The Dynamic Gastric Model

Tech ID: 02.301

Key Features

  • Accurate replication of gastric mixing, shear rates and peristalsis.
  • Provides an accurate biochemical environment for gastric contents, allowing for fed and fasted comparisons of the behaviour of dosage form with varying food types.
  • The ability to investigate the digestion of multiphase meals (i.e., real foods and/ or orally administered pharmaceutical preparations) as opposed to homogenised samples.
  • Automated dynamic adjustment of gastric residence time, acid and enzyme addition (quantity and rate) and physiological processing depending on the food matrix.
  • Controllable gastric emptying and discharge.
  • Full access for sampling at all stages of digestion allowing real time collection and detailed analysis, compartment specific modelling.
  • Fully automated, simple to use and sterilise.
  • Provides QA reporting on the digestive process, including residence time, emptying profiles, pH gradients, gastric additions/flow rates.

Description

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.

Patents

Published: WO/2007/010238

Granted: US 8,092,222; US 8,435,036; AU 2006271423; EP 1,907,108

References

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

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

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

Menard O et al (2014).  Validation of a new in vitro dynamic system to simulate infant digestion.  Food Chem; 145: 1039-1045.  https://doi.org/10.1016/j.foodchem.2013.09.036

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

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

Van den Abbeele P et al (2010).  Microbial community development in a dynamic gut model is reproducible, colon region specific, and selective for Bacteroidetes and Clostridium cluster IX.  Appl Environ Microbiol; 76(15): 5237-5246.  https://doi.org/10.1128/AEM.00759-10

Van den Abbeele P et al (2012).  Incorporating a mucosal environment in a dynamic gut model results in a more representative colonization by lactobacilli.  Microb Biotechnol; 5(1): 106-115.  https://doi.org/10.1111/j.1751-7915.2011.00308.x

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

Vermeiren J et al (2012).  Decreased colonization of fecal Clostridium coccoides/Eubacterium rectale species from ulcerative colitis patients in an in vitro dynamic gut model with mucin environment.  FEMS Microbiol Ecol; 79(3): 685-696.  https://doi.org/10.1111/j.1574-6941.2011.01252.x

Wickham M, Faulks R and Mills C (2009).  In vitro digestion methods for assessing the effect of food structure on allergen breakdown.  Mol Nutr Food Res; 53(8): 952-958.  https://doi.org/10.1002/mnfr.200800193

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

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

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

Contact: Dr Georgina Pope

Inventors
Dr Martin Wickham and Richard Faulks
Quadram Institute Bioscience