This study validated a mathematical model used to predict theoretical dissolution profiles in an artificial stomach-duodenum model. The model was developed using Berkeley Madonna software to predict drug concentration over time assuming no precipitation. Ketoconazole was used as the model drug. Experiments were conducted using the artificial stomach-duodenum model with variations in duodenum volume, gastric emptying time, and drug dose. The results showed good correlation between experimental and predicted profiles for area under the curve, maximum concentration, and time to maximum concentration for middle and low doses. High doses showed more variability possibly due to detection limits of the UV probes. The validated model will help determine the impact of supersaturation and precipitation during early formulation development.

1. Validation of mathematical model used to predict theoretical control
Artificial Stomach-Duodenum profiles
Dania Grant-Serroukh1, Mei Wong2, Debbie Kraus2
1 Department of Pharmaceutics, UCL School of Pharmacy 2Drug Product Design, Pharmaceutical Sciences, Pfizer, Sandwich
References
[1] McAllister, M., 2010, Dynamic dissolution: a step closer to predictive dissolution testing?: Mol Pharm, v. 7, p. 1374-87.
[2]Polster, C. S., Atassi, F., Wu, S. J and Sperry, D. C., 2010, Use of artificial stomach-duodenum model for investigation of dosing fluid effect
. on clinical trial variability: Mol Pharm, v. 7, p. 1533-8
[3] Carino, S. R., D. C. Sperry, and M. Hawley, 2006, Relative bioavailability estimation of carbamazepine crystal forms using an artificial
. stomach duodenum model: J Pharm Sci, v. 95, p. 116-25.
The first process that an orally administered drug must undergo in the body in order to exert a pharmacological response is dissolution. The
artificial stomach-duodenum (ASD) model has been proposed as an effective tool to gauge the supersaturation and precipitation profiles of a drug
formulation in the early stages of its development into a pharmaceutical product1,2.
Introduction
To help interpret the results obtained from the ASD model, the ASD
dissolution data can expressed relative to a control profile (i.e.
dissolution graph if no precipitation was to occur). Using Berkeley
Madonna software, a mathematical model has been developed to
predict the drug concentration profiles in the ASD model over time
assuming zero precipitation - saving both time and resources in the
initial formulation stages of the drug development process. The aim of
this study was to validate this mathematical model using an
experimental ASD set-up and establish a set of parameters for which
the predictions are accurate.
The ASD model as shown in Figure 1 consists of two separate compartments
representing the stomach and duodenum. The conditions in each compartment
are designed to simulate in vivo conditions. UV fibre optic probes in each
chamber detect the concentration of dissolved drug over time enabling us to see
dissolution, supersaturation and precipitation characteristics.
Artificial Stomach Duodenum (ASD) model
Stomach Chamber Duodenum ChamberGastric Fluid Duodenal Fluid Waste
Results and Discussion
-0.05
0.00
0.05
0.10
0.15
0.20
0.25
0.30
0.35
0.40
0 50 100 150 200
Concentration(mg/mL)
Time (min)
Stomach
Duodenum
TIME
c1,c2
Figure 1: Schematic of ASD
model
Summary and Further Work
The results from this study show that the Berkeley
Madonna differential equation model can be used
to predict control dissolution (no precipitation)
profiles for ketoconazole at low (30mg) and middle
doses (100mg) .
Figure 3 displays a typical ASD profile compared to
the Berkeley Madonna profile showing the transfer
of drug from the stomach to the duodenum chamber
before eventually being eliminated from the system.
It is assumed that the concentration of dissolved
drug in the duodenum is proportional to the amount
of drug that is available for absorption from the
intestinal tract and thus gives a direct representation
of bioavailability3.
The results show good correlation between the
experimental and predicted profiles for AUC, Cmax
and Tmax at middle (100mg) and low (30mg) doses
of ketoconazole (see Figure 4).
The variability seen in the AUC values of runs
where high doses (300mg) were used are possibly
due to limitations with the UV probes reaching the
upper limit of detection at concentrations between
0.6 - 0.8mg/mL.
Figure 3: Experimental (left) and predicted (right) concentration-time profiles for a
dose of 100mg, at t1/2 = 15min with 100mL duodenum volume.
This work was sponsored by the Centre of Doctoral Training in Advanced
Therapeutics and Nanomedicines and Pfizer. Special thanks to Alastair Coupe,
Mei Wong and Claudia Da Costa Matthews.
EPSRC grant EP/L01646X.
Methods
ASD experiments were run with three varying
factors; the volume within the duodenum
chamber, the half-life (gastric-emptying time) and
the dose of drug used. These parameters were
varied using Design of Experiments (DoE) as
shown in Table 1.
Table 1. DoE - 3 factors in 8 runs with 2 centrepoints
Run Duodenum
Volume (mL)
Half-life
(mins)
Dose
(mg)
1 100 15 100
2 40 5 300
3 250 5 30
4 40 30 300
5 250 30 30
6 40 5 30
6 250 5 300
7 40 30 30
8 250 30 300
9 100 15 100
10 100 15 100
Crystalline free base
pKa 2.94, 6.51
Solubility:
pH 2.0 – 12.07mg/mL
Ketoconazole was the model
compound for this study.
The structure and
physiochemical properties of
ketoconazole are shown in
Figure 2
Figure 2: Physiochemical properties
of Ketoconazole
Acknowledgements
Additional experiments are needed to understand the variability between experimental and
predicted AUC and Cmax values at high (300mg) doses and the use of shorter optical path
lengths in UV probes should be investigated. These findings will be used to refine the
Berkeley Madonna model which will be used alongside the ASD model to help in determining
the impact of supersaturation and precipitation on a drug or formulation.
a
b
c
Figure 4: Correlation between experimental and predicted
values for AUC (graph a), Cmax (graph b) and Tmax (graph c)
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10
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50
0 1 2 3 4 5 6 7 8 9 10
AUC
Run
Experimental Model Predictions
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Cmax(mg/ml)
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Experimental Model Predictions
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0 1 2 3 4 5 6 7 8 9 10
Tmax(mins)
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Experimental Model Predictions