Computer- aided biopharmaceutical characterization: gastrointestinal absorption simulation - Computer-Aided Applications in Pharmaceutical Technology - page 185

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-subject 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 in 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.

Summary of nimesulide input parameters employed
for GI simulation

Table 6.1

Parameter Model 1 Model 2

Molecular weight (g/mol) 308.31

logD (pH 7.4) 1.8 a 1.48 b

pK a

6.4 b

￿

￿ ￿

2.225 × 10 −4 c

2.002 × 10 −4 d

Human jejunal permeability (cm/s)

Dose (mg) 100

200 e

Dose volume (mL)
Solubility at pH 4.5 (mg/mL) 0.007 f 0.030 d

900 g Mean precipitation time (s)

Diffusion coeffi cient (cm 2 /s)

0.757 × 10 −5 c

1.2 g Drug particle density (g/mL)

5 d

25 g

Effective particle radius (μm)

88 e Body weight (kg)

0.1 h

/

First pass extraction (FPE) in liver (%)

( Continued )