# Dynamic in vitro gastric digestion of skimmed milk using the NERDT, an advanced human biomimetic digestion system

Jiajun Feng, Ines Greco, Olivia Ménard, Jeehyun Lee, Romain Jeantet, Didier

Dupont, Steven Le Feunteun

► To cite this version:

Jiajun Feng, Ines Greco, Olivia Ménard, Jeehyun Lee, Romain Jeantet, et al.. Dynamic in vitro gastric digestion of skimmed milk using the NERDT, an advanced human biomimetic digestion system. Food Research International, 2024, 195, pp.114898. 10.1016/j.foodres.2024.114898 . hal-04683381

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Distributed under a Creative Commons Attribution 4.0 International License
1 This file presents the accepted version of the following research article:

2 Feng, J., Greco, I., Ménard, O., Lee, J., Jeantet, R., Dupont, D., & Le Feunteun, S. (2024). Dynamic

3 *in vitro* gastric digestion of skimmed milk using the NERDT, an advanced human biomimetic

4 digestion system. Food Research International, 195, 114898.

5 https://doi.org/10.1016/j.foodres.2024.114898

6

7

8 Dynamic *in vitro* gastric digestion of skimmed milk using the NERDT, an

9 advanced human biomimetic digestion system

10

11 Jiajun Feng, Ines Greco, Olivia Ménard, Jeehyun Lee, Romain Jeantet, Didier Dupont, Steven

12 Le Feunteun*

13 INRAE, Institut Agro Rennes-Angers, UMR STLO, 35042 Rennes, France.

14

15 * Corresponding author: Steven Le Feunteun, steven.le-feunteun@inrae.fr

16

17
18 Abstract

19 The main objective of this study was to assess the ability of the NEar Real Digestive Tract
20 (NERDT), a computer-controlled biomimetic *in vitro* digestion system that considers the
21 biomechanics of the stomach, to reproduce physiologically relevant features of skimmed milk
22 gastric digestion. A second objective was to evaluate the influence of pepsin on the gastric
23 coagulation and emptying of milk proteins from experiments performed with and without
24 pepsin. A mass balance model over the stomach, assuming a perfectly stirred reactor
25 behaviour, has been developed. The results show that the NERDT can adequately reproduce
26 the targeted kinetics of gastric acidification and emptying, with a sieving effect that naturally
27 leads to a delayed emptying of caseins. Milk coagulated earlier and more chyme was emptied
28 towards the end of the experiments in the presence of pepsin than without, hence illustrating
29 the key influence of pepsin on the gastric coagulation of caseins and subsequent hydrolysis
30 and emptying of dairy particles. Overall, this study shows that the NERDT can be adequately
31 controlled to achieve desired gastric digestion conditions, and appears to be a very useful tool
32 to further improve the knowledge of the gastric digestion behaviour of complex foods such as
33 milk.

34

35 Keywords

36 *In vitro* digestion; Gastric biomechanics; Milk; Coagulation; Pepsin

37
38 For the purpose of Open Access, a CC-BY public copyright has been applied by the authors to

39 the present document and will be applied to all subsequent versions up to the Author

40 Accepted Manuscript arising from this manuscript.

<figure><img src="image_2.png" /></figure>

41

42 CC-BY 4.0 https://creativecommons.org/licenses/by/4.0

43
44

# 1. Introduction

Digestion is a complex physiological process that involves several biomechanical and biochemical mechanisms to break down food into nutrients that can be absorbed and used for various metabolic functions. Understanding the fate of food in the gastrointestinal tract is important to deepen our knowledge of the impact of food on human health, for the design of functional foods as well as for the establishment of new dietary recommendations.

Milk is a key source of nutrients for people of all ages, and its behaviour during digestion has been extensively studied. It has been shown that the whey protein fraction, which represents about 20% of bovine milk proteins, remains soluble and is rapidly emptied from the stomach into the duodenum with limited hydrolysis by pepsin (Boirie et al., 1997). Indeed, whey proteins are known to be quite resistant to pepsin in their native forms, in particular β-lactoglobulin, the main protein of bovine whey (Asselin et al., 1989; Kim et al., 2007). These proteins are also known to be rich in leucine, a branched-chain amino acid that can efficiently stimulate muscle protein synthesis (Rieu et al., 2006). Biologically, the rapid emptying of whey proteins into the small intestine is therefore thought to enable the activation of muscle synthesis metabolism soon after milk ingestion. In contrast, casein micelles, which represent approximately 80% of bovine milk proteins, coagulate in the stomach because of pepsin hydrolysis of κ-caseins (Tam & Whitaker, 1972; Ye et al., 2016, 2019) and of the acidic conditions in the stomach (Dalgleish & Corredig, 2012). *In vivo* studies with both animal (Miranda & Pelissier, 1981; Roy et al., 2022) and human models (Mahé et al., 1992) all show that the clotting of skimmed milk in the gastric phase leads to a delay in gastric emptying. The coagulation of caseins in the stomach slows down their transit towards the small intestine. Therefore, the constitutive amino acids of caseins are absorbed later and more progressively
67 than the ones contained in whey proteins. This slow arrival of amino acids is thought to enable
68 a prolonged muscle synthesis period after the metabolic pathways has been activated (Boirie
69 et al., 1997).

70 However, digestion studies performed *in vivo* on humans or animals are not always technically,
71 ethically and financially feasible. They are also associated with poor repeatability due to the
72 high individual variability (Dupont et al., 2019; Guerra et al., 2012). Moreover, *in vivo* studies
73 are of limited interest to deepen our understanding of some key mechanisms, such as the
74 respective influences of pepsin action, pH and biomechanics on the gastric digestion
75 behaviour of milk. In the past decades, numerous *in vitro* digestion models have been
76 developed as alternatives to *in vivo* trials. The most advanced dynamic *in vitro* digestion
77 systems include the TNO's gastrointestinal model (TIM) (Minekus et al., 1995), the human
78 gastric simulator (HGS) (Kong & Singh, 2010), the dynamic gastric model (DGM) (Wickham et
79 al., 2012), and the DIDGI system (Ménard et al., 2014). Currently, these models are widely
80 used to study the structural and biochemical changes that occur in different foods or drugs
81 under simulated gastrointestinal conditions (Hur et al., 2011). Compared to the *in vivo*
82 approach, *in vitro* methods are simpler, easier to operate, time-saving, and do not raise ethical
83 concerns (Shani-Levi et al., 2017). However, none of them consider the morphological and
84 anatomical characteristics of the actual stomach, which may hinder the accurate simulation
85 of gastric digestion *in vivo* (Wu & Chen, 2020). In recent years, several digestion models with
86 similar gastric morphology and dimension to the real human stomach have emerged, such as
87 the *in vitro* mechanical gastric system (IMGS) (Barros et al., 2016) and the gastric simulation
88 model (GSM) (Li et al., 2019). These types of gastric models may provide a valuable approach
89 to mimic the process of gastric digestion and emptying as it occurs *in vivo*.
Another biomimetic *in vitro* digestion simulator covering the entire gastrointestinal tract has recently been developed with consideration of the morphological features and anatomical details of the real stomach, including peristalsis. This system, previously named *Dynamic In Vitro* Human Stomach system (DHS-IV), is now commercially available under the name of NERDT, which stands for ‘NEar-Real Digestive Tract’. It has been used to investigate the digestive characteristics of several foods such as cheese (Peng et al., 2021), rice (Wang et al., 2022) and yoghurt (Zhang et al., 2023). In these studies, the NERDT was found to adequately simulate many important aspects of digestion, including the gradual decrease in gastric pH, the kinetics of gastric emptying, and the sieving effects on the emptied chyme.

Following on from this work, the objective of the present study was to evaluate the ability of this digestion system to reproduce the gastric digestion behaviour of milk *in vivo*, particularly with regard to the rapid emptying of whey proteins and the delayed emptying of caseins. The impact of pepsin on the gastric digestive behaviour of skimmed milk using NERDT was also investigated in a complementary series of experiments performed without the addition of pepsin.

## 2. Material and methods

### 2.1. Chemicals and reagents

A pasteurized skimmed milk powder (SMP) containing 33.8% of protein, 0.1% fat, and 55% lactose (w/w, dry basis) was kindly provided by Eurial, France. Porcine pepsin (P6887-5G), Pepstatin A (P5318-25MG) and ortho-phtalaldehyde (P0657-5G) were all bought from Sigma-Aldrich. The pepsin activity was determined to be 4395 U/mg using the pepsin enzymatic assay described in the supplementary material of Minekus et al. (2014). Pepstatin A was dissolved
112 in ethanol and stored at 4°C before use. Water was Milli-Q water and all other chemicals,
113 unless otherwise stated, were purchased from Sigma-Aldrich and were of standard analytical
114 grade.

115 2.2. Dynamic *in vitro* gastric digestion

116 2.2.1. Experimental devices

117 The *in vitro* gastric digestion experiments were performed using the NERDT shown in Fig. 1. It
118 is worth noting that gastric emptying is not controlled by a pump in this digestion system. It
119 has been designed to ensure that gastric emptying of food particles is naturally slower than
120 that of liquid food (i.e. not directly controlled), via various parameters such as a pyloric
121 opening synchronized with the gastric contractions provided by rollers, and a greater or lesser
122 pyloric opening to retain denser and/or larger particles. Similarly, the pH is not regulated
123 either. It results from a whole set of protocol parameters that are applied, which notably
124 include some step-wise procedures of syringe flow rates and of variation rates of the turntable
125 tilting angle. This operating logic implies some difficulties when willing to set the operating
126 parameters of the NERDT to reproduce *in vivo* data. The following paragraphs describe how
127 we proceeded. In addition, a balance (PS 6100.X2, Radwag Balance, Poland) connected to a
128 computer was used to automatically record the mass of the material emptied from the
129 stomach. A thermocouple (753-652, TC Direct, France) and a couple of soft pH probes
130 (Ohmega, MMS, The Netherlands), placed inside the stomach, were also used to continuously
131 monitor the gastric temperature and pH, respectively.

132 2.2.2. Target values based on *in vivo* observations
The *in vivo* situation that we wanted to reproduce *in vitro* corresponds to the gastric digestion of 400 mL skimmed milk in healthy human adults, as studied by Mahé et al. (1992). In order to define the operating parameters of the NERDT, we therefore started by defining some target kinetics or values to be achieved for gastric emptying, gastric pH, and pepsin activity, as follows:

- the target kinetics of gastric emptying was based on the results of Mahé et al. (1992), who reported that the emptying of the liquid phase of the gastric content can be approximated by an exponential decay with a half-gastric emptying time ($t_{1/2}$) of 25 ± 7 min. These considerations were used to compute a target kinetics for the gastric volume (V, mL) using:

$$V = V_{fed} \cdot e^{-k \cdot t} \qquad (1)$$

$$k = \left( -\frac{1}{t_{1/2}} \cdot \ln(1 - 0.5) \right) \qquad (2)$$

with $V_{fed}$ (mL) the volume of gastric content after feeding, and with $t_{1/2}$ = 25 min.

- the gastric pH was not monitored by Mahé et al. (1992). For this reason, we relied on the results obtained by Malagelada et al. (1979) with an ordinary homogenized meal in humans to define targeted values of gastric pH: pH 4.0 at about 0.5 × $t_{1/2}$ and pH 2.0 at about $t_{1/2}$.

- for pepsin addition, the target value was a pepsin activity of around 2000 U/mL after 2 $t_{1/2}$, an assumption that was based on both the INFOGEST recommendations (Brodkorb et al., 2019) and the results obtained by Nau et al. (2022) with pigs fed egg white gels.

It is noteworthy that the times associated to the target pH and pepsin activity depend on $t_{1/2}$.

Although this assumption may not be entirely reliable, it was considered convenient to put the results obtained across different studies with different meal compositions and sizes on a
common scale. The setting of the NERDT parameters enabling to fairly reproduce these
targeted values or kinetics, and which are provided in the following paragraphs, was
thereafter obtained after some preliminary calculations (section 2.3) and experiments.

## 2.2.3. Experimental protocol and setting of NERDT parameters

For each digestion experiment, 200 mL of reconstituted skimmed milk, obtained from SMP
rehydration into osmosis water to achieve a 10% dry matter content (w/w), was used. As
classically done in *in vitro* studies, the volume of the meal used was scaled down compared to
the *in vivo* situation (i.e. using a scaling factor of 2 in the present study) in order to fill the
stomach model of the NERDT to ~ 80% of its maximum capacity (~300 mL) after accounting
for the volumes of basal gastric secretions and Simulated Saliva Fluid (SSF). Assuming an *in
vivo* basal gastric fluid content of 24 mL (Maltby et al., 1986; Lydon et al., 1999), 12 mL of basal
gastric secretions, adjusted at pH 2, was added to the model stomach to mimic the fasted
state. Just before starting an experiment, the 200 mL of skimmed milk was mixed at room
temperature with 19 mL of SSF without salivary amylase (37°C). The mixture was then
introduced into the digestion system through the cone-shaped funnel within 2 min. The
driving device was programmed to produce 3 gastric contractions per minute by moving the
rollers in order to reproduce the mean frequency of gastric contractions observed in humans
(Quigley, 1996; Schulze, 2006). The pylorus was programmed to open at the end of each
gastric contraction using an opening size of 2.5 mm to avoid emptying of food particles larger
than ~ 2 mm (Schulze, 2006; Schwizer et al., 2006). Two independent syringe pumps were
used for a controlled addition of the simulated pepsin fluid, on the one hand, and of the
simulated acidic fluid, on the other hand. Both solutions were prepared using the electrolyte
concentrations recommended in the INFOGEST protocol (Brodkorb et al., 2019). In their way
177 to the gastric model, the tubes containing those solutions were heated to 37°C in a water bath
178 and mixed together via a T-shaped tubbing connection just before they reach the stomach
179 model. The final mixture, corresponding to the Simulated Gastric Fluid (SGF), had an HCl
180 concentration of 0.16 M (Leonard R et al., 2003) and a pepsin activity of 4390 U/mL. Since the
181 pepsin activity in the gastric contents depends on several parameters, such as the amount and
182 activity of the added SGF and the kinetics of emptying, the pepsin activity to be used in the
183 SGF was estimated by modelling using the same approach as the one described in section 2.3.

184 The applied SGF flow rate was maximal at 3 mL/min after the feeding phase, and was gradually
185 decreased down to 0.3 mL/min using a stepwise procedure consisting of 6 steps. The tilting
186 angle of the stomach model was also varied using a stepwise procedure consisting of 6 steps,
187 in a similar manner as in the study of (Wang et al., 2019). The flow rates of the syringe pumps
188 and the tilting angle rates are all provided in Table S1 of the Supplementary Material.

189 Both the digesta emptied from the stomach and the chyme inside the stomach model were
190 sampled after 5, 14, 20, 27, 40, 52, and 77 min of gastric digestion. These times were chosen
191 so that they correspond to multiples (0.125, 0.5, 1, 1.5, 2, and 3×) of the target t₁/₂ of 25 min
192 (Mahé et al., 1992) added with the 2 min of feeding duration. Some samples were collected
193 for immediate microscopic observations. Others were frozen after the addition of Pepstatin A
194 (0.5 mg/mL) for the measurement of protein hydrolysis and dry matter content. All
195 experiments were performed in triplicate.

196 2.3. Mathematical modelling

197 To help in the interpretation of the experimental data, a mathematical model that computes
198 mass balances over the stomach model was developed. It was used to predict the dynamic
199 evolution of the dry matter percentages as well as the casein and whey protein concentrations
of the digesta emptied from the stomach under the hypothesis that the stomach model behaves as a perfectly stirred reactor (PSR). Under this assumption, the dynamic evolution of the gastric content volume, V (mL), can be described as a function of time:

$$ \frac{dV(t)}{dt} = \Phi_{in}^{F} + \Phi_{in}^{S} - \Phi_{out} \quad (3) $$

where $\Phi_{in}^{F}$, is the feeding flux for $t < t_{feed}$ (the feeding duration) and 0 mL/min for $t > t_{feed}$. $\Phi_{in}^{S}$ (mL/min) is the flux of incoming secretions, that was controlled by the syringe pump rates, and $\Phi_{out}$ (mL/min) is the flux of material exiting the stomach.

After feeding, the gastric volume was expected to follow the targeted kinetics previously described (Eq. 1), which relies on the results of Mahé et al. (1992). For $t > t_{feed}$, it can thus be written that:

$$ \frac{d(V_{fed} \cdot e^{-k \cdot t})}{dt} = \Phi_{in}^{S} - \Phi_{out} \quad (4) $$

which leads to

$$ -V_{fed} \cdot k \cdot e^{-k \cdot t} = \Phi_{in}^{S} - \Phi_{out} \quad (5) $$

or

$$ \Phi_{out} = V_{fed} \cdot k \cdot e^{-k \cdot t} + \Phi_{in}^{S} \quad (6) $$

where $V_{fed}$ (mL), the gastric volume after feeding, was estimated to be 234 mL in our experimental conditions. The predicted cumulative mass of emptied material ($m_{out}$, g), as measured with the scale at the stomach exit, was calculated as:

$$ m_{out} = \rho \cdot \sum_{i=2}^{n} \Phi_{out} \cdot \Delta t_{i} = \rho \cdot \left( \sum_{i=2}^{n} V_{fed} \cdot k \cdot e^{-k \cdot t} \cdot \Delta t_{i} + \sum_{i=2}^{n} \Phi_{in}^{S} \cdot \Delta t_{i} \right) \quad (7) $$

with $\rho$ the density of the material emptied from the stomach, taken as 1 g/mL.
After feeding, it can also be approximated under the PSR hypothesis that:

$$ \frac{dm_{WP}(t)}{dt} = -\phi_{out} \cdot \frac{m_{WP}(t)}{V} \quad (8) $$

$$ \frac{dm_{Cas}(t)}{dt} = -\phi_{out} \cdot \frac{m_{Cas}(t)}{V} \quad (9) $$

which may be transformed into:

$$ m_{WP}(t + dt) = m_{WP}(t) - \phi_{out} \cdot \frac{m_{WP}(t)}{V} \cdot dt \quad (10) $$

$$ m_{Cas}(t + dt) = m_{Cas}(t) - \phi_{out} \cdot \frac{m_{Cas}(t)}{V} \cdot dt \quad (11) $$

where $m_{WP}$ and $m_{Cas}$ (g) are the masses of whey proteins and of caseins within the stomach, respectively.

Eq. 10 and 11 were used to predict the dynamic evolution of the whey protein and casein masses in the stomach. These masses were then converted into concentrations of whey proteins ($C_{WP}$, g/L) and caseins ($C_{Cas}$, g/L) in the stomach according to:

$$ C_{WP}(t) = 1000 \cdot \frac{m_{WP}}{V} \quad (12) $$

$$ C_{Cas}(t) = 1000 \cdot \frac{m_{Cas}}{V} \quad (13) $$

with $V$ (mL) the targeted gastric volume at corresponding times (Eq. 1).

Under the PSF assumption, the concentrations at the stomach exit are the same as those in the stomach. These simulations therefore also correspond to the predicted concentrations of whey proteins and caseins in the emptied digesta.

To compute the predicted dry mass percentages (DM, %) at the stomach exit, a similar set of equations were used to predict the gastric concentration of the other milk components ($C_{OMC}$,
239 g/L). The evolution of the dry mass coming from the secretions (SGF) was also taken into
240 account using:

$$
\frac{dm_{DM}^{S}(t)}{dt} = \Phi_{in}^{S} \cdot C_{DM}^{S} - \Phi_{out} \cdot \frac{m_{DM}^{S}(t)}{V} \cdot dt \quad (14)
$$

$$
242 \qquad m_{DM}^{S}(t + dt) = m_{DM}^{S}(t) + \Phi_{in}^{S} \cdot C_{DM}^{S} - \Phi_{out} \cdot \frac{m_{DM}^{S}(t)}{V} \cdot dt \quad (15)
$$

243 where $m_{DM}^S$ (g) is the gastric mass of dry matter coming from the secretions and $C_{DM}^S$ is the
244 corresponding dry matter concentration (estimated to be 0.018 g/mL in our experimental
245 conditions). The gastric concentration of the dry matter coming from the secretions ($C_{DM}^S$, g/L)
246 was then computed as in Eq. 12 and 13. The predicted dry mass percentages of the digesta
247 collected at the stomach exit (DM, %) was thereafter calculated as:

$$
248 \qquad DM(t) = 100 \cdot \frac{C_{WP} + C_{Cas} + C_{OMC} + C_{DM}^{S}}{1000 \cdot \rho} \quad (16)
$$

249 with the multiplication by 100 to express DM in %, the division by 1000 to convert the
250 concentrations in g/L into kg/L. An alternative simulation was computed by setting $C_{Cas}$ to
251 zero in Eq. 16 in order to predict the dry mass content of the emptied digesta in the case
252 where casein particles are never emptied from the stomach.

253 2.4. Dry matter content of the samples collected at the stomach exit

254 An Halogen Moisture Analyzer HE73 (Mettler Toledo SAS, Versailles, France) was used to
255 measure the dry matter content of the digesta collected at the stomach exit. For each
256 measurement, about 1 g of the sample was put onto a filter of the moisture analyzer.

257 2.5. Optical microscopy
Optical microscopy images of the samples collected from the inside of the stomach and at the stomach exit were acquired using a light microscope (BX51, Olympus, Japan) in a bright field mode with a 10 × magnification. These acquisitions were performed within 10 min maximum after sample collection. Immediately after collecting a sample, it was gently mixed manually, and a 3 mL plastic pipette was used to place a sample drop on a glass slide that was thereafter covered with a coverslip. Several image acquisitions were then performed in different sample locations. One image per time point, considered as representative of the set of images, was later selected in order to be included in the present paper.

## 2.6. OPA analysis

The concentration of free NH₂ groups in the samples collected at the stomach exit was measured by the OPA method using the protocol described in Lorieau et al. (2018), which relies on the method of Church et al. (1983) adapted to microplates. Briefly, one volume of sample was diluted with three volumes of sodium tetraborate buffer (50 mM). Then, 10 µL of the diluted sample were added to each well and 200 µL of the OPA reagent were added. The mixtures were incubated for 10 min at 37°C, and the absorbance at 340 nm was measured using a microplate reader (Thermo Fisher Scientific Oy, 1510, Finland) and converted into free NH₂ concentrations using a calibration curve made of L-methionine standard solutions.

## 2.7. 4th-derivative UV analysis

The 4th-derivative UV method proposed by (Lüthi-Peng & Puhan, 1999) was used to determine the casein and whey protein concentrations in the samples collected at the stomach exit. It should be emphasised that this method is not sensitive to protein hydrolysis, as it relies on the composition of aromatic amino acid residues contained in both kinds of proteins.
Therefore, the concentrations measured with that method, further referred to as 'casein' and 'whey protein' concentrations for the sake of simplicity, actually stand for the concentrations of both proteins and peptides coming from caseins and whey proteins. Briefly, a buffer solution containing 6 M guanidine-HCl and 0.1 M sodium acetate was prepared and the pH was adjusted to 5.0. The samples were diluted 100-fold with the buffer solution and vortexed, before measuring their UV spectra from 260 nm to 320 nm using a UV spectrophotometer (SPECTR 0021, SAFAS Monaco) and 1 cm quartz cuvettes. The 4th-derivative spectra were computed using the spectrometer software (SP2000V7, SAFAS Monaco). The total protein concentration and the whey protein to casein ratio of each sample were then estimated from the characteristic peaks of the 4th-derivative spectra (around 284 and 294 nm) and pre-established calibration curves prepared from whey protein isolate and native phosphocaseinate powder. The concentrations of whey proteins and caseins were then computed.

## 2.8. SDS-PAGE

SDS-PAGE was performed on the samples collected at the stomach exit using 4-12% polyacrylamide NuPAGE Novex Bis-Tris 15-well precast gels (Invitrogen, Carlsbad, CA) according to the manufacturer's instruction. Mark 12 Unstained Standard (Invitrogen, Carlsbad, CA) was used as a molecular weight marker. The samples, together with rehydrated milk, were loaded on the gels at a dilution factor of 40. The bands were also compared to porcine pepsin (P6887-5G, Sigma-Aldrich) solution prepared at 0.5 mg/mL. The gels were stained with colloidal Coomassie Blue (# 1610787, Bio-RAD, USA) and scanned using Image Scanner II (Amersham Biosciences).

## 2.9. Statistical analysis
Statistical analyses were conducted using R software (version 4.3.0). Unpaired Student's t-test at each sampling time were used to compare the effect of the conditions with and without pepsin on: the dry matter percentages, the concentrations of caseins and whey proteins measured by 4th-derivative UV, the concentrations of caseins and β-lactoglobulin measured by SDS-PAGE, and the concentrations in free NH₂. Unpaired Student's t-test were also used to determine whether the casein concentrations measured at 52 min and 77 min were different.

Differences were considered as statistically significant when p-value was strictly below 0.05.

Tendencies are also reported for p-values within the range 0.05-0.10.

## 3. Results

### 3.1. Gastric emptying

Fig. 2 shows the evolution of the cumulative mass of emptied material (A), of the gastric volume (B), and the visual appearance of the digesta remaining in the stomach at the end of the experiments (C). The increase in the mass of emptied material was close to the predictions of the mathematical model. By subtracting these results from the cumulative mass of material introduced into the stomach, the volume of the gastric content could be estimated. These results (Fig. 2B) show that the intragastric volume increased rapidly during 2 min of feeding, and then decreased in a trend similar to the targeted exponential decay (Eq. 1). The experimentally observed t₁/₂ was 25.3 ± 2.1 min and 22.3 ± 0.6 min for the conditions with and without pepsin, respectively. These results are also very close to the target value of 27 min (i.e. t₁/₂ + tfeed). The mass of chyme remaining in the stomach at the end of the digestion experiment (t = 77 min) was 23.3 ± 2.0 g and 27.3 ± 1.9 g for the conditions with and without
pepsin (p = 0.06), respectively. As shown in Fig. 2C, there was no clear difference in the visual appearance of the final digesta between the two conditions.

## 3.2. Gastric pH

Fig. 3 presents the pH profiles measured in the conditions with and without pepsin. The very first pH values, at around 2, correspond to the pH of the basal secretions. After feeding, the pH increased rapidly up to ~6.4, close to the pH of skimmed milk, and then gradually decreased in a bell-shaped trend. The pH reached 4 after ~18 min and 2 after ~35 min, and where thus reasonably close to the target values of pH 4.0 at about 13.5 min (0.5 × t₁/₂ + tfeed) and pH 2.0 at about 27 min (t₁/₂ + tfeed). The pH of the remaining chyme at the end of the gastric digestion was 1.36 ± 0.28 and 1.24 ± 0.22 for the conditions with and without pepsin, respectively.

## 3.3. Dry matter content of the emptied digesta

Fig. 4A and 4B show the evolution of the dry matter content and the visual appearance of the digesta collected at the stomach exit, respectively. In Fig. 4A, the solid line represents the predicted evolution of dry matter content of emptied digesta under the assumption that the stomach behaves as a perfectly stirred reactor. The dashed line represents the same predictions but with the further assumption that casein particles cannot exit from the stomach. At 5 min, the dry matter content of the emptied digesta for the conditions with and without pepsin were not statistically different, and were close to the model prediction accounting for simple dilution effects (solid line). At t = 14 min, the decrease in the dry matter content was higher than expected from simple dilution considerations, and in a greater extent for the condition with pepsin than for the condition without pepsin, although not statistically
different (p > 0.1). This statement was reinforced by the visual appearance of the collected samples (Fig. 4B) that appeared homogeneous and white in the absence of pepsin, only. From t = 20 to 52 min, all the results were well aligned with the dashed line, hence indicating that caseins were specifically retained within the stomach for both conditions in that period of time. This was confirmed by the visual aspect of the collected samples that all appeared translucent with a small amount of white precipitate that was attributed to casein particles.
At t = 77 min, the dry matter content measured in the condition without pepsin continued to be fairly described by the model predictions assuming that caseins did not exit the stomach.
However, in the presence of pepsin, the dry matter content increased substantially, leading to a significant higher value than that without pepsin (p = 0.04), hence indicating that pepsin promoted the emptying of the casein particles retained in the stomach. This finding was consistent with the increased turbidity of the supernatant of the emptied digesta at t = 77 min in the presence of pepsin (Fig. 4B). The material remaining in the stomach at the end of the experiment had a significant lower dry matter percentage (p < 0.02) for the condition with pepsin (9.7 ± 0.5%) than without (14.8 ± 2.0%). This also led to a significantly lower dry mass of remaining material (p < 0.002) for the condition with pepsin (2.26 ± 0.26 g) than without (4.02 ± 0.32 g).

### 3.4. Macro- and microstructures of the digesta

Some of the optical microscopic images of the samples collected outside and inside the stomach are presented in Fig. 5. A more extensive view of the images acquired are provided in Fig. S1 of the Supplementary Material. At t = 5 min, all digesta were liquid without particles.
At t = 14 min, milk particles were observed for both the conditions with and without pepsin, indicating that milk coagulation had started to take place in both conditions. In the absence
369 of pepsin, light grey strings of particles were observed in the background, suggesting that the
370 formation of milk particles was not yet complete. However, as these structures were not
371 observed in the background with pepsin, it is likely that milk intragastric coagulation was
372 complete in the presence of the gastric protease. From t = 14 min until the end of the
373 experiment, it seemed that all the particles constitutive of the outside digesta had size of
374 around 100 µm or below, whereas some larger particles were observed in the digesta
375 collected inside the stomach (Fig. S1). This observation suggests that a small sieving effect
376 took place during the gastric emptying of milk particles. At t = 77 min, many small particles
377 could be observed with and without pepsin in the digesta collected both inside and outside
378 the stomach. This demonstrates that the gastric biomechanics applied with the NERDT were
379 strong enough to induce a mechanical breakdown of the dairy particles and promote their
380 emptying. For the digesta collected at the stomach exit, it visually appeared that the particles
381 were more fragmented in the condition with pepsin than without pepsin, an observation that
382 would be consistent with the influence of protein hydrolysis by pepsin on gastric emptying.

383 3.5. Gastric emptying of proteins and protein hydrolysis

384 Fig. 6A and B show the protein profiles observed by SDS-PAGE of the samples collected at the
385 stomach exit for the conditions with and without pepsin, respectively. The intensity of the
386 casein bands was almost saturated at t = 5 min. It then decreased rapidly, from t = 14 min in
387 the presence of pepsin, and from t = 20 min in the absence of pepsin. From t = 20 min to t =
388 52 min, almost no caseins were observed in the emptied digesta, hence demonstrating that
389 caseins were specifically retained in the stomach during this period of time. Caseins were
390 observed again at t = 77 min, with a higher band intensity and a wide range of peptides for
391 the conditions with pepsin, as revealed by the appearance of a smear below the casein bands.
The bands corresponding to α-LA and β-LG persisted throughout the experiments for the condition with and without pepsin, with a gradual decrease of their intensity.

Fig. 7A and 7B show the concentrations of caseins and whey proteins in the emptied digesta as estimated from 4^th^-derivative UV, noting that these values are independent of the extent of protein hydrolysis. The relative concentrations of intact caseins and β-LG extracted from an image analysis of SDS-PAGE gels (Fig. 6), are superimposed for comparison purposes. Both sets of results show that the concentration of caseins in the emptied digesta suddenly decreased at around t = 14 min and then gradually increased until the end of the experiment.

The casein concentration at t = 14 min was higher in the absence of pepsin than in its presence, with p = 0.07 (trend) and p = 0.004 according to 4^th^-derivative UV and SDS-PAGE results, respectively. This suggests that casein coagulation occurred slightly earlier in the presence of pepsin. In the presence of pepsin, the casein concentration also increased significantly from t = 52 min and t = 77 min according to both kinds of measurements (p < 0.05), a phenomenon that was not observed in the absence of pepsin (p > 0.8). At t = 77 min, the casein concentration measured using 4^th^-derivative UV also tended to be higher in the presence of pepsin than in the absence of pepsin (p = 0.07). The decrease in the concentration of whey proteins (Fig. 6B) was more gradual and remained close to the model predictions accounting for simple dilution effects (solid line).

Fig. 7C presents the concentration of free NH₂ groups, which are carried by caseins, whey proteins and their peptides. This is the reason why the evolution of the free NH₂ concentrations showed an intermediate trend compared to that of whey protein and casein (Fig. 6A and 6B). The concentration of free NH₂ groups is also an indicator of the extent of protein hydrolysis by pepsin. However, from t = 5 min to t = 52 min, no significant differences
415 were observed between the conditions with and without pepsin. From t = 52 min to t = 77
416 min, the concentration of free NH₂ groups tended to increase in the presence of pepsin (p <
417 0.1), leading to a high probability (p = 0.06) that more peptides were produced in the presence
418 of pepsin than in its absence.

419 Overall, the concentrations of caseins, whey proteins and free NH₂ for the conditions with and
420 without pepsin evolved similarly during the first 52 min and only started to diverge at the end
421 of digestion, with some statistical trends and effects suggesting that a higher protein
422 hydrolysis took place in the condition with pepsin.
423 **4. Discussion**

424 This study demonstrates the ability of the NERDT digestion system to investigate the gastric
425 digestion of skimmed milk under physiologically relevant biochemical and biomechanical
426 conditions. It allowed many aspects of the gastric behaviour of milk to be reproduced in vivo,
427 in particular the rapid emptying of whey proteins and the delayed emptying of caseins.

428 Before discussing the mechanisms involved in the digestion of skimmed milk, it should first be
429 emphasised that the NERDT operating parameters could be adjusted to reproduce well the
430 target digestion conditions based on the *in vivo* literature. The evolution of the gastric volume
431 closely followed the targeted exponential decay (Fig. 2B) based on the human study of Mahé
432 et al. (1992), leading to an experimental t₁/₂ (25.3 ± 2.1 min) very close the target value (27
433 min). Similarly, intragastric pH values of 4.0 and 2.0 were measured at t = 18 and 35 min,
434 whereas these pH values should be reached at around 14 and 27 min (i.e. at 0.5 × t₁/₂ and at
435 t₁/₂), respectively, as targeted from the results of Malagelada et al. (1979). Indeed, the gastric
436 pH is not automatically regulated in the NERDT and the gastric emptying process relies on
437 various parameters such as the opening size and frequency of the pyloric valve, or the tilting
438 angle of the turntable on which the stomach model is mounted (Fig. 1). Although these
439 considerations make the setting of the operating parameters more complex, such a gastric
440 processing closely mimics the mechanisms taking place in the human stomach, reason why
441 the NERDT offers a real potential to investigate the gastric behaviour of complex foods, such
442 as milk, under fair biomimetic conditions.

443 The results obtained for the condition with pepsin are discussed below. During the first
444 minutes of gastric digestion, the milk was liquid. At t = 5 min, the dry matter content (Fig. 4),
445 the concentrations of whey proteins and caseins (Fig. 7) in the emptied digesta were therefore
adequately predicted by our mass balance model assuming that the stomach behaved as a
perfectly stirred reactor. Milk coagulated between t = 5 and 14 min under our experimental
conditions with pepsin. From t = 14 min to about 40 min, the great majority of the casein
content of the stomach could not be emptied (Fig. 7A), hence leading to a sudden decrease in
the dry matter (Fig. 4) and free amine (Fig. 7C) content of the digesta collected at the stomach
exit. It was also during this time period that the milk particles of the digesta inside the stomach
appeared visually the largest, while the few particles observed at the stomach exit appeared
comparatively smaller (Fig. 5 and Fig. 1S). From t = 40 min, the concentrations of casein
products in the emptied digesta gradually increased (Fig. 7A). According to our 4th-derivative
UV measurements, it reached 15.3 ± 3.6 g/L at t = 77 min, a value that can be compared with
the 0.7 ± 0.5 g/L measured at t = 20 min. This increase in the concentration of emptied casein
products at the end of the experiment, which included many peptides (Fig. 6A), is in very good
accordance with the sudden increase in the number of small and fragmented particles
observed in the emptied digesta (Fig. 5 and Fig. S1). These observations witness the breaking
down of milk particles by the combined action of pepsin and the gastric biomechanical
contractions.

This chronology of events is consistent with the scientific literature. As recently reviewed by
Huppertz & Chia (2021), both *in vivo* and *in vitro* studies have shown that unheated or mildly
heated milk coagulates rapidly after ingestion under the combined action of pepsin and pH,
typically in less than 30 min. For instance, an MRI study on women fed with whole bovine milk
reported that coagulation started within 10-20 min, with a strong coagulum forming after 30
min (Van Eijnatten et al., 2024). It is also known that in mildly heated milk, as used in the
present study, whey proteins are not directly incorporated in the casein curd (Ye et al., 2016),
469 which is why they continue to be emptied from the stomach in a regular flow, whereas caseins
470 are specifically retained. This phenomenon is even at the origin of the concept that whey
471 proteins and caseins can be considered as 'fast' and 'slow' digested proteins, respectively
472 (Boirie et al., 1997). In the late stages of digestion, the combined action of pepsin and of
473 biomechanical contractions allowed more casein proteins and peptides to be emptied from
474 the stomach, leading to an overall slow rate of casein nitrogen delivery to the small intestine
475 (Fig. 6A) as observed in humans (Mahé et al., 1996).

476 The second aim of this study was to evaluate the influence of pepsin on the gastric coagulation
477 and emptying of milk proteins. The skimmed milk coagulated faster in the presence of pepsin
478 than without according to the higher concentrations of caseins measured at the stomach exit
479 at t = 14 min by 4th-derivative UV (p = 0.07) and SDS-PAGE (p = 0.004). This was also supported
480 by the visual appearance of the collected samples, which, at this time, were still white (Fig. 4B)
481 and not yet fully coagulated (Fig. 5) in the absence of pepsin. This finding further confirms the
482 known influence of pepsin in the gastric coagulation of milk (Ye et al., 2016; Mulet-Cabero et
483 al., 2019). We may, however, acknowledge that the absence of pepsin did not delay much the
484 gastric coagulation of milk in comparison to previously reported data. This is because the acid
485 coagulation of milk takes place around pH 5 (Lucey & Fox, 1992), a value that was reached in
486 about 15 min in our experimental conditions in the absence of pepsin (Fig. 3). In the study of
487 Ye et al. (2016), in which gastric acidification and emptying were much slower (t1/2 of about
488 45 min), the gastric coagulation of milk in the presence of pepsin was visible after 8 min at pH
489 6, whereas it only appeared after 40 min and pH 5 without pepsin. In the study of Mulet-
490 Cabero et al. (2019), in which the gastric acidification and emptying was even more slow (t1/2
491 of about 100 min), the gastric coagulation of milk in the presence of pepsin was reported to
be visible within the first 10 min at pH 5.5-6, and only after 35 min at pH 5 in the absence of pepsin. These examples illustrate the difficulty to assess the true contribution of pepsin in the gastric coagulation of milk; a mechanism that can hardly be investigated *in vivo* and that is mostly determined by the experimental conditions used *in vitro* (e.g. kinetics of gastric pH, of gastric emptying and of pepsin addition).

With regard to the extent of proteolysis performed by pepsin after milk coagulation, the evolution of the concentrations of dry matter, caseins, whey proteins and free NH₂ groups was not affected by the presence or absence of pepsin from t = 20 to 52 min in our experimental conditions. The effects of pepsin action were only observed towards the end of the digestion experiments, at t = 77 min, on a number of variables that includes the casein concentration measured by both methods and the free NH₂ concentration, as well as on the dry mass of the material remaining in the stomach. This contrasts somehow with the results of Mulet-Cabero et al. (2019) who observed a significant increase of free NH₂ groups per gram of proteins at their first sampling time, at t = 35 min, which plateaued after ~ 100 min, when the t₁/₂ they considered was reached. This discrepancy can also be explained by the different experimental conditions used in both studies. Indeed, when we targeted to reach a pepsin activity of 2000 U/mL after two t₁/₂, the same level of pepsin activity was undoubtedly reached much earlier, presumably before t₁/₂, in the study of Mulet-Cabero et al. (2019). According to the study of Nau et al. (2022), who mapped the pepsin concentration and the extent of proteolysis in the stomach of pigs fed egg-white gels over 6 h postprandial period, a long delay may actually be needed before a significant level of intra-gastric proteolysis is reached. Although the pigs were fed with an important quantity of meal in that study, the authors did not observe any significant increase in proteolysis before 6 h of digestion, which corresponds
515 to about two t₁/₂ in their conditions. It is thus plausible that the INFOGEST semi-dynamic *in vitro* protocol used by Mulet-Cabero et al. (2019) recommends a rate of pepsin addition that is a bit high compared to the physiological reality, whereas we have to acknowledge that the opposite is presumably true for the protocol we used. These considerations actually call for more *in vivo* data on the rate of pepsin secretion.

520 Finally, the usefulness of combining a modelling approach together with the analysis of samples that are collected at the stomach exit, rather than collected from inside, can be highlighted. Indeed, samples collected from the inside of the stomach with a cut pipette were also considered and analysed during the experiment. However, it rapidly appeared that the measurements on those samples were associated with a huge variability linked to the difficulty to collect a representative sample from the heterogeneous contents of the stomach.
521
522
523
524
525
526 This is the reason why these data were not shown in the present paper except for the microscopic observations. The samples collected at the stomach exit exhibited less variability because of the sieving effect of the stomach model of the NERDT. Obviously, these samples do not directly reflect the intragastric contents and the interpretation of the results is less straightforward. In this context, the modelling approach developed proved to be very valuable to deepen our interpretation of the experimental data. Such a combined *in vitro-in silico* approach could be used in future studies to further optimize and validate that the sieving effect produced by the NERDT can adequacy mimic the physiological reality before investigating the behaviour of heterogeneous gastric contents, as typically encountered after the ingestion of complex foods or real meals.

536 **5. Conclusion**
This study has evaluated the suitability of the NERDT as a digestion system to replicate the *in vivo* gastric digestion behaviour on skimmed milk. A set of operating parameters allowing to reproduce target kinetics of gastric emptying and acidification were obtained after some preliminary experiments and with the help of a mass balanced model. In our experimental conditions, the intra-gastric coagulation of milk took place after about 10 min of digestion, and a sieving effect leading to a delayed emptying of casein particles was subsequently observed. The combined action of pepsin and of the biomechanical contractions allowed more casein proteins and peptides to be emptied from the stomach after more than one hour of gastric digestion. These results compare well with the *in vivo* literature but also suggest that the dynamic evolution of pepsin activity, for which *in vivo* data are scare, is key to properly understand and reproduce the slow gastric emptying of casein particles and their peptides.

Overall, the NERDT shows a great potential to deepen our understanding on the gastric behaviour of complex foods and meals with consideration of realistic particle size and gastric content heterogeneity.

551

552 Acknowledgements

The authors would like to thank Eurial, France for providing milk samples.
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707 treatment. *Food Chemistry*, 286, 216-225. https://doi.org/10.1016/j.foodchem.2019.02.010

708 Zhang, H., Duan, S., Yu, Y., Wu, R., Wang, J., Chen, X. D., Szeto, I. M.-Y., Wu, P., & Jin, Y.

709 (2023). Impact of casein-to-whey protein ratio on gastric emptying, proteolysis, and peptidome

710 profile of fermented milk during in vitro dynamic gastrointestinal digestion in preschool

711 children. *Food Chemistry*, 405, 134840. https://doi.org/10.1016/j.foodchem.2022.134840

712
<figure><img src="image_3.png" /></figure>

714

715 **Figure 1.** Picture of the 'NEar Real Digestive Tract' dynamic *in vitro* digestion system (NERDT).

716 1: funnel for the feeding; 2: esophageal model; 3: heating system; 4: gastric secretion tube; 5:

717 turntable to help controlling the emptying; 6: stomach rolling-extrusion device; 7: tube for

718 chyme collection; 8: scale; 9: thermocouple; 10: human stomach model; 11: soft pH probes;

719 12: pylorus valve. The scale (8), the thermocouple (9) and the soft pH probes (11) were all

720 connected to a computer to automatically record the mass of emptied material, the

721 intragastric temperature and pH, respectively.
<figure><img src="image_4.png" /></figure>

<figure><img src="image_5.png" /></figure>

<figure><img src="image_6.png" /><figcaption>**Figure 2.** Evolution of the cumulative mass of digesta emptied from the stomach (A), of the gastric volume (B), and visual appearance of the intragastric digesta at the end of the experiments (C) for the conditions with pepsin (●) and without pepsin (▲). Values are means ± SD over 3 replicates. In subplot (A), the solid line represents the values predicted by modelling. In subplot (B), the dotted line represents the targeted kinetics based on *in vivo* data.</figcaption></figure>
<figure><img src="image_7.png" /><figcaption>**Figure 3.** Evolution of the intragastric pH for the conditions with pepsin (●) and without pepsin (▲). Values are means ± SD over 3 replicates.</figcaption></figure>
<figure><img src="image_8.png" /><figcaption>**Figure 4.** Evolution of the dry matter content (A) and of the visual appearance (B) of emptied digesta for the conditions with (●) and without pepsin (▲). Values are means ± SD over 3 replicates. The solid line represents the values predicted by modelling under the assumption that the stomach behaves as a perfectly stirred reactor. The dashed line further assumes that caseins do not empty from the stomach.</figcaption></figure>
<figure><img src="image_9.png" /><figcaption>**Figure 5.** Optical microscopy images (magnification x10) of the digesta collected inside the stomach and at the stomach exit for the conditions with and without pepsin. The images acquired at all time points are presented in the supplementary material (Fig. S1).</figcaption></figure>
745

<figure><img src="image_10.png" /></figure>

746

747 **Figure 6.** SDS-PAGE profiles of the emptied digesta for the conditions with pepsin (A) and
748 without pepsin (B).

749
<figure><img src="image_11.png" /><figcaption>**Figure 7.** Evolution of the concentrations of caseins (A), of whey proteins (B) and of the free amines (C) in the emptied digesta for the conditions with pepsin (●, ○) and without pepsin (▲, △). Solid symbols represent the values measured using 4^th^-derivative UV, and open symbols represent the values estimated from SDS-PAGE results. Values are means ± SD over 3 replicates. The solid lines represent the values predicted by modelling.</figcaption></figure>