描述
动态胃模型Dynamic Gastric Model(DGM)是一种基于人工智能的台式体外系统,可模拟人类胃部的消化过程,能够实时精准预测并解析食品或药物制剂的行为特性。该模型已应用于口服药物研发与食物消化研究超过20年,并通过与人体内研究的广泛对比验证。DGM通过模拟人胃的胃底/胃体与胃窦部位,精准复刻复杂的生化组成及胃部力学环境,这对预测活性药物成分(API)及口服剂型的生物行为至关重要。
DGM作为一种具备生理相关性的筛选工具,可为新型及现有食品、膳食方案和药物制剂的评估提供宝贵数据。该模型为预测化合物、营养素及配方在吸收前的转化路径提供了精准可靠的方法,因此将成为产品开发中机制研究、稳定性评估及生物利用度分析的重要工具。DGM可广泛应用于各类产品研发、药物发现与开发项目,涵盖新型功能性食品、药物递送系统以及活性成分与营养素的生物利用度研究。经改良的配方产品可与原研产品进行直接性能比对,仿制药与创新药之间亦可开展同等对比。
通过预测生物利用度或生物等效性,DGM能够加速药物与食品的研发进程,降低临床试验失败风险,并提供经全面验证的动物试验替代方案。
主要特征
- 精确复制胃混合液,剪切速率和蠕动。
- 为胃内容物提供准确的生化环境,允许进食和禁食比较不同食物类型的剂型行为。
- 与均质样品相反,具有研究多相餐食(即,真实食品和/或口服给药的药物制剂)消化能力的能力。
- 根据食物基质自动动态调整胃停留时间,酸和酶的添加量(数量和速率)以及生理过程。
- 可控制的胃排空和排泄。
- 在消化的所有阶段均可进行采样,从而可以进行实时收集和详细分析,以及针对特定隔室的建模。
- 全自动,易于使用和消毒。
- 提供有关消化过程的质量检查报告,包括停留时间,排空曲线,pH梯度,胃添加量/流速。
参考文献:
Salt LJ, Mandalari G, Parker ML, Hussein M, Mills CE, Gray R, Berry SE, Hall W, Wilde PJ. Mechanisms of interesterified fat digestibility in a muffin matrix using a dynamic gastric model. Food Funct. 2023 Nov 13;14(22):10232-10239. doi: 10.1039/d3fo02963h. PMID: 37916919.
Ingrid Swanson Pultz et al (2021).Gluten Degradation, Pharmacokinetics, Safety, and Tolerability of TAK-062, an Engineered Enzyme to Treat Celiac Disease.Gastroenterology 2021;161:81–93.DOI:https://doi.org/10.1053/j.gastro.2021.03.019
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
Charlotte E Mills, Scott V Harding, Mariam Bapir, Giuseppina Mandalari, Louise J Salt, Robert Gray, Barbara A Fielding, Peter J Wilde, Wendy L Hall, Sarah E Berry, 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, The American Journal of Clinical Nutrition, Volume 113, Issue 5, May 2021, Pages 1221–1231, https://doi.org/10.1093/ajcn/nqaa413
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
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

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