at conditions for all six compounds. Also, the
applied method was able to accurately distinguish between minor and
signifi cant food effects. Therefore, it was concluded that biorelevant
solubility tests, in conjunction with physiologically based absorption
modeling, can be used to predict food effects caused by solubility and
dissolution rate limitations, and/or degradation. However, it was stressed
that the accuracy of a generated drug- specifi c absorption model needs to
be carefully verifi ed before proceeding to predict the effect of food.

An important issue emphasized from different studies (Mueller et al.,
1994; Schug et al., 2002a,b; Zhang et al., 2011) is related to the
formulation-d ependent food effects. Zhang et al. (2011) incorporated
gastric emptying time and different drug i n vivo solubilities under fasted
and fed states into the generated CBZ absorption model and observed
that co-a dministration of CBZ IR suspension with food resulted in
decreased Cm ax and prolonged t max , probably due to a prolonged gastric
emptying time, while co-a dministration of the IR tablet and XR capsule
with food resulted in increased Cm ax and earlier tm ax in comparison with
the PK parameters obtained under fasted state. A possible explanation of
this phenomenon was that the presence of a high-f at meal induced the
increase in bile salts concentration in the GI tract, thus enhancing the
dissolution rate of low soluble CBZ from the IR tablet and XR capsule.

Jones et al. developed a novel strategy for predicting human
pharmacokinetics in fasted and fed states, by using PBPK absorption
modeling across different species (Jones et al., 2006a). The proposed
strategy implies that the absorption models are fi rst generated for the
selected preclinical species (e.g. mouse, rat, dog, monkey) on the basis of
data generated during drug research and preclinical development, and
afterwards verifi ed thoroughly by comparing the simulation outcomes with
the results of in vivo animal studies. If the prediction was proven to be
accurate, then the same i n vitro absorption parameters and the same
assumptions can be used to predict human pharmacokinetics. However, if
the animal model was incomplete, further refi nement of the model is needed
in order to provide more accurate simulations in humans (F igure 6.11 ).

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Figure 6.11 PBPK prediction strategy for oral absorption prediction (proposed by Parrott and Lave, 2008)


Computer-aided applications in pharmaceutical technology

The overall concept of this strategy is illustrated in several papers
published by this group (Jones et al., 2006b; Parrott and Lave, 2008;
Parrott et al., 2009). For example, in one of these (Parrott et al., 2009),
GastroPlus™ PBPK absorption models for dog and human for two model
drugs (theophylline and aprepitant) were constructed in parallel by
integrating various predictive data, including drug physicochemical
properties, biorelevant solubility and dissolution, and in vivo study results.
Verifi cation of model assumptions was performed by comparing simulation
results to the food effects measured in carefully designed i n vivo dog studies,
whereas a good match of simulated and observed plasma concentrations in
the fasted and fed dogs indicated that the model has captured well the
mechanisms responsible for food effects, allowing a reliable prediction for
humans. The results indicated that the strategy to predict food effects via
PBPK modeling highly depended on drug biopharmaceutical properties.
For theophylline, a BCS class I compound, the food effects for immediate
and CR formulations could be well simulated by default GastroPlus™
models for both dog and human. However, simulations for aprepitant, a
BCS II drug, required several changes to the default GastroPlus™ models
(e.g. adjustment of regional solubility data, modifi cation of the diffusion
coeffi cient used to calculate the dissolution rate), indicating that PBPK
modeling based on i n vitro data for challenging drugs should be conducted
in conjunction with preclinical in vivo dog studies.

6.7 In vitro dissolution and in vitro–in vivo
There are two approaches enabling the GastroPlus™ generated drug-
specifi c absorption model to be used to assess the relationship between
the i n vitro and in vivo data: convolution to predict the plasma
concentration profi le, and deconvolution to estimate the i n vivo
dissolution profi le. Once an IVIVC is developed, an i n vitro dissolution
test can be used to identify changes that may affect the effi cacy and safety
of the drug product. In addition, biowaiver justifi cation could be discussed
in terms of whether dissolution from the dosage form is expected to be
the rate- limiting factor for drug in vivo absorption.

In the convolution approach, a set of in vitro data representing different
dissolution scenarios is used as the input function in GastroPlus™ software
to estimate the expected drug plasma concentration-t ime profi les. In the
next step, the obtained profi les are compared with the mean drug plasma

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concentration profi le observed i n vivo , in order to establish an IVIVC.
In the deconvolution approach, the GastroPlus™ generated i n vivo
dissolution profi le is plotted against the in vitro obtained dissolution
profi les, so that ‘bioperformance’ dissolution condition(s) can be identifi ed.

In the previously described case study of GLK IR tablets (Grbic et al.,
2011), a set of virtual in vitro data, based on the experimentally obtained
results (in media pH 1.2, 4.0, 4.5, 6.8, 7.2, and 7.4) and literature
reported data (Hong et al., 1998), was used as the input function in
GastroPlus™ software to estimate the expected GLK plasma concentration
profi les. The investigated i n vitro profi les (presented in F igure 6.12a )
were generated to refl ect the situation where:

I. less than 85% of the drug is dissolved – incomplete dissolution
(profi le a);

II. more than 85% of the drug is dissolved in 60 min (profi le b);
III. more than 85% of the drug dissolved in 45 min (profi le c);
IV. more than 85% of the drug dissolved in 30 min – ‘rapid’ dissolution

criteria (profi le d); or
V. more than 85% of the drug dissolved in 15 min – ‘very rapid’

dissolution criteria (profi le e).

The corresponding Cp –time profi les ( Figure 6.12b) , estimated on the
basis of the generated GLK-specifi c absorption model, were plotted
against the i n vivo observed data (Najib et al., 2002), in order to develop
a level A IVIVC model (F igure 6.13a) . The obtained correlation
coeffi cients and slopes of the regression lines are given in Table 6.10 .

The results indicated that variations in drug input kinetics were well
refl ected in the simulated in vivo profi les. However, it was evident that

Table 6.10 IVIVC statistical parameters for GLK IR tablets

in vitro inputs C onvolution approach Deconvolution approach
a r a r

profi le a 0.440 0 .382 2.289 0.875
profi le b 0.894 0 .897 1.031 0.894
profi le c 0.896 0 .910 1.056 0.929
profi le d 0.898 0 .923 0.946 0.896
profi le e 0.867 0 .947 1.189 0.999
a – slope of the regression line, r – coeffi cient of correlation

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Computer-aided applications in pharmaceutical technology

(a) Virtual GLK dissolution profi les, and (b) the
Figure 6.12

corresponding simulated in vivo profi les, along with
the actual in vivo data (from Najib et al., 2002) (the
simulated profi les b, c, and d overlap)

differences observed in vitro were less pronounced in the predicted PK
profi les (the simulated profi les b, c, and d overlapped). The highest degree
of deviation from the in vivo observed profi le was demonstrated for
profi le a, representing a scenario in which less than 85% of the drug is
dissolved. On the other hand, values of the slope close to unity, as well
as high coeffi cients of correlation, indicated the presence of a level A
correlation for the profi les b, c, d, and e.

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Figure 6.13 IVIVC plot for GLK IR tablets: (a) convolution approach;
(b) deconvolution approach

In the attempt to establish IVIVC for the same data set using the
deconvolution approach, the hypothetical GLK i n vivo absorption profi le
estimated by GastroPlus™ was compared with previously described in
vitro dissolution profi les. Since in vitro drug dissolution was faster than
the corresponding i n vivo process, it was necessary to rescale the time
axis when progressing from in vitro to in vivo . The IVIVC plot of the
percentage dissolved i n vitro vs. the percentage absorbed i n vivo , is
presented in Figure 6.13b . The outcomes of deconvolution revealed that
the i n vitro profi le e (stretched by 12-fold linear rescaling of the time axis)
has the same general shape (morphology) as the estimated hypothetical
in vivo dissolution profi le, although a good correlation was also achieved
for the in vitro profi les b, c, and d (T able 6.10) . These results were in
accordance with those obtained by the convolution approach. Since both
convolution and deconvolution approaches were successful in establishing
a level A IVIVC, it was suggested that dissolution specifi cation of more
than 85% GLK dose dissolved in 60 min may be considered as biorelevant
dissolution acceptance criteria for GLK IR tablets.

Other examples can also serve as a good illustration of how GIST can
be used to develop IVIVC. In our previous work (Kovacevic et al., 2009),
a convolution based approach was applied to simulate CBZ plasma
concentration-t ime profi les based on different i n vitro dissolution rates,
with the aim to evaluate whether IVIVC for IR and CR CBZ tablets could
be established. Dissolution studies of the investigated IR and CR CBZ
tablets were performed in the United States Pharmacopoeia (USP) rotating
paddle apparatus at 75 rpm, using 900 mL of various dissolution media.

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In the case of IR tablets, the employed media included water, 0.1, 0.25,
0.5, and 1% sodium lauryl sulfate (SLS) aqueous solution, 0.1 M HCl,
USP acetate buffer pH 4.5, and USP phosphate buffer pH 6.8. In the case
of CR tablets, drug release studies were performed in water, 1% SLS, and
according to the half-c hange methodology (HCM). The obtained
dissolution data were later used as the input function in the GastroPlus™
Single Simulation Mode, to evaluate the infl uence of i n vitro drug
dissolution rate on the predicted CBZ plasma concentration- time profi les.
The dissolution profi les used as inputs, and the corresponding C p –time
profi les, are presented in Figure 6.14 . PK parameters predicted on the
basis of different input CBZ dissolution rates and the relevant prediction
error statistics are given in T ables 6.11 and 6.12. F igures 6.14b and d
illustrate that, in the case of CBZ IR tablets, the simulated i n vivo profi les

Table 6.11 The PK parameters predicted based on CBZ IR tablets
dissolution in various media

Dissolution C m ax % PE AUC 0 →t A UC 0→∞ % PE t m ax (h) F a (%)
media (mg/L) ( μ g h/mL) ( μ g h/mL)
pH 6.8 3.29 -12.96 194.4 220.9 -3.58 16.5 95.5
0.1% SLS 3.60 -4.76 199.7 225.8 -1.44 10.5 97.6
0.25% SLS 3.61 -4.5 200.6 226.5 -1.13 9.9 98.9
0.5% SLS 3.79 0.26 202.1 227.7 -0.61 6.6 98.4
1% SLS 3.77 -0.26 201.9 227.5 -0.70 6.9 98.3
In vivo obs* 3 .78 2 24.6 229.1 6.0 N/A

* Refers to the data obtained/calculated based on the mean Cp -t profi le observed for
the reference product in the relevant i n vivo BE study.

Table 6.12 The PK parameters predicted based on CBZ CR tablets
dissolution in various media

Dissolution C max % PE A UC 0→t A UC 0→∞ % PE t m ax (h) F a (%)
media (mg/L) ( μ g h/mL) ( μ g h/mL)
Water 2.06 − 35.42 1 31.6 143.2 −39.37 2 1.2 59.6
Half- change 3.45 8 .15 2 10.6 228.1 − 3.43 13.8 95.6
1% SLS 3.56 1 1.60 2 15.6 232.8 − 1.44 9.9 9 7.5
In vivo obs* 3 .19 2 23.9 236.2 14.0 N/A

* Refers to the data obtained/calculated based on the mean Cp -t profi le observed for
the reference product in the relevant i n vivo BE study.

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Figure 6.14 CBZ IR (a–d) and CR tablets (e, f) dissolution profi les
in various dissolution media and the corresponding
simulated in vivo profi les (open square symbols refer
to the actual in vivo data (f))

did not appear to be strongly affected by the differences in drug dissolution
rate. The best match between the predicted and the observed Cm ax and
AUC values was accomplished for drug dissolution in 0.5 and 1% SLS.
An interesting phenomenon concerned the deviations between the
predicted Cm ax and tm ax values obtained for different pH dissolution media
(water, media pH 1.2, 4.5, and 6.8), which were not consistent with the
almost superimposable i n vitro dissolution profi les in these media
( Figures 6.14c and d) . It was postulated that the obtained differences were
caused by a simulation artifact resulting from the software approximation
of the time needed to accomplish 100% drug dissolution, which was
estimated as 5.5 and 15.4 h for water and pH 6.8 media, respectively. In
the case of CR tablets, the simulated profi les based on CBZ dissolution in
1% SLS and HCM were in best agreement with the in vivo observed data,

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while the PK profi le predicted on the basis of the CR tablets dissolution in
water indicated slow and incomplete drug absorption. It was noted that
such results were in accordance with the software calculated 39.29 h to be
the time needed for 100% drug dissolution to be accomplished, which
exceeds the physiologically relevant GI transit time.

In order to develop a level A IVIVC, CBZ plasma concentration profi les
simulated on the basis of drug dissolution data obtained in water and
media containing 1% SLS for IR and CR tablets (F igure 6.15) were
plotted against the in vivo observed data. Linear regression analysis of
the pooled data for both the generic and reference IR and CR tablets
indicated high level A IVIVC, especially for predictions based on the in
vitro data observed in 1% SLS (F igure 6.16) . According to these results,
it was suggested that 1% SLS might be considered as the ‘bioperformance’
dissolution medium for both the IR and CR CBZ tablets. However, it was
noted that the possibility to obtain a universal IVIVC model for both IR
and CR products resulted from the fact that CBZ in vivo behavior is
determined by its PK characteristics (i.e. long elimination half-l ife) rather
than the dosage form properties, and that any further generalization of
this concept to other compounds should be carefully evaluated.

Figure 6.15 Comparative dissolution data for generic (dotted line)
and reference (solid line) CBZ tablets in water
(triangle) and 1% SLS (circle) (open symbols refer to
CR tablets, closed symbols refer to IR tablets)

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Figure 6.16 IVIVC plot for CBZ tablets in (a) water and (b) 1% SLS
(test IR tablets (▲); reference IR tablets (▲); test CR
tablets (●); reference CR tablets (●))

Another example considering identifi cation of the predictive in vitro
dissolution of CBZ formulations was given by Zhang et al. (2011). The
authors reviewed a set of i n vitro dissolution data obtained under different
conditions for different CBZ products, which were submitted to the
FDA, and made a selection of the representative in vitro dissolution
profi les, which were compared with the GastroPlus™ predicted CBZ in
vivo dissolution profi les in the fed and fasted states. The data collected
demonstrated that in vitro dissolution of CBZ from the IR suspension,
conducted in 900 mL water using USP Apparatus 2 with a rotation speed
of 50 rpm, was slower than the simulated in vivo dissolution in the fed
state but faster than in vivo dissolution in the fasted state, indicating that
the employed i n vitro dissolution test conditions for CBZ IR suspension
could not be considered biorelevant ( Figure 6.17a ). In the case of the
CBZ IR tablet, i n vitro dissolution profi les obtained in 900 mL media
containing 0.1% SLS, using USP Apparatus 2 with paddle speed of
75 rpm, were close to the i n vivo dissolution in the fed state (F igure 6.17b ).
For the CBZ XR tablet, the dissolution profi le obtained in 900 mL buffer
(pH 1.1, 4.5, and 6.8), using USP Apparatus 1 at 100 rpm, correlated
well with in vivo dissolution under fed conditions (F igure 6.17c) . For the
XR capsule, the best relationship between i n vitro and i n vivo data under
both fasted and fed conditions was achieved with the dissolution profi le
obtained in 900 mL buffer containing 0.1% SLS using USP Apparatus 2
at 50 rpm (F igure 6.17d) . In addition, the repeated simulations performed
for fasted state, using the same solubility as for the fed state, gave an
almost identical i n vivo dissolution rate to that obtained under the fed
state, indicating that the differences in in vivo dissolution rates between

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Comparison of in vitro dissolution, Weibull CR profi les, and in vivo dissolution profi les for different dosage
Figure 6.17

forms: (a) IR suspension; (b) IR tablet; (c) XR tablet; and (d) XR capsule (reprinted from Zhang et al.,
2011; with permission of Springer)


Computer-aided biopharmaceutical characterization

fasted and fed states, for both IR and XR formulations, were caused by
the difference in in vivo solubility under fasted and fed states.

Another example of using computer simulations to establish IVIVC
referred to etoricoxib solid oral dosage forms (Okumu et al., 2008).
Dissolution profi les of etoricoxib from the fi lm- coated tablets were
performed in USP Apparatus 2 at 75 rpm, using conventional dissolution
media: simulated gastric fl uid (SGF) and USP-simulated intestinal fl uid
(USP-SIF) (900 mL), and fasted state simulated intestinal fl uid (FaSSIF)
(500 and 900 mL) as ‘biorelevant’ media. The in vitro data obtained were
then used as input functions in GastroPlus™ to predict the corresponding
drug absorption profi les ( Figure 6.18 ). A comparison of the simulated
profi les with the in vivo observed data (T able 6.13 ) indicated that the
profi les obtained in SGF and 900 mL FaSSIF appeared to simulate the i n
vivo profi le better when compared with that in SIF and 500 mL FaSSIF.
These results suggested that USP-SIF might not be the best choice of
media, and that recommended 500 mL FaSSIF (Galia et al., 1998;
Marques, 2004) may not be the right choice of volume for ‘biorelevant’
in vitro testing of etoricoxib tablets. However, the simulation results
based on the dissolution data obtained in 900 mL FaSSIF and SGF
provided a comparatively good IVIVC (r2 = 0.899 and 0.898, respectively).

Etoricoxib: (a) comparison of dissolution profi les in the
Figure 6.18

USP Apparatus 2 (n = 3); (b) comparison of simulated
profi les and observed in vivo data (60 mg tablet) using
dissolution data as input function in GastroPlus. The
simulated curves of 0.01 M HCl and 900 mL FaSSIF
are super- imposable and predict the observed data
well; however, the simulated curves using SIF or
500 mL FaSSIF as input function show lower Cm ax
values (reprinted from Okumu et al., 2009; with
permission of Elsevier)

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Table 6.13 Etoricoxib: regression analysis output, and percent prediction error statistics

Medium/method Power of prediction values* AUC (×10 4 ng h/mL) C max ( μ g/mL)
r 2 SSE RMSE MAE p redicted % PE** predicted % PE**

Solution 0.900 0.193 0 .101 0 .054 1.943 − 6.88 1 .007 1 0.05
FaSSIF-900 mL 0.899 0.195 0 .101 0 .058 1.942 − 6.82 0 .989 1 1.69
0.01 M HCl 0.898 0.197 0 .102 0 .054 1.941 − 6.77 0 .990 1 1.59
USP–SIF 0.676 0.613 0 .180 0 .093 1.939 − 6.66 0 .844 2 4.64
FaSSIF-500 mL 0.593 0.820 0 .208 0 .114 1.937 − 6.55 0 .806 2 8.04

* r 2 – coeffi cient of determination, SSE – sum of squared errors, RMSE – root mean square error, MAE – mean absolute error
** In comparison to the observed values: AUC = 1.818 × 104 ng h/mL, Cm ax = 1.12 μ g/mL


Computer-aided biopharmaceutical characterization

6.8 Biowaiver considerations
The role of biowaivers in the drug approval process has been emphasized
since the introduction of BCS (Amidon et al., 1995) and the release of
FDA guidance on waiver of in vivo bioavailability and BE studies (US
Food and Drug Adminstration, 2000). In this context, the term biowaiver
refers to the situations in which i n vivo BE studies can be substituted with
the relevant i n vitro data. The main premise, when adopting the biowaiver
concept, was to reduce time and costs, and to offer benefi ts in terms of
ethical considerations. The most common type of biowaiver adopted by
the regulatory authorities includes the application of the BCS-based
scheme (similar or rapid/very rapid dissolution profi les of the test and
reference product in pH 1.2, 4.5, and 6.8 media) or the application of
IVIVC. According to the FDA, biowaivers for IR drug products may be
requested solely in the cases of highly soluble and highly permeable
substances (BCS class I) when the drug product is (very) rapidly dissolving
and exhibits similar dissolution profi le to the reference product, while the
IVIVC-based approach has been narrowed down to applications for XR
products (US Food and Drug Administration, 2000, 1997). The EMA
and WHO issued guidelines widened the eligibility for biowaiver to some
BCS class III (eligible if very rapidly dissolving) (European Medicines
Agency, 2010; WHO Expert Committee on Specifi cations for
Pharmaceutical Preparations, 2006) and BCS class II drugs (eligible for
biowaiver if the dose-t o-solubility ratio at pH 6.8 is 250 mL or less and
high permeability is at 85% absorbed) (WHO Expert Committee on
Specifi cations for Pharmaceutical Preparations, 2006). Also, it was
pointed out that the biowaiver concept concerning BCS II and III drugs
should be further relaxed (e.g. BCS class II drugs eligible for biowaiver
under the assumption that the drug dissolves completely during the GI
passage (Yu et al., 2002), and BCS class III compounds eligible if rapidly
dissolving (Tsdume and Amidon, 2010)).

Several examples from the literature describe how GIST can be used to
identify BCS class(es) of drugs eligible for biowaiver. In the previously
mentioned i n vitro- in silico study of GLK IR tablets, simulation results
demonstrated that differences in GLK in vitro dissolution kinetics, such
as 85% drug dissolved within the 15 to 60 min time frame, are not
expected to refl ect on the in vivo PK profi le. These results support the
assumption that, in the case of BCS class II drugs, complete i n vivo
dissolution might occur at later time points than for highly soluble BCS
class I drugs. This would allow wider biorelevant i n vitro dissolution
specifi cation, than very rapid/rapid i n vitro dissolution, to be set. In

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addition, i n vitro results indicated that GLK solubility and dissolution
from IR tablets are not expected to be the rate-l imiting factors for GLK
in vivo absorption, and since this was a highly permeable drug, there was
a rationale to postulate that biowaiver extension might be applicable in
the case of GLK IR tablets (Grbic et al., 2011).

Another example is the work of Okumu et al. (2009), who combined
in vitro results with i n silico simulations using GastroPlus™, in order to
support biowaiver for IR etoricoxib solid oral dosage forms. They used
in vitro measured solubility and dissolution data in different media, along
with caco −2 assessed drug permeability as input functions in the program
in order to predict etoricoxib absorption profi le. The simulation results
revealed that drug absorption after tablet administration was similar to
the absorption of an oral solution, indicating fast and complete drug
absorption. Furthermore, solubility results indicated etoricoxib behaves
like a BCS class I drug in an acidic environment, and the dissolution
transfer model confi rmed that the drug stays in solution when transferred
from the acidic environment of the stomach into the small intestine.
Therefore, it was concluded that etoricoxib might be a suitable candidate
for biowaiver.

In our CBZ study (Kovacevic et al., 2009), biowaiver justifi cation for
this BCS class II drug was elaborated upon. The GastroPlus™ generated
CBZ-specifi c absorption model was used to predict drug plasma
concentration- time profi les based on different in vitro dissolution rates as
input function. The results revealed that high dissolution rates (i.e. >85%
of drug dissolved in <10 min) were not related to the signifi cant increase
in Cm ax in comparison to the i n vivo observed values, thus indicating
that the predicted plasma concentration profi les were rather insensitive
to the differences in drug input kinetics. Following these results, it
was concluded that there is a rationale for considering CBZ biowaiver
extension. However, it was stressed that, at present, other factors such as
CBZ narrow therapeutic index and vital indication are the limitations for
granting marketing authorization based on the in vitro data alone.

Tubic-Grozdanis et al. (2008) also demonstrated that GI simulation of
oral drug absorption can aid in identifi cation of BCS class II biowaiver
candidate drugs. They used several weakly acidic (i.e. ibuprofen,
ketoprofen, diclofenac, mefenamic acid, and piroxicam) and weakly
basic (i.e. verapamil, miconazole, and terbinafi ne) BCS class II model
compounds, and GIST as a tool to study how differences in dissolution
rates would affect drug bioavailability and other PK properties.
Theoretical dissolution profi les of two IR drug products, namely ‘rapid
IR’ (released >90% of the dose within 10 min) and ‘slow IR’ (released

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80% in 45 min) were designed and used to predict plasma concentrations
vs. time and absorption curves for each compound used in the simulations.
Depending on the drug properties, either GastroPlus™ Single Simulation
Mode or Virtual Trial (e.g. for verapamil, which is a highly variable drug)
were selected for the simulations. PSA was performed in order to assess
the infl uence of drug properties (i.e. particle size, solubility, precipitation
time) on the fraction of drug absorbed. According to the obtained results,
and supported by previously published biopharmaceutical data on the
selected model drugs, it was deduced that ibuprofen, ketoprofen,
diclofenac, piroxicam, and terbinafi ne could be considered as candidates
for biowaiver. However, GI simulation indicated that mefenamic acid
and miconazole were not eligible for granting a biowaiver. According to
the predictions, mefenamic acid exhibited solubility and dissolution
limited absorption in the small intestine. Moreover, this drug lacked a
predictive dissolution method which would indicate its biopharmaceutical
properties. In the case of miconazole, it was found that oral drug
absorption was limited by dissolution rate and by the saturated solubility,
indicating that a highly dosed drug would probably precipitate in the
GI milieu.

Tsume et al. (2010) investigated the ability of GIST to predict oral
absorption of the selected BCS class I (propranolol and metoprolol) and
BCS class III drugs (cimetidine, atenolol, amoxicillin), and performed
in silico BE studies to estimate the feasibility of extending biowaivers to
BCS class III drugs. In addition, the signifi cance of ‘rapid dissolution’ and
‘very rapid dissolution’ criteria for BCS class III drugs was evaluated. The
GastroPlus™ Virtual Trial model was used to assess the infl uence of drug
dissolution kinetics on oral drug absorption, Cm ax , AUC, and BE. A set of
virtual in vitro dissolution data (corresponding to 85% release in 15, 30,
45, 60, 90, 120, and 180 min) was used as input function in GastroPlus™
to predict the drug PK profi le. For each BCS class III drug, virtual trial
(500 subjects) with T8 5% = 15 min (‘very rapid dissolution’), and virtual
trials (24 subjects) at different release rates (from T8 5% = 30 min to T 85% =
180 min) were performed as ‘reference’ and ‘samples’, respectively. The
results of the predictions (mean C max and AUC0 -inf ± SDs), with different
release rates used as ‘samples,’ were compared with the reference results
to determine BE. The results demonstrated BE up to T 85% = 45 min (for
amoxicillin) or T 85% = 60 min (in the cases of cimetidine and atenolol)
compared to the reference result of T8 5% = 15 min, including BE between
very rapid (>85% solubility in 15 min) and rapid dissolution (>85%
solubility in 30 min). These fi ndings indicated that the permeability of
BCS class III compounds was the rate-l imiting step for oral drug

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Computer-aided applications in pharmaceutical technology

absorption rather than their dissolution. Overall, the obtained results
suggested that extending the biowaiver to include IR dosage forms of
BCS class III drug products is feasible, and moreover, that biowaivers for
BCS class III drug products with suitably rapid dissolution would ensure
‘bioperformance’ of these pharmaceutical products.

Crison et al. (2012) employed i n silico modeling to justify biowaiver for
BCS class III drug metformin hydrochloride. GastroPlus™ modeling was
performed within the range of gastric transit times expected in human
subjects, to show the broad range of release rates that are expected to
have no impact on AUC and C max , and therefore result in drug products
BE. It should be noted that, although metformin exhibits nonlinear
pharmacrokinetics with respect to dose, the absorption model developed
in this study was based on 500 mg data, so the simulation results were
limited to that dose. Two clinical studies for IR formulations were used in
the model development and additional clinical studies, one for IR and one
for ER formulation, were used to confi rm that the model was predictive
over a wide range of drug release times. Drug release profi les representing
100% of metformin released in 5 min up to 14 h were used as inputs for
the model. The simulations to predict plasma concentrations of metformin
corresponding to different release rates were performed as virtual trials,
so that inter-s ubject variability could be introduced into the predictions.
In order to prove model predictability, the results of virtual trial simulations
(defi ned as ‘test’) were compared with the observed clinical data (defi ned
as ‘reference’). According to the simulation results, metformin release
rates within 100% of drug, dissolved in 5 min up to 2 h did not have a
statistically signifi cant effect on Cm ax and AUC0 -t . In addition, it was shown
that within this range of dissolution rates, metformin products are
expected to be bioequivalent, irrespective of the results of the f 2 test. In
conclusion, the results illustrated that the described i n vitro-i n silico
approach might be used to waive i n vivo BE studies for metformin drug
product. Furthermore, it was deduced that i n silico modeling and
simulation, which includes all the key parameters that fully defi ne the
absorption of BCS class III compounds (i.e. dissolution, permeability, and
GI residence time), should be more mechanistically accurate and robust
for BE evaluation than statistical comparison of i n vitro release profi les.

6.9 Conclusions
The various examples presented demonstrate that GI modeling has become
a powerful tool to study oral drug absorption and pharmacrokinetics. This

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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 i n vitro-i n 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-e ffective 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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new information regarding drug biopharmaceutical properties being
collected, it is expected that GI modeling will be more often used by
formulation scientists. In this context, it should be stressed that large
amounts of valuable data on drug biopharmaceutical properties still lie
within pharmaceutical companies and regulatory agencies, and even
partial access to these data would be helpful to generate and/or validate
in silico absorption models. Published examples of the successful
application of in silico techniques would also assist in promoting their
wider acceptance.

6.10 References
Abuasal , B.S. , Bolger , M.B. , Walker, D.K. , and Kaddoumi , A . (2 012 ) ‘I n silico

modeling for the nonlinear absorption kinetics of UK-343,664: a P-gp and
CYP3A4 substrate ’, Mol. Pharm. , 9 ( 3 ): 492 – 504 .

Aburub , A . , R isley , D .S. , and Mishra, D . (2 008 ) ‘ A critical evaluation of fasted
state simulating gastric fl uid (FaSSGF) that contains sodium lauryl sulfate and
proposal of a modifi ed recipe ’, Int. J. Pharm. , 347 ( 1 – 2 ): 16 – 22 .

Agoram , B . , W oltosz , W .S. , and Bolger , M .B. (2 001 ) ‘ Predicting the impact of
physiological and biochemical processes on oral drug bioavailability ’, Adv.
Drug Deliv. Rev. , 50 ( Suppl 1 ): S41 – 67 .

Al-Salami , H. , Butt , G. , Tucker, I. , and M ikov , M. ( 2008 ) ‘ Infl uence of the
semisynthetic bile acid (MKC) on the ileal permeation of gliclazide in healthy
and diabetic rats ’, Pharmacol. Rep. , 60 ( 4 ): 532 – 41 .

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