# Structuring foods to improve the bioavailability of bioactives and nutrients

Didier Dupont

► To cite this version:

Didier Dupont. Structuring foods to improve the bioavailability of bioactives and nutrients. Nutrition Symposium Institut Pasteur de Lille: Nutrevent conference, Jun 2017, Lille, France. 2017. hal-01542570

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<figure><img src="image_1.png" /></figure>

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# The structure of dairy products drives the kinetics of proteolysis and lipolysis in the GI tract and the bioavailability of nutrients

<figure><img src="image_2.png" /></figure>

INRA
SCIENCE & IMPACT

Dr Didier DUPONT

INRA, Rennes, France

<figure><img src="image_3.png" /></figure>
# Why are we interested in understanding food digestion?

<figure><img src="image_4.png" /></figure>

Diet-related diseases ↑
Prevent these pathologies rather than
cure them

Gut = interface between food and human body
Digestion releases food components that can have a beneficial or a deleterious effect on human health

<figure><img src="image_5.png" /></figure>

... but the mechanisms of food disintegration in the gastrointestinal tract remain unclear and the digestive process has been considered as a black box so far

**By increasing our knowledge on food digestion, we will increase our knowledge on the effect of food on human health**
# Our goals

<figure><img src="image_6.png" /></figure>

* To understand the mechanisms of breakdown of food matrices and their constituents in the gut and identify the beneficial/deleterious food components released during digestion

* To determine the impact of the structure of food matrices on these mechanisms

* To model these phenomena in order to develop a reverse engineering approach
<figure><img src="image_7.png" /></figure>

# The structure of dairy products modulate their kinetics of digestion

<figure><img src="image_8.png" /></figure>

INRA
SCIENCE & IMPACT

Barbé F.¹, Ménard O.¹, Le Gouar Y.¹, Buffière C.², Famelart M.-H.¹, Laroche B.³, Le Feunteun S.⁴, Rémond D.² and Dupont D.¹

<figure><img src="image_9.png" /></figure>

¹ INRA STLO Rennes, France

² INRA UNH Clermont-Ferrand, France

³ INRA MIA Jouy-en-Josas, France

⁴ INRA GMPA Grignon, France
# Objective: to compare kinetics of digestion of dairy products of identical composition but different structure

Ultra Low Heat powder

<figure><img src="image_10.png" /></figure>

<figure><img src="image_11.png" /></figure>

rehydration in water 14.5%

unheated milk ("raw" milk)

<figure><img src="image_12.png" /></figure>

heat treatment
90°C-10 min

microstructure

heated milk

<figure><img src="image_13.png" /></figure>

<figure><img src="image_14.png" /></figure>

24h-20°C,
GDL 3 % w/w +
mixer 2 min

macrostructure

<figure><img src="image_15.png" /></figure>

rennet gel

24h-20°C,
rennet 0.003 % v/w

<figure><img src="image_16.png" /></figure>

pH 6.6

<figure><img src="image_17.png" /></figure>

rennet gel

24h-20°C,
rennet 0.3 % v/w

<figure><img src="image_18.png" /></figure>

pH 6.6

<figure><img src="image_19.png" /></figure>

acid gel

24h-20°C,
GDL 3 % w/w

<figure><img src="image_20.png" /></figure>

pH 4

<figure><img src="image_21.png" /></figure>

pH 4

stirred acid gel

Fat-free matrices: 40 g/L caseins, 10 g/L whey proteins, 95 g/L lactose and minerals + marker of the meal transit (Cr²⁺-EDTA) → Gastric emptying half-time

<figure><img src="image_22.png" /></figure>
Gastric emptying half-time

<figure><img src="image_23.png" /></figure>

Ultra Low Heat
powder

<figure><img src="image_24.png" /></figure>

96 min

unheated milk
("raw" milk)

<figure><img src="image_25.png" /></figure>

rennet gel

? min

<figure><img src="image_26.png" /></figure>

pH 6.6

<figure><img src="image_27.png" /></figure>

heated milk

<figure><img src="image_28.png" /></figure>

rennet gel

352 min

<figure><img src="image_29.png" /></figure>

pH 6.6

<figure><img src="image_30.png" /></figure>

96 min

148 min

<figure><img src="image_31.png" /></figure>

pH 4

<figure><img src="image_32.png" /></figure>

stirred acid gel

124 min

<figure><img src="image_33.png" /></figure>

pH 4
Milk proteins in the duodenum (ELISA)

Casein

<figure><img src="image_34.png" /></figure>

- Intense and early peak with milk

- Lower and delayed with gels

- Intermediate behaviour with stirred gel

- Low concentrations with rennet gel but casein release tends to increase over time

- Only traces of milk proteins found in the jejunum

- Dairy products remain highly digestible

β-Ig

<figure><img src="image_35.png" /></figure>
# Bioactive peptides released during digestion differ from one matrix to another

More than 4000 peptides were identified in the gut lumen!!!

<table><thead><tr><th>Protein</th><th>Sequence</th><th>Activity</th><th>Reference</th></tr></thead><tbody><tr><td>αs1</td><td>1-23</td><td>EMUL</td><td>Shimizu et al. (1984)</td></tr><tr><td>αs1</td><td>23-34</td><td>HYP</td><td>Maruyama &amp; Suzuki (1982)</td></tr><tr><td>αs1</td><td>30-45</td><td>MB</td><td>Meisel et al. (1991)</td></tr><tr><td>αs1</td><td>40-52</td><td>MB</td><td>Adamson &amp; Reynolds (1996)</td></tr><tr><td>αs1</td><td>43-58</td><td>MB</td><td>Meisel et al. (1991)</td></tr><tr><td>αs1</td><td>91-100</td><td>STRE</td><td>Miclo et al. (2001)</td></tr><tr><td>αs1</td><td>99-109</td><td>MIC</td><td>McCann et al. (2006)</td></tr><tr><td>αs1</td><td>167-180</td><td>MIC</td><td>Hayes et al. (2006)</td></tr><tr><td>αs1</td><td>180-193</td><td>MIC</td><td>Hayes et al. (2006)</td></tr><tr><td>αs2</td><td>1-24</td><td>MB</td><td>Miquel et al. (2005)</td></tr><tr><td>αs2</td><td>124-146</td><td>MB</td><td>Miquel et al. (2005)</td></tr><tr><td>αs2</td><td>183-206</td><td>TRAN</td><td>Kizawa et al. (1996)</td></tr><tr><td>αs2</td><td>183-207</td><td>MIC</td><td>Recio &amp; Visser (1999)</td></tr><tr><td>αs2</td><td>189-197</td><td>HYP</td><td>Maeno et al. (1996)</td></tr><tr><td>αs2</td><td>190-197</td><td>HYP</td><td>Maeno et al. (1996)</td></tr><tr><td>β</td><td>1-24</td><td>MB</td><td>Bouhallab et al. (1999)</td></tr><tr><td>β</td><td>33-52</td><td>MB</td><td>Miquel et al. (2005)</td></tr><tr><td>β</td><td>60-80</td><td>OPI</td><td>Jinsmaa &amp; Yoshikawa (1999)</td></tr><tr><td>β</td><td>98-105</td><td>OXI</td><td>Rival et al. (2001)</td></tr><tr><td>β</td><td>114-119</td><td>OPI</td><td>Jinsmaa &amp; Yoshikawa (1999)</td></tr><tr><td>β</td><td>132-140</td><td>HYP</td><td>Robert et al. (2004)</td></tr><tr><td>β</td><td>192-209</td><td>IMM</td><td>Coste et al. (1992)</td></tr><tr><td>β</td><td>193-202</td><td>IMM</td><td>Kayser &amp; Meisel (1996)</td></tr><tr><td>β</td><td>193-209</td><td>IMM</td><td>Coste et al. (1992)</td></tr><tr><td>κ</td><td>18-24</td><td>HYP</td><td>Lopez-Exposito et al. (2007)</td></tr><tr><td>κ</td><td>106-116</td><td>THR</td><td>Jolles et al. (1986)</td></tr><tr><td>β-Ig</td><td>32-40</td><td>HYP</td><td>Pihlanto-Leppala et al. (2000)</td></tr><tr><td>β-Ig</td><td>92-100</td><td>MIC</td><td>Pellegrini et al. (2001)</td></tr><tr><td>β-Ig</td><td>142-148</td><td>HYP</td><td>Mullally et al. (1997)</td></tr></tbody></table>

<figure><img src="image_36.png" /></figure>

<figure><img src="image_37.png" /></figure>

<table><thead><tr><th>Protein</th><th>Sequence</th><th>Activity</th><th>Reference</th></tr></thead><tbody><tr><td>αs1</td><td>40-52</td><td>MB</td><td>Adamson & Reynolds (1996)</td></tr><tr><td>αs1</td><td>43-58</td><td>MB</td><td>Meisel et al. (1991)</td></tr><tr><td>αs1</td><td>99-109</td><td>MIC</td><td>McCann et al. (2006)</td></tr><tr><td>αs1</td><td>167-180</td><td>MIC</td><td>Hayes et al. (2006)</td></tr><tr><td>αs1</td><td>180-193</td><td>MIC</td><td>Hayes et al. (2006)</td></tr><tr><td>αs2</td><td>1-24</td><td>MB</td><td>Miquel et al. (2005)</td></tr><tr><td>αs2</td><td>189-197</td><td>HYP</td><td>Maeno et al. (1996)</td></tr><tr><td>β</td><td>33-52</td><td>MB</td><td>Miquel et al. (2005)</td></tr><tr><td>β</td><td>166-175</td><td>HYP</td><td>Hayes et al. (2007)</td></tr><tr><td>β</td><td>193-202</td><td>IMM</td><td>Kayser & Meisel (1996)</td></tr><tr><td>β-Ig</td><td>92-100</td><td>MIC</td><td>(8)</td></tr><tr><td>β-Ig</td><td>142-148</td><td>HYP</td><td>(9)</td></tr></tbody></table>

<figure><img src="image_38.png" /></figure>

<figure><img src="image_39.png" /></figure>

* More bioactive peptides identified during digestion of acid gel than rennet gel

* Nature of peptides is identical (clearly defined by the digestive enzyme specificity)

* Kinetics of release are different
## 2) effect on absorption

<figure><img src="image_40.png" /></figure>

### milk gelation:

→ delayed proteins transit → delayed AA absorption

maximal AA concentration in the plasma

## 3) potential effect on satiety

ghrelin (gastrointestinal hormone → appetite stimulation)

<figure><img src="image_41.png" /></figure>

### milk gelation:

↓ postprandial ghrelin concentration =

↑ satiety ?
In silico model of transit and absorption

Le Feunteun et
al. Food
Bioprocess
Tech 2014

<figure><img src="image_42.png" /></figure>

Better understanding of the food behaviour in the stomach
Predictive model???
# Differential behaviour of acid/rennet gels in gastric conditions

<figure><img src="image_43.png" /></figure>

- Acid/Rennet gel: identical composition, similar rheological properties and pore size

<figure><img src="image_44.png" /></figure>

- ≠ Time of residence in the stomach (Acid 148 min / Rennet 352 min)

<figure><img src="image_45.png" /></figure>

- How can we explain this difference? Dynamic *in vitro* digestion of the 2 gels

DIDGI®

StoRM® software

Ménard *et al.*
Food Chem 2014

Stomach

Small intestine

- Pepsine

- Gastric lipase

- Simulated gastric fluid

- HCl

- Pancreatin

- Bile

- Simulated intestinal fluid

- NaHCO₃

<figure><img src="image_46.png" /></figure>

Emptying :
Elashoff's model

<figure><img src="image_47.png" /></figure>

Emptying :
Elashoff's model
<figure><img src="image_48.png" /></figure>

<figure><img src="image_49.png" /></figure>

Formation of a strong coagulum with rennet gel → slow down the gastric emptying of caseins

**The structure that a food adopts in the stomach is essential to understand its digestion**
<figure><img src="image_50.png" /></figure>

# Understanding the mechanisms of dairy gel particles degradation in the stomach

<figure><img src="image_51.png" /></figure>

INRA
SCIENCE & IMPACT

Floury J.¹, Cardoso Bianchi T.L.¹, Thévenot J.¹, Dupont D.¹, Jamme F.², Lutton E.³, Panouillé M.³, Boué F.³, Le Feunteun S.⁴

<figure><img src="image_52.png" /></figure>

<figure><img src="image_53.png" /></figure>

<figure><img src="image_54.png" /></figure>

¹ INRA STLO Rennes, France

² SOLEIL Synchrotron Gif-sur-Yvette, France

³ INRA GMPA Grignon, France
<figure><img src="image_55.png" /></figure>

Soleil is a particle (electron) accelerator that produces the synchrotron radiation, an extremely powerful source of light that permits exploration of inert or living matter

<figure><img src="image_56.png" /></figure>

DISCO is a VUV to visible beamline dedicated to biochemistry, chemistry and cell biology. The spectral region is optimized between 60 and 700 nm with conservation of the natural polarization of the light

☞ Allow the imaging of protein intrinsic fluorescence with a UV microscope

<figure><img src="image_57.png" /></figure>

<figure><img src="image_58.png" /></figure>
Kinetics of gel particles disintegration: comparison of rennet/acid gel

<figure><img src="image_59.png" /></figure>

Rennet Gel

<figure><img src="image_60.png" /></figure>

Acid Gel
<figure><img src="image_61.png" /></figure>

# Nutrition of the neonate - Effect of the fat globule homogenization on the digestion of milk macronutrients

<figure><img src="image_62.png" /></figure>

INRA
SCIENCE & IMPACT

Bourlieu C.¹, Ménard O.¹, De Langle A.¹, Rousseau F.¹, Madec M.-N.¹, Deglaire A.¹, Pezennec S.¹, Robert B.¹, Bouhallab S.¹, Carrière F.², Dupont D.¹

<figure><img src="image_63.png" /></figure>

¹ INRA STLO Rennes, France

² CNRS EIPL Marseille, France
# Human/ bovine milk / Infant Formula
## Protein structures

<figure><img src="image_64.png" /><figcaption>Casein micelle</figcaption></figure>

(Turck et al, 2010)

Ø = 64 nm
(β, κ-casein)

<figure><img src="image_65.png" /><figcaption>Whey Proteins</figcaption></figure>

<figure><img src="image_66.png" /><figcaption>Human milk</figcaption></figure>

<figure><img src="image_67.png" /><figcaption>Bovine milk</figcaption></figure>

<figure><img src="image_68.png" /><figcaption>Infant Formula</figcaption></figure>

Ø = 182 nm
(αS1, β-casein)

<figure><img src="image_69.png" /></figure>

<figure><img src="image_70.png" /></figure>

<figure><img src="image_71.png" /></figure>
# Human/ bovine milk / Infant Formula

## Lipid globule structure

<figure><img src="image_72.png" /><figcaption>Bovine milk</figcaption></figure>

<figure><img src="image_73.png" /><figcaption>Infant Formula</figcaption></figure>

<figure><img src="image_74.png" /><figcaption>Native milk fat globule</figcaption></figure>

<figure><img src="image_75.png" /></figure>

(Lopez, 2010)

<figure><img src="image_76.png" /><figcaption>Lipid droplets</figcaption></figure>

Triacylglycerols

(0,2 - 1 µm)

(Lopez and Briard-Bion, 2007)
# Do technological processes have an impact on the kinetics of lipolysis in the stomach?

## Effect of homogenization

### 2 model infant formulas standardized in fat and proteins

(1.8 % proteins 40:60 caseins/whey proteins, 3.2 % fate with either native or homogenized globules)

Raw formula
(M1)

Homogenized formula
(M2)

<figure><img src="image_77.png" /></figure>
The increased lipolysis of the homogenized formula can be explained by the increase in specific surface of the o/w interface

<figure><img src="image_78.png" /></figure>

Specific surface [m²/g of lipid]

M1
1.81

M2
31.90

Bourlieu *et al.* Food Chem. 2015

<figure><img src="image_79.png" /></figure>
# In vivo study in the preterm infant

NCT02112331 (ClinicalTrials.gov)

<figure><img src="image_80.png" /></figure>

* Preterm hospitalized infants (GA < 32 wk)

* Nasogastric tube feeding

* Enteral feeding ≥ 120 mL/kg/day at 3 h intervals

CHU
Rennes

<figure><img src="image_81.png" /></figure>

## GROUP A

HM from their **own mother**

<figure><img src="image_82.png" /></figure>

Raw HM

→ collected < 24h
before feeding

<figure><img src="image_83.png" /></figure>

Past HM

→ 1 pool aliquoted
in 6 bottles

## GROUP B

HM from **anonymous donor**

<figure><img src="image_84.png" /></figure>

Past HM

→ The same pool from one donor
was used for the two types of milk

<figure><img src="image_85.png" /></figure>

P+Homog HM

De Oliveira *et al.*
Am J Clin Nutr. 2016

## HM bank

Holder pasteurization

<figure><img src="image_86.png" /></figure>

Indirect homogenization
by ultrasonication

→ 595 W, 3 periods of
5 min interrupted by
30s of pause

<figure><img src="image_87.png" /></figure>
# Homogenization affected the initial structure and the emulsion disintegration of HM

(n = 5 infants)

## Gastric disintegration

### Initial structure

<figure><img src="image_88.png" /></figure>

Past HM: **4.1 ± 1.2 m²/g of fat**

P+Homog HM: **25.5 ± 3.8 m²/g of fat**

### Disintegration of the emulsion structure after 35 min of gastric digestion

* Differences in terms of aggregates morphology

* Persistence of native fat globules throughout gastric digestion

35 min

<figure><img src="image_89.png" /></figure>

<figure><img src="image_90.png" /></figure>

60 min

<figure><img src="image_91.png" /></figure>

<figure><img src="image_92.png" /></figure>

90 min

<figure><img src="image_93.png" /></figure>

<figure><img src="image_94.png" /></figure>

Apolar lipids

Amphiphiles

Proteins

INSTITUT
CARNOT
QUALIMENT
# Homogenization impacted gastric lipolysis

De Oliveira et al.
Clin Nutr. 2017

## Instantaneous lipolysis level

<figure><img src="image_95.png" /></figure>

Pre-lipolysis: 4.4 ± 1.0%

<figure><img src="image_96.png" /></figure>

Past HM

<figure><img src="image_97.png" /></figure>

P+Homog HM

* Same milk composition, initial pre-lipolysis degree and inactivation of BSSL

* Different structure

<figure><img src="image_98.png" /></figure>

* Increase of specific surface of droplets facilitating HGL adsorption

Human milk homogenization accelerates gastric lipolysis: what are the consequences on the growth and health of pre-term neonates?
<figure><img src="image_99.png" /></figure>

# Infant formulas
Can we create lipid structures biomimetic of the
native fat globule?

<figure><img src="image_100.png" /></figure>

INRA
SCIENCE & IMPACT

Le Huërou-Luron I.¹, Bouzerzour K.², Ferret-Bernard S.¹, Ménard O.², Le Normand L.¹, Perrier C.¹, Le Bourgot C.¹, Jardin J.², Bourlieu C.², Carton T.³, Le Ruyet P.⁴, Cuinet I.⁴, Bonhomme C.⁴, Dupont D.²

<figure><img src="image_101.png" /></figure>

¹ INRA ADNC Rennes, France

² INRA STLO Rennes, France

³ BIOFORTIS, Saint-Herblain, France

⁴ LACTALIS, Retiers, France
# Infant formulas: can we create lipid structures biomimetic on the native fat globule?

<figure><img src="image_102.png" /><figcaption>Formula T1</figcaption></figure>

Interface 100 % Proteins
100% vegetable oil

<figure><img src="image_103.png" /><figcaption>Formula T2</figcaption></figure>

Interface 100 % phospholipids
100% vegetable oil

<figure><img src="image_104.png" /><figcaption>Formula T3</figcaption></figure>

Interface 100 % phospholipides
40% vegetable oil + 60% milk fat

<figure><img src="image_105.png" /></figure>
# Can the composition of Infant Formula modulate the physiological response of the neonate?

<figure><img src="image_106.png" /></figure>

T2

T3

<figure><img src="image_107.png" /></figure>

Veg

<figure><img src="image_108.png" /></figure>

Veg + PL

<figure><img src="image_109.png" /></figure>

Dairy Fat + PL

Automatic meal delivery (10 meals/ day)

<figure><img src="image_110.png" /></figure>

<figure><img src="image_111.png" /></figure>

Mother-fed piglets
(MF = + control)

<figure><img src="image_112.png" /></figure>

Rehydration at 20%

Mesenteric Lymph Nodes (MLN)

Collect of effluents and tissues

<figure><img src="image_113.png" /></figure>

Slaughtering
after

7 days

28 days

(90 min postprandial)

Effluents:

-SDS-PAGE

-Elisa

Tissues:

-Morphometry

-Enzyme Activities

-Intestinal Permeability

-Local immune response

-Microbiota
<figure><img src="image_114.png" /></figure>

# Protein Digestion

## Casein

### Jejunum

<figure><img src="image_115.png" /></figure>

### Ileum

<figure><img src="image_116.png" /></figure>

## β-lactoglobulin

<figure><img src="image_117.png" /></figure>

<figure><img src="image_118.png" /></figure>

Milk Proteins better resist to intestinal digestion in the presence of dairy fat
→ Modification of the interface

(Granger et al 2005; Davies et al, 2001)
# Secretory activity of MLN

## Interferon-γ (Th1 pro-inflammatory)

<figure><img src="image_119.png" /></figure>

<figure><img src="image_120.png" /></figure>

Mesenteric lymph
nodes (MLN)

## Interleukine-10 (Th2 anti-inflammatory)

<figure><img src="image_121.png" /></figure>

Milk lipids → maturation of the piglet's immune system more similar than with sow's milk

Le Huerou-Luron et al.
Eur J Nutr. 2016
Microbiota by DHPLC

D7 & D28

<figure><img src="image_122.png" /></figure>

D28

<figure><img src="image_123.png" /></figure>

Y

Y

<figure><img src="image_124.png" /></figure>

The composition/structure of the infant formula « orientates » the microbiota

More Proteobacteria with milk fat /
More Firmicutes with plant oil

<figure><img src="image_125.png" /></figure>

<figure><img src="image_126.png" /></figure>
# Conclusion

The structure/composition of dairy products regulate the kinetics of protein digestion and the release of amino acids in the bloodstream

Gastric emptying rate will highly depend on the structure that the product will adopt in the stomach cavity

Understanding the mechanisms of food particle breakdown in the stomach is critical to control the structure a food will adopt in gastric conditions

Being able to design food structures for controlling the kinetics of hydrolysis of macronutrients will allow to obtain food particularly adapted to specific population

<figure><img src="image_127.png" /></figure>
# The Bioactivity & Nutrition team

<figure><img src="image_128.png" /></figure>

## Head

Didier DUPONT - Senior Scientist

## Scientists

Rachel BOUTROU - Junior Scientist

Amélie DEGLAIRE - Lecturer

Juliane FLOURY - Lecturer

Catherine GUERIN - Lecturer

Joëlle LEONIL - Senior Scientist

Françoise NAU - Professor

Frédérique PEDRONO - Lecturer

Jonathan THEVENOT - Post-doc

## PhD students

Lucie LORIEAU (2016-2019)

Linda LEROUX (2016-2019)

Manon HIOLLE (2016-2019)

Yohan REYNAUD (2016-2019)

## Technicians

Gwenaële HENRY

Yann LE GOUAR

Nathalie MONTHEAN

## Engineers

Julien JARDIN

Olivia MENARD

Jordane OSSEMOND

## Masters students
# Improving health properties of food by sharing our knowledge on the digestive process

## International Network

Dr. Didier DUPONT, Senior Scientist, INRA, France

<figure><img src="image_129.png" /></figure>

<figure><img src="image_130.png" /></figure>
INFOGEST

<figure><img src="image_131.png" /></figure>

Tech Univ Denmark Univ Aarhus Univ Copenhagen MTT Univ Oulu Univ Eastern Finland
Norwegian Univ Life Sci Chalmers Univ Tech NIZO
Nofima Riga Stradin Univ VTT Univ Ljubljana
Univ Zagreb

Canada

<figure><img src="image_132.png" /></figure>

<figure><img src="image_133.png" /></figure>

Laval Univ
Univ Guelph

Czech Univ Prague
Inst Chem Technol

KTU Food Inst

Lithuanian Univ HS

Gdansk Univ Tech

Maize Res Inst

NGO

Polish Academy of Sci

Univ Belgrade

Univ Novi Sad

Centr Food Res Inst

Ben Gurion Univ

Technion

New Zealand

Pom Med Univ

<figure><img src="image_134.png" /></figure>

Riddett Inst

Plant Food Res

USA

<figure><img src="image_135.png" /></figure>

Univ
California
Davis

Montenegro

<figure><img src="image_136.png" /></figure>

Albania

<figure><img src="image_137.png" /></figure>

Agric Univ Tirana

<figure><img src="image_138.png" /></figure>

Australia

Deakin Univ

Univ Queensland

CONICET

Univ Buenos Aires

Argentina

350 scientists - 130 institutes – 38 countries
# Industry involvement

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~ 40 European companies are involved in INFOGEST

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INFOGEST

<figure><img src="image_142.png" /><figcaption>Chair
Didier Dupont - France</figcaption></figure>

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<figure><img src="image_145.png" /><figcaption>Vice-chair
Alan Mackie - UK</figcaption></figure>

www_cost-infogest.eu

In vitro/in vivo
correlations
WG1

<figure><img src="image_146.png" /><figcaption>Didier Dupont</figcaption></figure>

In vitro semi-
dynamic
model of
digestion
WG2

<figure><img src="image_147.png" /><figcaption>Alan Mackie</figcaption></figure>

Models for
specific
populations
WG3

<figure><img src="image_148.png" /><figcaption>Uri Lesmes</figcaption></figure>

Digestive
lipases and
lipid digestion
WG4

<figure><img src="image_149.png" /><figcaption>Myriam Grundy</figcaption></figure>

Digestive
amylases and
starch
digestion
WG5

<figure><img src="image_150.png" /><figcaption>Nadja Siegert</figcaption></figure>

In silico
models of
digestion
WG6

<figure><img src="image_151.png" /><figcaption>Choi-Hong Lai</figcaption></figure>

<figure><img src="image_152.png" /><figcaption>Guy Vergeres</figcaption></figure>

The "Mind-
the-Gap"
group

<figure><img src="image_153.png" /><figcaption>Frederic Carriere</figcaption></figure>

<figure><img src="image_154.png" /><figcaption>Fred Warren</figcaption></figure>

<figure><img src="image_155.png" /><figcaption>Steven Le Feunteun</figcaption></figure>
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We are pleased to announce the next
6th International Conference on Food Digestion

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in Granada, Spain, April 2019
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Thanks for your kind attention !!!

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