Open Access Article. Published on 19 September 2018. Downloaded on 9/25/2018 11:20:51 AM.
This article is licensed under a Creative Commons Attribution-NonCommercial 3.0 Unported Licence.

RSC Advances
View Article Online

PAPER

View Journal | View Issue

Cite this: RSC Adv., 2018, 8, 32440

Implementation of a dynamic intestinal gut-on-achip barrier model for transport studies of lipophilic
dioxin congeners†
Kornphimol Kulthong, abc Loes Duivenvoorde,b Barbara Z. Mizera,dDeborah Rijkers,b Guillaume ten Dam,b Gerlof Oegema,b Tomasz Puzyn,dHans Bouwmeester‡a and Meike van der Zande‡*b
Novel microﬂuidic technologies allow the manufacture of in vitro organ-on-a-chip systems that hold great
promise to adequately recapitulate the biophysical and functional complexity of organs found in vivo. In thisstudy, a gut-on-a-chip model was developed aiming to study the potential cellular association andtransport of food contaminants. Intestinal epithelial cells (Caco-2) were cultured on a porous polyester
membrane that was tightly clamped between two glass slides to form two separate ﬂow chambers. Glasssyringes, polytetraﬂuoroethylene tubing and glass microﬂuidic chips were selected to minimize surface
adsorption of the studied compounds (i.e. highly lipophilic dioxins), during the transport studies.
Confocal microscopy studies revealed that, upon culturing under constant ﬂow for 7 days, Caco-2 cells
formed complete and polarized monolayers as observed after culturing for 21 days under static
conditions in Transwells. We exposed Caco-2 monolayers in the chip and Transwell to a mixture of 17
dioxin congeners (7 polychlorinated dibenzo-p-dioxins and 10 polychlorinated dibenzofurans) for 24 h.
Gas chromatography-high resolution mass spectrometry was used to assess the cellular association andtransport of individual dioxin congeners across the Caco-2 cell monolayers. After 24 h, the amount oftransported dioxin mixture was similar in both the dynamic gut-on-a-chip model and the static

Received 25th June 2018
Accepted 10th September 2018

Transwell model. The transport of individual congeners corresponded with their number of chlorine
atoms and substitution patterns as revealed by quantitative structure–property relationship modelling.

DOI: 10.1039/c8ra05430d

These results show that the gut-on-a-chip model can be used, as well as the traditional static Transwellrsc.li/rsc-advances

system, to study the cellular association and transport of lipophilic compounds like dioxins.

1. Introduction
Determination of the oral bioavailability of compounds aer
ingestion is a crucial element in the toxicological risk assessment of food chemicals and environmental contaminants.
Caco-2 cells, a cell line established from human epithelial
adenocarcinoma cells, grown in Transwells® have been widelyused as a general in vitro model for the prediction of in vivo
intestinal food chemicals and drug absorption and to investigate local eﬀects on the intestinal epithelium.1–3 However,aDivision of Toxicology, Wageningen University, P.O. Box 8000, 6700 EA, Wageningen,
The Netherlandsb

RIKILT-Wageningen Research, P.O. Box 230, 6700 AE, Wageningen, The Netherlands.
E-mail: meike.vanderzande@wur.nlcNational Nanotechnology Center (NANOTEC), National Science and Technology
Development Agency, Pathum Thani 12120, ThailanddLaboratory of Environmental Chemometrics, Faculty of Chemistry, University ofGdansk, Gdansk, Poland† Electronic supplementary
10.1039/c8ra05430d

information‡ These authors jointly directed this work.

32440 | RSC Adv., 2018, 8, 32440–32453(ESI)

available.

See

DOI:

grown under static conditions, such a model may not fully
capture the physical and extracellular complexity found in vivo.
Two main shortcomings of this model are the relatively longculture period that is needed for these cells to diﬀerentiate,4and the static culturing conditions which fail to mimic theluminal and blood plasma ows as observed in vivo.
Recent advances in micro engineering and microuidics
have led to unprecedented organ-on-a-chip models that recapitulate not only in vivo uid ows, but also the structure andfunction of human tissues.5–9 For this reason, microuidic
technology has been introduced into the eld of drug discovery.
This already now has resulted in a variety of models recapitulating specic organ functions, including the small intestine, inso-called gut-on-a-chip models.5,6,10,11 In a limited number ofstudies, drug transport has been reported on Caco-2 cell-based
microuidic devices to evaluate the permeability potential ofthese models.12,13 However, for the purpose of gut-on-a-chipmodel development and optimization, its eﬃciency forcompound permeability should not only be investigated for
drugs, but also for chemical substances in general. The gut
epithelium acts as the important barrier in defence to protect

This journal is © The Royal Society of Chemistry 2018

View Article Online

Open Access Article. Published on 19 September 2018. Downloaded on 9/25/2018 11:20:51 AM.
This article is licensed under a Creative Commons Attribution-NonCommercial 3.0 Unported Licence.

Paper

Fig. 1

RSC Advances

Structures of investigated compounds.

the body from exogenous substances as present in food.
Therefore we assessed the barrier properties of the gut-on-achip model for dioxins which are an important class foodcontaminants. Furthermore, the predictive capacity of a gut-ona-chip model should be evaluated in terms of the comparison to
the Transwell system that has been used in the past decades forthis type of studies.
The term dioxins, also called polychlorinated dibenzodioxins, refers to two groups of compounds: polychlorinateddibenzo-p-dioxins (PCDDs) and dibenzofurans (PCDFs) (seeFig. 1). These are a group of chemically-related compounds (i.e.
congeners) that are classied as persistent organic pollutants(POPs) and cause concern over environmental and humanhealth impact. Dioxins are highly toxic and longer term exposure can cause reproductive and developmental problems, skinlesions, damage to the immune system, interference with thehormonal balance, and lastly dioxins may also cause cancer.14,15
Because of their lipophilic properties, or high log P values (thepartition coeﬃcient of a molecule between an aqueous andlipophilic phase, usually octanol and water; Table 1), dioxins
accumulate in the food chain, mainly in the fatty tissue of farmanimals. Consumption of contaminated food, like contaminated beef and dairy products, is the primary source of dioxin
entry into the human body.16–18 In fact, more than 90% of

The predicted log P values of dioxin congeners,25 and limit ofquantiﬁcation (LOQ) by GC-HRMS
Table 1

Congenerslog P

LOQ (pg mL1)

2,3,7,8-TCDF
1,2,3,7,8-PeCDF
2,3,4,7,8-PeCDF
1,2,3,4,7,8-HxCDF
1,2,3,6,7,8-HxCDF
2,3,4,6,7,8-HxCDF
1,2,3,7,8,9-HxCDF
1,2,3,4,6,7,8-HpCDF
1,2,3,4,7,8,9-HpCDF
OCDF
2,3,7,8-TCDD
1,2,3,7,8-PeCDD
1,2,3,4,7,8-HxCDD
1,2,3,6,7,8-HxCDD
1,2,3,7,8,9-HxCDD
1,2,3,4,6,7,8-HpCDD
OCDD

6.5
6.9
6.9
6.9
6.9
7.1
7.4
7.9
7.5
8.5
6.4
6.6
8.4
7.4
7.4
7.5
8.1

0.086
0.201
0.185
0.137
0.161
0.186
0.212
0.165
0.133
0.210
0.095
0.184
0.175
0.185
0.145
0.147
0.300

This journal is © The Royal Society of Chemistry 2018human dioxin exposure occurs through food ingestion.14,19
About 20 congeners of dioxins can be absorbed through daily
meals,20,21 17 of these congeners are present in a reference
mixture in the legal food monitoring programmes of the EU.22
The molecular composition (i.e. degree of chlorination), solubility, and molecular size of each congener inuences its
absorption rate, toxicity, tissue distribution, and elimination.23
In this study, we aimed to compare the transport of 17
diﬀerent dioxin congeners (7 PCDDs and 10 PCDFs) ina dynamic microuidic Caco-2 model with their transport ina traditional static Transwell Caco-2 model. For this, we developed a dynamic in vitro microuidic gut-on-a-chip model with
Caco-2 cells for the purpose of mimicking the in vivo conditions.
Quantication of the dioxin mixture in the cellular fraction as
well as the amount transported across the intestinal monolayers
was performed using gas chromatography-high resolution mass
spectrometry (GC-HRMS). The GC-HRMS method is considereda highly sensitive method suitable for dioxin measurements at
low concentration (0.08–0.30 pg mL1) ranges (Table 1).24 The
resulting cellular association and transport of dioxins were not
only compared between the two models, but we also performed
quantitative structure–property relationship modelling to
explain diﬀerent biological behaviour of the dioxin congeners.

2.

Experimental

2.1. The gut-on-a-chip device
The microuidic gut-on-a-chip device consists of three resealable glass slides, with the same width (15 mm) and length (45mm), upon assembly resulting in two ow chambers separated
by a middle layer that contains a cell culture membrane (Fig. 2).
In more detail, a polyester (PET) membrane xed on a glass
slide, with a 0.4 mm pore size, 12 mm thickness, 1.6  106 poredensity and 1 cm2 surface area, separates the upper apical (AP)and the lower basolateral (BL) chambers (Micronit, Enschede,
The Netherlands). Both the upper and lower glass slides werespaced from the middle layer membrane by 0.25 mm thicksilicone bands resulting in a volume of 110 mm3 and 75 mm3for the AP and BL side respectively, which makes the total
volume of the device 185 mm3 (mL). The distance between the
middle layer and the top layer was 0.25 mm and 0.65 mm for theglass and membrane section, respectively. The distance
between the middle layer and bottom layer was 0.25 mm. The
chip was mounted with a quick locking mechanism in the chip

RSC Adv., 2018, 8, 32440–32453 | 32441

View Article Online

Open Access Article. Published on 19 September 2018. Downloaded on 9/25/2018 11:20:51 AM.
This article is licensed under a Creative Commons Attribution-NonCommercial 3.0 Unported Licence.

RSC Advances

Paper

Fig. 2 (A) Schematic design of the microﬂuidic system. (B) Image of the chip holder used to clamp the chip together and allowing connection of
the tubing.

holder constructed for connecting external tubing to the chip
via specic ferrules to ensure tight connections and a leak-freesystem. Constant ow was carried out using a microsyringe
pump (NE-4000, New Era Pump Systems, Inc.) equipped with
two glass syringes (5 mL, Luer lock tip, Fortuna®, Sigma), witheach syringe connected to either the AP or the BL compartmentof the chip with PTFE (Teon) tubing (0.25 mm inner diameter,14.5 cm length). On the outlets of the chip the same tubing wasused and connected with glass collecting reservoirs. Before
starting experiments, all tubing and chip parts were sterilizedby autoclave and 70% ethanol and the tubing was lled withmedium in order to eliminate air bubbles in the microuidic
system. The entire system was put in an incubator at 37  C tomaintain cell culture conditions. All experiments were performed at a constant ow rate of 25 mL h1 resulting in a shear
rate in the AP compartment of 0.0001–0.0005 dyne per cm2 (atthe membrane and glass surface, respectively) and in the BL
compartment of 0.0005 dyne per cm2.

sensitive CALUX bioassay that is routinely used in our laboratories, as described previously.26 Briey, rat H4IIE hepatoma
cells, stably transfected with an AhR-controlled luciferase
reporter gene plasmid (pGudLuc1.1), were grown in 48 well
microtiter plates. Upon cell conuence, they were exposed intriplicate to standards and the dioxin mixture dissolved inculture medium. Aer 20–24 h of exposure, the cells were
washed, lysed and an aliquot was used to determine the luciferase content using a plate reader (Luminoskan, Labsystems).
Dioxin concentrations in the culture medium samples are
expressed as total bioanalytical equivalent (BEQ) of 2,3,7,8-tetrachlorodibenzo-p-dioxin (TCDD), and were estimated froma calibration curve of the reference dioxin mixture samples
whose response was tted with an exponential curve t. The
entire experiment was repeated twice and the average of bothexperiments is reported.

2.3. Culture of human intestine epithelial cells
2.2. Optimization of the gut-on-a-chip system for dioxin
mixture absorption properties
Before starting the transport experiment, the fraction of dioxins
absorbed by the diﬀerent types of tubing and syringes to be
used in the microuidic system was determined. The following
conditions were evaluated: glass or polypropylene syringes
equipped with ethylene tetrauoroethylene (ETFE), stainlesssteel or Teon tubing. Diﬀerent combinations (n ¼ 2 for eachcombination) were tested by rinsing with cell culture medium
containing 0.2 ng mL1 dioxin mixture (in 1% DMSO) at a ow
rate of 25 mL h1 for 24 h at 37  C (conditions as used in the nal
transport experiment). The amount of total dioxins in culture
medium before and aer owing through the microuidic
systems was determined by using a most cost eﬀective and32442 | RSC Adv., 2018, 8, 32440–32453

The Caco-2 cell line (HTB-37), derived from a human colorectal
adenocarcinoma, was obtained from The American Type
Culture Collection (ATCC, Manassas, VA, USA). They weregrown (at passage number 29-45) in Dulbecco's Modied Eagle
Medium (Lonza) supplemented with 10% heat-inactivated fetal
bovine serum (Gibco, Waltham, MA USA), 1% penicillin–
streptomycin (Sigma), 1% non-essential amino acids (Gibco),
further referred to as DMEM+.
The cells were seeded at density of 40 000 cells per cm2 in 12
well Transwell polyester inserts (0.4 mm pore size, 1.12 cm2surface area, Corning Amsterdam, The Netherlands) cultured in
DMEM+. During culture period, medium was changed for every
other day.
In the microuidic chip cells were seeded at a density of
75 000 cell per cm2, the cells were allowed to attach to theThis journal is © The Royal Society of Chemistry 2018

View Article Online

Open Access Article. Published on 19 September 2018. Downloaded on 9/25/2018 11:20:51 AM.
This article is licensed under a Creative Commons Attribution-NonCommercial 3.0 Unported Licence.

Paper

RSC Advances

membrane without ow for 72 h and then were perfused withlow sodium bicarbonate (10 mM) DMEM+ (Sigma) for optimizing pH buﬀering capacity, through the AP and BL side for 7
days.

Germany) for 3 h. The plate was read at 440 nm and 640 nm
using a microplate reader (BioTek, Winooski, VT). The background signal at 640 nm, which is directly correlated with thecell quantity, was subtracted from the reaction signal at 440 nm.

2.4. Immunohistochemistry

2.7. Dioxin mixture transport experiments

For morphological assessment of the monolayers grown in the
microuidic chips or Transwell cells were seeded (n ¼ 3) ina separate experiment. Aer 10 days of culture (i.e. 7 days underow conditions) the microuidic chips were opened, and cells
were xed on the membrane. The monolayer of cells wassequentially xed with 4% formaldehyde at room temperaturefor 10 min and rinsed with PBS. Cells were then permeabilizedwith 0.25% Triton X100 in PBS for 10 min, rinsed with PBS andblocked with 1% acetylated bovine serum albumin in PBS for
30 min. Tight junctions were stained with 10 mg mL1 of the
conjugated antibody ZO-1/TJP1-Alexa Fluor 594 (Invitrogen,
Waltham, MA). The nuclei were stained with 5 mg mL1 of DAPI
(Invitrogen, Waltham, MA) and 4 U mL1 of Phalloidin Alexa
Fluor 488 (Life technologies) was used to stain actin laments(i.e. cytoskeleton). The incubation time for all stainings was
10 min. Each membrane was then cut out and placed between
two cover slips separated by a spacer (0.12 mm depth  20 mm
diameter). Anti-fading mounting medium was dropped on themembrane. The cells cultured on Transwell membranes for 21
days were stained with the same methods and conditions. The
stained monolayers of cells were analysed using a confocal
microscope (LSM 510 UVMETA; Carl Zeiss, Germany). Samples
were excited with 405, 488 and 543 nm lasers. Multi-tracked
images were captured to avoid bleed through. The usedpinholes were in the range of 148–152 mm at magnication
40.The gain and oﬀset for the diﬀerent channels were kept
constant during the entire experiment.

In the dynamic gut-on-a-chip model, cells were cultured for 10days, of which 7 days under continuous ow of 25 mL h1. At day
10, DMEM+ containing 0.2 ng mL1 of each congener of dioxin(in 1% DMSO) was pumped into the apical channel, andDMEM+ without the dioxin mixture was perfused into thebasolateral channel. Aer 24 h, a total of 600 mL of eluent fromeach compartment was collected for further analysis.
In the Transwell model, cells were cultured for 21 days, andsubsequently exposed to 600 mL DMEM+ containing 0.2 ng
mL1 of the dioxin mixture (in 1% DMSO) in the apicalcompartment. The basolateral compartment was lled with 600
mL DMEM+. Aer 24 h, all DMEM+ was collected from bothcompartments.
In both the chip and Transwell models, cells were collected
at the end of the exposure to determine compound accumulation in the cells. For this, cells were trypsinized with 0.025%
Trypsin/EDTA, rinsed with HBSS and lysed by three freeze–thaw
cycles. To establish a complete mass balance, the fraction ofabsorbed dioxins on membranes, tubing and culture chambersaer exposure was collected by rinsing all compartments with
200 mL hexane. The experiment was performed in triplicate, allcollected samples were analysed together using GC-HRMS.

2.5. Compounds
The dioxin mixtures were obtained from Cambridge Isotope
Laboratories, Inc. (Tewksbury, USA) and contained 10 PCDF
congeners: 2,3,7,8-TCDF, 1,2,3,7,8-PeCDF, 2,3,4,7,8-PeCDF,
1,2,3,4,7,8-HxCDF,
1,2,3,6,7,8-HxCDF,
2,3,4,6,7,8-HxCDF,
1,2,3,7,8,9-HxCDF, 1,2,3,4,6,7,8-HpCDF, 1,2,3,4,7,8,9-HpCDF
and OCDF, and 7 PCDD congeners: 2,3,7,8-TCDD, 1,2,3,7,8PeCDD, 1,2,3,4,7,8-HxCDD, 1,2,3,6,7,8-HxCDD, 1,2,3,7,8,9HxCDD, 1,2,3,4,6,7,8-HpCDD and OCDD.
2.6. Cell viability
To exclude cytotoxicity of the dioxin mixture at the concentration used for the transport studies, a WST-1 assay was performed on Caco-2 cells. The WST-1 assay quanties cellular
mitochondrial activity, which directly correlates to cell viability
and thus cytotoxicity. For this, cells were seeded in 96 wellplates at a density of 50 000 cells per cm2 and were incubatedovernight and were subsequently exposed to the dioxin mixture
in DMEM+ at various concentration ranging from 0.025 to 2.0 ng
mL1, for 24 h. Aer exposure, the cells were washed with
Hank's Balanced Salt Solution (HBSS) and incubated with 10 mL
WST-1 reagent (Roche Diagnostics GmbH, Mannheim,

This journal is © The Royal Society of Chemistry 2018

2.8. GC-HRMS analysis
Samples were analysed by GC-HRMS using previously describedmethods.24 In short, prior to the extraction, the samples werespiked with 13C-isotope labelled internal standards (50 pg ofeach of the 7 PCDDs and of 9 out of 10 PCDFs (except 2,3,4,6,7,8HxCDF)). Consequently, 1 mL of methanol was added andextracted with once 4 mL hexane and twice with 5 mL hexane.
The hexane phases were merged and the samples were puried
as described in earlier studies.24 Using an automated clean-up(PowerPrep system, Fluid Management Systems, Waltham,USA), extracts were puried on an acid silica column, a neutral
silica column, a basic alumina column and an activated carbon/
Celite column. For the elution of the columns, custom madesolvents and mixtures were used, respectively being hexane,
hexane/dichloromethane (1 : 1, v/v), ethyl acetate/toluene (1 : 1,v/v) and toluene. The volume of the nal extract was reduced to
0.5 mL using an automated evaporation system with a xed
endpoint of 0.5 mL. The recovery standards 13C-labelled 1,2,3,4TCDD and 2,3,4,6,7,8-HxCDF, 100 pg, were added for the analysis of PCDD/F, aer which the volume of the extracts wereagain reduced to 0.5 mL. PCDD/F analysis was performed by
GC/HRMS using an Agilent (Wilmington, USA) 6890 Series gas
chromatograph and an AutoSpec Ultima high resolution mass
spectrometer (Waters, Milford, USA) operated at a resolution of
10 000 (10% peak valley). The GC column was a DB5 MS (60 m,
0.25 mm i.d., 0.25 mm; J&W, Folson, USA). The mass spectrometer was operated in electron impact ionization mode,

RSC Adv., 2018, 8, 32440–32453 | 32443

View Article Online

Open Access Article. Published on 19 September 2018. Downloaded on 9/25/2018 11:20:51 AM.
This article is licensed under a Creative Commons Attribution-NonCommercial 3.0 Unported Licence.

RSC Advances

using selected-ion monitoring. A large volume injector (LVI) wasused to inject 100 mL of the extract containing PCDD/Fs on the
GC. The results were corrected for recovery of the 13C-labelledinternal standards and the performance was checked throughan in-house reference sample sunower oil spiked at approximately 1 pg TEQ g1 fat PCDD/F, 0.8 pg TEQ g1 fat dl-PCBs and15 ng g1 fat non-dioxin like-PCBs. The performance of themethods is regularly checked by participation in PT-tests (EURL
dioxins and PCBs twice a year, Folkehelse Institute, once a year),showing good performance.

2.9. Quantitative structure–property relationship modelling
Quantitative structure–property relationship (QSPR) modellingis based on the assumption that the structure of compound (i.e.
geometry, mass, surface) is responsible for its chemical, physical and biological properties.27 For the QSPR modelling data
were logarithmized to make them more linear and sorted byincreasing values. To be able to validate the QSPR models thedata were split into two sets: a training set (to calibrate of model
equations), and a validation set (used for verifying the predictive
ability of the developed models). For this we used the “Z:1”
algorithm, which places every Zth compound in the validation
set (objects from this set are marked as #2) and all theremaining objects in the training set (those objects are marked
as #1). We took Z ¼ 5 for cellular uptake and association data
and Z ¼ 4 for transport data.
Molecular structures of the PCDD and PCDF congeners werebuild using the ConGENER soware.28 The molecular geometries energy was optimized in Gaussian package (version 09,
Gaussian Inc, Wallingford, Connecticut USA) with the B3LYP/6311+G* density functional method as described before.29,30 Aer
optimization the molecular structures were used to calculate
the molecular descriptors, in order to obtain a numericalrepresentation of the compound structure. To this end, we used
DRAGON soware (version 6.0),31 which allowed us to obtaina set of 2622 molecular descriptors.
Based on the experimental data and molecular descriptors
we calibrated the models equations using a Multiple Linear
Regression (MLR) method.32 In order to select from such a large
descriptors set the models that best correlate with the biological
endpoint we used genetic algorithm (GA)33 implemented in the
QSARINS soware (version 2.2.2).34 The genetic algorithm isable to search for the best solutions from a large number ofpossibilities by a maximizing/minimizing tness function. The
procedure of GA was inspired by evolution mechanism, where
the data respond to chromosomes and genes.35 In our study GA
was applied with the following parameters: the population size:
200, the mutation rate: 20%. Due to the small number ofcompounds in the models training sets we chose 2 descriptors
for each model, to avoid their overtting.36,37 The chosen
descriptors are presented in the Table 4. We selected four
autocorrelation descriptors: ATSC3v and GATS5v for uptake
and/or cellular association models, ATS5m and MATS5m fortransport models. In general autocorrelation descriptors are
based on the autocorrelation function ACk presented in theeqn (1).

32444 | RSC Adv., 2018, 8, 32440–32453

Paper

ACk ¼

ðb
a

f ðxÞf ðx þ kÞdx(1)

where f(x) is a function of the variable x, k is the lag representing
an interval of x and a, b dene the total studied interval of thefunction.37
To meet the requirements of QSPR models dened by theOrganization for Economic Co-operation and Development,37
we carried out a two-stage model validation of each model: forthe internal validation we chose the leave-one-out method(cross-validation),38 and for the external validation we used thevalidation set of data.
Aer the validation process we examined the applicability
domain (AD) of our models. AD is a theoretical space determined by values of molecular descriptors, and so resulting fromstructural similarity of the compounds, in which the predictions are reliable. For this purpose we calculated leverage values(h) and the standardized residual values. The h values thatdenes the similarity of ith compound to the training set were
calculated according to the eqn (2), where xi is a vector containing descriptors for ith compound and X is a matrix containing descriptors for every compound from the training set.
hi ¼ xTi (XTX)1xi(2)

When the h value for a compound is higher than the criticalvalue h* (calculated based on the eqn (3)), then the model
predicted the endpoint value for this compound by
extrapolation.
h* ¼ 3pn1(3)where p is the number of variables in model plus one, n is thenumber of the compounds in the training set.39
We veried the tting of the models by determination coefcient (R2) and root mean square error of calibration (RMSEC).
Their robustness and stability was evaluated by the crossvalidation coeﬃcient (QCV2) and the root mean square error ofcross-validation (RMSECV). The predicting capabilities of themodels were examined by external validation coeﬃcient (QEXT2)
and root mean square error of prediction (RMSEEXT).40
2.10.

Statistical analysis

SPSS (IBM) was used for all statistical evaluations. The cellularaccumulation and transport data was evaluated using an independent paired t-test for the dioxin mixture transport experiment and a one-way ANOVA with Dunnett's post hoc test wasused for the cell viability test. A p-value of #0.05 was consideredsignicant.

3.

Results and discussion

3.1. Microuidic model development for lipophiliccompounds
Dioxins are highly lipophilic, and thus tend to adsorb tosurfaces of materials used in experiments depending on theirchemical composition and their subsequent compound–This journal is © The Royal Society of Chemistry 2018

View Article Online

Open Access Article. Published on 19 September 2018. Downloaded on 9/25/2018 11:20:51 AM.
This article is licensed under a Creative Commons Attribution-NonCommercial 3.0 Unported Licence.

Paper

RSC Advancesmaterial interactions.41,42 Polydimethylsiloxane (PDMS) is oenused to create microuidic devices because of its low cost, easeof use, high compliance, microscope compatibility and fast
fabrication properties.43–45 However, PDMS has a number ofimportant limitations regarding its use in biological studies.46
One of those limitations is adsorption of various molecules by
PDMS because of its permeable and hydrophobic properties.47 It
has been reported that PDMS based material adsorbed more
small-molecules and hydrophobic compounds compared topolystyrene based material,48,49 and glass,50 and thus dramatically aﬀecting free compound concentrations.49 Obviously, thedegree of adsorption relies on lipophilic properties ofcompounds.50,51 While it has been shown that continuous
rinsing with buﬀer might result in desorption of compounds
from PDMS,50 it is clear that PDMS might not be the ideal
material for highly lipophilic compounds like dioxins. Therefore, in this study, glass-based chips were used, to reduce theeﬀect of surface adsorption as much as possible. Selection of
the optimal type of tubing for the compounds of interest ina microuidic system is also an essential element of the
microuidic model development, especially since the (relative)
surface areas of the capillary tubing is large. Therefore, theconcentration of the dioxin mixture in cell culture medium wasdetermined aer use in diﬀerent materials of syringes andtubing (i.e. glass vs. polypropylene syringes and ethylene tetrauoroethylene (ETFE), stainless steel and Teon tubing), under
the conditions as used in the nal experiments. The fraction ofdioxin mixture adsorbed onto the material surface aer incubation was determined by assessing the amount of dioxins in
the cell culture medium expressed in BEQ using the dioxin
CALUX bioassay, as a cost eﬀective measurement technique.
Highest residual amounts of the dioxin mixture in cell culture
media were detected when using glass syringes and Teontubes, about 60% higher compared to the set-up with the lowest
yield using polypropylene syringes and ETFE tubing (Table 2).
Therefore Teon tubing is used in the nal experiments.
Dioxin adsorption to the microuidic system was also
assessed in the exposure/translocation experiment by GCHRMS. For this, the system was rinsed with hexane aer the
exposure and the amount of dioxins in this fraction was determined with GC-HRMS and compared to the exposure mixture.
On average, hexane rinses of the chip compartments aer
incubation with the dioxin mixture contained 3% of the total
amount of dioxins as present in the exposure mixture in both
systems, indicating low adsorption (see ESI Table 1†). This
Table 2 Eﬀect of diﬀerent tubing and syringe material of the microﬂuidic systems on recovery of the dioxin mixture measured by CALUX(n ¼ 2)Syringes and tubing

Dioxin concentration(ng BEQ per ml)

Recoverya (%)

Polypropylene + ETFE
Glass + ETFE
Glass + steel
Glass + Teon

0.085  0.005
0.199  0.006
0.212  0.012
0.219  0.003

26  161  265  467  1a

The initial concentration of Dioxin in DMEM+ is 0.328 ng BEQ per ml.

This journal is © The Royal Society of Chemistry 2018

information, together with an average measured mass balanceof 93%, (the sum of apical, basolateral and cellular fractions, as
discussed in detail later) shows a good recovery in the chipsystem.
3.2. Morphology of intestinal barriers
Monolayer integrity of Caco-2 cells grown under dynamic ow
and static conditions was evaluated using uorescence confocal
microscopy. Representative images are shown in Fig. 3. In thechip the Caco-2 cells were grown using a continuous ow of 25mL h1 for 7 days. Cells cultured under continuous ow havebeen described to form intact and polarized monolayers fastercompared to Caco-2 cells cultured using traditional static
conditions.6 This is conrmed in the present study by showing
complete and polarized monolayers of cells at day 10 (i.e. 7 daysof ow) in the chip model. For comparative analysis Caco-2 cells
were grown for 21 days under static conditions in Transwells.
Cells grown under continuous ow showed a comparable
monolayer formation to cells grown without continuous ow, as
can be seen from the tight junction immunouorescencestaining (Fig. 3A and B). Cell polarisation was visualized using
immunouorescence staining of actin laments, nuclei, andtight junctions (Fig. 3C–H). Core bundles of actin laments inthe microvilli were seen in cells grown under both conditions.
Aer cell polarisation, the cells grown without continuous ow
exhibited densely packed actin laments on the apical side in
the microvilli of the cells, while cells grown under dynamic
conditions also exhibited pronounced actin laments on thebasolateral side (Fig. 3E and F). Likely this is caused by theshear stress (0.0001–0.0005 dyne per cm2), induced by thecontinuous ow, as shown before for endothelial cells exposed
to a shear stress of 15 dyne per cm2,52 and placenta (i.e. BeWo)
cells exposed to shear stresses ranging from 0.001 to 0.12 dyne
per cm2.53 In addition, we observed that cells grown undercontinuous ow seem to be larger compared to the cells grownwithout continuous ow. Likely this can also be explained by
the shear stress exposure. Since actin laments are associatedwith cell adhesion and mechanics, remodelling of actin laments might alter cell spread, migration, elongation, or
enlargement of the cells.52,53 While the thickness of the monolayers of cells grown under both conditions was comparable (10mm height; Fig. 3G and H), the continuous ow seemed toinduce a more pronounced 3D structure, which has more
prominently been shown in Caco-2 containing chip models that
also employ stretchable membranes (cyclic strain of 0.15 Hz
frequency together with a shear stress of 0.02 dyne per cm2).6
3.3. Comparative cellular uptake and absorption, andtransport of dioxins
The human gut epithelium acts as an important barrier in
defence to protect the body from exogenous substances that canbe present in our food.54,55 To further optimize and implement
dynamic gut-on-a-chip models, we determined the transport of
17 individual dioxin congeners across a monolayer of Caco-2
cells under dynamic and static culturing and exposure
conditions.

RSC Adv., 2018, 8, 32440–32453 | 32445

View Article Online

Paper

Open Access Article. Published on 19 September 2018. Downloaded on 9/25/2018 11:20:51 AM.
This article is licensed under a Creative Commons Attribution-NonCommercial 3.0 Unported Licence.

RSC AdvancesFig. 3 Morphology of Caco-2 cells cultured in a Transwell for 21 days versus in chip for 10 days (of which 7 days under a continuous ﬂow of 25 mLh1) visualized by confocal microscopy. Tight junctions of cells cultured in (A) a Transwell and in (B) a chip were stained for ZO-1/TJP1 (red). Actinﬁlaments on the apical side of the cells, stained by Phalloidin (green), and tight junctions of cells were visualized on merged images in (C)a Transwell and (D) a chip. Merged images showing nuclei (DAPI; blue) and actin ﬁlaments on the basolateral side of cells cultured in (E)a Transwell, and (F) a chip. Note the more pronounced expression of actin in the chip. Vertical cross-section of the cell monolayer in (G)a Transwell and (H) a chip. The scale bar represents 10 mm.

Transport studies can only be reliably performed using
dioxin concentrations that do not aﬀect the viability of cells
used in the experiment. A WST-1 assay was used to select a nontoxic dioxin mixture concentration. As can be seen in Fig. 4,
decreased cell viability was only observed following an exposure
to $0.6 ng mL1 dioxin mixture for 24 h (P < 0.05, one way
ANOVA). For the cellular association and transport studies an
exposure concentration of 0.2 ng mL1 was used.
Aer 24 hours of exposure the cells were harvested from thechip and Transwell membranes and the concentration of individual congeners in these fractions, as well as in the medium
fractions from the apical and basolateral compartments of boththe chip and Transwell, was determined using GC-HRMS. Also,
hexane rinses of both the chip and Transwell were evaluated.
The so-called mass balance was determined (Table 3), which is32446 | RSC Adv., 2018, 8, 32440–32453

the sum of the amount of congeners detected in the AP and BL
compartments and the cellular fraction. This indicated massbalances ranging from 75 to 99%, concluding the method andexperimental setup suitable for dioxin transport studies.
Transport of dioxin congeners across the two barrier models
was low and ranged from 0.6% to 3.3% for OCDF and 1,2,3,7,8PeCDF respectively in the dynamic model, and ranged from
0.2% to 4.4% in the static model for OCDF and 1,2,3,7,8-PeCDF
respectively (Fig. 5A). Comparison of the transport and cellularassociation of the individual congeners between both models
showed no signicant diﬀerences. In addition, a similar trend
in congener transport was observed (R2 ¼ 0.96) between the twomodels (Fig. 5C). The larger error bars in the chip datacompared to the Transwell data could be explained by the more
complicated (technical) setup of the chip experiments

This journal is © The Royal Society of Chemistry 2018

View Article Online

Open Access Article. Published on 19 September 2018. Downloaded on 9/25/2018 11:20:51 AM.
This article is licensed under a Creative Commons Attribution-NonCommercial 3.0 Unported Licence.

Paper

Cytotoxicity of the dioxin mixture in Caco-2 cells after 24 h
exposure given as a percentage of viable cells (SEM) of the total
number of cells (n ¼ 3).
Fig. 4

RSC Advances

a steady-state amount of approximately 13% cellular association
has been reported following 90 minutes of exposure to 3.4  102mCi mL1 U-14C-TCDD and less than 1.5% of apical concentration
was transported to the basal compartment.63 Our data illustrates
an amount of 2,3,7,8-TCDD in the cellular fraction of approximately 39 and 56% in Transwell and chip model, respectively. We
observed that the dioxin mixture was more retained in thecellular fraction than passed through the monolayer to the basalside under both experimental conditions. This can be explainedby the high lipophilicity of the dioxin congeners and its inuenceon their partition coeﬃcient, so they are likely to remain in
phospholipid bilayers and only slowly diﬀuse across it.64 This has
been reported also for polychlorinated biphenyls, PCB#52, #118,#153 and #180, which have an extremely high hydrophobicity(log P > 5).65

3.4. QSPR modelling for PCDDs and PCDFs
compared to Transwells. In an earlier study the transport acrossa monolayer of Caco-2 cells of only 2,3,7,8-TCDD has beenstudied.56 The authors quantied the TCDD concentration
using a luciferase HepG2 cell assay. They found that 15% of
TCDD was transported across the Caco-2 cell monolayer in
24 h.56 This is higher than the 3% transported 2,3,7,8-TCDD
observed by us for both models, which might be explained by
diﬀerences in exposure concentration. In their study, a 10 foldhigher concentration of 2.5 nM of 2,3,7,8-TCDD was used,
whereas we used 0.26 nM of 2,3,7,8-TCDD as part of a mixture ofdioxins.
Upon 24 h of exposure to the mixture of dioxins the cellular
fraction contained relative amount of each congener ranging
from 33% for 1,2,3,7,8,9-HxCDD to 61% for OCDD of the total
exposure concentration (based on a mass balance calculation)in the dynamic model and relative amount ranging from 22.5%
for 1,2,3,7,8,9-HxCDD to 51.8% for OCDD in the static model(Fig. 5B). While the fraction of the dioxin mixture in the cellular
fraction seems to be increased in cells cultured under dynamic
conditions this was not signicantly diﬀerent. Cellular association and accumulation of dioxins is likely caused by their lipophilicity as reported in the previous studies.57,58 Under both
culture conditions individual congeners were present in thecellular fraction in a comparable pattern, as a strong correlation
between the chip and Transwell cellular amount was observed
for the whole series of test compounds (R2 ¼ 0.98; Fig. 5D).
Dioxins have been investigated extensively in vitro with moststudies focusing on their potential toxicity and metabolism,
focussing specically on 2,3,7,8-TCDD-aryl hydrocarbon receptor(AhR) interactions. 2,3,7,8-TCDD can induce expression of cytochrome P450 1A1 (CYP1A1), a phase I drug metabolism enzyme,
and UDP-glucuronosyltransferase (UGT) isoforms UGT1A6 andUGT1A9, phase II drug metabolism enzymes, in Caco-2 cells.59–62
On the contrary, intestinal uptake and transport of dioxins has
only very limitedly been studied in vitro before. We identied only
one study that reports cellular association, in this study onlya single dioxin congener has been used. For 2,3,7,8-TCDD,

This journal is © The Royal Society of Chemistry 2018

To further explore the relationship between the physicochemical properties of the used dioxins and their uptake and/or
cellular association we used QSPR modelling. QSPR modellingis used to identify correlations between structure and biologicaleﬀects. In order to build a QSPR model experimental (chip) datais necessary, once a QSPR model is validated it could be used forprioritization of (new) specic compounds for (on-chip) testing
based on their molecular structure. Combinations of in vitro
studies and computational approaches have been identied asa powerful approach to develop alternative testing strategies
without the use of animals.66,67 For each model equation wechose 2 descriptors, to avoid their overtting.37 The chosen
descriptors are presented in the Table 4.
We have developed four statistically signicant QSPR models
describing relationship between the chemical structures of
PCDDs and PCDFs and their uptake and/or cellular association

Mass balance from dioxin transport experiments analysed by
GC-HRMS (n ¼ 3)

Table 3Mass balance (%)
Components

Chip

Transwell

Congener
2,3,7,8-TCDF
1,2,3,7,8-PeCDF
2,3,4,7,8-PeCDF
1,2,3,4,7,8-HxCDF
1,2,3,6,7,8-HxCDF
2,3,4,6,7,8-HxCDF
1,2,3,7,8,9-HxCDF
1,2,3,4,6,7,8-HpCDF
1,2,3,4,7,8,9-HpCDF
OCDF
2,3,7,8-TCDD
1,2,3,7,8-PeCDD
1,2,3,4,7,8-HxCDD
1,2,3,6,7,8-HxCDD
1,2,3,7,8,9-HxCDD
1,2,3,4,6,7,8-HpCDD
OCDD

82  588  593  797  794  597  791  697  597  796  885  592  698  892  593  597  7100  875  186  182  285  293  187  284  293  183  279  281  285  284  292  193  183  282  2

RSC Adv., 2018, 8, 32440–32453 | 32447

View Article Online

Paper

Open Access Article. Published on 19 September 2018. Downloaded on 9/25/2018 11:20:51 AM.
This article is licensed under a Creative Commons Attribution-NonCommercial 3.0 Unported Licence.

RSC AdvancesFig. 5 Comparison of dioxin congeners in the cellular fraction and transported over the cell barrier between a dynamic ( ) versus a static ( )model. (A) Transport of dioxin congeners through the monolayer of cells cultured in chips and Transwells given as a percentage (SEM) of dioxin
congeners in the basal side compared to the total exposed amount. (B) Fraction of dioxin congeners in the cell fraction given as a percentage(SEM) of dioxin congeners in the cellular fraction compared to the total exposed amount. (C) Correlation between transported dioxin
congeners in the dynamic versus static model. (D) Correlation between dioxin congeners in the cellular fraction in the dynamic versus static
model, 1 ¼ OCDF; 2 ¼ 1,2,3,4,7,8,9-HpCDF; 3 ¼ 1,2,3,4,6,7,8-HpCDD; 4 ¼ OCDD; 5 ¼ 1,2,3,4,7,8-HxCDD; 6 ¼ 1,2,3,4,7,8-HxCDF; 7 ¼
1,2,3,4,6,7,8-HpCDF; 8 ¼ 1,2,3,7,8,9-HxCDF; 9 ¼ 2,3,4,6,7,8-HxCDF; 10 ¼ 2,3,4,7,8-PeCDF; 11 ¼ 1,2,3,7,8,9-HxCDD; 12 ¼ 1,2,3,6,7,8-HxCDF; 13¼ 1,2,3,6,7,8-HxCDD; 14 ¼ 1,2,3,7,8-PeCDD; 15 ¼ 2,3,7,8-TCDD; 16 ¼ 2,3,7,8-TCDF; 17 ¼ 1,2,3,7,8-PeCDF.

Table 4 Molecular descriptor pairs for the developed QSPR models

Pair of models

Descriptors

Cell QSPR models

ATSC3v
GATS5v
ATS5m
MATS5mTransport QSPR models

32448 | RSC Adv., 2018, 8, 32440–32453

Centred Broto–Moreau autocorrelation of lag 3 weighted by van der Waals volume
Geary autocorrelation of lag 5 weighted by van der Waals volume
Centred Broto–Moreau autocorrelation of lag 5 weighted by massMoran autocorrelation of lag 5 weighted by massThis journal is © The Royal Society of Chemistry 2018

View Article Online

Paper
Table 5

RSC Advances
Parameters of quality evaluation for developed models

Open Access Article. Published on 19 September 2018. Downloaded on 9/25/2018 11:20:51 AM.
This article is licensed under a Creative Commons Attribution-NonCommercial 3.0 Unported Licence.

2

Model 1Model 2Model 3Model 4

R

RMSEC

QCV2

0.83
0.86
0.91
0.92

3.83
3.60
0.07
0.01

0.73
0.80
0.86
0.14

RMSECVQEXT2RMSEEXT

4.83
4.39
0.08
0.14

0.87
0.72
0.87
0.96

3.11
4.31
0.08
0.07

and transport across the monolayer of cells. In the process ofmodel calibration we obtained the following eqn (3)–(6):Model 1 (Uptake-chip): Y ¼ 9.9324 ATSC3v 52.8616 GATS5v+ 121.637(4)

Model 2 (Uptake-Transwells): Y ¼ 12.7950 ATSC3v  57.3662
GATS5v + 118.7720(5)Model 3 (Transport-chip): log Y ¼ 1.0760 ATS5m  0.6038
MATS5m + 4.4399(6)
Model 4 (Transport-Transwells):
log Y ¼ 1.7296 ATS5m  0.5969 MATS5m+ 7.0661(7)

The values of determination coeﬃcients (R2), internal andexternal validation coeﬃcients (QCV2 and Q2EXT) and root meansquare errors (RMSEC, RMSECV, RMSEEXT) presented in Table 5
indicate good quality, stability and predictive capabilities of themodels.

Fig. 6 Experimental and predicted values of endpoint for each developed model, the training (>) and validation set (-). 1 ¼ 1,2,3,7,8,9-HxCDD;
2 ¼ 1,2,3,6,7,8-HxCDD; 3 ¼ 1,2,3,6,7,8-HxCDF; 4 ¼ 1,2,3,4,6,7,8-HpCDF; 5 ¼ 1,2,3,7,8-PeCDF; 6 ¼ 2,3,7,8-TCDF; 7 ¼ 1,2,3,7,8,9-HxCDF; 8 ¼
2,3,7,8-TCDD; 9 ¼ 1,2,3,7,8-PeCDD; 10 ¼ 2,3,4,6,7,8-HxCDF; 11 ¼ 2,3,4,7,8-PeCDF; 12 ¼ 1,2,3,4,6,7,8-HpCDD; 13 ¼ 1,2,3,4,7,8-HxCDF; 14 ¼
1,2,3,4,7,8,9-HpCDF; 15 ¼ OCDF; 16 ¼ 1,2,3,4,7,8-HxCDD; 17 ¼ OCDD.

This journal is © The Royal Society of Chemistry 2018

RSC Adv., 2018, 8, 32440–32453 | 32449

View Article Online

Open Access Article. Published on 19 September 2018. Downloaded on 9/25/2018 11:20:51 AM.
This article is licensed under a Creative Commons Attribution-NonCommercial 3.0 Unported Licence.

RSC Advances

Paper

Fig. 7 Williams plots for developed models. The lines on the plot (3s and h*) deﬁne the applicability domain of the model, the training (>) andvalidation set (-).

This evaluation is additionally conrmed by high correlation
between experimental and predicted values of endpoints presented at the Fig. 6.
To verify models applicability domains (AD) we applied Williams
plots11 (values of standardized residuals versus the leverage values).
AD is limited by the critical values of standardized residuals (threestandard deviation units-3s) and the critical leverage value (h*). The
developed Williams plots are presented in Fig. 7.
All the compounds are situated in the range of 3 standard
deviations from 0 and none of them exceeded the critical leveragevalue (h*). It means that all the compounds used to develop themodels are structurally similar and the uncertainly of model
predictions for them is acceptable. Moreover, it conrms theassumption that PCDDs and PCDFs may belong to the common
AD.
Interpretation of QSPR models can be used to hypothesize on
mechanism of cellular uptake (and/or association) and subsequenttransport of dioxins across the monolayer of cells. Molecular
descriptors chosen for the model equation indicate which structural features determine the values of the endpoint. For ourmodels we selected four autocorrelation descriptors: ATSC3v and32450 | RSC Adv., 2018, 8, 32440–32453

GATS5v for uptake (and/or association) models, ATS5m andMATS5m for transport models. Autocorrelation descriptors can
encode not only the structure of the molecule but also physicochemical properties attributed to atoms, therefore descriptors of
this type are eﬀectively used for modelling compounds' interactions with membranes or their cytotoxicity.68–71 For the cellularassociation models we developed (Model 1 and 2) there is a clear
relationship between values of the ATSC3v descriptor and thenumber of chlorine atoms in the compound. These quantities areinversely proportional, but this descriptor is preceded in model
equations by negatives coeﬃcients. Increasing number of chlorine
atoms in the molecule is therefore associated with increasing valueof PCDDs and PCDSs cellular association. The second descriptor
used in uptake models-GATS5v-is connected to the substitution
pattern. It takes on higher values when the distribution of chlorine
atoms in the molecule is less symmetrical. Thus the GATS5v
descriptor describes the distribution of the charge in thecompound. This means that the uptake of PCDDs and PCDFs ishigher in the case of less symmetrical molecules. This conclusion
is also conrmed by the fact that GATS5v descriptor assumes
higher values for PCDFs compared to PCDDs.

This journal is © The Royal Society of Chemistry 2018

View Article Online

Open Access Article. Published on 19 September 2018. Downloaded on 9/25/2018 11:20:51 AM.
This article is licensed under a Creative Commons Attribution-NonCommercial 3.0 Unported Licence.

Paper

The results are relatively similar in case of models developed
for the transport of PCDDs and PCDFs: rst selected descriptor(ATS5m) is also inversely proportional to the number of chlorine atoms in a molecule. It shows, that compounds with higher
number of chlorine atoms are more easily transported. The
values of the second descriptor, MATS5m, depend on thesubstitution pattern.

RSC Advances

as mass spectrometry to measure the transported amount ofdrugs or metabolites in the system in real-time.73–75

Conﬂicts of interest
There are no conicts of interest to declare.

Conclusion

Acknowledgements

Here we have implemented a dynamic ow gut-on-a-chip modelthat can be used to study the transport and the cellular accumulation of lipophilic compounds like dioxins. The experimental conditions were optimized to avoid uncontrollable
adsorption. By using a very sensitive, dioxin congener specic
GC-HRMS detection method, we show that the transport andcellular accumulation prole of dioxins of Caco-2 cells grownunder continuous ow and static conditions is comparable.
Physicochemical properties, of the dioxin congeners allowed us
to predict their transport proles in vitro.
Caco-2 cell models have been used to study the absorption
and local eﬀects of a variety of compounds, and are recognized as usefully predictive in vitro models for the study ofpassage of substances through the gut wall. However, theirconventional culture methods provide some disadvantages,
for example, long term cultures time (3 weeks), not fully
mimicking in vivo conditions and inability to do on-line
measurement. Therefore, a gut-on-a-chip system has beendeveloped and proposed to be used as an in vitro alternativemodel that recapitulates not only in vivo uid ows, but also
the structure, transport, of the gut epithelium.
The results illustrate that Caco-2 cells cultured in a dynamic
system for 10 days shows similar polarisation and morphological properties as cells cultured for 21 days in a static system.
Furthermore, the gut-on-a-chip system also showed transport
properties similar to the static system indicating its applicability for transport studies. Thus it now is possible to use anadvanced gut-on-a-chip models next to the traditional Transwellmodel as a screening assay to study eﬀects of compounds on
dioxin absorption in order to prevent dioxin ingestion inhuman. Clearly, there are remaining technical challenges to beaddressed in working with organ-on-chips, to allow an easier
implementation of these models into routine testing. Precisely
controlled injection systems, with ow and pressure control,and easy to use inert chip materials, are the most important
technical challenges.72 This is the rst report comparing thetransport of 17 dioxin congener using an in vitro system andrevealing their structure–transport relationship. Interestingly,
using quantitative structure–property relationship modelling
we have been able to reveal the relationship between the degreeof chlorination and the chlorination patterning on the cellularassociation and transport of the individual dioxin congeners
across a monolayer of Caco-2 cells.
The implemented gut-on-a-chip system can be used for dioxintransport screening which might be useful for other lipophiliccompound study as well. Moreover, the microuidic organ-on-achip model can be coupled to downstream analysis systems, such

This research was nanced by the Dutch Ministry of Agriculture,
Nature and Food Quality and K. K. is supported by the Royal
Thai government Scholarship.

This journal is © The Royal Society of Chemistry 2018

References
1 C. Pereira, J. Costa, B. Sarmento and F. Araújo, in Concepts
and Models for Drug Permeability Studies, Woodhead
Publishing, 2016, DOI: 10.1016/B978-0-08-100094-6.000055, pp. 57–81.
2 R. B. van Breemen and Y. Li, Expert Opin. Drug Metab.
Toxicol., 2005, 1, 175–185.
3 A. M. Marino, M. Yarde, H. Patel, S. Chong and
P. V. Balimane, International journal of pharmaceutics, 2005,
297, 235–241.
4 M. J. Briske-Anderson, J. W. Finley and S. M. Newman, Proc.
Soc. Exp. Biol. Med., 1997, 214, 248–257.
5 H. J. Kim and D. E. Ingber, Integr. Biol., 2013, 5, 1130–1140.
6 H. J. Kim, D. Huh, G. Hamilton and D. E. Ingber, Lab Chip,
2012, 12, 2165–2174.
7 M. Gori, M. C. Simonelli, S. M. Giannitelli, L. Businaro,
M. Trombetta and A. Rainer, PLoS One, 2016, 11, e0159729.
8 E. W. Esch, A. Bahinski and D. Huh, Nat. Rev. Drug Discovery,
2015, 14, 248–260.
9 K. J. Jang and K. Y. Suh, Lab Chip, 2010, 10, 36–42.
10 N. S. Bhise, J. Ribas, V. Manoharan, Y. S. Zhang, A. Polini,
S. Massa, M. R. Dokmeci and A. Khademhosseini, J.
Controlled Release, 2014, 190, 82–93.
11 J. D. Wang, E. Khafagy, K. Khanafer, S. Takayama andM. E. H. Elsayed, Mol. Pharmaceutics, 2016, 13, 895–906.
12 J. H. Yeon and J. K. Park, Anal. Chem., 2009, 81, 1944–1951.
13 G. J. Mahler, M. B. Esch, R. P. Glahn and M. L. Shuler,
Biotechnol. Bioeng., 2009, 104, 193–205.
14 W. H. Organization, Exposure to Dioxins and Dioxin-like
Substances: A Major Public Health Concern, World HealthOrganization, Geneva, 2010.
15 E. F. S. Authority, Dioxin and PCBs, (accessed 30 January2018).
16 M. O. Milbrath, Y. Wenger, C. W. Chang, C. Emond,
D. Garabrant, B. W. Gillespie and O. Jolliet, Environ. Health
Perspect., 2009, 117, 417–425.
17 G. Charnley and J. Doull, Food Chem. Toxicol., 2005, 43, 671–
679.
18 J. K. Huwe and G. L. Larsen, Environ. Sci. Technol., 2005, 39,
5606–5611.
19 A. K. Liem, P. Furst and C. Rappe, Food Addit. Contam., 2000,
17, 241–259.

RSC Adv., 2018, 8, 32440–32453 | 32451

View Article Online

Open Access Article. Published on 19 September 2018. Downloaded on 9/25/2018 11:20:51 AM.
This article is licensed under a Creative Commons Attribution-NonCommercial 3.0 Unported Licence.

RSC Advances

20 H. Poiger and C. Schlatter, Chemosphere, 1986, 15, 1489–
1494.
21 K. Kitamura, M. Nagahashi, M. Sunaga, S. Watanabe and
M. Nagao, J. Health Sci., 2001, 47, 591–59.
22 E. F. S. Authority, EFSA J., 2012, 10, 2832.
23 M. Vandenberg, J. Dejongh, H. Poiger and J. R. Olson, Crit.
Rev. Toxicol., 1994, 24, 1–74.
24 R. Hoogenboom, G. Ten Dam, M. van Bruggen,
S. M. F. Jeurissen, S. P. J. van Leeuwen, R. M. C. Theelen
and M. J. Zeilmaker, Chemosphere, 2016, 150, 311–319.
25 S. Kim, P. A. Thiessen, E. E. Bolton, J. Chen, G. Fu,
A. Gindulyte, L. Y. Han, J. E. He, S. Q. He,
B. A. Shoemaker, J. Y. Wang, B. Yu, J. Zhang and
S. H. Bryant, Nucleic Acids Res., 2016, 44, D1202–D1213.
26 R. Hoogenboom, T. Bovee, W. Traag, R. Hoogerbrugge,
B. Baumann, L. Portier, G. van de Weg and J. de Vries,
Mol. Nutr. Food Res., 2006, 50, 945–957.
27 A. Tropsha, Mol. Inf., 2010, 29, 476–488.
28 M. Haranczyk, T. Puzyn and P. Sadowski, QSAR Comb. Sci.,
2008, 27, 826–833.
29 A. Rybinska, A. Sosnowska, M. Barycki and T. Puzyn, J.
Comput.-Aided Mol. Des., 2016, 30, 165–176.
30 T. Puzyn, N. Suzuki, M. Haranczyk and J. Rak, J. Chem. Inf.
Model., 2008, 48, 1174–1180.
31 A. Mauri, V. Consonni, M. Pavan and R. Todeschini, MATCH,
2006, 56, 237–248.
32 X. J. Yao, A. Panaye, J. P. Doucet, R. S. Zhang, H. F. Chen,
M. C. Liu, Z. D. Hu and B. T. Fan, J. Chem. Inf. Model.,2004, 44, 1257–1266.
33 T. Ghafourian and M. T. D. Cronin, SAR QSAR Environ. Res.,
2005, 16, 171–190.
34 P. Gramatica, N. Chirico, E. Papa, S. Cassani and S. Kovarich,
J. Comput. Chem., 2013, 34, 2121–2132.
35 S. E. Haupt and R. L. Haupt, Practical Genetic Algorithms,
John Wiley & Sons, Inc., New Jersey, 2nd edn, 2004.
36 A. Cherkasov, E. N. Muratov, D. Fourches, A. Varnek,
I. I. Baskin, M. Cronin, J. Dearden, P. Gramatica,
Y. C. Martin, R. Todeschini, V. Consonni, V. E. Kuz'min,
R. Cramer, R. Benigni, C. H. Yang, J. Rathman, L. Teroth,
J. Gasteiger, A. Richard and A. Tropsha, J. Med. Chem.,
2014, 57, 4977–5010.
37 OECD, Principles for the validation, for regulatory purposes, of(quantitative) structure-activity relationship models, OECD,
Paris, 2004.
38 J. H. Wu, J. A. Mei, S. X. Wen, S. Y. Liao, J. C. Chen and
Y. Shen, J. Comput. Chem., 2010, 31, 1956–1968.
39 P. Gramatica, QSAR Comb. Sci., 2007, 26, 694–701.
40 N. Chirico and P. Gramatica, J. Chem. Inf. Model., 2011, 51,
2320–2335.
41 T. Cseh, S. Sanschagrin, J. Hawari and R. Samson, Appl.
Environ. Microbiol., 1989, 55, 3150–3154.
42 J. K. Unger, G. Kuehlein, A. Schroers, J. C. Gerlach and
R. Rossaint, Biomaterials, 2001, 22, 2031–2037.
43 J. C. McDonald, D. C. Duﬀy, J. R. Anderson, D. T. Chiu,
H. K. Wu, O. J. A. Schueller and G. M. Whitesides,
Electrophoresis, 2000, 21, 27–40.

32452 | RSC Adv., 2018, 8, 32440–32453

Paper

44 A. L. Thangawng, R. S. Ruoﬀ, M. A. Swartz and
M. R. Glucksberg, Biomed. Microdevices, 2007, 9, 587–595.
45 C. W. Tsao, Micromachines, 2016, 7(12), 225.
46 E. Berthier, E. W. K. Young and D. Beebe, Lab Chip, 2012, 12,1224–1237.
47 K. Y. Chumbimuni-Torres, R. E. Coronado, A. M. Mfuh,
C. Castro-Guerrero, M. F. Silva, G. R. Negrete, R. Bizios andC. D. Garcia, RSC Adv., 2011, 1, 706–714.
48 M. W. Toepke and D. J. Beebe, Lab Chip, 2006, 6, 1484–1486.
49 B. J. van Meer, H. de Vries, K. S. A. Firth, J. van Weerd,
L. G. J. Tertoolen, H. B. J. Karperien, P. Jonkheijm,
C. Denning, I. J. AP and C. L. Mummery, Biochem. Biophys.
Res. Commun., 2017, 482, 323–328.
50 N. Li, M. Schwartz and C. Ionescu-Zanetti, Journal of
biomolecular screening, 2009, 14, 194–202.
51 J. D. Wang, N. J. Douville, S. Takayama and M. ElSayed, Ann.
Biomed. Eng., 2012, 40, 1862–1873.
52 S. Noria, F. Xu, S. McCue, M. Jones, A. I. Gotlieb and
B. L. Langille, Am. J. Pathol., 2004, 164, 1211–1223.
53 S. Miura, K. Sato, M. Kato-Negishi, T. Teshima and
S. Takeuchi, Nat. Commun., 2015, 6.
54 K. R. Groschwitz and S. P. Hogan, J. Allergy Clin. Immunol.,
2009, 124, 3–20, quiz 21–22.
55 J. Konig, J. Wells, P. D. Cani, C. L. Garcia-Rodenas,
T. MacDonald, A. Mercenier, J. Whyte, F. Troost and
R. J. Brummer, Clin. Transl. Gastroenterol., 2016, 7, e196.
56 Y. Natsume, H. Satsu, Y. Hatsugai, H. Watanabe, R. Sato,
H. Ashida, R. H. Tukey and M. Shimizu, Food Sci. Technol.
Res., 2003, 9, 364–366.
57 M. Casalegno, G. Raos and G. Sello, Phys. Chem. Chem. Phys.,2016, 18, 17731–17739.
58 M. Laznicek and A. Laznickova, J. Pharm. Biomed. Anal.,
1995, 13, 823–828.
59 M. Hamada, H. Satsu, Y. Natsume, S. Nishiumi, I. Fukuda,
H. Ashida and M. Shimizu, J. Agric. Food Chem., 2006, 54,
8891–8898.
60 P. A. Munzel, S. Schmohl, H. Heel, K. Kalberer, B. S. BockHennig and K. W. Bock, Drug Metab. Dispos., 1999, 27,
569–573.
61 M. Daujat, S. Charrasse, I. Fabre, P. Lesca, Y. Jounaidi,
C. Larroque, L. Poellinger and P. Maurel, Eur. J. Biochem.,
1996, 237, 642–652.
62 W. J. de Waard, J. M. M. J. G. Aarts, A. A. C. M. Peijnenburg,
H. Baykus, E. Talsma, A. Punt, T. M. C. M. de Kok, F. J. van
Schooten and L. A. P. Hoogenboom, Toxicol. in Vitro, 2008,
22, 396–410.
63 S. Cavret, C. Feidt and F. Laurent, J. Agric. Food Chem., 2005,
53, 2773–2777.
64 H. Lodish, A. Berk and S. L. Zipursky, et al., in Molecular Cell
Biology, W. H. Freeman, New York, 4th edn, 2000, ch. 15.1.
65 A. G. Oomen, J. Tolls, M. Kruidenier, S. S. D. Bosgra,
A. J. A. M. Sips and J. P. Groten, Environ. Health Perspect.,
2001, 109, 731–737.
66 T. Hartung, Nature, 2009, 460, 208–212.
67 T. Hartung, T. Luechtefeld, A. Maertens and A. Kleensang,
Alternatives to Animal Experimentation, 2013, 30, 3–18.

This journal is © The Royal Society of Chemistry 2018

View Article Online

Open Access Article. Published on 19 September 2018. Downloaded on 9/25/2018 11:20:51 AM.
This article is licensed under a Creative Commons Attribution-NonCommercial 3.0 Unported Licence.

Paper

68 M. Wagener, J. Sadowski and J. Gasteiger, J. Am. Chem. Soc.,
1995, 117, 7769–7775.
69 H. Bauknecht, A. Zell, H. Bayer, P. Levi, M. Wagener,
J. Sadowski and J. Gasteiger, J. Chem. Inf. Model., 1996, 36,
1205–1213.
70 L. Saiz-Urra, M. P. Gonzalez and M. Teijeira, Bioorg. Med.
Chem., 2007, 15, 3565–3571.
71 B. Bordas, I. Belai and T. Komives, J. Agric. Food Chem., 2011,59, 2863–2869.

This journal is © The Royal Society of Chemistry 2018

RSC Advances

72 C. Probst, S. Schneider and P. Loskill, Curr. Opin. Biomed.
Eng., 2018, 6, 33–41.
73 S. F. Mao, D. Gao, W. Liu, H. B. Wei and J. M. Lin, Lab Chip,
2012, 12, 219–226.
74 D. Gao, H. Li, N. Wang and J. M. Lin, Anal. Chem., 2012, 84,9230–9237.
75 D. Gao, H. X. Liu, J. M. Lin, Y. N. Wang and Y. Y. Jiang, Lab
Chip, 2013, 13, 978–985.

RSC Adv., 2018, 8, 32440–32453 | 32453