Advances in dynamic microphysiological organ-on-a-chip: Design principle and its biomedical application

Accepted Manuscript Title: Advances in Dynamic Microphysiological Organ-on-a-Chip: Design Principle and its Biomedical Application Authors: Sang Hun Lee, Bong-Hyun Jun PII: DOI: Reference:

S1226-086X(18)30848-7 https://doi.org/10.1016/j.jiec.2018.11.041 JIEC 4278

To appear in: Received date: Revised date: Accepted date:

29 September 2018 18 November 2018 20 November 2018

Please cite this article as: Sang Hun Lee, Bong-Hyun Jun, Advances in Dynamic Microphysiological Organ-on-a-Chip: Design Principle and its Biomedical Application, Journal of Industrial and Engineering Chemistry https://doi.org/10.1016/j.jiec.2018.11.041 This is a PDF file of an unedited manuscript that has been accepted for publication. As a service to our customers we are providing this early version of the manuscript. The manuscript will undergo copyediting, typesetting, and review of the resulting proof before it is published in its final form. Please note that during the production process errors may be discovered which could affect the content, and all legal disclaimers that apply to the journal pertain.

Advances in Dynamic Microphysiological Organ-on-a-Chip:

SC RI PT

Design Principle and its Biomedical Application

Sang Hun Leea, Bong-Hyun Junb,*

Department of Bioengineering, University of California Berkeley, 94720, CA, USA

b

Department of Bioscience and Biotechnology, Konkuk University, 1 Hwayang-dong, Gwanjin-gu,

U

a

M

A

N

Seoul 143-701, Korea

*Corresponding author: Prof. Bong-Hyun Jun, Ph.D

A

CC

EP

Graphical Abstract

TE

D

Email address: [email protected], Tel. +82-2-450-0521

1

SC RI PT U N

Abstract

A

Recently, microfluidic organomimetic technology with precise spatiotemporal fluid control has

M

offered unprecedented benefits to create physiologically-relevant in vitro organ models by

D

recapitulating subtle organ-specific variations. The fundamental design principle of the microfluidic

TE

organ-on-a-chip (OoC) platform is founded on ‘reverse engineering’ living organs, which are deconstructed to recapitulate their essential function. In addition, OoC has leveraged recapitulation of

EP

multiorgan-level function with inter-connection and has modeled human pathophysiology. This review aims to highlight recent advances of the microphysiological dynamic OoC platform, exploring its

CC

biomedical and personalized medicine applications. We will discuss the critical aspects of OoC

A

development and provide guidance to researchers to build physiologically-relevant OoCs in terms of cell source, perfusion flow, micro-sized biomimetic organ architecture, and mechanobiological motion. Finally, future directions for multi-OoCs are discussed along with the technical challenges encountered in drug development pipelines of the pharmaceutical industry.

2

Keywords: Organ-on-a-Chip (OoC); Microphysiological Systems; induced pluripotent stem cell; dynamic perfusion flow; biomimetic organ architecture; mechanobiological motion; multi organ-on-a-

SC RI PT

chip; pharmacokinetic-pharmacodynamics model

1. Introduction

Organs-on-chips (OoCs) are emerging as innovative platforms to allow academic and industrial researchers to investigate organ physiology and to discover previously unknown drug candidates [1-4]. During the last few years, several organ-on-a-chip systems have been developed, including the brain

U

[5-7], blood-brain-barrier (BBB) [8-11], heart [12-14], liver [15, 16], lung [17-20], kidney [21],

N

gastrointestinal tract [22], pancreatic islet equivalents [23-25], vascular networks [26], and cancer

A

models [27, 28], and several OoC platforms have already shown promising results. In this sense,

M

advancements in the OoC platform can provide a robust and miniaturized platform to interpret disease

D

mechanism and progression (Fig. 1). It can also help to perform efficient clinical trials of drugs and to

TE

identify molecular biomarkers at a personalized level [29]. In particular, the current pipelines for new drug development require billions of dollars to approve a drug and significant amounts of time,

EP

including long periods of drug-discovery, preclinical stage, multiple clinical stages I, II, and III [30].

CC

Therefore, plugging OoCs into the drug development pipeline could considerably improve the drug development process by reducing the cost and periods for preclinical stages [31]. In addition, a variety

A

of high-profile programs from various funding sources have been underway over the past several years [32, 33]. The Defence Advanced Research Projects Agency (DARPA) and the National Institutes of Health (NIH) have funded ten-organoid projects and an OoC program in a major organ for drug screening, respectively [31, 34]. Despite this enormous potential at the organ level, the field is still mostly in its early stages. Because creating an OoC platform is a complicated process, there are 3

numerous obstacles to be overcome. Remaining challenges to overcome include i) reproducing the architectural complexity of living organs in a miniaturized fashion, ii) coupling individual organs into the interconnected single platform to recapitulate the human organ interactions, iii) building a fully automated analysis system for more accurate representations as a substitute of natural organs, and iv)

SC RI PT

clinical trials and the regulatory hurdles for Food and Drug Administration (FDA) approval. As mentioned above, this approach provides unprecedented value in fundamental and industrial research, beyond what has been accomplished with traditional culture systems or animal models [35]. Specific factors for human organ-level function are as follows: i) the dynamic flow provides an in vivo-

U

like microenvironment with mechanical stimuli as well as insight into cellular behavior. By mimicking

N

dynamic blood flow and investigating how different tissues physically interface with each other in

A

living organs, OoC offers a more systematic approach to test new drug candidates as well as human

M

pathophysiology than other in vitro methods. ii) The intercommunication between multiple organs in the human body plays an important role in determining cost-effective pharmaceutical drug trials, while

D

this intercommunication is absent in most traditional in vitro cell culture systems. For instance, static

TE

cell culture models at a single organ level often fail to fully recapitulate the critical aspects of human physiology because conventional cell culture approaches could not be adapted for the inter-

EP

communication study between multiple tissues. Most OoC systems model only one or two aspects at a

CC

time, and it remains to be seen if one platform will emerge as a model of all aspects of human physiology [36].

A

In this article, we summarize recent research efforts aimed at the dynamic microphysiological

OoCs by reconstructing physiological microenvironments in vitro. Therefore, the focus of this review is fundamental design principles of OoCs that require critical reconstitution of the organ-organ interface with perfusion flow, living organ-relevant microarchitecture, and mechanobiological motions as well as a reliable cell source. 4

2. Design factors of organ-on-a-chip for cellular physiology The fundamental design principle of OoC development is the conceptual reconstruction of a

SC RI PT

living organ system in vitro to emulate their essential functions as a whole [3, 37, 38]. In this section, we will discuss the critical aspects, such as structural, functional, biochemical, and mechanical features, of OoCs and give more guidance to researchers to construct more physiologically relevant OoCs as shown in Fig. 2. For instance, one representative pioneering work in the OoC field is the biomimetic lung-on-a-chip microdevice [18, 19]. Huh et al. created a microfluidic OoC device to reproduce human

U

alveolar-capillary interfaces emulating a fundamental unit of the human lung through 3D compartments,

N

vacuum-mediated cyclic mechanobiological stimulation, and medium perfusion. This excellent work

A

includes most functional aspects of the OoC to be considered for human organ physiology; however,

M

most OoCs do not manage all aspects or consider a few aspects to answer simple biological questions.

D

Therefore, specific factors should seriously be considered to recapitulate the human organ-level

EP

TE

function regarding the following four aspects.

CC

2.1 Cell sources for personalized medicine Tailoring OoC platforms to the individual patient is a powerful aspect of this technology, and it

A

aligns well with the current paradigm shift towards personalized medicine applications. The basic issue in OoCs is that each organ must correctly exhibit the authentic functionalities of the organs it is representing. For this purpose, various organ-specific cells can be used for OoCs to display their functionality. Therefore, the most critical consideration for the OoC model is the selection of a suitable cell from an appropriate cell source to reproduce the desired organ function. In most cases in OoCs, 5

immortalized organ-specific cell lines, which were obtained from primary cells via genetic modification, have been widely utilized due to the facile handling and growth. However, it is known that they do not perfectly exhibit the organ-specific functions as seen in the original organ because they are immortalized through genotypic and phenotypic drifting modification [39]. Therefore, primary cells

SC RI PT

isolated from the specific organs without any genetic modification have been used in the OoC platform. These primary cells are mature cell types that exhibit the most phenotypically similar functions to their native in vivo environment. Notwithstanding this significant advantage for use in OoCs, their functionality, such as gene and protein expression, are altered after only a few days in in vitro culture. In particular, the isolation and in vitro culture of adult primary cells (e.g., brain) are more challenging

U

due to the post-mitotic nature of these cells [25]. In sum, it is difficult to use for routine cell-based

N

assay due to the unstable availability of organ-specific cells, their finite lifespan, and insufficient

M

A

phenotypic organ-level functions [40].

As a promising alternative approach, it is now possible to engineer human in vitro systems

D

amenable to pathophysiological study and disease progression with human induced pluripotent stem

TE

cells (iPSCs) derived from patients [12, 41, 42]. The iPSC harboring genetic information from patients, which allowed for reprogramming of fibroblast into stem cells, can be differentiated into specific cell

EP

lineages, such as neurons, several blood lineage cells, and cardiomyocytes [43-48]. Therefore,

CC

advances in generating iPSCs derived from human organ-specific tissues offer an unprecedented opportunity to create self-organizing mini-organ systems, which are better predictors of drug toxicity

A

[49, 50]. For instance, iPSCs allow the use of patient-specific cells and gene-editing techniques, such as CRISPR-Cas, enabling studies into the effects of specific gene mutations on organ functionality and how subsequent therapeutic treatments interact mechanistically. Additionally, there are some commercially available organ-specific cells derived from human iPSCs, and many studies have been published suggesting that organ-specific functions can be induced with human iPSCs. Schepers et al. 6

reported a perfusable 3D liver-on-a-chip, which employed human iPSCs from a patient [51]. This platform described a human liver model based on hepatocyte aggregates from iPSC-derived cells to characterize liver functions and had a lifetime of 28 days under continuous perfusion. As another example, Mathur and colleagues at UC Berkeley reported that human iPSC-derived cardiac tissue-

SC RI PT

based pharmacological studies validated their functionality by comparing the drug effect at half maximal inhibitory/effective concentration values (IC50/EC50) [49].

2.2 Dynamic flow-mediated precision perfusion control

U

Tissues and multicellular structures in living organs experience various interfacial mechanical

N

forces, such as blood flow, compression, and tension, and are remodeled for precise control of cellular

A

behaviors, which has been recognized as important factors for various physiological processes [18, 52].

M

The precise control of flow permits accurate models of the living organ to be built at the microscale [53,

D

54]. In this context, perfusion is one of the most critical aspects in OoC, as it provides shear stress that

TE

affects gene expression, cellular morphology, and cell polarity [55]. Further, perfusion of 3D culture is almost reserved in only microfluidic technology since the compartmentalized characteristics of

EP

microfluidics allow precise perfusion control of the adjacent culture media. Benefits associated with perfusion flow include stable nutrient supply, removal of waste metabolites, and control of oxygen

CC

tension. In addition, it is useful to understand the relationship between the flow rate of culture media

A

and chamber height, which cooperatively estimate the fluid velocity within the cell culture chamber [56]. If the cell culture chamber has a rectangular shape, the average fluid velocity is given by: 𝑣=

7

𝑄 𝑤ℎ

where, v: average fluid velocity (m/sec), Q: culture media flow rate (m3/sec), w: chamber width (m), and h: chamber height (m). If the high mass transport of oxygen and nutrients at a given chamber height is needed, the flow rate should be increased. As a simultaneous event, cells are exposed to the increased shear stress. If a constant fluid velocity is desired, increasing the chamber height causes

SC RI PT

increased consumption of media (increased Q) in the non-recirculating system. One significant fact is that endothelial cells in the human body are exposed to fluid-induced shear stress across the blood vessel between the cells and the surrounding matrix [57]. Therefore, in the initial stage of OoCs, the dynamic flow-induced shear stress has been widely used to study the effects

U

of interfacial flow on cellular adhesion, mechanics, morphology, and growth [58]. Recent studies have

N

focused on reproducing physiologically relevant shear stresses to understand their effects regarding the

A

functionality of specific tissues and organs [59, 60]. In addition, the flow-induced shear stress can

M

affect the cellular polarity, which has also been extensively investigated in absorptive cells types facing the lumen of an organ, such as the kidney and intestine [61]. In particular, the apical membrane surface

D

of endo/epithelial cells facing the organ lumen has the more specialized domain because the apical

TE

surface includes most of the proteins essential for organ-specific functions, such as nutrient absorption or resorption and digestion [62]. Such mechanical stimulation has also been known as critical

EP

determinants for differentiated functions of cells and tissues in the physiological process [63, 64].

CC

Another significant factor is gradient-driven biochemical signaling, which is found in many biological phenomena, including angiogenesis, invasion, and migration [65-67]. A diffusive mixing of laminar

A

flow in OoCs at a low Reynolds number results in generating spatially and temporally controlled stable gradients of biochemical molecules. For instance, microfluidic gradient generators are focused on creating different types of biomolecular gradients and understanding their biological effects in 2D cellular microenvironments (i.e., chemotaxis) [68, 69]. Numerous studies have reported 3D biochemical microenvironments to reproduce biological processes occurring in the human body. For 8

example, hydrogel-incorporating microfluidic OoCs between surface-accessible microchannels have been used to study angiogenesis with the gradients of soluble growth factors in in vivo-like 3D environments [70-72]. To reproduce the dynamic flow in OoCs, pump or pumpless fluidic actuation, perfusion path,

SC RI PT

and circulation method should be considered to leverage the circulatory system of a living organ as shown in Fig 3. First, the pumps are commonly used to generate and regulate microflow [73]. However, the number of samples and tube connections could be problematic, especially when the throughput is increased. Therefore, a pump-integrated device, such as pressure-driven flow (which has a high

U

fabrication cost) [74], or pump-free approaches have been demanded. A pump-free OoCs platform,

N

which is a passive microflow generating system, is divided into three categories based on its fluid

A

actuation mechanisms: gravity-driven [75, 76], surface tension-driven [77, 78], and osmosis-driven

M

flow [79, 80]. In addition, by altering the microfluidic channel geometry and applied flow rates, more complex gradient patterns are possible. Since microfluidics enable spatial control over fluids, the

D

gradients can be precisely controlled and used for angiogenesis and tumor invasion assays. For

TE

example, a biomimetic liver-on-a-chip reconstructs hepatic microarchitecture through the endotheliallike barrier. The functional unit of this liver-on-a-chip consists of a central chamber for culturing the

EP

liver cell and a surrounding microchannel for the nutrient supply. It is isolated by microfabricating the

CC

in vivo barrier-like structure with narrow slits (2 µm in width) that imitate the highly permeable biomimetic barrier structure between the liver sinusoid and hepatocytes. This biomimetic device

A

closely approximates the transport of nutrients and waste products in the liver sinusoid. Quasi-Vivo (Kirkstall, UK) developed a commercially available perfusion chamber bioreactor, which has the interconnected cell culture flow system and provides in vivo-like conditions for cell growth [81]. The main feature of this system is the ability to apply various flow rates dependent on the cell type and to provide constant nutrient turnover to cells without imposing high shear stress or turbulent flow [82, 83]. 9

Static organ culture Static fluidic connections via the Transwell between individual organs depend on the physical proximity of the organ compartments through the diffusion force [37, 84]. For instance,

SC RI PT

transport of biochemical factors and cell-cell communication in the Transwell are facilitated by diffusion and gravitational forces to direct the bottom side in the same well [85]. As an alternative approach, multiple organ types can be cultured within wells (each containing a tissue-specific medium)

Unidirectional perfusion through microfluidic inter/intra-connections in

N

Single-pass perfusion flow

U

and then connected via the tube or microfluidic channel to each other [86].

A

multiple organ chambers enables modeling of cellular signaling molecules transport as it enters the

M

vascular system and travels through all individual organ chamber. These organ systems can be designed to arrange the individual chambers in serial, parallel, or both. Among various perfusion

D

methods, the gravity-driven fluid flow can be used for this purpose, which eliminates the need for an

TE

external pump. This flow circulation method has been successfully validated in many anti-cancer and anti-vascular drugs. However, the unidirectional flow only enables organs located downstream, thereby

CC

EP

upstream feedback signaling in the in vivo circulatory environment is not feasible.

Multiple passes/recirculating perfusion flow

Microfluidic

connections

with

continuous

A

perfusion to more closely mimic the in vivo circulatory system at physiological cell volume-to-liquid flow rate ratios. These microfluidic perfusions rely on recirculation of the outgoing media from a single-pass flow through predetermined microfluidic paths to reproduce organ communication both upstream and downstream [87]. The recirculation of drugs, their secretome, and metabolites also enable long-term studies of pharmacokinetics (PK) [74, 88]. For instance, Miller and Shuler have developed a 10

pumpless 14 compartment microphysiological platform [89]. This system was designed to mimic organ-level physiological interaction by capturing the relationship between the organ volume and the residence time of 13 organs. The gravity-driven flow on a custom programmed rocker platform enabled pumpless operation. Another platform adopted pneumatic pressure-driven medium circulation with a

SC RI PT

microplate-sized format for open-well access [74]. This multi OoC design enabled the simplified fluidic actuation setup and improved potential compatibility with pipette-friendly liquid handling and analytical tools, including liquid chromatography and mass spectrometry analysis. Another approach for perfusion allowed connection of two or more independently perfused, parallel porous microchannels lined with different cell types on opposite sides, to create alveolar-capillary or blood-

A

N

U

brain-barrier interfaces within a single microfluidic device [87, 90].

M

2.3 Biomimetic organ-like microarchitecture

D

The transition to 3D cell culture techniques is an important step in a trend towards biomimetic tissue

TE

and organ models [91]. Current advances in microfabrication technique have shaped more complex and physiologically relevant microenvironments for organ culture that reconstruct in vivo-like

EP

microstructures. From several reported OoC systems, researchers successfully demonstrated that cells cultured within these OoCs showed physiological functions that are similar to those exhibited within in

CC

vivo organs. Such examples demonstrate the potential of OoC systems that correctly reproduce the

A

physiological organ-level structural functions to screen new therapeutics [92]. In particular, as shown in Fig. 4, there are major approaches to reproduce physiologically relevant organ-like microarchitectures: endothelial-like fluidic barrier in 2D single-layer microfluidic devices, 3D compartmentalization, such as a vertical stack by a porous membrane [93] or 3D spheroid-like cellular aggregation, and hydrogel-assisted 3D networks. 11

The organ models require regulation in 3D environments for better physiological accuracy, such as the functional units of liver sinusoids, brain cerebral cortex structures, bone marrow hematopoietic niches, and pancreatic islets [5, 6, 23, 94]. The in vivo organ-like microarchitecture can more closely reproduce 3D microarchitectures and physiological interfaces with closely interrelated multiple cell

SC RI PT

types in the OoC platform [38]. Therefore, the critical aspect of developing complex living organ systems is the ability to control and mimic 3D biological architecture with distinct modules and spatiotemporal chemical microenvironments [95]. Mainly, a porous membrane-based geometrical 2D and 3D compartmentalization can broadly mimic organ structures and functions and create isolation

The microfluidic neuron-on-a-chip device as a 2D geometrical

A

Geometrical 2D compartment

N

U

between differentiated well-defined environments [96].

M

compartment has been used to organize directed axonal growth of neuronal cells to construct the physiological networks between neurons as found in vivo. This OoC platform for neurons consists of

D

narrow microgrooves in the center position to bridge the cell culture chambers and the neuron culture

TE

chambers in two side channels. Taylor et al. also developed a compartmentalized microfluidic device to

EP

guide the growth of axons and dendrites by using parallel microgrooves, which allowed them to visualize and manipulate synapses, such as pre- and post-synaptic cell bodies [5]. Peyrin et al.

CC

fabricated a similar microfluidic system with two compartments connected by an asymmetrical microchannel, ‘axon diodes,’ to generate oriented neuronal networks [97]. As another interesting

A

approach, Takeda et al., reported that the transfer of tau between neurons using microfluidic neuronon-a-chip [98]. This OoC comprises three distinct neuron culture compartments that are connected through microgrooves to form the synaptic connections between neurons from different compartments. The axonal input of tau species from the 1st neuron compartment was transferred into the 2nd neuron

12

compartment, indicating that the tau species was involved in neuronal uptake and propagation along with coupled neurons. The geometrical compartment can also be used as a shield from direct convective flow by the microfabricated structure. Luke Lee group at the University of California Berkeley reported that the

SC RI PT

individual culture unit, which was designed with a C-shaped ring, effectively decoupled the central cell growth regions from the outer fluid transport channels (shear stress: < 0.01 Pa) [99]. The chamber layout reproduced physiological tissue conditions by implementing an outer channel for convective blood flow that fed cells through diffusion into the low shear interstitial space. A high-aspect-ratio, C-

U

shaped cell culture chamber achieved greater uniformity in mass transport properties without limiting

N

mass transport (10 sec nutrient turnover). A similar approach was used in a liver‐on‐a‐chip mimicking

A

hepatic microarchitecture containing a permeable endothelial barrier between hepatocytes and the liver

M

sinusoid (scale bar, 50 µm) [100, 101]. In the geometrical 2D compartment, microstructure-based compartments, such as micro posts, and parallel cell culture chambers are widely used in various

D

research fields. A microfluidic bone‐on‐a‐chip model comprising four parallel channels isolated by 100

TE

µm gaps via micro posts allowed for paracrine communication between ECs and stromal cells

CC

EP

(connective tissue) during vessel formation.

Porous membrane-based 3D compartment

Recently, thin and flexible porous membrane-

A

based vertical 3D compartments to recapitulate biomimetic organ-like microarchitecture were widely used in OOC devices, although this approach is typically associated with 2D culture [102]. The compartmentalized organ chamber was separated by an ECM-coated thin porous PDMS, polyester, polycarbonate, and polyethylene terephthalate (PET) membranes between two vertical PDMS microfluidic stacks. As an example, the concept of 3D microarchitecture and physiological interfaces 13

applied to lung-on-a-chip devices by constructing vertical stacks via multi-layer microfluidic devices. Ingber group at Harvard University described the human lung ‘small airway-on-a-chip,’ which consisted of bronchiolar epithelium and underlying functional vascular endothelium to mimic air flow for the lung pathophysiology. To construct the airway-on-a-chip, a microfluidic device was devised

SC RI PT

with an upper channel separated from a parallel lower vascular channel by a thin, porous polyester membrane at 10 µm in thickness and 0.4 µm in pore size [103]. This small airway chips lined with chronic obstructive pulmonary disease (COPD) epithelia recapitulated features of the disease, including selective cytokine hypersecretion, increased neutrophil recruitment, and clinical exacerbations by exposure to pathogens. Similar approaches were used to develop microfluidic models, such as kidney,

U

liver, intestine, skin, and cancer models. The skin‐on‐a‐chip device comprised of three PDMS

N

interlayers and two PET porous membranes (pore size: 0.4 μm), which allowed for stacking of multiple

A

cell types present in the skin, such as epidermal (HaCat), dermal (fibroblast), and endothelial (human

M

umbilical vein endothelial cell, HUVEC) components [104]. Skin inflammation and edema were

D

induced by applying a tumor necrosis factor (TNF)-alpha on the dermal layer to demonstrate the

TE

functionality of the system. A microfluidic kidney‐on‐a‐chip model was integrated with a stacked PDMS channel (upper layer) and a PDMS well (bottom layer) separated by a porous polyester material,

EP

which allowed for physiologically relevant 3D kidney microarchitecture [105]. As a model cell,

CC

primary rat kidney IMCD cells were cultured, and the fluidic shear stress of 1 dyne/cm2 for a time period of 5 h was applied to generate in vivo-like tubular environments. The cell polarization and

A

rearrangement of the cytoskeleton and cell junctions were enhanced in kidney IMCD cells. A gut‐on‐a‐ chip device was developed containing a flexible porous ECM‐coated membrane lined by gut epithelial cells in the middle and vacuum chambers on both sides to mimic intestinal peristalsis [106]. A similar two-layer microfluidic device with a porous membrane was also designed to produce a 3D metastatic

14

cancer model to study the interactions between circulating breast cancer cells and microvascular endothelium under physiological flow conditions [107].

The spheroids can mainly be comprised of homotypic-

SC RI PT

3D Spheroid-based microarchitecture

(single cell type) and heterotypic-spheroids (multiple cell types), which can have different ratios of cells to better mimic the cellular heterogeneity found in a living organ (i.e., pancreatic islet) [23, 25, 35]. All cells in a 3D spheroid grow in close contact, thus replicating the cellular signaling and physical interface observed in living organs. In particular, 3D spheroid models are thought to be suitable in vitro

U

model for screening anticancer therapeutics in oncology due to their ability to more accurately mimic

N

the key features found in solid tumors, such as cellular heterogeneity (different cell layers), 3D

A

microstructure containing cell-cell physical interface, cellular signaling, growth kinetics, and drug

M

resistance [108, 109]. Currently, there are commercially available products for physiological study and high throughput screening of anticancer therapeutics. Further, several techniques are available to

D

effectively form spheroids, including hanging drop method, liquid overlay technique, and microfluidic

TE

compartment- and spinner flasks-based techniques [110-112]. Moreover, different methods are used to

EP

generate 3D spheroids, which are categorized into two main parts: scaffold-based spheroid (hydrogel and inserts) and scaffold-free spheroid. In the scaffold-based spheroid, cells are anchored to a 3D

CC

matrix, such as collagen, chitosan, and polycaprolactone, which mimics the extracellular matrix (ECM) architecture. In contrast, scaffold-free spheroids are formed by cellular aggregates, commonly known

A

as spheroids.

Recently, Lee et al. at the University of California Berkeley reported on a microphysiological OoC platform that allows the uniform 3D spheroid formation of pancreatic β-cell islets for an oxidative stress-induced diabetes model study [23]. This pancreas-on-a-chip device consisted of a half-sphere15

shaped islet spheroid culture chamber surrounded by a perfusion flow network, which was inspired by the in vivo pancreatic islet structure. This approach demonstrated that a chronic glucolipotoxic substance exposure to the pancreatic islet spheroids could cause a state of redox imbalance and oxidative stress, which could lead to β-cell dysfunction in insulin biosynthesis and apoptosis, finally

SC RI PT

resulting in metabolic perturbations associated with diabetic diseases. Another example is a spheroidbased 3D liver-on-a-chip to investigate the interactions between hepatocytes and hepatic stellate cells (HSC) [113]. The osmotic pump-based continuous medium flow assisted the formation and long-term maintenance of spheroids. The co-cultured hepatocyte spheroids with HSCs showed improvement of liver-specific function, such as albumin secretion, urea synthesis, and glycogen storage, via the

U

formation of tight cell-cell contacts. Choi et al. reported on size-controllable networked neurospheres

N

as a 3D neuronal tissue model for an Alzheimer’s disease study [114]. The concave microwell array-

A

based 3D neurosphere was used to study the neurotoxicity of amyloid beta. The neurite degradation

M

after amyloid beta exposure and neurosphere apoptosis was observed in a similar manner with the

D

appearance of pathophysiological features of Alzheimer’s disease. Wang et al. constructed the colon-

TE

on-a-chip device using a microwell array (150 µm in diameter, 150 µm in depth) to capture colon spheroid [115]. The captured colon spheroids were then embedded in a Matrigel to provide an ECM

EP

environment, which is crucial for spheroid growth. As a cancer-on-a-chip model, a co-culture

CC

environment for the 3D spheroid was created using a microfluidic hanging drop system. Frey et al., demonstrated a continuously perfused chamber array containing both liver and colorectal cancer

A

spheroids [116]. This system was used to study drug metabolism and toxicity. The liver spheroids metabolized a chemotherapeutic compound, thus they were exposed to the colorectal cancer spheroids downstream of the metabolized drugs. Kilic and colleagues describe that multi-layer brain-on-a-chip platform to mimic the CNS microenvironment [117]. This brain OoC platform was designed to differentiate the human pluripotent 16

stem cell into a mixed population of mature neuronal and glial cells and utilized to study a variety of chemotaxes, such as neurotoxicity and drug delivery studies. Another interesting approach was described as a 3D Alzheimer’s disease (AD) brain platform, which is able to culture three types of neurons [118, 119]. This brain OoC model consisted of two chambers mimicking the in vivo AD

SC RI PT

environment to observe the interaction between neuron, astrocytes, and microglia. This AD brain model mirrored microglial recruitment, neurotoxic activities, showing beta-amyloid aggregation, phosphorylated tau accumulation, and neuro-inflammatory activity. In addition, 3D brain model for tetra cell culture was reported to reproduce the BBB model [120]. This tetra culture platform comprised an endothelial cell-lined vascular compartment and an ECM-embedded brain tissue compartment to

N

U

culture neuroblastoma, microglia, and astrocytes.

A

Recently, Pamies and his colleagues at Johns Hopkins University have created an iPSC-derived

M

‘3D mini-brain model, comprised of differentiated mature neurons and glial cells, including astrocytes and oligodendrocytes [121, 122]. This mini-brain model could mature over eight weeks and showed

D

critical brain-like structures and functionality. To evaluate its brain-like functionality, researchers

TE

placed a mini-brain on the microelectrode array and measured the spontaneous electrical interconnection between neurons as test drugs were added. This neurophysiological ‘mini-brain’ model

EP

can be used to investigate neuro-neuroglia function as well as pathogenic mechanisms including

A

CC

Alzheimer’s and Parkinson’s diseases, and multiple sclerosis.

Hydrogel-incorporated 3D microarchitecture

In classical culture techniques, the spatial control

to study cell migration and invasion is usually achieved by a membrane in a Boyden chamber, which consists of a cylindrical cell culture insert with a polycarbonate membrane at the bottom and the larger cell culture plate nesting the insert [123, 124]. A recent trend is the microfluidic OoC systems 17

embedded hydrogels that offer a more physiologically relevant 3D matrix in the OoC [125-127]. In particular, hydrogels provide an intrinsic microenvironment in which cell can cluster together without the need for surface adhesion. In addition, spatial control over hydrogels can be achieved using guiding structures, such as ridges, pillars, or posts [128, 129]. Hydrogels used in the OoC platform can be

SC RI PT

categorized into two distinct groups: natural and synthetic hydrogels. Natural hydrogels are comprised of collagen, fibrin, hyaluronic acid, Matrigel, and derivatives of natural materials, such as chitosan, alginate, and silk fibers. These hydrogels are known as the most physiological hydrogels, which have a similar composition to in vivo ECM components; however, it is difficult to reproducibly control their microstructures and properties. In contrast, synthetic hydrogels, such as poly(ethylene glycol)

U

diacrylate, poly(vinyl alcohol), and poly(acrylamide), are more reproducible, and their microstructure

N

depends on precise polymerization conditions. This synthetic hydrogel offers more diverse strategies

M

A

for tuning mechanical properties via changing chemical compositions. Bischel et al. demonstrated an interesting technique to pattern cells inside a hydrogel in the

D

microfluidic channel [130]. Due to the fluidic properties and differences in viscosity and pressure, a

TE

liquid can create a lumen inside the hydrogel. In order to fabricate standardized microfluidic OOC platforms, Trietsch et al. demonstrated a microfluidic 3D OoC device in a conventional 96-plate

EP

containing individually accessible chambers, which were patterned with hydrogel via phase-guides

CC

[131]. Perfusion flow was sustained by passive leveling between two reservoirs without the use of external pumps. Another example, by patterning a hydrogel between two fluids, stable and predictable

A

linear gradients were formed. Many reports have constructed the gradient formulation of various biochemical factors using the microfluidic network in combination with a perfusion channel [127]. Zheng et al. constructed the microfluidic vascular networks with 3D collagen scaffolds and demonstrated their functionality, such as morphology, mass transfer process, and long-term stability, by forming an endothelialized lumen in vitro [132]. Cho et al. demonstrated a 3D model of BBB on a 18

collagen-incorporated OoC platform. To validated the tightness function of the BBB model for brain, its disruption by neuroinflammation mediator with TNF-was measured, and the protective effect by drugs was also proven [133]. Another example from Roger Kamm group at MIT is that a hydrogelincorporating microfluidic migration/invasion assay can facilitate cell interactions in 3D ECM

SC RI PT

scaffolds to better investigate the complex behaviors [134]. The interior of the center channel was coated with Poly-D-lysine (PDL) to promote 3D capillary morphogenesis and two side channels acted as a control channel and a condition channel, which was filled with a hydrogel solution (type I collagen gel) to control gel stiffness. The angiogenic response to a gradient in vascular endothelial growth factor

A

2.4 Mechanobiological motion of dynamic organs

N

U

(VEGF) was evaluated.

M

The mechanobiological aspects, such as active strain, compression, and air/liquid interfaces, are

D

another functional aspect that should be seriously considered using the microphysiological OoC

TE

platform. These characteristics have a crucial role in the maintenance of many mechanically stressed tissues, such as skeletal and cardiac muscle, bone, cartilage, and blood vessels, as well as a few organs,

EP

such as the heart, lung, and intestine [50, 58, 60]. Further, the lung is normally facing the air-blood barrier for breathing movements, the gastro-intestinal (GI) tract for peristalsis motion, the skin for

CC

stretching, and the urothelium for stretching due to hydrostatic pressure. As an example of in vivo

A

mechanobiological motion, diseases of the lung, such as fibrosis, can be leveraged on a lung-on-a-chip. The basement membrane of the lung is distended to about 4% linear strain in normal breathing, while the distal area of the lung tissue undergoes a larger deformation of up to 12% during deep breathing. These distension levels of the lung alveoli strongly depend on the mechanical properties of the lower airways. In the case of lung fibrosis, the lung tissue stiffens due to a pathological accumulation of ECM 19

proteins secreted by epithelial cells and fibroblast. The stiffness of healthy lung is about 2 kPa while the stiffness of approximately 16 kPa was observed in stiffer fibrotic tissues. This leads to an increase in airways resistance to inflation and thus to a decrease in the mechanical strain in the parenchymal area. Therefore, to accurately recapitulate organ-specific dynamic mechanical microenvironments and

SC RI PT

diseases, the advanced systems need to be incorporated into OoCs to generate dynamic physical forces [135]. A number of microfluidic OOC platforms have been reported to recapitulate various types of mechanical forces. For example, it is possible to create more complex structures, such as the coupling of a microfluidic channel with a stretchable membrane. The PDMS, which exhibits great mechanical properties (its Young’s modulus: ~1 MPa) that can easily be deformed, was widely utilized as a

U

culturing membrane. For example, heart-on-a-chip models reflect the mechanobiological motion

N

measurement, such as the contractility of mature cardiomyocytes (which are highly polarized) and

A

contractile cells [13, 14]. Parker group at Harvard University has demonstrated the 3D-printed heart-

M

on-a-chip device, which could allow studies on the heart without further testing [12]. This platform

D

consisted of multilayer flexible cantilevers, an embedded strain sensor, and human iPSC-derived

over several weeks.

TE

cardiomyocytes (hiPSC-CMs), and it facilitated non-invasive analyses of tissue contractile strength

EP

As another example, as shown in Fig 5, it is possible to reconstitute breathing motions in a

CC

lung-on-a-chip by applying cyclic suction for stretching and relaxing of microchambers that run parallel to the cell culture channels, which are implemented with a thin, elastic membrane [18, 20].

A

Lung epi- and endothelial cells have been cultured on both sides to recreate the air-blood barrier at the air-liquid interface. The membrane can be stretched unidirectionally via the action by providing a vacuum in two adjacent chambers. This system was used to investigate the inflammatory response upon exposure to nanoparticles and more recently to recreate drug toxicity-induced pulmonary edema. This approach was further used to mimic the peristalsis movement of the intestinal barrier. In the 20

intestinal system, the bowel movements, such as macroscopic peristalsis-like movements and microscopic motility of villus intestinal epithelium, primarily promote the proliferation and differentiation of epithelial cells in the intestine. The ‘Gut-on-a-chip’ employs the cellular intestinal components, such as villus epithelium, gut microbiota, and immune components, as well as the

SC RI PT

mechanical components (i.e., peristalsis-like motion and flow) to reconstitute the transmural 3D lumencapillary tissue interface [22, 136, 137]. Kim et al. described the novel approach that the polymeric cell stretching modules have employed in a biomimetic human ‘Gut-on-a-chip’ microphysiological system to introduce cyclic mechanical distortions on a microfluidic device [136]. Interestingly, mechanical

N

U

deformations can restrict the aberrant overgrowth of microbial cells in the intestinal lumen [138].

A

3. Microphysiological multi organ-on-a-chip for disease modeling and PK-PD

M

Recently, the multi OoC as an advanced microphysiological analysis system has encouraged

D

researchers to challenge a more physiological in vitro platform through so-called ‘body-on-a-chip’ or

TE

‘human-on-a-chip’ [139, 140]. The multi organ-on-a-chip can provide more realistic models of human diseases. Importantly, the potential for industry adaptation of OoC may be realized sooner for

EP

mechanistic studies of disease modeling and new insights into the disease progression and mechanisms

CC

of drug action. Early development of a body-on-a-chip platform came from the need for new innovative model systems for human toxicology and drug discovery to overcome the limitation of traditional cell

A

culture approaches and animal testing [141]. To recapitulate complex organ-level dynamic responses, a variety of tissues with the critical features of the specific organ as organ equivalents should be cultured in a human-like metabolizing environment. The advantage of the physiological multi-OoC platform is an explicit adjustable fluid flow and a controllable local tissue-to-fluid ratio in microchannels and the dynamic human organ-organ interaction [142]. However, these systems still suffer from reduced cross21

conditioning and molecular crosstalk among tissues, due to the small cell count and significant substance dilution [143], and need to be improved. The Shuler group at Cornell University pioneered in this field and demonstrated one of the first body-on-a-chip devices [88, 139, 144]. To construct multiple organ-level interactions, colon cancer

SC RI PT

cells, hepatoma cells, and myeloblasts were used to test how a drug (tegafur) was metabolized by liver cells into the active metabolite 5-fluorouracil that exhibits tumor cytotoxicity [144]. Zhang and colleagues described a 3D microfluidic cell culture system with media recirculation that combined individual cell culture compartments, such as the human liver, lung, kidney, and adipose cells [145].

U

Physiological flow velocity and substance residence times were maintained to control the fluid-to-

N

tissue ratio. Recently, the Griffith group at MIT developed the body-on-a-chip platform that

A

interconnects up to 10 human organs modeled by microenvironments on a chip [146]. These

M

interconnected microphysiological systems can accurately reproduce interactions between diverse human organs and the circulation of blood substitute for up to 4 weeks. In particular, this pioneered

D

work can offer more relevant results for their efficacy, toxicity, and metabolism of new drug candidates

TE

before drug trials in humans. A similar approach by Hesperos, Inc. (Orlando, USA) was created to offer multi-organ systems with built-in biological sensors (mechanical and chemical) for systemic

EP

bioanalytics and toxicology [36]. The key technology was a pumpless four-organ (heart, liver, neuron,

CC

and skeletal muscle) system, in which the assessment of the toxicological and functional responses of five drugs was demonstrated. The incorporation of built-in sensors offered an improved ability for readouts

from

cells,

such as

contraction frequency for cardiomyocytes

and

A

functional

electrophysiological recording for neurons. Another important aspect is that these multi-OoC devices could link disease modeling and pharmacokinetics (PK)-pharmacodynamics (PD) models, which reflect ADME (absorption, distribution, metabolism, and excretion) of various drug administration routes in the physiological arrangement 22

[147]. To model ADME as the human body, the clinical data from animal model testing have been used to determine the layout and directions of flow in OOCs that can match the critical functions needed for the drug study. For example, the path of drug administration involves absorption into the bloodstream, subsequent distribution throughout the whole body, metabolism in the liver, and excretion via the

SC RI PT

kidney [148]. This ADME is related to PK and PD models, which are mathematical tools frequently used in the pharmaceutical industry as shown in Fig. 6. Simply, the PK model is useful for tasks such as dose optimization and animal-to-human extrapolation, and the PD model is used to predict and interpret the physiological effect of drugs. First, the PK model is important in the simulation and prediction of multi-organ interactions. As mimicking multi-organ interactions is important in the field

U

of OoCs, the multicomponent PK model can be applied to design and construct such an OoC system. In

N

contrast, the PD model refers to the pharmacological effect of a drug in the human body, such as the

A

drug concentration whether linear or nonlinear. As a more complex form, the PD model can also be

M

used to incorporate a time delay depending on the drug’s mechanisms, such as an anticancer drug.

D

As another serious issue, there must be a means of ensuring continuous recirculation that

TE

culture medium should be perfused and circulated through multiple organ-specific cell chambers in the multi-OoC platform. Importantly, the continuous recirculation of media over an extended period (up to

EP

a month) allows for the accumulation of secreted biological factors in the media to physiological levels.

CC

Thus, a universal medium must be developed for several organ chambers containing different cell types [41]. Therefore, the long-term circulation of a universal serum-free medium through multiple organ

A

compartments is another key innovation to solve this issue. In this context, the exclusion of serum in cell culture media is necessary to reduce variability, which could lead to variable drug testing results. However, it is also important to note that there are several important proteins present in serum. Therefore, removal of serum could be followed by substituting certain supplementary additives that are important for delivery of appropriate bio/chemical molecules to specific organs [36]. Recently, a 23

serum-free media as a universal media was used in multiple organ systems to predict drug toxicity within a four interconnected organ-on-a-chip, such as intestine, liver, skin, and kidney equivalents [84, 143]. To demonstrate the feasibility of combining multiple organs with barriers controlling drug

SC RI PT

transport, multiple organs were connected via a fixed microfluidic interconnection.

4. Future directions and remaining challenges

As mentioned previously, based on encompassing multidisciplinary approaches, we are able to design, fabricate, and apply these OoC platforms to the biomedical field. The significant efforts have

U

been made for practical use of OoC in many areas, such as drug metabolism study, efficacy, and

N

toxicity, which are typically time-consuming, expensive, and often fail to translate to clinical drug trials

A

[149]. However, OoCs provide an opportunity to reconstruct the time-dependent dynamics of multi-

M

organ interactions and reveal underlying mechanisms of drug that have yet to be studied. To achieve

D

this purpose, several engineering challenges still remain:

TE

i) The most important issue might be that the lack of available human organ-specific cells will be a limitation in OoC research as well as the entire field of human physiology. Even human cells, such

EP

as organ-specific cell lines and primary cells, are not sufficiently stable for long-term in vitro cultures

CC

that should exhibit the authentic functionality and metabolic activity of the specific organ [150, 151]. One possible alternative is the use of a stem cell. However, further development of stem cells is

A

deterred by ethical concerns. In this context, human iPSCs will be a promising alternative since it is possible to isolate them from an individual patient to permit personalized medicine. The iPSCs can also be used to differentiate to a specific organ and to analyze organ-level responses and fundamental disease mechanisms in the pharmaceutical and biomedical industry [49].

24

ii) The second challenge is the development of customized cell culture media for the multi-OoC for different purposes. Typically, the multi-organ chip platform incorporated with multiple cell types require universal cell culture medium because of the need for media recirculation through individual organ compartment. However, individual cells grow in widely discrete cell culture media with diverse

SC RI PT

compositions. In addition, it is difficult to culture multiple cell types in a single media while maintaining optimal cellular behavior. The possible alternative ways for multiple organs are an adaptation of cells to common media, the use of serum-free media, or development of a universal media formulation, which can be customized by osmolarity, pH, supplements, and an optimal balance

U

of nutrients.

N

iii) The third challenge is the need to incorporate detection and analysis methods into the

A

microfluidic device, if needed. Although nearly all current microfluidic OoC platforms employ optical

M

microscopy techniques, which allow high-resolution and real-time imaging for analyses of physiological responses [152], the closed nature of microfluidic systems hinders the analysis of

D

physiological responses. Recently, there has also been intensive research approaches to incorporate

TE

traditional, biochemical, and immunological detection methods, such as PCR and ELISA, onto microfluidic platforms [4, 23, 100]. However, drug screening or clinical trials of drug candidates

EP

replacing animal testing will require more synchronized measurement of various physiological

CC

dynamic responses that will need to be quantitatively evaluated in a high-spatially and temporally resolved manner [140, 153]. Therefore, state-of-the-art detection methods need to be integrated into the

A

microfluidic platform, such as mass spectrometry, electrophoresis, and electrochemistry [74, 154-156]. Another aspect to be considered, microfluidic OoC platforms will require more user-friendly interfaces for easy operation to be employed as platforms for fundamental and practical applications [157]. Clearly, this involves simplification and automation of device operation for common use by unskilled

25

users, such as physicians, medical chemists, and bio- or physiologists. In addition, reproducibility and reliability by unskilled users should be guaranteed as an analytical platform.

SC RI PT

5. Conclusion As described above, microfluidic OoC platforms are emerging as powerful tools to study human physiology. These pioneering efforts have replicated in vivo-relevant environments on an in vitro platform, which could leverage critical cell-blood flow, cell-biochemical factors, and multiple cellular interactions that influence cellular behaviors. Therefore, the collaborative work of material and

U

analytical chemists, cellular biologists, pharmacologists, physicians, and mechanical, electrical,

N

chemical engineers on microfluidic OoC platform will be required for significant innovation of

A

developing a comprehensive in vitro organotypic culture model that allows the simulation of multi-

M

organ drug toxicity and pharmacogenetics and a fundamental understanding of human physiology. We

EP

Acknowledgements

TE

D

hope this review article will help improve your understanding of OoCs.

This research was supported by the KU Research Professor Pro-gram of Konkuk University & funded

A

CC

by the Korean Health Tech-nology R&D Project, Ministry of Health & Welfare (HI17C1264).

Competing Interests The authors declare no conflict of interest.

26

References

[1] Z. Li, Z. Cui. Biotechnol Adv. 32 (2014) 243. [2] T. Takebe, B. Zhang, M. Radisic. Cell Stem Cell. 21 (2017) 297.

SC RI PT

[3] V.v. Duinen, S.J. Trietsch, J. Joore, P. Vulto, T. Hankemeier. Curr Opin Biotechnol 35 (2015) 118. [4] E.W. Esch, A. Bahinski, D. Huh. Nat Rev Drug Discov. 14 (2015) 248.

[5] A.M. Taylor, M. Blurton-Jones, S.W. Rhee, D.H. Cribbs, C.W. Cotman, N.L. Jeon. Nat Methods. 2 (2005) 599.

[6] J.W. Park, B. Vahidi, A.M. Taylor, S.W. Rhee, N.L. Jeon. Nat Protoc. 1 (2006) 2128.

[7] N.L. Jeon, H. Baskaran, S.K.W. Dertinger, G.M. Whitesides, L.V.D. Water, M. Toner. Nat Biotech. 20 (2002) 826.

U

[8] J.A. Brown, V. Pensabene, D.A. Markov, V. Allwardt, M.D. Neely, M. Shi, C.M. Britt, O.S. Hoilett, Q.

N

Yang, B.M. Brewer, P.C. Samson, L.J. McCawley, J.M. May, D.J. Webb, D. Li, A.B. Bowman, R.S. Reiserer, J.P. Wikswo. Biomicrofluidics. 9 (2015) 054124.

A

[9] M.v.d. Helm, A.v.d. Meer, A.v.d.B. Jan Eijkel, L. Segerink. Tissue Barriers. 4 (2016) e1142493.

M

[10] D.T. Phan, H.F. Bender, J.W. Andrejecsk, A. Sobrino, S.J. Hachey, S.C. George, C.C. Hughes. Exp Biol Med. 242 (2017) 1669.

Biomaterials. 115 (2017) 30.

D

[11] P. P.Partyka, G. A.Godsey, J. R.Galie, M. C.Kosciuk, N. K.Acharya, R. G.Nagele, P. A.Galiea.

TE

[12] J.U. Lind, T.A. Busbee, A.D. Valentine, F.S. Pasqualini, H. Yuan, M. Yadid, S.-J. Park, A. Kotikian, A.P. Nesmith, P.H. Campbell, J.J. Vlassak, J.A. Lewis, K.K. Parker. Nat Mater. 16 (2017) 303. [13] A. Agarwal, J.A. Goss, A. Cho, M.L. McCain, K.K. Parker. Lab Chip. 13 (2014) 3599.

EP

[14] A. Grosberg, P.W. Alford, M.L. McCain, K.K. Parker. Lab Chip. 11 (2011) 4165. [15] S. Knowlton, S. Tasoglu. Trends Biotechnol. 34 (2016) 681.

CC

[16] K. Domansky, W. Inman, J. Serdy, A. Dash, M.H.M. Lim, L.G. Griffith. Lab Chip. 10 (2010) 51. [17] D. Huh. AnnalsATS. 12 (2015) 42. [18] D. Huh, B.D. Matthews, A. Mammoto, M. Montoya, H.Y. Hsin, Donald E. Ingber. Science. 328 (2010)

A

1662.

[19] D. Huh, D.C. Leslie, B.D. Matthews, J.P. Fraser, S. Jurek, G.A. Hamilton, K.S. Thorneloe, M.A. McAlexander, D.E. Ingber. Science Trans Med. 4 (2012) 1. [20] K.H. Benam, R. Villenave, C. Lucchesi, A. Varone, C. Hubeau, H.-H. Lee, S.E. Alves, M. Salmon, T.C. Ferrante, J.C. Weaver, A. Bahinski, G.A. Hamilton, D.E. Ingber. Nat Methods. 13 (2016) 151. [21] M.J. Wilmer, C.P. Ng, H.L. Lanz, P. vulto, L. Suter-Dick, R. Masereeuw. Trends Biotechnol. 34 (2016) 156. 27

[22] H.J. Kim, D. Huh, G.A. Hamilton, D.E. Ingber. Lab Chip. 12 (2012) 2165. [23] S.H. Lee, S. Hong, J. Song, B. Cho, E.J. Han, S. Kondapavulur, D. Kim, L.P. Lee. Adv Health Mater. 7 (2018) 1701111. [24] F.R. Castiello, K. Heileman, M. Tabrizian. Lab Chip. 16 (2016) 409. [25] S. Bauer, C.W. Huldt, K.P. Kanebratt, I. Durieux, D. Gunne, S. Andersson, L. Ewart, W.G. Haynes, I. Maschmeyer, A. Winter, C. Ämmälä, U. Marx, T.B. Andersson. Sci Rep. 7 (2017) 14620.

SC RI PT

[26] S. Massa, M.A. Sakr, J. Seo, P. Bandaru, A. Arneri, S. Bersini, E. Zare-Eelanjegh, E. Jalilian, B.-H. Cha, S. Antona, A. Enrico, Y. Gao, S. Hassan, J.P. Acevedo, M.R. Dokmeci, Y.S. Zhang, A. Khademhosseini, S.R. Shin. Biomicrofluidics. 11 (2017) 044109.

[27] L.L. Bischel, D.J. Beebe, K.E. Sung. BMC Cancer. 15 (2015) 12.

[28] Y.S. Zhang, Y.-N. Zhang, W. Zhang. Drug Discov Today. 32 (2017) 1392.

[29] M. Rothbauer, D. Wartmann, V. Charwat, P. Ertl. Biotechnol Adv. 33 (2015) 948.

[30] J.A. Dimasi, L. Feldman, A. Seckler, A. Wilson. Clin Pharmacol Ther. 87 (2010) 272.

U

[31] A. Skardal, T. Shupe, A. Atala. Drug Discov Today. 21 (2016) 1399.

N

[32] S. Reardon. Nature. 518 (2015) 285. [33] M. Baker. Nature 471 (2011) 661.

A

[34] National Institutes of Health (2014).

M

[35] E. C.Costa, A. F.Moreira, D. Melo-Diogo, V. M.Gaspar, M. P.Carva, l. J.Correia. Biotechnol Adv. 34 (2016) 1427.

D

[36] B. Zhang, M. Radisic. Lab Chip. 17 (2017) 2395. [37] D.E. Ingber. Cell. 164 (2016) 1105.

TE

[38] S. Bhatia, D.E. Ingber. Nat Biotechnol. 32 (2014) 760. [39] S. Ahadian, R. Civitarese, D. Bannerman, M.H. Mohammadi, R. Lu, E. Wang, L. Davenport‐Huyer, B. Lai,

EP

B. Zhang, Y. Zhao, S. Mandla, A. Korolj, M. Radisic. Adv Health Mater. 7 (2018) 1700506. [40] Y.I. Wang, C. Carmona, J.J. Hickman, M.L. Shuler. Adv Health Mater. 7 (2017) 1701000. [41] T. Kanamori, S. Sugiura, Y. Sakai. Drug Metab Pharmacokinet. 33 (2018) 40.

CC

[42] B.W. Ellis, A. Acun, U.I. Can, P. Zorlutuna. Biomicrofluidics. 11 (2017) 024105 [43] I.-H. Park, N. Arora, H. Huo, N. Maherali, T. Ahfeldt, A. Shimamura, M.W. Lensch, C. Cowan, K.

A

Hochedlinger, G.Q. Daley. Cell. 134 (2008) 877. [44] K. Takahashi, S. Yamanaka. Cell. 126 (2006) 663. [45] M.J. Workman, J.P. Gleeson, E.J. Troisi, H.Q. Estrada, S.J. Kerns, C.D. Hinojosa, G.A. Hamilton, S.R. Targan, C.N. Svendsen, R.J. Barrett. Cell Mol Gastroenterol Hepatol. 5 (2018) 669. [46] T. Vierbuchen, A. Ostermeier, Z.P. Pang, Y. Kokubu, T.C. Südhof, M. Wernig. Nature. 463 (2010) 1035. [47] J. Kim, J.A. Efe, S. Zhu, M. Talantova, X. Yuan, S. Wang, S.A. Lipton, K. Zhang, S. Ding. Proc Natl Acad Sci. 108 (2011) 7838. 28

[48] J. Ellis, M. Bhatia. Clin Pharmacol Ther. 89 (2011) 666639. [49] A. Mathur, P. Loskill, K. Shao, N. Huebsch, S. Hong, S.G. Marcus, N. Marks, M. Mandegar, B.R. Conklin, L.P. Lee, K.E. Healy. Sci Rep. 5 (2015) 8883. [50] T. Sato, H. Clevers. Science. 340 (2013) 1190. [51] A. Schepers, C. Li, A. Chhabra, B.T. Seney, S. Bhatia. Lab Chip. 16 (2016) 2644. [52] E. Meier, M.T. Lam. JSM Biotechnol Bioeng. 3 (2016) 1060.

SC RI PT

[53] K. Sugimura, P.-F. Lenne, F. Graner. Development. 143 (2016) 186. [54] S. Chanet, A.C. Martin. Prog Mol Biol Transl Sci 126 (2014) 317. [55] KarlMatter, IraMellman. Curr Opin Cell Biol. 6 (1994) 545. [56] L. Kim, Y.-C. Toh, J. Voldman, H. Yu. Lab Chip. 7 (2007) 681. [57] J.W. Song, L.L. Munn. Proc Natl Acad Sci. 13 (2011) 15342.

[58] K.L. Sellgren, B.T. Hawkins, S. Gregoa. Biomicrofluidics. 9 (2015) 061102. [59] Y.-K. Wang, C.S. Chen. J Cell Mol Med. 17 (2013) 823.

N

K.W. Joo, Y.S. Kim, D.K. Kim. Exp Biol Med. 354 (2017) 48.

U

[60] S.H. Yang, D. Huh, H.A. Jo, S. Kim, C.S. Lim, J.C. Lee, H.C. Kim, H.M. Kwon, C.W. Jeong, C. Kwak,

[61] J. Theobald, A. Ghanem, P. Wallisch, A.A. Banaeiyan, M.A. Andrade-Navarro, K. Taskova, M. Haltmeier,

A

A. Kurtz, H. Becker, S. Reuter, R. Mrowka, X. Cheng, S. Wölfl. ACS Biomaterials Science & Engineering. 4

M

(2017) 78.

[62] S.-Y. Chang, E.J. Weber, K.P.V. Ness, D.L. Eaton, E.J. Kelly. Clin Pharmacol Ther. 100 (2016) 464.

D

[63] Kshitiz, J. Park, P. Kim, W. Helen, A.J. Engler, A. Levchenko, D.-H. Kim. Integr Biol. 4 (2012) 1008. [64] K.-J. Jang, H.S. Cho, D.H. Kang, W.G. Bae, T.-H. Kwon, K.-Y. Suh. Integr Biol. 3 (2011) 134.

TE

[65] S.M.N. Rao, U. Tata, V.K. Lin, J.-C. Chiao. Micromachines. 5 (2014) 13. [66] D.-H.T. Nguyen, S.C. Stapleton, M.T. Yang, S.S. Cha, C.K. Choi, P.A. Galie, C.S. Chen. Proc Natl Acad

EP

Sci. 110 (2013) 6712.

[67] J.W. Song, J. Daubriac, J.M. Tse, D. Bazou, L.L.M. author. Lab Chip. 12 (2012) 5000. [68] P.J. Hung, P.J. Lee, P. Sabounchi, R. Lin, L.P. Lee. Biotechnol Bioeng. 89 (2005) 1.

CC

[69] Y.J. Choi, S. Chae, J.H. Kim, K.F. Barald, J.Y. Park, S.-H. Lee. Sci Rep. 3 (2013) 1921. [70] S. Chung, R. Sudo, I.K. Zervantonakis, T. Rimchala, R.D. Kamm. Adv Mater. 21 (2009) 4863.

A

[71] Y. Shin, S. Han, J.S. Jeon, K. Yamamoto, I.K. Zervantonakis, R. Sudo, R.D. Kamm, S. Chung. Nat Proc. 7 (2012) 1247. [72] J.W. Song, L.L. Munn. Proc Natl Acad Sci. 108 (2011) 15342. [73] C.K. Byun, K. Abi-Samra, Y.-K. Cho, S. Takayama. Electrophoresis. 35 (2014) 245. [74] T. Satoh, S. Sugiura, K. Shin, R. Onuki-Nagasaki, S. Ishida, K. Kikuchi, M. Kakiki, T. Kanamori. Lab Chip. 17 (2017) 115.

29

[75] V.v. Duinen, A.v.d. Heuvel, S.J. Trietsch, H.L. Lanz, J.M.v. Gils, A.J.v. Zonneveld, P. Vulto, T. Hankemeier. Sci Rep. 7 (2017) 18071. [76] G.S. Jeong, J. Oh, S.B. Kim, M.R. Dokmeci, H. Bae, S.-H. Lee, A. Khademhosseini. Lab Chip. 7 (2014) 4213. [77] T.E.d. Groot, K.S. Veserat, E. Berthier, D.J. Beebe, A.B. Theberge. Lab Chip. 16 (2016) 334. [78] I. Meyvantsson, J.W. Warrick, S. Hayes, A. Skoien, D.J. Beebe. Lab Chip. 8 (2008) 717.

SC RI PT

[79] J.Y. Park, S.-K. Kim, D.H. Woo, E.J. Lee, J.H. Kim, S.H. Lee. Stem Cells. 27 (2009) 2646. [80] J.Y. Park, S.J. Yoo, C.M. Hwang, S.-H. Lee. Lab Chip. 15 (2009) 2194. [81] B. Lam, Z. Fang, E.H. Sargent, S.O. Kelley. Anal Chem. 84 (2012) 21.

[82] P. Chandorkar, W. Posch, V. Zaderer, M. Blatzer, M. Steger, C.G. Ammann, U. Binder, M. Hermann, P. Hörtnagl, C. Lass-Flörl, D. Wilfingseder. Sci Rep. 7 (2017) 11644.

[83] D. Mazzei, M.A. Guzzardi, S. Giusti, A. Ahluwalia. Biotechnol Bioeng. 106 (2010) 127.

[84] I. Wagner, E.-M. Materne, S. Brincker, U. Su¨ßbier, C. Fra¨drich, M. Busek, F. Sonntag, D.A. Sakharov,

U

E.V. Trushkin, A.G. Tonevitsky, R. Laustera, U. Marx. Lab Chip. 13 (2013) 3038.

N

[85] H.E. Abaci, K. Gledhill, Z. Guo, A.M. Christiano, M.L. Shuler. Lab Chip. 15 (2015) 882. [86] L. Vernetti, A. Gough, N. Baetz, S. Blutt, J.R. Broughman, J.A. Brown, J. Foulke-Abel, N. Hasan, J. In, E.

A

Kelly, O. Kovbasnjuk, J. Repper, N. Senutovitch, J. Stabb, C. Yeung, N.C. Zachos, M. Donowitz, M. Estes, J.

M

Himmelfarb, G. Truskey, J.P. Wikswo, D.L. Taylor. Sci Rep. 7 (2017) 42296 [87] E.-M. Maternea, A.P. Rammea, A.P. Terrasso, M. Serra, P.M. Alves, C. Brito, D.A. Sakharov, A.G.

D

Tonevitskye, R. Lauster, U. Marx. J Biotechnol. 205 (2015) 36. [88] J.H. Sung, A. Dhiman, M.L. Shuler. J Pharm Sci. 98 (2009) 1885.

TE

[89] P.G. Miller, M.L. Shuler. Biotechnol Bioeng. 113 (2016) 2213. [90] O. Kilic, D. Pamies, E. Lavell, P. Schiapparelli, Y. Feng, T. Hartung, A. Bal-Price, H.T. Hogberg, A.

EP

Quinones-Hinojosa, H. Guerrero-Cazares, A. Levchenko. Lab Chip. 16 (2016) 4152. [91] L. Sundstrom, T. Biggs, A. Laskowski, L. Stoppini. Expert Opin Drug Discov. 9 (2014) 335. [92] J.B. Lee, J.H. Sung. Biotechnol J. 8 (2013) 1258.

CC

[93] A.K.H. Achyuta, A.J. Conway, R.B. Crouse, E.C. Bannister, R.N. Lee, C.P. Katnik, A.A. Behensky, J. Cuevas, S.S. Sundaram. Lab Chip. 13 (2013) 542.

A

[94] P.J. Lee, P.J. Hung, L.P. Lee. Biotechnol Bioeng. 97 (2007) 1340. [95] A.M. Taylor, N.L. Jeon. Curr Opin Neurobiol. 5 (2010) 640. [96] F. Zheng, F. Fu, Y. Cheng, C. Wang, Y. Zhao, Z. Gu. Small. 12 (2016) 2253. [97] J.-M. Peyrin, B. Deleglise, L. Saias, M. Vignes, P. Gougis, S. Magnifico, S. Betuing, M. Pietri, J. Caboche, P. Vanhoutte, J.-L. Viovy, B. Brugg. Lab Chip. 11 (2011) 3663.

30

[98] S. Takeda, S. Wegmann, H. Cho, S.L. DeVos, C. Commins, A.D. Roe, S.B. Nicholls, G.A. Carlson, R. Pitstick, C.K. Nobuhara, I. Costantino, M.P. Frosch, D.J. Mu¨ller, D. Irimia, B.T. Hyman. Nat Comm. 6 (2015) 8490. [99] P.J. Lee, P.J. Hung, V.M. Rao, L.P. Lee. Biotechnol Bioeng. 94 (2006) 5. [100] S.A.M. Shaegh, F.D. Ferrari, Y.S. Zhang, M. Nabavinia, N.B. Mohammad, J. Ryan, A. Pourmand, E. Laukaitis, R.B. Sadeghian, A. Nadhman, S.R. Shin, A.S. Nezhad, A. Khademhosseini, M.R. Dokmeci.

SC RI PT

Biomicrofluidics. 10 (2016) 044111.

[101] P.J. Lee, P.J. Hung, L.P. Lee. Biotechnol Bioeng. 97 (2007) 1340.

[102] D. Huh, H. Fujioka, Y.-C. Tung, N. Futai, R. Paine, J.B. Grotberg, S. Takayama. Proc Natl Acad Sci. 104 (2007) 18886.

[103] K.H. Benam, R. Villenave, C. Lucchesi, A. Varone, C. Hubeau, H.-H. Lee, S.E. Alves, M. Salmon, T.C. Ferrante, J.C. Weaver, G.A. Hamilton, A. Bahinski, D.E. Ingber. Nat Methods. 13 (2016) 151.

[104] M. Wufuer, G. Lee, W. Hur, B. Jeon, B.J. Kim, T.H. Choi, S.H. Lee. Sci Rep. 21 (2016) 37471.

U

[105] K.-J. Jang, K.-Y. Suh. Lab Chip. 10 (2010) 36.

N

[106] H.J. Kim, D. Huh, G. Hamilton, D.E. Ingber. Lab Chip. 12 (2012) 2165. [107] Z. Xu, E. Li, Z. Guo, R. Yu, H. Hao, Y. Xu, Z. Sun, X. Li, J. Lyu, Q. Wang. ACS Applied Materials and

A

Interfaces. 8 (2016) 25840.

M

[108] W.J. Ho, E.A. Pham, J.W. Kim, C.W. Ng, J.H. Kim, D.T. Kamei, B.M. Wu. Cacner Sci. 101 (2010) 2637.

D

[109] D. Lv, Z. Hu, L. Lu, H. Lu, X. Xu. Oncol Lett. 14 (2017) 6999. [110] E. Fennema, N. Rivron, J. Rouwkema, C.v. Blitterswijk, J.d. Boer. Trends Biotechnol. 31 (2013) 108.

TE

[111] E.C. Costa, V.M. Gaspar, P. Coutinho, I.J. Correia. Biotechnol Bioeng. 111 (2014) 1672. [112] G.S. Jeong, D.Y. No, J. Lee, J. Yoon, S. Chung, S.-H. Lee. Nat Comm. 7 (2016) 11269.

EP

[113] S.-A. Lee, D.Y. No, E. Kang, J. Ju, D.-S. Kim, S.-H. Lee. Lab Chip. 13 (2013) 3529. [114] Y.J. Choi, J. Park, S.-H. Lee. Biomaterials. 34 (2013) 2938. [115] Y. Wang, A.A. Ahmad, P.K. Shah, C.E. Sims, S.T. Magness, N.L. Allbritton. Lab Chip. 13 (2013) 4625.

CC

[116] O. Frey, P.M. Misun, D.A. Fluri, J.G. Hengstler, A. Hierlemann. Nat Comm. 5 (2014) 4250. [117] O. Kilic, D. Pamies, E. Lavell, P. Schiapparelli, Y. Feng, T. Hartung, A. Bal-Price, H.T. Hogberg, A.

A

Quinones-Hinojosa, H. GuerreroCazares, A. Levchenko. Lab Chip. 16 (2016) 4152. [118] J. Park, I. Wetzel, I. Marriott, D. Dréau, C. D’Avanzo, D.Y. Kim, R.E. Tanzi, H. Cho. Nat Neurosci. 21 (2018) 941. [119] H. Cho, T. Hashimoto, E. Wong, Y. Hori, L.B. Wood, L. Zhao, K.M. Haigis, B.T. Hyman, D. Irimia. Sci Rep. 3 (2013) 1823. [120] Y. Koo, B.T. Hawkins, Y. Yun. Sci Rep. 8 (2018) 2841.

31

[121] D. Pamies, P. Barreras, K. Block, G. Makri, A. Kumar, D. Wiersma, L. Smirnova, C. Zhang, J. Bressler, K.M. Christian, G. Harris, G.-l. Ming, C.J. Berlinicke, K. Kyro, H.j. Song, C.A. Pardo, T. Hartung, H.T. Hogberg. ALTEX. 34 (2017) 362. [122] D. Pamies, K. Block, P. Lau, L. Gribaldo, C. Pardo, P. Barreras, L. Smirnova, D. Wiersma, L. Zhao, G. Harris, T. Hartung, H.T. Hogberg. Toxicol Appl Pharmacol. 354 (2018) 101. [123] N. Kramer, A. Walzl, C. Unger, M. Rosner, G. Krupitza, M. Hengstschla¨ger, H. Dolznig. Mutat Res Rev

SC RI PT

Mutat Res. 752 (2013) 10.

[124] J. Tóvári, K. Futosi, A. Bartal, E. Tátrai, A. Gacs, I. Kenessey, S. Paku4. Cell Adh Migr. 8 (2014) 509. [125] N.W. Choi, M. Cabodi, B. Held, J.P. Gleghorn, L.J. Bonassar, A.D. Stroock. Nat Mater. 2 (2007) 908. [126] Y. Zheng, J. Chen, M. Craven, N.W. Choi, S. Totorica, A. Diaz-Santana, P. Kermani, B. Hempstead, C. Fischbach-Teschl, J.A. López, A.D. Stroock. Proc Natl Acad Sci. 109 (2012) 9342.

[127] J.S. Miller, K.R. Stevens, M.T. Yang, B.M. Baker, D.-H.T. Nguyen, D.M. Cohen, E. Toro, A.A. Chen, P.A. Galie, X. Yu, R. Chaturvedi, S.N. Bhatia, C.S. Chen. Nat Mater. 11 (2012) 768.

U

[128] M. Kang, W. Park, S. Na, S.-M. Paik, H. Lee, J.W. Park, H.-Y. Kim, N.L. Jeon. Small. 11 (2015) 2789.

N

[129] J. Choi, S. Kim, J. Jung, Y. Lim, K. Kang, S. Park, S. Kang. Biomaterials. 32 (2011) 7013. [130] L.L. Bischel, E.W.K. Young, B.R. Mader, D.J. Beebe. Biomaterials. 34 (2013) 1471.

A

[131] S.J. Trietsch, G.D. Israëls, J. Joore, T. Hankemeier, P. Vulto. Lab Chip. 13 (2013) 3548.

M

[132] Y. Zhenga, J. Chen, M. Craven, N.W. Choi, S. Totorica, A. Diaz-Santana, P. Kermani, B. Hempstead, C. Fischbach-Teschl, J.A. López, A.D. Stroock. Proc Natl Acad Sci. 109 (2012) 9342.

D

[133] H. Cho, J.H. Seo, K.H.K. Wong, Y. Terasaki, J. Park, K. Bong, K. Arai, E.H. Lo, D. Irimia. Sci Rep. 5 (2015) 15222.

TE

[134] Y. Shin, S. Han, J.S. Jeon, K. Yamamoto, I.K. Zervantonakis, R. Sudo, R.D. Kamm, S. Chung. Nat Protoc. 7 (2012) 1247.

EP

[135] C. Moraes, Y. Sun, C.A. Simmons. Integr Biol. 3 (2011) 959. [136] H.J. Kim, H. Li, J.J. Collins, D.E. Ingber. Proc Natl Acad Sci. 14 (2015) E7. [137] H.J. Kim, D. Ingber. Integr Biol. 5 (2013) 1130.

CC

[138] A. Bein, W. Shin, S. Jalili-Firoozinezhad, M.H. Park, A. Sontheimer-Phelps, A. Tovaglieri, A. Chalkiadaki, H.J. Kim, D.E. Ingber. Cell Mol Gastroenterol Hepatol 5 (2017) 659.

A

[139] J.H. Sung, M.L. Shuler. Lab Chip. 9 (2009) 1385. [140] S.H. Lee, J.H. Sung. Adv Health Mater. 7 (2017) 1700419. [141] J.H. Sung, B. Srinivasan, M.B. Esch, W.T. McLamb, C. Bernabini, M.L. Shuler, J.J. Hickman. Exp Biol Med. 239 (2014) 1225. [142] H. Kimura, Y. Sakai, T. Fujii. Drug Metab Pharmacokinet. 33 (2018) 43. [143] I. Maschmeyer, A.K. Lorenz, K. Schimek, T. Hasenberg, A.P. Ramme, J. Hübner, M. Lindner, C. Drewell, S. Bauer, A. Thomas, N.S. Sambo, F. Sonntag, R. Laustera, U. Marxab. Lab Chip. 15 (2015) 2688. 32

[144] J.H. Sung, C. Kama, M.L. Shuler. Lab Chip. 10 (2010) 446. [145] C. Zhang, Z. Zhao, N.A.A. Rahim, D.v. Noort, H. Yu. Lab Chip. 9 (2009) 3185. [146] C.D. Edington, W.L.K. Chen, E. Geishecker, T. Kassis, L.R. Soenksen, B.M. Bhushan, D. Freake, J. Kirschner, C. Maass, N. Tsamandouras, J. Valdez, C.D. Cook, T. Parent, S. Snyder, J. Yu, E. Suter, M. Shockley, J. Velazquez, J.J. Velazquez, L. Stockdale, J.P. Papps, I. Lee, N. Vann, M. Gamboa, M.E. LaBarge, Z. Zhong, X. Wang, L.A. Boyer, D.A. Lauffenburger, R.L. Carrier, C. Communal, S.R. Tannenbaum, C.L. Stokes, D.J.

SC RI PT

Hughes, G. Rohatgi, D.L. Trumper, M. Cirit, L.G. Griffith. Sci Rep. 8 (2018) 4530. [147] H.E. Abaci, M.L. Shuler. Integr Biol. 7 (2015) 383.

[148] K. Ronaldson-Bouchard, G. Vunjak-Novakovic. Cell Stem Cell. 22 (2018) 310. [149] D. Kim, X. Wu, A.T. Young, C.L. Haynes. Acc Chem Res 47 (2014) 1165. [150] S.R. Khetani, S.N. Bhatia. Nat Biotechnol. 1 (2008) 120.

[151] N.S. Bhise, V. Manoharan, S. Massa, A. Tamayol, M. Ghaderi, M. Miscuglio, Q. Lang, Y.S. Zhang, S.R. Shin, G. Calzone, N. Annabi, T.D. Shupe, C.E. Bishop, A. Atala, M.R. Dokmeci, A. Khademhosseini.

U

Biofabrication. 8 (2016) 014101.

N

[152] T. Nakayama, H.M. Hiep, S. Furui, Y. Yonezawa, M. Saito, Y. Takamura, E. Tamiya. Anal Bioanal Chem. 396 (2010) 457.

A

[153] S.H. Lee, H.J. Ko, T.H. Park. Biosens Bioelectron. 25 (2009) 55.

M

[154] S.-B. Huang, S.-S. Wang, C.-H. Hsieh, Y.C. Lin, C. Laid, M.-H. Wu. Lab Chip. 13 (2013) 1133. [155] S.H. Lee, O.S. Kwon, H.S. Song, S.J. Park, J.H. Sung, J. Jang, T.H. Park. Biomaterials. 33 (2012) 1722.

D

[156] S.H. Lee, S.B. Jun, H.J. Ko, S.J. Kim, T.H. Park. Biosens Bioelectron. 24 (2009) 2659. [157] Y.S. Zhang, J. Aleman, S.R. Shin, T. Kilic, D. Kim, S.A.M. Shaegh, S. Massa, R. Riahi, S. Chae, N. Hu, H.

TE

Avci, W. Zhang, A. Silvestri, A.S. Nezhad, A. Manbohi, F.D. Ferrari, A. Polini, G. Calzone, N. Shaikh, P. Alerasool, E. Budina, J. Kang, N. Bhise, J. Ribas, A. Pourmand, A. Skardal, T. Shupe, C.E. Bishop, M.R.

EP

Dokmeci, A. Atala, A. Khademhosseini. Proc Naatl Acad Sci. 114 (2017). [158] L. Ren, W. Liu, Y. Wang, J.-C. Wang, Q. Tu, J. Xu, R. Liu, S.-F. Shen, J. Wang. Anal Chem. 85 (2013) 235.

CC

[159] M.B. Esch, H. Ueno, D.R. Applegate, M.L. Shuler. Lab Chip. 16 (2016) 2719.

A

[160] J.H. Sung, J. Yu, D. Luo, M.L. Shuler, J.C. March, Lab Chip. 11 (2011) 389. [161] D. Huh, H.J. Kim, J.P. Fraser, D.E. Shea, M. Khan, A. Bahinski, G.A. Hamilton, D.E. Ingber, Nat Protoc. 8 (2013) 2135. [162] W. Shin, H.J. Kim, Method Cell Biol. 146 (2018) 135.

33

Figure Legends Fig. 1. Development pathway for organ-on-a-chip (OoC) technology. The development pathway for microphysiological OoC platform is an iterative process that starts with the discovery/ideation,

SC RI PT

followed by invention/prototyping, fabrication of the scalable platform, and preclinical and clinical testing. In the early stage of development, functional characteristics for the in vitro organ model, multiparametric questions, and analytical readout methods, including on-line monitoring, should be considered to answer the intended biological questions. In addition, the appropriate complexity for prototyping OoCs should be defined, and bench tests should be executed to validate the OoC platform.

U

After carrying out this iterative process, appropriate preclinical and clinical evaluation in the validation

N

stage must proceed to discover the functional changes, drug responses, and multi-organ interactions

M

A

over periods of weeks to years.

D

Fig. 2. Schematic diagram illustrating conceptual OoC design through critical consideration, its

TE

fabrication, and OoC-based physiological data acquisition to improve physiological relevance. The advanced OoC platform represents functional human organs as a series of interconnected organ

EP

compartments. The microfluidic OoC platform can be reconstructed by interconnecting individual

CC

organ compartments, which represent different living organs, and can enable stem cell-derived organoids in the organ-like microarchitecture. A blood substitute was also recirculated through

A

surrounded microchannels during operation. In addition, the use of integrated analytics suggested that trace amounts of metabolites could be identified from each experimental unit, enhancing collection of analytical results. This synergistic strategy for constructing microphysiological OoCs can lead to advances of previous OoC models and holds the potential to simulate human drug responses in the diseased organ model. 34

Fig. 3. Perfusion and recirculation flow of OoC platforms for recreating organ–organ interactions in vitro. A) Four types of static OoC platforms presented: a) transwell, b) microtunnel, c) micropattern, and d) wells-in-a-well. B) The single-pass OoC platform connects all organ modules in a single fluid path (route 1), or with additional paths (e.g., route 2). C) Pump-driven recirculating OoC platform

SC RI PT

interconnects organ modules in a closed-loop path (loop 1) or separate fluidic pools or loops (e.g., loop 2). D) Pumpless recirculating platforms via gravity-driven flow and a rocking system to drive fluid. On, organ module; Pn, pump; Rn, reservoir; Cn, medium collector; Dn, debubbler. n represents the index of

U

a specific module. Reproduced with permission of John Wiley & Sons, Inc. [40].

N

Fig. 4. Microfluidic design for various organ microarchitectures. (a) Micropillar array-based horizontal

A

2D compartment for cardiac cell culture. (i) Device with four ports. 1, 2: central inlet and outlet, 3, 4:

M

side inlet and outlet, (ii) micropillar arrays in a microfluidic device used to construct the interface between the blood vessel and myocardial tissue. (iii) Schematic showing how the microfluidic device

D

may be used to study various microenvironmental states. Reproduced with permission of the American

TE

Chemical Society [158]. (b) Porous membrane-based vertical layer-by-layer 3D compartment. (i) Each

EP

organ chip is seeded with cells that represent the appropriate tissue: Caco-2 cells were seeded onto the GI tract chip 16 days prior to use in the device, and human primary liver cells were seeded 9 days and 2

CC

days before use in the device. Both tissues matured independently of each other before being combined for 14 days of co-culture. Reproduced with permission of the Royal Society of Chemistry [159]. (c)

A

Microfluidic 3D spheroid architecture. (i) Schematic illustration of microfluidic 3D spheroid architecture under gravity‐driven continuous hydrodynamic medium flow. Human 1.1B4 pancreatic β cells were injected into the microfluidic device and then cultured for 5 days. (ii) Scanning electron microscope (SEM) images of the islet-on-a-chip showing the endothelial-like perfusion barrier connecting the nutrient channel and spheroid culture chamber. (iii) Computational simulations of flow 35

characteristics of islet‐on‐a‐chip. Cross‐sectional images indicated as an A–A′. Streamline (red lines) and magnitude of the flow velocity of culture medium with perfusion channel (flow rate 5 nL min −1). Reproduced with permission of John Wiley & Sons, Inc. [23]. (d) Hydrogel embedded 3D architecture. (i) Photograph of the bottom of an OrganoPlate for the intestinal tubules (Mimetas, Netherlands)

SC RI PT

showing 40 microfluidic channel networks. The photo on the right side represents a single microfluidic channel network comprising three channels that join in the center. (ii) Schematic illustration of OrganoPlate. The center of a chip comprises a tubule, extracellular matrix gel, and perfusion lane. Two phase-guides (white bars) are present that define the three distinct lanes in the central channel. An extracellular matrix gel (light gray) is patterned by two phase-guides (dark gray). Reproduced with

A

N

U

permission of the Nature Publishing Group [3].

M

Fig. 5. Mechanobiological motion of organ-on-a-chips. (a) A breathing lung-on-a-chip platform. (i) Intrapleural pressure leads to stretching and contracting of the alveolar-capillary interface in the living

D

lung. (ii) A thin and elastic PDMS membrane serving as an alveolar-capillary barrier was positioned

TE

between two microfluidic channels. Cyclic motion by vacuum suction to the side chambers causes

EP

stretching of the PDMS membrane, imitating physiological breathing. Reproduced with permission [18]. Copyright 2010, American Association for the Advancement of Science. (b) Cardiac-on-a-chip

CC

model. (i) 3D-printed cardiac microphysiological device with a functional readout of cardiac contractility. The contraction of cardiac tissue leads to deflection of the cantilever that may be recorded

A

as a resistance alteration proportional to the contractile strength of the cardiac tissue. (ii) Representative traces of contractile twitch stress generated by the laminar hiPS-CMs tissues on day 2 and 28. Immunostained laminar hiPS-CMs tissues on device cantilevers at day 2 and day 28 after seeding. Scale bars 10 µm. Blue: DAPI nuclei stain. White: actinin stain. (iii) schematic illustration of the 3D printed device, showing a fluorescent image of immunostained cardiac tissues on the surface of the 36

cantilever. Reproduced with permission [12]. Copyright 2016, Nature Publishing Group. (c) Gut-on-achip with peristalsis-like motion. (i) Fluorescence image of Caco-2 cells for villi model. Caco-2 cells cultured on collagen scaffold were visualized by immunostaining of actin (green) and nucleic acid (blue). (ii) Top-view of Caco-2 villi in a gut-on-a-chip. Caco-2 villi were reconstructed in microfluidic

SC RI PT

gut-on-a-chip under peristalsis-like mechanobiological motion and fluid flow. An inset designates the vertical location of the cross-section. Scale bar represents 25 µm. (iii) Experimental workflow to reconstruct intestinal microenvironment for the gut-on-a-chip. (upper images) Schematic illustrations of the bi-layered microchannels. (bottom images) The photographs show porous membranes and coculture of intestinal villi with gut microbiome for villus morphology. Reproduced with permission of

A

N

U

the Royal Society of Chemistry [160], Nature Publishing Group [161], and the Elsevier [162].

M

Fig. 6. The concept of microfluidic multi-OoC platform. (a) Schematic illustration of pharmacokineticpharmacodynamic (PK-PD) models for (i) single and (ii) two-organ compartment PK model.

D

Reproduced from [92] with permission of John Wiley & Sons, Inc. (b) Four different organs can be

TE

incorporated into a single OoC platform through connections to a microfluidic circulatory channel. A

EP

blood substitute is perfused through the entire multi-OoC to ensure the maintenance of physiologically relevant conditions to each organ. Reproduced from [92] with permission of John Wiley & Sons, Inc.

CC

(c) The four organ-on-a chip device has four tissue culture compartments comprising intestine (1), liver (2), skin (3), and kidney (4) tissues and is interconnected by a surrogate blood flow (pink-color) and

A

excretory flow channels (yellow-color). This device consists of two polycarbonate cover plates and a PDMS-glass chip. Reproduced from [143] with permission of the Royal Society of Chemistry. All rights reserved.

37

D

TE

EP

CC

A

SC RI PT

U

N

A

M

Fig 1.

38

D

TE

EP

CC

A

SC RI PT

U

N

A

M

Fig 2.

39

D

TE

EP

CC

A

SC RI PT

U

N

A

M

Fig 3.

40

D

TE

EP

CC

A

SC RI PT

U

N

A

M

Fig 4.

41

D

TE

EP

CC

A

SC RI PT

U

N

A

M

Fig. 5

42

D

TE

EP

CC

A

SC RI PT

U

N

A

M

Fig. 6

43