Tech ID: 02.301
The Dynamic Gastric Model (DGM) is a bench-top computer controlled in vitro system that simulates digestion in the human stomach, allowing accurate prediction and understanding of the behaviour of foods or drug preparations within the human gut during digestion in real time. The DGM was developed at the Quadram Institute Bioscience (formerly the Institute of Food Research) and is the first known in-vitro model developed to combine emerging scientific knowledge of the physical, mechanical and biochemical environments experienced by the luminal contents of the human stomach, in a single predictive system.
The DGM fully replicates both the complex biochemical conditions and the array of gastric forces crucial for the prediction of the bio-behaviour of API’s (Active Pharmaceutical Ingredient’s) and dosage forms for oral delivery (e.g. capsule, tablet, powder and liquid). Samples can be taken at any time during the process and analysed to predict the availability for uptake (bio-accessibility) of active components such as nutrients and drugs.
The DGM is based on many years of underpinning MRI studies in humans and has been validated for food and pharmaceutical applications in both the commercial and academic sectors, providing a physiological, cost effective and ethical alternative to animal studies.
For further detailed information please download the non-confidential summary pdf.
Under licence from PBL, the Danish Contract Research Organisation (CRO) Bioneer A/S utilises the DGM to provide pharmaceutical services and contract R&D within the field of drug development. DGM units can also be built to order and supplied to the research and development community. For more information and to receive a quotation, please contact Dr Georgina Pope.
Granted: US 8,092,222; EP 1,907,108; CA 2,613,980
Selected paper references regarding the development and application of the Dynamic Gastric Model. Papers with pharmaceutical focus are highlighted in Bold.
Ballance S et al (2013). Evaluation of gastric processing and duodenal digestion of starch in six cereal meals on the associated glycaemic response using an adult fasted dynamic gastric model. Eur J Nutr; 52(2): 799-812. https://doi.org/10.1007/s00394-012-0386-5
Burnett G R et al (2002). Interaction between protein allergens and model gastric emulsions. Biochem Soc Trans; 30(Pt 6): 916-918. https://doi.org/10.1042/bst0300916
Butler J et al (2019). In vitro models for the prediction of in vivo performance of oral dosage forms: Recent progress from partnership through the IMI OrBiTo collaboration. Eur J Pharm Biopharm; 136: 70-83. https://doi.org/10.1016/j.ejpb.2018.12.010
Chessa S et al (2014). Application of the Dynamic Gastric Model to evaluate the effect of food on the drug release characteristics of a hydrophilic matrix formulation. Int J Pharm; 466(1-2): 359-367. https://doi.org/10.1016/j.ijpharm.2014.03.031
Edwards C H et al (2021). Structure-function studies of chickpea and durum wheat uncover mechanisms by which cell wall properties influence starch bioaccessibility. Nat Food; 2: 118-126. https://doi.org/10.1038/s43016-021-00230-y
Grassby T et al (2017). In vitro and in vivo modeling of lipid bioaccessibility and digestion from almond muffins: The importance of the cell-wall barrier mechanism. Journal of Functional Foods; 37: 263–271. https://doi.org/10.1016/j.jff.2017.07.046
Lo Curto A et al (2011). Survival of probiotic lactobacilli in the upper gastrointestinal tract using an in vitro gastric model of digestion. Food Microbiology; 28(7): 1359-1366. https://doi.org/10.1016/j.fm.2011.06.007
Mandalari G et al (2018). Understanding the Effect of Particle Size and Processing on Almond Lipid Bioaccessibility through Microstructural Analysis: From Mastication to Faecal Collection. Nutrients; 10(2): 213. https://doi.org/10.3390/nu10020213
Mandalari G et al (2018 Epub 2016). Durum wheat particle size affects starch and protein digestion in vitro. Eur J Nutr; 57(1): 319-325. https://doi.org/10.1007/s00394-016-1321-y
Mandalari G et al (2016). The effect of sun-dried raisins (Vitis vinifera L.) on the in vitro composition of the gut microbiota. Food & Function; 7: 4048-4060. https://doi.org/10.1039/c6fo01137c
Mandalari G et al (2013). Bioaccessibility of pistachio polyphenols, xanthophylls, and tocopherols during simulated human digestion..Nutrition; 29(1): 338-44. https://doi.org/10.1016/j.nut.2012.08.004
Mandalari G et al (2008). Potential prebiotic properties of almond (Amygdalus communis L.) seeds. Appl Environ Microbiol; 74(14): 4264-4270. https://doi.org/10.1128/AEM.00739-08
Marciani L et al (2007). Enhancement of intragastric acid stability of a fat emulsion meal delays gastric emptying and increases cholecystokinin release and gallbladder contraction. Am J Physiol Gastrointest Liver Physiol; 292(6): G1607-1613. https://doi.org/10.1152/ajpgi.00452.2006
Mercuri A et al (2011). 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 study. Pharm Res; 28(7): 1540-1551. https://doi.org/10.1007/s11095-011-0387-8
Mercuri A et al (2009). Assessing drug release and dissolution in the stomach by means of Dynamic Gastric Model: a biorelevant approach. J Pharm Pharmacol; 61 Supplement 1: A-5
Mercuri A et al (2008). Dynamic gastric model (DGM): a novel in vitro apparatus to assess the impact of gastric digestion on the droplet size of self-emulsifying drug-delivery systems. J Pharm Pharmacol; 60 Supplement 1: A-2
Mills CE et al (2021). Palmitic acid-rich oils with and without interesterification lower postprandial lipemia and increase atherogenic lipoproteins compared with a MUFA-rich oil: A randomized controlled trial..Am J Clin Nutr; 113(5):1221-1231. https://doi.org/10.1093/ajcn/nqaa413
Pitino L et al (2011). Survival of Lactobacillus rhamnosus strains inoculated in cheese matrix during simulated human digestion. Food Microbiol; 28(7): 1359-66. https://doi.org/10.1016/j.fm.2012.02.013
Pitino I et al (2010). Survival of Lactobacillus rhamnosus strains in the upper gastrointestinal tract. Food Microbiol; 27(8): 1121-1127. https://doi.org/10.1016/j.fm.2010.07.019
Rodes L et al (2014). Enrichment of Bifidobacterium longum subsp. infantis ATCC 15697 within the human gut microbiota using alginate-poly-l-lysine-alginate microencapsulation oral delivery system: an in vitro analysis using a computer-controlled dynamic human gastrointestinal model. J Microencapsul; 31(3): 230-238. https://doi.org/10.3109/02652048.2013.834990
Thuenemann EC et al (2015). Dynamic Gastric Model (DGM). In: Verhoeckx K et al (eds). The Impact of Food Bioactives on Health: in vitro and ex vivo models [Internet]. Cham (CH): Springer; 2015. Chapter 6. https://doi.org/10.1007/978-3-319-16104-4_6
Vardakou M et al (2011). Achieving antral grinding forces in biorelevant in vitro models: comparing the USP dissolution apparatus II and the dynamic gastric model with human in vivo data. AAPS PharmSciTech; 12(2): 620-626. https://doi.org/10.1208/s12249-011-9616-z
Vardakou M et al (2011). Predicting the human in vivo performance of different oral capsule shell types using a novel in vitro dynamic gastric model. Int J Pharm; 419(1-2): 192-199. https://doi.org/10.1016/j.ijpharm.2011.07.046
Wickham M J S et al (2012). The Design, Operation, and Application of a Dynamic Gastric Model. Dissolution Technologies; 19(3): 15-22. https://doi.org/10.14227/DT190312P15
Zhang Q et al (2014). Differential digestion of human milk proteins in a simulated stomach model. J Proteome Res; 13(2): 1055-1064. https://doi.org/10.1021/pr401051u