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The aim of this review paper is to evaluate the predictive quality of a combination of in vitro dynamic gastrointestinal models, mucosal transit models and in silico kinetic modeling. The TNO gastro-Intestinal Model (TIM) is a computer-controlled system, mimicking essential gastrointestinal parameters of the stomach, small intestine and large intestine. The systems have dialysis or filtration units connected to the intestinal compartments. TIM settings are adapted to the condition that has to be simulated, such as fasted and fed state, age, and co-medication. In this way the transit and digestibility of food, release, dissolution, and bioaccessibility of nutrients, drugs, and metabolites can be studied. The TIM Systems have been validated in comparison to human studies for various food products and oral drugs, published in peer-reviewed journals. The results show the potential availability for absorption, called 'bioaccessibility'. Combining TIM with mucosal transit assays, it is possible to also analyze the intestinal absorption. But for predicting bioavailability and plasma concentrations in time it needs additional kinetic data, such as distribution, metabolism, and excretion. TIM bioaccessibility data and (published) kinetic data can be used as input in commercial in silico models or specifically developed in silico modeling. Validation studies show a high predictive quality for human nutrient and drug bioavailability and plasma concentrations. Maybe not (yet) in all cases the predictions will cover for 100% the human data, so there is room for improvement. However, the reviewed studies clearly show the strength of combining a validated gastrointestinal model with physiological kinetic data in in silico modeling. It certainly will replace animal experiments and will strongly increase the success rate of follow-up human studies, saving time and costs.
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2 Minekus M, Alminger M, Alvito P et al. A standardized static in vitro digestion method suitable for food international consensus. Food Function 2014;5:1113-1124. doi:10.1039/c3fo60702.
3 Alminger M, Aura,A-M, Bohn T et al. In vitro models for studying secondary plant metabolite digestion and bioaccessibility. Compreh. Rev. Food Sci. Food Safety 2014;13:413-436.
4 Dupont D, Alric M, Blanquet-Diot S et al. Can dynamic in vitro digestion systems mimic the physiological reality? Crit. Rev. Food Sci. Nutr. 2018;Jan23:1-17. doi:10.1080/10408398.2017.1421900
5 Marze S. Bioavailability of nutrients and micronutrients: Advances in modeling and in vitro approaches. Annu. Rev. Sci. Techn. 2017;8:35-55.
6 Ting Y, Zhao Q, Xia C et al. Using in vitro and in vivo models to evaluate the oral bioavailability of neutraceuticals. J. Agric. Food Chem. 2015;63:1332-1338.
7 Kostewicz ES, Abrahamsson B, Brewster M et al. In vitro models for the prediction of in vivo performance of oral dosage forms. Eur. J. Pharm. Sci. 2014;57(1):342-366.
8 Butler J, Hens B, Vertzoni M et al. 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 . Pharmaceutics Biopharmaceutics, 2019;136:70-83.
9 Lex TR, Rodriguez JD, Zhang L et al. Development of in vitro dissolution testing methods to simulate fed conditions for immediate release solid oral dosage forms. The AAPS Journal 2022;24:40.
10 Williams CF, Walton GE, Jiang L et al. Comparative analysis of intestinal tract models. Ann. Rev. Food Sci. Technol. 2015;6:329-350.
11 Minekus M, Marteau P, Havenaar R et al. A multi compartmental dynamic computer-controlled model simulating the stomach and small intestine. Alternatives Lab. Animals (ATLA) 1995;23:197-209.
12 Minekus M, Smeets-Peeters MJ, Bernalier A et al. A computer-controlled system to simulate conditions of the large intestine with peristaltic mixing, water absorption and absorption of fermentation products. Appl. Microb. Biotechn. 1999;53:108-114.
13 Aguirre M, Eck A, Koenen ME et al. Evaluation of an optimal preparation of human standardized fecal inocula for in vitro fermentation studies. J. Microbiol. Methods 2015;117: 78-84.
14 Aguirre M, Jonkers DM, Troost F et al. In vitro characterization of the impact of different substrates on metabolite production, energy extraction and composition of gut microbiota from lean and obese subjects. Plos One 2014;Nov.26:1-23. doi:10.1371/journal.pone.0113864.
15 Tabernero M, Venema K, Maathuis, AJ et al. Metabolite production during in vitro colonic fermentation of dietary fiber: Analysis and comparison of two European diets. J. Agric. Food Chem. 2011;59(16):8968-8975.
16 Van Nuenen HM, Venema K, Van der Woude JC et al. The metabolic activity of fecal microbiota from healthy individuals and patients with inflammatory bowel disease. Digestive Dis. Sci. 2004;49(3):485-491.
17 Rose DJ, Venema K, Keshavarzian A et al. Starch-entrapped microspheres show a beneficial fermentation profile and decrease in potentially harmful bacteria during in vitro fermentation in faecal microbiota obtained from patients with inflammatory bowel disease. Br. J. Nutr. 2010;103:1514-1524.
18 Kovatcheva-Datchary P, Egert M, Maathuis A et al. Linking phylogenetic identities of bacteria to starch fermentation in an in vitro model of the large intestine by RNA-based stable isotope probing. Environmental Microbiol. 2009;11(4):914–926.
19 Rajilic-Stojanovic M, Maathuis A, Heilig H et al. Evaluating the microbial diversity of an in vitro model of the human large intestine by phylogenetic microarray analysis. Microbiology 2010;156: 3270-3281.
20 Verwei M, Minekus M, Zeijdner E et al. Evaluation of two dynamic in vitro models simulating fasted and fed state conditions in the upper gastrointestinal tract (TIM-1 and tiny-TIM) for investigating the bioaccessibility of pharmaceutical compounds from oral dosage forms. Int. J. Pharm. 2016;498:178-186.
21 Liu J, Nagapudi K, Dolton MJ et al. Utilizing tiny-TIM to assess the effect of acid-reducing agents on the absorption of orally administered drugs. J. Pharmaceutical Sci. 2021;110:3020−3026.
22 Reis PM, Raab TW, Chuant JY et al. Influence of surfactants on lipase fat digestion in a model gastrointestinal system. Food Biophysics 2008'3:370-381.
23 Domoto N, Koenen ME, Havenaar R et al. The bioaccessibility of eicosapentaenoic acid was higher from phospholipid food products than from mono- and triacylglycerol food products in a dynamic gastrointestinal model. Food Sci. Nutr. 2013;1(6):409-415.
24 Minekus M, Jelier M, Xiao J.-Z et al. Effect of partially hydrolyzed guar gum (PHGG) on the bioaccessibility of fat and cholesterol. Biosci. Biotechnol. Biochem. 2005;69(5):932-938.
25 Van Loo-Bouwman CA, Naber TH, Minekus M et al. Food matrix effects on bioaccessibility of β-carotene can be measured in an in vitro gastrointestinal model. J. Agric. Food Chem. 2014;62(4):950-955.
26 Havenaar R, Anneveld B, Hanff LM et al. In vitro gastrointestinal model (TIM) with predictive power, even for infants and children? Internat. J. Pharm. 2013;457:327-332.
27 Fondaco D, AlHasawi F, Lan Y et l. Biophysical aspects of lipid digestion in human breast milk and Similac infant formulas. Food Biophysics 2015;10:282-291.
28 Maathuis A, Havenaar R, He T et al. Protein digestion and quality of goat and cow milk infant formula and human milk under simulated infant conditions. J. Pediatric Gastroenterol. Nutr. 2017;65 (6):661-666. doi:10.1097/MPG.0000000000001740.
29 Denis S, Sayd T, Georges A et al. Digestion of cooked meat proteins is slightly affected by age as assessed using the dynamic gastrointestinal TIM model and mass spectrometry. Food Function 2016;7(6):2682-2691.
30 Bellmann S, Lelieveld J, Gorissen T et al. Development of an advanced in vitro model and its evaluation versus human gastric physiology. Food Res. Internat. 2016;88:191-198.
31 Hopgood M, Reynolds G, Barker R. Using Computational Fluid Dynamics to Compare Shear Rate and Turbulence in the TIM-Automated Gastric Compartment With USP Apparatus II. J. Pharmaceutical Sci. 2018;107(7):1911-1919.
32 Smeets-Peeters MJ, Minekus M, Havenaar R et al. Description of a dynamic in vitro model of the dog gastrointestinal tract and an evaluation of various transit times for protein and calcium. Alternatives Lab. Animals (ATLA) 1999;27:935-949.
33 Avantaggiato G, Havenaar R, Visconti A. Assessment of the muli-mycotoxin binding efficacy of a carbon/aluminosilicate based product in an in vitro gastrointestinal model. J. Agricul. Food Chem. 2007;55:4810-4819.
34 Martinez RCR, Cardarelli HR, Borst W et al. Effect of galactooligosaccharides and Bifidobacterium animalis Bb-12 on growth of Lactobacillus amylovorus DSM 16698, microbial community structure, and metabolic production in an in vitro colonic model set up with human or pig microbiota. FEMS Microbiol. Ecol. 2013;84:110-123.
35 Williams GA, Koenen ME, Havenaar R et al. Survival of Mycobacterium bovis BCG oral vaccine during transit through a dynamic in vitro model simulating the upper gastrointestinal tract of badgers. Plos One 2019;14(4):e0214859. doi.org/10.1371/journal.pone.0214859.
36 Brito-de la Fuente E, Secouard S, Siegert N et al. Determination of dissolution profile and bioaccessibility of ketosteril using an advanced gastrointestinal In vitro model. Dissolution Technol. May2019. doi.org/10.14227/DT260219P30.
37 Havenaar R, Barker R, Butler J et al. Repeatability and reproducibility of simulated fasted and fed state gastrointestinal conditions and paracetamol bioaccessibility in the dynamic in vitro models TIM-1 and tiny-TIMsg. 2022.(submitted for publication).
38 Havenaar R, Maathuis A, De Jong A et al. Herring roe protein had a high digestible indispensable amino acid score (DIAAS) using a dynamic in vitro gastrointestinal model. Nutr. Res. 2016;36:798-807.
39 Helbig A, Silletti E, van Aken GA et al. Lipid digestion of protein stabilized emulsions investigated in a dynamic in vitro gastro-intestinal model system. Food Dig. 2013;4:58–68. doi:10.1007/s13228-012-0029-6.
40 Lafond M, Bouza B, Eyrichine S et al. In vitro gastrointestinal digestion study of two wheat cultivars and evaluation of xylanase supplementation. J. An. Sci. Biotechnol. 2015;6(1)Art.#5.
41 Haraldsson A-K, Rimsten L, Alminger M et al. Digestion of barley malt porridges in a gastrointestinal model: Iron dialysability, iron uptake by Caco-2 cells and degradation of ß-glucan. J. Cereal Sci. 2005;42:243-254.
42 Salovaara S, Larsson-Alminger M, Eklund-Jonsson C et al. Prolonged transit time through the stomach and small intestine improves iron dialyzability and uptake in vitro. J. Agric. Food Chem. 2003;51:5131-5136.
43 Verwei M, Arkbåge K, Havenaar R et al. Folic acid and 5-Methyl-tetrahydrofolate in fortified milk are bioaccessible as determined in a dynamic in vitro gastrointestinal model. J. Nutr. 2003;133:2377-2383.
44 Richelle M, Sanchez B, Tavazzi I et al. Lycopene isomerisation takes place within enterocytes during absorption in human subjects. Br. J. Nutr. 2010;103:1800-1807.
45 Mateo Anson N, Havenaar R, Bast A et al. Antioxidant and anti-inflammatory capacity of bioaccessible compounds from wheat fractions after gastrointestinal digestion. J. Cereal Sci. 2010;51(1):110-114.
46 Lila MA, Ribnicky DM, Rojo LE et al. Complementary approaches to gauge the bioavailability and distribution of ingested berry polyphenols. J. Agric. Food Chem. 2012;60:5763-5771.
47 Brouwers J, Anneveld B, Goudappel GJ et al. Food-dependent disintegration of immediate release fosamprenavir tablets: In vitro evaluation using magnetic resonance imaging and a dynamic gastrointestinal system. Eur. J. Pharmaceutics Biopharmaceutics 2011;77:313–319.
48 Van den Abeele J, Schilderink R, Schneider F et al. Gastrointestinal and systemic disposition of diclofenac under fasted and fed state conditions supporting the evaluation of in vitro predictive tools. Mol. Pharmaceutics 2017. doi:10.1021/acs.molpharmaceut.7b00253.
49 Van Den Abeele J, Kostantini C, Barker R et al. The effect of reduced gastric acid secretion on the gastrointestinal disposition of a ritonavir amorphous solid dispersion in fasted healthy volunteers: an in vivo-in vitro investigation. Eur. J. Pharmaceutical Sci. 2020;105377. doi.org/10.1016/j.ejps.2020.105377.
50 Souliman S, Blanquet S, Beysac E et al. A level A in vitro/in vivo correlation in fasted and fed states using different methods: Applied to solid immediate release oral dosage from. Eur. J. Pharmaceutical Sci. 2006;27:72-79.
51 Souliman S, Beyssac E, Cardot J-M et al. Investigation of the biopharmaceutical behavior of theophylline hydrophilic matrix tablets using USP methods and an artificial digestive system. Drug Development Industrial Pharm. 2007;33(4):475-483.
52 Dickinson PA, Abu Rmaileh R, Ashworth L et al. An investigation into the utility of a multi-compartmental, dynamic, system of the upper Gastrointestinal tract to support formulation development and establish bioequivalence of poorly soluble drugs. AAPS Journal 2012;14(2):196-205.
53 Barker R, Abrahamsson B, Kruusmägi M. Application and validation of an advanced gastro-intestinal in vitro model for evaluation of drug product performance in pharmaceutical development. J. Pharm. Sci. 2014;103 (11):3704-3712. doi:10.1002/jps.24177.
54 Ting Y, Jiang Y, Lan Y et al. Viscoelastic emulsion improved the bioaccessibility and oral bioavailability of crystalline compound: A mechanistic study using in vitro and in vivo models. Mol. Pharmaceutics 2015;12(7):2229-2236.
55 López Mármol A, Fischer PL, Wahl A et al. Application of tiny-TIM as a mechanistic tool to investigate the in vitro performance of different itraconazole formulations under physiologically relevant conditions. Eur. J. Pharmaceutical Sci. 2022;173:106165.
56 Effinger A, McAllister M, Tomaszewska I et al. Investigating the impact of Crohn’s Disease on the bioaccessibility of a lipid-based formulation with an in vitro dynamic gastrointestinal model. Mol. Pharmaceutics 2021;18:1530−1543.
57 Shah A, Liu MC, Vaughan D et al. Oral bioequivalence of three ciprofloxacin formulations following singledose administration: 500 mg tablet compared with 500 mg/10 mL or 500 mg/5 mL suspension and the effect of food on the absorption of ciprofloxacin oral suspension. J. Antimicrob. Chemother. 1999;43:49−54.
58 Tenjarla S, Romasanta V, Zeijdner E et al. Release of 5-aminosalicylate from an MMX mesalamine tablet during transit through a simulated gastrointestinal tract system. Adv. Therapy. 2007;24(4):826-840.
59 Schilderink R, Protopappa M, Fleth-James J et al. On the usefulness of compendial setups and tiny-TIM system in evaluating the in vivo performance of oral drug products with various release profiles in the fasted state: Case example sodium salt of A6197. Eur. J. Pharmaceutics Biopharmaceutics. 2020;149:154-162.
60 Verwei M, Arkbåge K, Groten JP et al. The effect of folate binding proteins on bioavailability of folate from milk products. Trends Food Sci. Techn. 2005;16:307-310.
61 Déat E, Blanquet-Diot S, Jarrige J-F et al. Combining the dynamic TNO-gastrointestinal tract system with a Caco-2 cell culture model: Application to the assessment of lycopene and r-tocopherol bioavailability from a whole food. J. Agric. Food Chem. 2009;57:11314-11320. Correction of Fig. 4: JAFC:11314.
62 Etcheverry P, Grusak MA, Fleige LE. Application of in vitro bioaccessibility and bioavailability methods for calcium, carotenoids, folate, iron, magnesium, polyphenols, zinc, and vitamins B6, B12, D, and E. Frontiers Physiology 2012;p1-22. doi:10.3389/fphys.2012.00317.
63 Westerhout J, van de Steeg E, Grossouw D et al. A new approach to predict human intestinal absorption using porcine intestinal tissue and biorelevant matrices. Eur. J. Pharmaceutical Sci. 2014;63:167–177.
64 Westerhout J, Bellmann S, van Ee R et al. Prediction of oral absorption of nanoparticles from biorelevant matrices using a combination of two physiologically relevant in vitro models. J. Food Chem. Nanotechn. 2017. doi.org/10.17756/jfcn.2017-046.
65 Kubbinga M, Augustijns P, García MA et al. The effect of chitosan on the bioaccessibility and intestinal permeability of acyclovir. Eur. J. Pharmaceutics Biopharmaceutics 2019;136:147-155.
66 Moxon TE, Gouseti O, Bakalis S. In silico modeling of mass transfer and absorption in the human gut. J. Food Engeneering 2016;176:110-120.
67 Del Rio AR, Van der Wielen N, Gerrits JJ et al. In silico modelling of protein digestion: A case study on solid, liquid and blended meals. Res. Internat. 2022;157. dio.org/10.1016/j.foodres.2022.111271.
68 Ekins S, Mestres J, Testa B. In silico pharmacology for drug discovery: Applications to targets and beyond. Brtit. J. Pharmacol. 2007;Sept. doi:10.1038/sj.bjp.0707305.
69 Piñero J, Furlong LI, Sanz F. In silico models in drug development: Where we are. Current Opinion Pharmacol. 2018;42:111-121.
70 Sögren E, Thörn , Tamergren C. In silico modeling of gastrointestinal absorption: prediction performance of three physiologically based absorption models. Mol. Pharmaceutics 2016;13:1763-1778.
71 Verwei M, Freidig AP, Havenaar R et al. Predicted serum folate concentrations based on in vitro studies and kinetic modeling are consistent with measured folate concentrations in humans. J. Nutr. 2006;136(12):3074-3078.
72 Verwei M, Arkbåge K, Mocking H et al. The binding of folic acid and 5-methyltetrahydrofolate to folate-binding proteins during gastric passage differs in a dynamic in vitro gastrointestinal model. J. Nutr. 2004;134:31-37.
73 Bellmann S, Minekus M, Sanders P et al. Human glycemic response curves after intake of carbohydrate foods are accurately predicted by combining in vitro gastrointestinal digestion with in silico kinetic modeling. Clin. Nutr. Experimental 2018;17:8-22. doi.org/10.1016/j.yclnex.2017.10.003.
74 Bellmann S, Krishnan S, de Graaf A et al. Appetite ratings of foods are predictable with an in vitro advanced gastrointestinal model in combination with an in silico artificial neural network. Food Res. Internat. 2019;122:77-86.
75 Naylor TA, Connolly PC, Martini LG et al. Use of a gastro-intestinal model and GastroplusTM for the prediction of in vivo performance. Applied Therapeutic Res. 2006;(1):15-19.
76 Ojala K, Schilderink R, Nykänen P et al. Predicting the effect of prandial stage and particle size on absorption of ODM-204. Eur. J. Pharmaceutics Biopharmaceutics 2020;156:75–83.
77 Luo L, Thakral NK, Schwabe R et al. Using tiny-TIM dissolution and in silico simulation to accelerate oral product development of a BCS Class II compound. AAPS Pharm. Sci. Tech. 2022;23:185. doi.org/10.1208/s12249-022-02343-4.
78 Chiang P-C, Liu J, Nagapudi K et al. Evaluating the IVIVC by combining tiny-TIM outputs and compartmental PK model to predict oral exposure for different formulations of ibuprofen. J. Pharmaceutical Sci. 2022; 111(7):2018-2029. doi.org/10.1016/j.xphs.2022.01.024.