INFOGEST static in vitro simulation of gastrointestinal food digestion

1. InfoGest Training School – Gdansk
Simulating Digestion: in vitro and
in silico modeling
Alan Mackie, IFR – UK
alan.mackie@ifr.ac.uk

2. Part1: Introduction
• Why are we interested in simulating
digestion?
• What aspects of digestion are tractable to
simulation?
• How do the different models work?
• When are different models of digestion
appropriate to use?

3. Introduction
The road to a healthier meal? Optimised for health?
Introduction: Food

4. Introduction: Obesity
Obesity levels increase with age, due to
evolutionary drive for a small positive energy
balance. More than 50% of the population
are overweight.
However, modest and sustainable reductions in
energy intake could reduce the age related increase
in obesity levels and related health problems.
Obesity and related health conditions are an
increasing problem in the UK and identified as
a national priority.
Introduction: Food

5. Introduction: Obesity
We need to understand the rules governing satiety
and satiation in order to help design healthier foods.
Education: Educating the population about the
benefits of eating more healthily is not working
fast enough.
Diet: Eating a more satiating and slower
release diet could work if it is also highly
palatable
Introduction: Food

6. Introduction: Food
?
Full!

7. Introduction: Obesity
Introduction: Pharma
Many diseases and medical conditions are
treated orally and this is the patient preferred
route of delivery.
Diet: The interaction of drug formulations
with food is important for understanding
bioavailability
We need to understand the rules governing bioavailability
to say “take the drug before or after a meal.”

8. Introduction: Pharma
?

9. Why in vitro and not in vivo?
• Ethical constraints and costs
• Sampling heterogeneity is a challenge
• Foods need to be liquidised to allow sampling
• There can be issues with the sheer induced by
drawing through a 3mm nasogastric tube
altering food structure.
• Sampling or measurement of certain parameters
may simply not be possible.

10. Physiology
• Why are we interested in simulating
digestion?
• What aspects of digestion are tractable to
simulation?
• How do the different models work?
• When are different models of digestion
appropriate to use?

11. Physiology: Introduction

12. The many stages of digestion
• Pre-ingestion
– Psychological effects, Pavlovian responses, etc
• Oral processing
– Chewing, sensing, bolus formation, etc
• Gastric processing
– Acidification, proteolysis, physical processing?
• Small intestine
– Proteolysis, lipolysis, amylolysis, absorption
• Large intestine
– Fermentation, absorption, etc
Not Possible to model in vitro
Simulation of digestion is possible but
what about modelling whole body
effects like GI hormone and neural
interactions?

13. Oral Processing: What is tractable?
Food type
Liquid -> Is an oral stage needed?
Solid -> What do we need to mimic?
Chewing
Sensing
texture
Dispersing
tasting
Bolus
formation
Salivary
amylase
Aroma
release
lubrication

14. The Riddet Mouth
Development of a model mouth containing an artificial tongue to measure
the release of volatile compounds. Benjamin, O.; Silcock, P.; Kieser, J. A.; et al.
INNOVATIVE FOOD SCIENCE & EMERGING TECHNOLOGIES 15, 96-103 (2012)
Dispersing
Salivary
amylase
Aroma
release
Chewing
Bolus
formation
Salivary
amylase
Oral Processing: What is tractable?

15. Oral Processing: What is tractable?
Chewing Needed for solid meals
Bolus formation Needed for solid meals
Aroma release Needed?
Saliva Containing what?
Amylase Needed for starchy meals
Lipase Humans Do NOT secrete lingual lipase
C. Salles et al. Journal of Food Engineering 82 (2007) 189–198

16. Gastric Processing: What is tractable?
Low pH?
protease
lipase
Phase
separation
shearing
Mixing?
Gastric
retention
Sensing
fullness
Absorption?

17. Using an MRI scanner we can
take a series of pictures like
taking slices through a loaf of
bread
Looking inside the body
The slices can then be used to
provide information in 3-D

18. Food in the Stomach
Liver Spine Stomach
Liver Spine Stomach
Can we link layering to physiology?
We have eaten the food. What happens next ?

19. Measuring Stomach Volume
Measuring Stomach Volume
Taking slices through the body (as highlighted)

20. Can food structure affect a physiological response such as an
allergic reaction?
Four volunteers in a cross-over
study which followed the
EuroPrevall DBPCFC dosing
protocol.
Dose
Protein dose Chocolate bars (Peanut) Dessert
1 3 µg 1g of 0.0006% bar 1ml of 1:19 dilution of low
2 30 µg 1 segment (5g) of 0.0012% bar 1ml of 1:1 dilution of low
3 300 µg 1 segment (5g) of 0.012% bar 5 ml low
4 3 mg 1 segment (5g) of 0.12% bar 50 ml low
5 30 mg 1 segment (5g) of 1.2% bar 6 ml of high
6 100 mg 1 segment (5g) of 4% bar 20 ml of high
7 300 mg 1 segment (5g) of 12% bar 60 ml of high
8 1 g 2 bars of 4% 200 ml of high
9 3 g 2 bars of 12% 2 top dose bars
Physiological response

21. Volume Data: Dessert
Mean secretion/emptying rate: -0.189 +/- 0.057 ml/minute

22. Volume Data: Chocolate Bars
Mean secretion/emptying rate: +0.068 +/- 0.066 ml/minute

23. Gastric Data ( volume measured – volume consumed)
Mean emptying rate: Dessert -11.3 ml / hour vs Chocolate +4 ml / hour
-40.0
-20.0
0.0
20.0
40.0
60.0
80.0
100.0
120.0
-50 0 50 100 150 200
Volume(ml)
Time (minutes)
Physiological response

24. Allergic Reactions
0.00%
10.00%
20.00%
30.00%
40.00%
50.00%
60.00%
70.00%
80.00%
90.00%
100.00%
3.00E-06 3.00E-05 3.00E-04 3.00E-03 3.00E-02 0.1 0.3 1 3 3+
Cumulativefrequency
Protein dose (g)
Bar
Dessert
First to show that fat continuous foods have a higher threshold due to gastric
emptying
Mackie et al., Mol. Nutr. & Food Res. (2012) 56, 1708-1714
Physiological response

25. Conclusions
• A statistical analysis of the objective symptoms was
undertaken and showed that the chocolate bars gave a
significantly higher threshold for objective symptoms than
the dessert.
• This data correlated with gastric volume measurements on
4 separate volunteers undergoing the same protocol as
the allergic patients.
• The gastric emptying data showed that the dessert was
emptied from the stomach at a faster rate than the
chocolate bars and confirms the initial hypothesis.

26. Stomach volume changing with time (as highlighted)
Measuring Changes in Stomach Volume

27. Two isocaloric meals: The impact on Gastric retention
Liquid - - - - , Solid
t½ = 70 min. and 100 min.

28. Two isocaloric meals: The impact on plasma CCK
Liquid - - - - , Solid

29. Two isocaloric meals (same fat, protein, carbohydrate)
Liquid - - - - , Solid
Not at all hungry Very hungry

30. Enzymes
How much is enough?
• Constant defined quantity
• Constant defined activity
• Constant defined secretion rate
• Variable secretion rate (as in vivo)
Based on: Amount of substrate? Food weight?
Food volume? Nutrient content? A Combination
of factors?

31. Enzymes
• Pepsin
• Gastric lipase
Pepsin is secreted by chief cells in the form of
pepsinogen. This is then cleaved into pepsin will digest up
to 20% of ingested amide bonds by cleaving preferentially
after the N-terminal of aromatic amino acids such as
phenylalanine, tryptophan, and tyrosine.
This is NOT a good guide to the specificity of the enzyme
Does NOT require bile or co-lipase to work efficiently
A limitation of acidic lipases is that they remove only
one fatty acid from each triglyceride

32. Gastric Processing: What is tractable?
pH profile with time Needed
Pepsin Needed
Gastric lipase Needed?
Physical processing Needed for solid meals
Gastric retention Needed
Fullness and particulate sensing Needed?
Ferrua et al. Trends in Food Science & Technology. (2011) 22(9) 480–491

33. Small intestine: What is tractable?
neutral pH proteases
lipases
bile
amylase
mixing
motility
chemo
sensing
absorption

34. Enzymes
• Trypsinogen -> trypsin
• Chymotrypsinogen -> chymotrypsin
• Elastase
• Carboxypeptidase
• Pancreatic lipase
• Amylase
• ETC

35. Enzymes
• Trypsin
• Chymotrypsin
Is a Serine protease that cleaves peptide chains mainly at
the carboxyl side of the amino acids lysine or arginine,
except when either is followed by proline.
This is NOT a good guide to the specificity of the enzyme
Chymotrypsin preferentially cleaves peptide amide bonds
where the carboxyl side of the amide bond is tyrosine,
tryptophan or phenylalanine. Other amide bonds are
cleaved at slower rates, particularly those containing
leucine and methionine.
This is NOT a good guide to the specificity of the enzyme

36. Enzymes
• Pancreatic Lipase
• Amylase
Cleaves triglycerides into 1 monoglyceride and two fatty
acids. In the presence of bile it needs colipase to work
effectively
Cleaves the α(1-4) glycosidic linkages of amylose to yield
dextrin and maltose (comprising 2 x glucose).
This is NOT a good guide to the specificity of the enzyme

37. Absorption: Specific limitations
Hydrolysis products
absorption
Mucus
Diffusion
Self-assembly
Viscosity
Entrapment
Receptor density and activity
Environment

38. Mucus Barrier:

39. Introduction:
• Varying composition and thickness
throughout the GI Tract
• Association between thickness and
function
– Prevent toxins / pathogens / digestive
enzymes reaching the epithelium
– Allowing nutrient absorption and waste
transport

40. Duodenum: mouse
MUC2 WGA DAPI

41. Colon: mouse

42. Mucous – Fibre Interactions:
5 min 20 min 60 min
1.00E-11
1.00E-10
1.00E-09
1.00E-08
1.00E-07
0 500 1000 1500 2000 2500
D(m2/s)
Time (s)
Diffusion of alginate

43. Absorption
Caco2? No!
There are more appropriate immortal cell lines
or tissue explants

44. Absorption: conclusions
• Dietary fibre can penetrate the mucus layer of
the upper GI tract.
• The permeation of dietary fibre into the
mucus increases it viscosity and decreases the
ability of particles to diffuse through it.
• Absorption by enterocytes can be affected by
gut contents.

45. Small intestine: What is tractable?
pH (near neutral) Needed
Proteases Needed
Lipases Needed
Amylase Needed
Mixing Needed
Bile (bile salts, PC, cholesterol, etc) Needed
Absorption Needed?
Chemo-sensing Needed?

46. Large intestine: What is tractable?
near
neutral pH
Fibre
processing
absorption
fermentation
Bacteria
Low water
Anaerobic

47. Large intestine: What is tractable?
Gibson & Roberfroid, J. Nutr. 125: 1401-1412, 1995.

48. Large intestine: What is tractable?
pH (near neutral) Needed
Bacterial load Needed
Processing of Fibre Needed
Low water Needed?
Anaerobic Needed
Absorption Needed?
Chemo-sensing Needed?
TIM-2 model from TNO

49. Oral
Stomach
IntestineColon
Pylorus
Brain
CCK
Satiation
Pancreas
GIP
PYY
Ghrelin
GLP-1
Decreased flow
The role of gastrointestinal hormones: Its complicated
Whole body: What is tractable?

50. Summary of part 1
• The main features of oral, gastric , small intestinal
and colonic digestion appear to be tractable to in
vitro methods.
• There some features of digestion that are not at
present tractable.
• The key is to know what to include and what can be
left out in order to answer YOUR QUESTION.
• A model is only useful if it has some predictive
power and should therefore be based on
physiology.
Any Questions?

51. Part 2: The models
• Why – are we interested in simulating
digestion?
• What – aspects of digestion are tractable to
simulation?
• How do the different models work?
• When are different models of digestion
appropriate to use?

52. InfoGest Summer School – Gdansk
Simulating Digestion: in vitro and
in silico modeling
Alan Mackie, IFR – UK
alan.mackie@ifr.ac.uk

53. Part 2: The models
• Why – are we interested in simulating
digestion?
• What – aspects of digestion are tractable to
simulation?
• How do the different models work?
• When are different models of digestion
appropriate to use?

54. In vitro models
Fully integrated models of the upper GI tract.
• The TNO TIM models designed by Rob Havenaar and Mans Minekus
(Netherlands)
• The IFR DGM designed by Martin Wickham and Richard Faulks
coupled to Birmingham Dynamic Duo (UK)
• The INRA in vitro model (France)
• The Riddet model (New Zealand)
• The Laval model and the Guelph model (Canada)
• There are also many others …
We will now discuss what the simulated digestion models comprise in the
following phases:
• Oral
• Gastric
• Small intestinal (duodenal, jejunal, ileal)

55. Models of the oral phase
Requirements:
Chewing Needed for solid meals
Bolus formation Needed for solid meals
Aroma release Needed?
Texture sensing Needed?
Saliva Containing what?
Amylase Needed for starchy meals
Oral models fall into 2 groups:
1. Models of only the oral phase for testing aroma
release, texture, etc.
2. Models where the oral processing is a precursor to
simulated gastric digestion
If we have a “liquid” meal do we need an oral phase?

56. Oral phase only methods
Sensing texture (tribology) using a pigs tongue
Dresselhuis et al. Food Hydrocolloids 22 (2008) 323–335
No chew, no bolus formation, no amylase, no
measurement of aroma release.
Chewing simulator for food breakdown
and the analysis of in vitro flavour release
Salle et al. J. Food Eng. 82 (2007) 189–198
Chewing, bolus formation, aroma release,
amylase?
No next step!

57. Oral phase method
Simulate mastication
After addition of simulated salivary
fluid (with salivary amylase)
Formed into boluses of ~5ml volume for delivery to the gastric phase
“Chewing”, “bolus formation”, simulated saliva including amylase,

58. Oral phase method
Liquid food samples: no oral phase
Solid food samples: 50 g of food e.g. cooked pasta “chewed” in the mincer
Ratio Food / Simulated salivary fluid (SSF): 50/50 w/v or more to create a paste-like consistency.
Time of chew: 2 min
Add 5 g food + 5 mL Simulated salivary fluid (SSF)
Add Human salivary alpha amylase: 150 IU/ mL in the SSF (Sigma A1031; definition of units on corn
starch).
Add 0.5 µL of CaCl2 (H2O)2 (588 g/L) per mL SSF, (in this case add 2.5 µL to the 5 mL of SSF)
Final oral volume = 10 mL

59. Models of the gastric phase
Requirements:
pH profile with time Needed
Pepsin Needed
Gastric lipase Needed but?
Physical processing Needed for solid meals
Gastric retention Needed
Fullness and particulate sensing Needed?
Sampling Needed?
These fall into 3 groups:
1. Static – conditions fixed at start
2. Semi-dynamic – pH gradually reduced (enzymes gradually
added)
3. Fully dynamic – pH, enzyme secretion and shearing
mimics in vivo situation.

60. 1. Static gastric model
Ratio Food (including oral phase) / Simulated gastric fluid (SGF) : 50/50 w/v
Time of gastric digestion: 2 hours
Check the pH of the solution: 3
Add 10 mL of liquid sample or 10 mL oral content (solid samples) + 10 mL
simulated gastric fluid (SGF)
Add Porcine pepsin 1000 U/mL SGF (Unit on CN; purity)
Bolus delivered from the oral digestion (10 mL) + 0.15 μL of CaCl2 (H2O)2 (588 g/L,
w/v) per mL of SGF. Fill up to a final volume of 20 ml with SGF.
Proposition for non-standard gastric procedure:
Add Phospholipids (PC) 0.17 mM final concentration (in this case the volume
of the digesta is 20 mL).
Bolus delivered from the oral digestion (10 mL) + 0.267 mL PC/SGF solution (10
mg/mL of the gastric PC solution) + 0.15 μL of CaCl2 (H2O)2 (588 g/L, w/v) per mL
of SGF. Fill up to a final volume of 20 ml with SGF

61. 1. Static gastric model
Why 2 hours? Why pH 3?
Do these make sense in all cases?
t½ = 100 min. for our semi-solid meal & 70 min. for the liquid.
Christian et al. (1980) showed that the average t½ for
emptying meals consisting of meat, vegetables, and drinks
were 277, 146, and 77min for 1692, 900, and 300 g meals
respectively. The t½ for the liquid part of these meals was 178,
81, and 38min. respectively.
Gastric emptying is controlled so that about 2 to 4 kcal/min
caloric content is delivered to the duodenum (dynamic model)
Kong et al., J. FOOD SCI. 73(5), R67-R80, 2008

62. 2. Semi-dynamic gastric model
• pH follows a predefined profile as a function of time
• Sampling periodically to go into a duodenal step?
Gastric
Duodenal
periodic sampling
throughout
Each gastric sample goes
through a separate
duodenal phase
Gastric
Duodenal

63. 2. Semi-dynamic gastric model
(120 g Egg Beaters, 2 slices of bread; 30 g
of strawberry jam and 120 ml of water)
Gastric pH in the fasted state and after
food intake (pH 6, 458 calories, and 400
mL total volume) in 10 healthy volunteers
(Malagelada et al. 1976).

64. 2. Semi-dynamic model
(1) gastric compartment
(2) small intestine
(3) pH electrodes
(4) Secretion of lipases and pepsin
(5) secretion of pancreatic juice and bile
(6) hollow fibre (kidney dialysis)membranes
simulating the absorption of digested
products
Krul C, Luiten-Schuite A, Baandagger R, Verhagen H,
Mohn G, Feron V, Havenaar R. 2000. Application of a
dynamic in vitro gastrointestinal tract model to study the
availability of food mutagens, using heterocyclic
aromatic amines as model compounds. Food Chem
Toxicol 38(9):783–92

65. Main Body:
Gentle 3 contraction
wave per min cycle
In-homogenously mixed
Antrum:
High shear well mixed
environment
Shear at 10-100 sec-1
Phase II contraction waves
Inventors: Martin Wickham, Richard Faulks
Licensed to PBL
Dynamic Gastric Model (DGM): Full simulation of gastric forces and motility
3. Fully dynamic gastric model

66. Kong & Singh, J. Food Sci. 75(9) E627, 2010
3. Fully dynamic gastric model

67. Elashoff’s power exponential equation is as follows (Marshall et al. 2005):
1. y(t) = 2−(t/T1/2 )β
y(t) is the fractional meal retention at time t in minutes
T1/2 is the time required empty half the contents
β is a constant that determines the shape of the curve.
Siegel and others (1988) further modified Elashoff’s model to account for
the lag phase:
2. y(t) = 1 − (1 − e−kt)β
k is the gastric emptying rate per minute.
A value of β >1.0 indicates an initial delay in emptying as for the solid foods,
whereas a value of β < 1.0 indicates an initial rapid emptying as for liquid foods
(Siegel et al. 1988).
The half-time (t 1/2) can be calculated using y(t) = 0.5 and solving for t,
3. t1/2 = (−1/k) · ln(1 − 0.51/β)
3. Fully dynamic gastric model

68. 0
100
200
300
400
500
600
700
0 50 100 150 200
Volume(ml)
Time (min.)
Control
Active
Control Active
T1/2 69.6 92.0
beta 1.09 0.93
V0 4.26 0
Vmax 568.89 609.0
y(t) = 2−(t/T1/2 )β
3. Fully dynamic gastric model

69. Models of the intestinal phase
Requirements:
Proteases Needed
lipases Needed
amylase Needed for starchy meals
Bile Needed
Product removal Needed?
Nutrient sensing Needed?
Sampling Needed?
These fall into 2 groups:
1. Static – conditions fixed at start
2. Dynamic – enzymes gradually added (products removed)

70. Small intestinal models
Ratio Food (gastric content) / Simulated duodenal fluid (SDF): 50/50 w/v
Time of duodenal digestion: 2 hours
Check the pH of the solution: 7
Add 20 mL gastric content + 20 mL SDF (in this case the final volume of
the duodenal digesta is 40 mL. This volume will be decreased if gastric
samples have been taken.)
20 mL gastric content + 3.0 μL of CaCl2 (H2O)2 (588 g/L, w/v) + Bile (to give
10mM bile salt final concentration) + fill up to a final volume of 40 mL with
SDF to reach the same volume as the gastric digesta (20mL).
At this point there are two options in how to proceed.
1. Use pancreatin
2. Use individual enzymes

71. Pancreatin
Add sufficient pancreatin to provide 100 U/ml (final volume of SDF +
digesta) of trypsin. Thus the concentrations of the other duodenal
enzymes should be sufficient.
The proteolytic, lipolytic and amylolytic activity should be determined.
Add Pancreatin 100 U/mL of trypsin activity of the final volume (TAME
Units)
Add Bile (final concentration in total fluid 10 mM)
There are two options for bile for the duodenal stage, which is to use
either:
Bile extract (e.g. B8631-100G from Sigma) or
Fresh porcine bile (available from several InfoGest members including IFR
(160 mM stock)). The SDF the concentration is made up to 20mM.
Small intestinal models

72. Individual enzymes
Add Pancreatic Amylase 200 U/mL final concentration (Units on corn
starch) Add Porcine trypsin 100 U/mL final concentration (TAME
Units)
Add Chymotrypsin 50 U/mL final concentration
Add Porcine intestinal lipase 2000 U/mL (Units, tributyrin as
substrate)
Add Co-lipase (2:1 co-lipase/lipase molar excess)
Final duodenal volume = 40 mL
What about product removal?
Small intestinal models

73. Small intestinal models
1. No removal
1. Proteolysis will generally run to completion (generation of small
peptides)
2. Starch hydrolysis will generally run to completion (generation of
maltose)
3. Lipolysis is more complicated!

74. Lipolysis
lipid drop
Lipase/
colipase Breakdown
products
Aqueous phase
Lipid phase
TG
FA, MG
lipase co-lipasebile saltspolar
lipids
mixed
micelle
fatty acids protein
The role of the different players
The players

75. Lipolysis

76. Small intestinal models
Removal of mixed micelles

77. Dynamic Duodenal Model: combining
segmented and peristaltic flow.
Bostjan Hari, Serafim Bakalis, Peter Fryer
25 cm
3cm
pancreatic juice (NaHCO3,
amylase,…)
segmentation
movements
peristaltic
movements
velocity
profile
absorption of
glucose
gastric
acid
(HCl,…)
pylorus

78. Small changes in vicosity affect mixing
in the model duodenum
Water (0.001Pa.s) Thickener (CMC) solution (0.2 Pa.s)
These data suggest that “modest” changes in viscosity in the
upper intestine could modulate rates of digestion and hence
physiological responses such as glycaemic index

79. Conclusions to in vitro modelling
• For simple systems in vitro can be predictive of
in vivo
• Can allow access to parameters that are hard
to access in vivo
• Can perform “unethical” experiments
• May not be representative in complex systems
• Others?

80. In silico models
Pros:
Well defined
No variability
No ethical considerations
etc
Cons:
Too simplistic
Not physiologically relevant
etc

81. In silico models
• Reaction rates
• Dissolution modelling
• Computational fluid dynamics (CFD) –
flow / mixing
• Absorption modelling
• A more holistic approach

82. Reaction rates
Enzyme driven reaction rates
Michaelis–Menten kinetics
assumes
E = enzyme
S = substrate
P = product
Vmax = maximum rate of reaction
Km = concentration of S that gives half Vmax
Does this hold for lipases?

83. Dissolution modelling
The main roles of IVIVC are:
• To use dissolution test as a surrogate for human studies.
• To supports and/or validate the use of dissolution methods and
specifications.
• To assist in quality control during manufacturing and selecting appropriate
formulations
An In-vitro in-vivo correlation (IVIVC) is defined by the U.S. FDA as "a predictive
mathematical model describing the relationship between an in-vitro property of a
dosage form and an in-vivo response"

84. Dissolution modelling
Parameters of interest
Cmax: maximum concentration
Tmax: time to Cmax
AUC: Area under the curve

85. CFD of gastric flow
Here, vector u is the flow field, ρ is the fluid
density, μ is the fluid viscosity and vector f is the
gravity term.
Analysis of Flow Phenomena in Gastric Contents
Induced by Human Gastric Peristalsis Using CFD
Hiroyuki Kozu, Isao Kobayashi, Mitsutoshi Nakajima,
Kunihiko Uemura, Seigo Sato & Sosaku Ichikawa
Food Biophysics 5 330 (2010)

86. CFD of gastric flow
b = SGF, density = 0.989, viscosity = 0.73 mPa.s
c = SGF + yogurt [50% (v/v)] density = 1.016
Viscosity = 3.8 mPa.s
Time course of the concentration
distribution of pepsin in the
modelled antrum. The model gastric
contents used are b and c. The antral
contraction wave progresses from
lower right to upper left

87. CFD of gastric flow
Ferrua & Singh, J. Food Sci (2010), 75(7) R151
Low Viscosity System
(Water)

88. CFD of intestinal flow
The time-averaged streamline patterns of (a) show both a macroscale outer eddy formed
by the moving lid, and micro-scale eddies generated by the oscillatory motions of the
two-dimensional villi.
Wang et al. Phil. Trans. R. Soc. A (2010) 368 2863.

89. Absorption modelling
BA = Benzoic acid (compound of interest)
Q = flow rate
CL = clearance rate
The gut is split into 5 compartments
1. Lumen
2. Enterocytes
3. Mucosal blood
4. Serosal blood
5. Serosa and other tissue
Cong et al. Drug Metab. Disposit. (2001) 29, 1539
Too Simple!

90. Absorption modelling
Too many unknowns

91. Enteric
nervous
GLP-1
-
-
Selected physiological control mechanisms (stomach, small intestine)
George van Aken
Pylorus
+
duodenum jejunum ileum
L-cells
Transit speed
Storage
pressure
bile pancreas
I-cells
intake
-
CCK
+
stomach
PYY
-
No
hunger
CCK
Large reservoir of nutrient
present in small intestine
CCK-B
Next
meal
PYY
Computer
modelingFed by: • physiological literature
• in vitro studies
(in vitro gastric, Simfyd, tiny TIM)
-End
Meal
Feeling full

92. Gastric
volume
First model
Together to the next level
Intake
(water,
protein,fat,
carbohydrate
as a function
of time)
Fundus
Corpus
Antrum Duodenum Jejunum 1
Jejunum 2
Jejunum 3 Ileum 1
Ileum 2
Ileum 5
Ileum 6 Colon
Ileum 3
Ileum 4
FFAFFA
FFA
FFA
FFA
FFA
FFA
FFA
FFA
FFA
Total absorbable
nutrients
PYY
pylorus
CCK
Fullness
Hunger
absorption
Phase
separation

93. Simulation, for each consecutive segment;
calibrated on literature data
•Gastric emptying
•Small intestinal transport speed
•Rate of lipolysis
•Rate of absorption
•CCK released proportional to FFA from the first 2/3 of the small intestine; CCK is
removed from the blood by the liver (0.5 min-1)
•PPY released proportional to FFA from the last 1/3 of the small intestine; PYY is
removed from the blood by the liver (0.03 min-1)
•Lipase released from a slowly refilling reservoir proportional to TG in first small
intestine segment

94. Simulation: Gastric volume
Qualitatively similar but the details underlie different behaviour

95. Part 2: The models
• Why – are we interested in simulating
digestion?
• What – aspects of digestion are tractable to
simulation?
• How do the different models work?
• When are different models of digestion
appropriate to use?

96. Using the right tools for the job
Why not use the most sophisticated model each time?
COST
Why not use the simplest model each time?
Define the end point! What is the question?
Not realistic

97. Pharma example
GSK have tested approximately 80 compounds using a
complex dynamic in vitro model for various applications and
investigations, namely:
• Formulation bio-enhancement – screening and
comparisons
• Predictions of Food effects
• Precipitation/ solubility issues
• Pro-drug stability
• Process variability
• Alcohol effects
• Performance of different salts
• Prediction of performance in different state patients, e.g.
co-administration with PPI’s (proton pump inhibitors)

98. Pharma example
Example – Compound X
• Patients to be dosed with 100mg Compound X and in addition, with
and without co-administration of PPI
• Prediction of the clinical outcome was investigated using the in vitro
digestion in the fasted state:
• by using a standard fasted protocol, ramping the gastric pH from
pH 2, down to pH1.2 – 1.5 during the experiment.
• By using a standard fasted protocol but maintaining gastric pH 4
for the duration of the experiment
• Explored 3 formulation types, all containing 100mg Compound X
• Spray Dried Dispersion Suspension
• Tablet formulation
• Capsule formulation

99. Pharma example

100. Pharma example
The in vitro digestion results showed that the rate and extent of
dissolution of drug was higher using the standard gastric pH conditions
(pH2) then when an elevated pH was used (pH4) to mimic a patient co-
administration with PPI’s.
- The in vitro data predicted the clinical data
The in vitro data indicated that the potential exposure of the drug would
be higher in the fed state than in the fasted.
- The in vitro data predicted the clinical data (and there was more
variability in the fasted state).
in vitro successfully modelled human data

101. Simple example ?
Digestion of a protein solution to test the release of
specific bioactive peptides
What do we need to include? You decide!
Liquid solution -> no oral phase
What if the protein is casein and precipitates
under acid conditions?
Gastric conditions
Intestinal conditions

102. Conclusion
• Always undertake experiments in humans• Always undertake experiments in humans
• Keep the model simple enough to be easy/possible to
do
• Keep the model physiologically relevant
• Be consistent and use standardised InfoGest protocols
• Others ?

103. Some Literature
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3. Mercuri, A.; Passalacqua, A.; Wickham, M. S. J.; Faulks, R. M.; Craig, D. Q. M.; Barker, S. A., The Effect of Composition and Gastric Conditions
on the Self-Emulsification Process of Ibuprofen-Loaded Self-Emulsifying Drug Delivery Systems: A Microscopic and Dynamic Gastric Model
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or White Rice. Food Biophys. 2013, 8, (1), 12-23.
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7. Carey, M. C.; Small, D. M.; Bliss, C. M., Lipid Digestion and Absorption. Annual Review of Physiology 1983, 45, 651-677.
8. Chaudhri, O. B.; Small, C.; Bloom, S. R., Gastrointestinal hormones regulating appetite. Phil. Trans. R. Soc. B 2006, 361 1187-1209.
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12. Goetze, O.; Steingoetter, A.; Menne, D.; van der Voort, I. R.; Kwiatek, M. A.; Boesiger, P.; Weishaupt, D.; Thumshirn, M.; Fried, M.; Schwizer,
W., The effect of macronutrients on gastric volume responses and gastric emptying in humans: a magnetic resonance imaging study.
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