TY - JOUR
T1 - Mathematical modelling of food hydrolysis during in vitro digestion
T2 - From single nutrient to complex foods in static and dynamic conditions
AU - Le Feunteun, Steven
AU - Verkempinck, Sarah
AU - Floury, Juliane
AU - Janssen, Anja
AU - Kondjoyan, Alain
AU - Marze, Sebastien
AU - Mirade, Pierre Sylvain
AU - Pluschke, Anton
AU - Sicard, Jason
AU - van Aken, George
AU - Grauwet, Tara
PY - 2021/10
Y1 - 2021/10
N2 - Background: In vitro digestion methods are widely used to investigate the effect of food properties on the hydrolysis of the main macronutrients: starch, lipid and protein. The growing quantity of experimental data calls for strategies to quantitatively compare the effect of food composition and structure on their hydrolysis kinetics. Mathematical modelling is a powerful tool for this purpose as it allows to summarize complex phenomena into a few equations, and quantify relevant model parameters. Scope and approach: This review focuses on modelling in vitro digestion data, more particularly the hydrolysis of the main macronutrients at the gastric and small intestinal stages. Both static and dynamic in vitro conditions are considered, giving an overview of the modelling strategies available for each macronutrient. Besides, ongoing efforts to model the effects of food micro- and macrostructure as well as the interplay between macronutrient hydrolysis are summarized. A view on how modelling may help to bridge the gap between in vitro and in vivo studies is also provided. Key findings and conclusions: In vitro digestion and mathematical modelling are highly complementary methods. Mathematical models can provide a full and quantitative picture of the phenomena taking place, meanwhile in vitro experiments offer an excellent framework to test modelling concepts and assumptions. Some hybrid strategies, combining in vitro and in silico approaches have also been proposed to more accurately translate in vitro observations into in vivo predictions. Although very young, this field of research appears very promising to complement, or offer an alternative to experimental studies.
AB - Background: In vitro digestion methods are widely used to investigate the effect of food properties on the hydrolysis of the main macronutrients: starch, lipid and protein. The growing quantity of experimental data calls for strategies to quantitatively compare the effect of food composition and structure on their hydrolysis kinetics. Mathematical modelling is a powerful tool for this purpose as it allows to summarize complex phenomena into a few equations, and quantify relevant model parameters. Scope and approach: This review focuses on modelling in vitro digestion data, more particularly the hydrolysis of the main macronutrients at the gastric and small intestinal stages. Both static and dynamic in vitro conditions are considered, giving an overview of the modelling strategies available for each macronutrient. Besides, ongoing efforts to model the effects of food micro- and macrostructure as well as the interplay between macronutrient hydrolysis are summarized. A view on how modelling may help to bridge the gap between in vitro and in vivo studies is also provided. Key findings and conclusions: In vitro digestion and mathematical modelling are highly complementary methods. Mathematical models can provide a full and quantitative picture of the phenomena taking place, meanwhile in vitro experiments offer an excellent framework to test modelling concepts and assumptions. Some hybrid strategies, combining in vitro and in silico approaches have also been proposed to more accurately translate in vitro observations into in vivo predictions. Although very young, this field of research appears very promising to complement, or offer an alternative to experimental studies.
KW - Bioaccessibility
KW - Digestion
KW - Enzymatic hydrolysis
KW - In silico
KW - In vitro
KW - Modelling
U2 - 10.1016/j.tifs.2021.08.030
DO - 10.1016/j.tifs.2021.08.030
M3 - Article
AN - SCOPUS:85114385464
SN - 0924-2244
VL - 116
SP - 870
EP - 883
JO - Trends in Food Science and Technology
JF - Trends in Food Science and Technology
ER -