A microfluidic in vitro model of the blood-brain barrier

The blood-brain barrier (BBB) limits entry of most molecules into the brain and complicates the development of brain-targeting compounds, necessitating novel BBB models. This dissertation describes the first microfluidic BBB model allowing the study of BBB properties in relation to various chemical compounds by enabling tunable wall shear stress (WSS) via dynamic fluid flow, cell-cell interaction through a thin co-culture membrane, time-dependent delivery of test compounds, and integration of sensors into the system, resulting in significant reduction of reagents and cells required and shorter cell seeding time. Use of parallel channels first enabled simultaneous monitoring of multiple cell populations under a wide range (~x15) of WSS. The microfluidic model formed the BBB by incorporating brain endothelial (b.End3) and glial (C6/C8D1A) cells at the intersection of two crossing microchannels, respectively representing luminal and abluminal sides, fabricated in a transparent polydimethylsiloxane (PDMS) substrate utilizing high-precision soft lithography techniques. The utilized cells were adopted from immortalized cells for high consistency over repeated passages and pure and proliferative culture. The developed microfluidic BBB model was validated by (1) expression of tight junction protein ZO-1 and glial protein GFAP by fluorescence imaging, and P-gp activity by Calcein AM, confirming key BBB proteins; (2) high trans-endothelial electrical resistance (TEER) of co-cultures exceeding 250Ωcm2 confirming sufficiently contiguous cell layer formation; (3) chemically-induced barrier modulation, with transient TEER loss by 150μM histamine (~50% for 8-15min), and increase in permeability at elevated pH (10.0); (4) size-dependent (668-70,000Da) compound permeability mimicking in vivo trends; and (5) highly linear correlation (R2>0.85) of clearance rates of seven selected neural drugs with in vivo brain/plasma ratios. We demonstrated the effects of WSS (0-86dyn/cm2) on bEnd.3 properties under increasing WSS, including increase in (6) TEER, (7) cell re-alignment toward flow direction, and (8) protein expression of ZO-1/P-gp, and (9) decrease in tracer permeability. The developed in vitro microfluidic BBB model provides distinct advantages for monitoring and modulating barrier functions and prediction of compound permeability. Thus, it would provide an innovative platform to study mechanisms and pathology of barrier function as well as to assess novel pharmaceuticals early in development for their BBB clearance capabilities.

A MICROFLUIDIC IN VITRO MODEL OF THE BLOOD-BRAIN BARRIER by Ross Hunter Booth A dissertation submitted to the faculty of The University of Utah in partial fulfillment of the requirements for the degree of Doctor of Philosophy Department of Bioengineering The University of Utah December 2014 Copyright © Ross Hunter Booth 2014 All Rights Reserved The University of Utah Graduate School STATEMENT OF DISSERTATION APPROVAL The dissertation of Ross Hunter Booth has been approved by the following supervisory committee members: Hanseup Kim , Chair 07/21/2014 Date Approved Alan Dorval , Member 07/21/2014 Date Approved Hamid Ghandehari , Member 07/21/2014 Date Approved Carlos Mastrangelo , Member 07/21/2014 Date Approved Florian Solzbacher , Member 07/21/2014 Date Approved and by Patrick Tresco , Chair/Dean of the Department/College/School of Bioengineering and by David B. Kieda, Dean of The Graduate School. ABSTRACT The blood-brain barrier (BBB) limits entry of most molecules into the brain and complicates the development of brain-targeting compounds, necessitating novel BBB models. This dissertation describes the first microfluidic BBB model allowing the study of BBB properties in relation to various chemical compounds by enabling tunable wall shear stress (WSS) via dynamic fluid flow, cell-cell interaction through a thin co-culture membrane, time-dependent delivery of test compounds, and integration of sensors into the system, resulting in significant reduction of reagents and cells required and shorter cell seeding time. Use of parallel channels first enabled simultaneous monitoring of multiple cell populations under a wide range (~x15) of WSS. The microfluidic model formed the BBB by incorporating brain endothelial (b.End3) and glial (C6/C8D1A) cells at the intersection of two crossing microchannels, respectively representing luminal and abluminal sides, fabricated in a transparent polydimethylsiloxane (PDMS) substrate utilizing high-precision soft lithography techniques. The utilized cells were adopted from immortalized cells for high consistency over repeated passages and pure and proliferative culture. The developed microfluidic BBB model was validated by (1) expression of tight junction protein ZO-1 and glial protein GFAP by fluorescence imaging, and P-gp activity by Calcein AM, confirming key BBB proteins; (2) high trans-endothelial electrical resistance (TEER) of co-cultures exceeding 250Ωcm2 confirming sufficiently contiguous cell layer formation; (3) chemically-induced barrier modulation, with transient TEER loss by 150μM histamine (~50% for 8-15min), and increase in permeability at elevated pH (10.0); (4) size-dependent (668-70,000Da) compound permeability mimicking in vivo trends; and (5) highly linear correlation (R2>0.85) of clearance rates of seven selected neural drugs with in vivo brain/plasma ratios. We demonstrated the effects of WSS (0-86dyn/cm2) on bEnd.3 properties under increasing WSS, including increase in (6) TEER, (7) cell re-alignment toward flow direction, and (8) protein expression of ZO-1/P-gp, and (9) decrease in tracer permeability. The developed in vitro microfluidic BBB model provides distinct advantages for monitoring and modulating barrier functions and prediction of compound permeability. Thus, it would provide an innovative platform to study mechanisms and pathology of barrier function as well as to assess novel pharmaceuticals early in development for their BBB clearance capabilities. iv TABLE OF CONTENTS ABSTRACT.................................................................................................................. iii LIST OF FIGURES ..................................................................................................... ix LIST OF TABLES ....................................................................................................... xi ABBREVIATIONS ..................................................................................................... xii ACKNOWLEDGEMENTS ...................................................................................... xvi CHAPTERS 1. INTRODUCTION .................................................................................................. 1 1.1 Motivation and Significance ......................................................................... 1 1.2 Summary of Innovations ............................................................................... 5 1.3 Research Objectives ...................................................................................... 6 1.4 References ..................................................................................................... 8 2. BACKGROUND ................................................................................................... 11 2.1 Structure and Function of the Blood-Brain Barrier .................................... 11 2.1.1 Introduction to the Neurovascular Unit ............................................ 11 2.1.2 BBB Physiological Features............................................................. 12 2.1.2.1 Role of Endothelial Cell Tight Junctions ............................... 13 2.1.2.2 Role of Membrane Transporters ............................................ 14 2.1.2.3 Role of Astrocytes ................................................................. 15 2.1.2.4 Role of Shear Stress ............................................................... 16 2.2 Previous Models of the Blood-Brain Barrier .............................................. 16 2.2.1 In Vivo Models ................................................................................. 17 2.2.2 In Vitro Models ................................................................................ 18 2.2.2.1 Transwell Systems ................................................................. 19 2.2.2.2 Dynamic In Vitro BBB Models ............................................. 20 2.3 MEMS and Microfluidics ........................................................................... 21 2.3.1 Previous Microfluidic Cell Culture Systems .................................... 22 2.3.2 Fabrication Methods ......................................................................... 23 2.3.2.1 Hard Micromachining Methods ............................................. 23 2.3.2.2 Soft Micromachining Methods .............................................. 24 2.3.2.3 Bonding Methods ................................................................... 25 2.3.2.4 Packaging and Preparation ..................................................... 27 2.4 In Vitro Model Characteristics .................................................................... 27 2.4.1 Constituent Cell Types ..................................................................... 28 2.4.2 Porous Membrane ............................................................................ 29 2.4.3 Adhesion-Promoting Treatments ..................................................... 30 2.4.4 Cellular Media .................................................................................. 30 2.4.5 Microfluidic Structure ...................................................................... 31 2.5 Methods of Model Characterization ........................................................... 32 2.5.1 TEER Measurement ......................................................................... 33 2.5.2 Trans-BBB Permeability Methods ................................................... 34 2.5.3 Imaging Methods .............................................................................. 35 2.5.4 Protein Expression Techniques ........................................................ 35 2.5.4 Microfluidics Simulations ................................................................ 36 2.6 References ................................................................................................... 36 3. CHARACTERIZATION OF A MICROFLUIDIC IN VITRO MODEL OF THE BLOOD-BRAIN BARRIER (μBBB) .................................................. 50 3.1 Abstract ....................................................................................................... 50 3.2 Introduction ................................................................................................. 50 3.3 Structure and Fabrication ............................................................................ 56 3.3.1 Structure ........................................................................................... 56 3.3.2 Fabrication ........................................................................................ 58 3.4 Cell Culture ................................................................................................. 59 3.5 Testing Methodology .................................................................................. 62 3.5.1 Imaging ............................................................................................. 62 3.5.2 TEER Measurement ......................................................................... 63 3.5.3 Permeability ..................................................................................... 64 3.6 Results and Discussion ............................................................................... 65 3.6.1 Imaging ............................................................................................. 65 3.6.2 TEER ................................................................................................ 68 3.6.2.1 Steady-State TEER Measurements ........................................ 69 3.6.2.2 Dynamic TEER Measurements ............................................. 70 3.6.3 Permeability ..................................................................................... 71 3.7 Conclusions................................................................................................. 73 3.8 Acknowledgements ..................................................................................... 73 3.9 References ................................................................................................... 74 4. A MULTIPLE-CHANNEL, MULTIPLE-ASSAY PLATFORM FOR CHARACTERIZATION OF FULL-RANGE SHEAR STRESS EFFECTS ON VASCULAR ENDOTHELIAL CELLS ................................... 79 vi 4.1 Abstract ....................................................................................................... 79 4.2 Introduction ................................................................................................. 80 4.3 Structures and Fabrication .......................................................................... 86 4.3.1 Microfluidic Parallel-Channel Structure .......................................... 86 4.3.2 Integrated Micro-Flow Sensor Array ............................................... 89 4.4 Cell Culture ................................................................................................. 91 4.5 Testing Methodology .................................................................................. 91 4.5.1 Prediction of the Wall Shear Stress by Simulation .......................... 92 4.5.2 Shear Stress Measurement with Integrated Micro-Flow Sensors .... 94 4.5.3 Application of Shear Stress to Cultured Endothelial Cells .............. 95 4.5.4 Morphometric Analysis .................................................................... 96 4.5.5 Permeability Assay ........................................................................... 98 4.5.6 TEER Assay ..................................................................................... 99 4.5.7 Western Blot ................................................................................... 100 4.6 Results and Discussion ............................................................................. 100 4.6.1 Shear Stress Simulation and Measurement .................................... 100 4.6.2 Morphometric Analysis .................................................................. 103 4.6.3 Permeability ................................................................................... 105 4.6.4 TEER .............................................................................................. 106 4.6.5 Western Blot Analysis .................................................................... 108 4.7 Conclusions............................................................................................... 109 4.8 Acknowledgements ................................................................................... 110 4.9 References ................................................................................................. 110 5. PERMEABILITY ANALYSIS OF NEUROACTIVE DRUGS THROUGH A DYNAMIC MICROFLUIDIC IN VITRO BLOOD-BRAIN BARRIER MODEL .............................................................................. 117 5.1 Abstract ..................................................................................................... 117 5.2 Introduction ............................................................................................... 118 5.3 Structure and Fabrication of the Microfluidic BBB Model ...................... 122 5.4 Materials and Cell Culture ........................................................................ 124 5.4.1 CNS-targeting Compounds ............................................................ 124 5.4.2 Cell Culture .................................................................................... 125 5.5 Testing Methodology ................................................................................ 127 5.5.1 Fluorescent Imaging of Endothelial Cell Morphology .................. 127 5.5.2 Dynamic Flow Experiments ........................................................... 127 5.5.3 Cytotoxicity Testing ....................................................................... 128 5.5.4 Trans-Endothelial Electrical Resistance (TEER) ........................... 129 5.5.5 Drug Permeability .......................................................................... 129 5.5.6 Sample Compound Quantification (HPLC-UV/LC-MS) ............... 131 5.6 Results and Discussion ............................................................................. 132 5.6.1 Chromatographic Analysis ............................................................. 132 5.6.2 Morphology .................................................................................... 134 5.6.3 Cytotoxicity .................................................................................... 134 vii 5.6.4 Trans-Endothelial Electrical Resistance ......................................... 137 5.6.5 Drug Permeability .......................................................................... 139 5.7 Conclusions............................................................................................... 144 5.8 Acknowledgements ................................................................................... 145 5.9 References ................................................................................................. 145 6. CONCLUSIONS ................................................................................................. 151 6.1 Summary and Impact ................................................................................ 151 6.2 Unpublished Results ................................................................................. 153 6.3 Further Commentary ................................................................................. 158 6.4 Future Work .............................................................................................. 161 6.4.1 μBBB Model Optimization ............................................................ 161 6.4.1.1 Primary Cells and Cell Culture Properties ........................... 161 6.4.1.2 Membrane Materials ............................................................ 163 6.4.1.3 Electrode Properties ............................................................. 163 6.4.1.4 Direct Comparison with an Animal Model .......................... 164 6.4.1.5 Adoption of the Model by Industry ..................................... 166 6.4.2 Screening of Novel BBB-Crossing Macromolecules..................... 167 6.4.3 Toward a Complete Neurovascular Unit ........................................ 168 6.4.4 Integration into a Body-on-a-Chip ................................................. 170 6.5 References ................................................................................................. 171 viii LIST OF FIGURES 2.1 Structure of brain capillaries .................................................................................. 13 2.2 Traditional in vitro BBB models ........................................................................... 19 3.1 Motivation and background for μBBB development ............................................ 52 3.2 Structure and design of the developed μBBB ....................................................... 56 3.3 Components of the μBBB ...................................................................................... 58 3.4 Testing setup for validating the μBBB .................................................................. 60 3.5 Representative images of cells in μBBB ............................................................... 67 3.6 TEER levels of static and dynamic experiments over time, beginning on D0 of endothelial culture ............................................................................................. 68 3.7 Steady-state TEER levels of each base condition .................................................. 69 3.8 Continuous response to histamine exposure in three samples at each concentration ......................................................................................................... 71 3.9 Permeabilities of culture μBBB under different conditions .................................. 72 4.1 Studying the relationship between vascular wall shear stress (WSS) and endothelial cell (EC) physiology ........................................................................... 82 4.2 The presented parallel channel array allows multiple high-throughput characterization assays of WSS effects on cultured endothelial cells ................... 85 4.3 Multichannel device structure and fabrication ...................................................... 86 4.4 Microflow sensor array structure and fabrication .................................................. 90 4.5 Shear stress calculation methods ........................................................................... 93 4.6 Testing methodology ............................................................................................. 97 4.7 WSS characterization results ............................................................................... 101 4.8 Morphometry results ............................................................................................ 104 4.9 Permeability of FITC-conjugated dextran 4 kD and propidium iodide at WSS magnitudes ranging from 0.35 to 84 dyn cm-2 ........................................... 106 4.10 TEER measured following high shear stress was increased at about 0.8 unit resistance per unit WSS ....................................................................................... 107 4.11 Densitometric relative band analysis of western blots from cell lysates of brain endothelial cells grown to confluence and exposed to 24 h WSS was compared with static controls grown in 6-well plates ......................................... 108 5.1 Microfluidic blood-brain barrier models ............................................................. 120 5.2 Microfluidic blood-brain barrier chip for permeability assays ............................ 123 5.3 Linear standard curves for chromatographic detection ....................................... 133 5.4 Immunostaining of the brain endothelial cell line bEnd.3 cell line used for the BBB models in this study and extracted primary brain endothelial cells from the rat for reference ..................................................................................... 135 5.5 Cytotoxicity of each drug tested in this study as measured by LDH expression following twenty-four hour exposure to different concentrations. .... 136 5.6 TEER levels of prepared BBB models, 4 days after endothelial cell seeding as quality control ................................................................................................. 138 5.7 Permeability coefficients of each compound used in the study ........................... 141 5.8 In vivo correlation of averaged permeability coefficients ................................... 141 5.9 Comparison of average static/dynamic BBB permeability coefficients between static and dynamic models .................................................................... 143 6.1 Relative Calcein AM uptake by bEnd.3 cells ...................................................... 155 6.2 Morphological images of both astrocyte cell lines used in this dissertation, stained on day 2 of culture ................................................................................... 156 6.3 Size-exclusion elution profiles of FITC-conjugated dextrans used in Chapter 3 permeability assays following 3 years of storage in aqueous solution ............. 157 6.4 Permeability measurement of the BBB in vivo .................................................... 165 6.5 The microfluidic neurovascular unit concept (μNVU) ........................................ 169 x LIST OF TABLES 3.1 Qualitative comparison of standard BBB models with the μBBB proposed in this article ............................................................................................................... 52 4.1 Comparison of flow-based in vitro systems for characterizing WSS effects on vascular endothelial cells .................................................................................. 84 5.1 Compounds tested in this study ........................................................................... 125 5.2 Permeability results of each compound used in the study ................................... 140 6.1 Comparison of microfluidic BBB studies reported at the time of this dissertation ........................................................................................................... 152 6.2 Physicochemical properties and dynamic in vitro results of each of the compounds tested in this dissertation .................................................................. 160 ABBREVIATIONS μBBB microfluidic in vitro blood brain barrier model μCCA microscale cell culture analogue μNVU microfluidic in vitro neurovascular unit μTAS micro total analysis system AD Alzheimer's Disease ABC ATP-binding cassette ADMET absorption, distribution, metabolism, excretion, toxicity APTES aminopropyltriethoxysilane AUC are under the curve B/P brain/plasma ratio BBB blood-brain barrier BCRP breast cancer resistance protein bEnd.3 brain endothelial cell line BMEC brain microvascular endothelial cell BSA bovine serum albumin C8-D1A astrocyte type I cell line CAD computer-aided drafting CMC Comparative Medicine Center CNS central nervous system CVD chemical vapor deposition DAPI 4',6-diamidino-2-phenylindole DMEM Dulbecco's Modified Eagle Medium DRIE deep reactive-ion etching EC endothelial cell ECS extracellular space ELS elastin-like polypeptide ESEM environmental scanning electron microscopy F12 Ham's Nutrient Mixture F12 FBS fetal bovine serum FITC fluorescein isothiocyanate GDNF glial-derived neurotrophic factor GFAP glial fibrillary acidic protein HBSS Hanks' Balanced Salt Solution HEPES 4-(2-hydroxyethyl)-1-piperazineethanesulfonic acid HPLC high-performance liquid chromatography IGF-I insulin-like growth factor I Kp brain uptake ratio LC liquid chromatography LC-MS liquid chromatography-mass spectrometry LogP logarithm of the octanol/water partition coefficient LogPe logarithm of endothelial permeability coefficient xiii LPCVD low-pressure chemical vapor deposition LRP-1 low density lipoprotein receptor-related protein 1 MDR1 multidrug resistance protein 1, P-glycoprotein MEA multi-electrode array MEMS microelectromechanical systems MMD multilayered microfluidic device MRP multidrug resistance protein MS mass spectrometry NBM neurobasal medium NMDA N-Methyl-D-aspartate Nrf2-ARE NF-E2-related factor 2-antioxidant response element NVU neurovascular unit P-gp P-glycoprotein PAμBBB parallel array in vitro blood brain barrier model PC polycarbonate PD pharmacodynamic PDMS polydimethylsiloxane PECVD plasma-enhance chemical vapor deposition PFA paraformaldehyde PBPK physiologically-based pharmacokinetic models PI propidium idodie PK Pharmacokinetic PS permeability surface area product xiv PVDF polyvinylidene fluoride RAM random access memory RIE reactive-ion etching SEM scanning electron microscope SI shape index siRNA small interfering ribonucleic acid SMBB Sorenson Molecular Biotechnology Building TCD thermal conductivity detector TEER trans-endothelial electrical resistance TJ tight junctions TNF-α tumor necrosis factor alpha VEC vascular endothelial cells WB western blot WSS wall shear stress ZO-1 zonal occludin-1 xv ACKNOWLEDGEMENTS I would foremost like to thank my advisor and mentor, Dr. Hanseup Kim, for continuous guidance and support throughout the described studies, and for having faith in my abilities to conduct such research independently. Thank you for your continual motivation and drive. I would like to thank my advisory committee members, Chuck Dorval, Hamid Ghandehari, Carlos Mastrangelo, and Florian Solzbacher. I would also like to acknowledge Moses Noh, whose work on the micro-flow sensor contributed to the study in Chapter 4. The majority of this research was funded by USTAR startup fund, without which it may not have been possible. Finally, I wish to thank my friends and family for their continuous support, especially my wife Stella, for her patience during all my late nights away at the lab. CHAPTER 1 INTRODUCTION This dissertation aims to address the feasibility of modeling the blood-brain barrier (BBB) by developing an innovative chip-based microfluidic platform. This chapter describes the significance of such a system, and overviews the project and approach taken in this dissertation to develop and characterize the described platforms. 1.1 Motivation and Significance There is currently a prevalent and increasing burden on the healthcare industry over the growing number of patients suffering from neurodegenerative disorders of the central nervous system (CNS), notably Alzheimer's Disease (AD), which is diagnosed in an estimated 24 million patients worldwide, and is projected to double every 20 years [1]. However, CNS drug development progress is comparatively slower than other healthcare areas [2,3]. The distinguishing pharmacokinetic hurdle [4] to drug development for CNS disorders is the BBB [3], which effectively blocks nearly all nonpolar compounds larger than ~500Da from entering neural tissue [5]. Due to this prevalent role in drug development, innovative preclinical models of the BBB are in high demand. BBB models primarily have two applications: (1) to monitor barrier function and investigate changes 2 induced by chemical and physical stimuli; and to (2) predict the rate of delivery of compounds across the BBB. The first application is extremely useful for basic research on BBB physiological mechanisms, and to study the BBB's role in CNS disease progression [6]; the second application can be used to test the passage of novel drugs [7] or drug delivery vehicles [8] across the BBB during stages of prescreening and optimization of CNS treatments prior to animal and clinical studies [9]. This dissertation aims to include feasibility of the use of the innovative system for both of these applications within its scope. In vitro models are a valuable precursor to animal models due to lower cost, time, and ethical constraints [10], and enable more focused, controllable, and repeatable experimentation, as well as more massively-parallel environments. The validity of an in vitro model is dependent on how closely it reproduces the key physiological characteristics of its in vivo archetype. The key characteristics of the BBB include: Structurally, (1) a contiguous monolayer of endothelial cells containing strongly expressed tight junctions [11]; (2) astrocytes in close contact with the endothelial monolayer, which play a key role in modulating barrier function through cell signaling from endfoot processes [12]; functionally having strong expression of (3) membrane-bound transport components for receptor-mediated transport and efflux transport [13]; a microenvironment experiencing (4) fluidic shear stress, which is known to have a mechanotransductive effect on endothelial cell phenotype [14,15]; Model conditions should show highly (5) selective permeability from the constituted structures to dissolved compounds; and (6) maintenance of high electrical resistance indicating the contiguity of the endothelial cell monolayer and soundness of tight junctions. Additionally, the reliable, 3 rapid measurement of these physiological conditions is an important component to a valid BBB model. This is particularly the case for trans-membrane properties, thus an effective BBB model must allow reliable measurement of tracer compound permeability and trans-endothelial electrical resistance (TEER). The commercially-available current state-of-the art in vitro models comprises a simple transwell insert [16]. Transwell inserts comprise of a porous membrane attached to a cup-shaped insert for placement in multiwell plates of multiple sizes. However, they are limited to represent only static environments, without continuous luminal flows. Luminal flows are known to cause fluid shear stress [5] that imposes mechanotransductive effects on endothelial cell phenotypes in vitro and in vivo [17], thus influencing a myriad of molecular pathways [18] activated via membrane-bound receptors [19], inducing proliferative responses including tight junction proteins [20], membrane efflux transporters [14], and cytoskeletal restructuring and cell reorientation [15] in a manner dependent on flow direction [21]. Thus, a truly representative in vitro model should have physiologically relevant flow conditions. In 1996, a dynamic in vitro BBB (DIV-BBB) [22-24] model was developed which utilizes hollow fibers to mimic BBB architecture and flow conditions. However, the DIV-BBB has wall thickness (150μm) significantly higher than transwell membranes (10μm), discouraging cell-cell interaction and decreasing background permeability, take significantly longer (~3x) to reach steady-state barrier permeability [10,22] than 2D models, and lack the potential for integration of biosensors and compartmentalized or parallel array setups due to the simplicity of their design and fabrication as simple hollow fiber bundles in a bulky cartridge [10,22], having diameters of approximately 1mm, more 4 than 10x larger than brain capillaries, failing to accurately represent in vivo flow conditions. To address these short-comings of existing systems, this dissertation presents a microfluidic in vitro BBB model (μBBB) [25] that includes several practical advantages: (A) Significantly lower costs, timescales, and ethical issues than in vivo studies; (B) Massively-parallel, controlled and repeatable environments, and easier elucidation of molecular mechanisms than in vivo models; (C) Dynamic microenvironment providing shear stress stimulation to cultured endothelial cells, allowing controlled delivery of test compounds and improved permeability analysis compared to in vitro static models; (D) Much thinner culture membrane, decreasing the distance between co-cultured cells for compound diffusion, compared to in vitro DIV-BBB models. (E) Smaller functional volumes for quicker media exchange, material conservation, and scales closer to true in vivo dimensions; (F) A 2D culture surface allowing complete initial seeding and shorter times to steady-state barrier resistance for a more rapid turn-around time, shortening experiments and allowing a more high-throughput approach to experimentation. The impact of this dissertation involves development of an innovative platform for BBB modeling with the aforementioned practical advantages, and characterization and validation of such a system in both scientific and engineering aspects for use in basic research and pharmaceutical drug development for the following two applications: (1) The system was developed and utilized to test responses of the cultured cells to chemical and physical stimuli, including chemical stimulation [26] and shear stress [27]. (2) The system was also used for proof-of-concept as a drug delivery test platform for predicting clinical clearance through the BBB. The combined impact of these studies will prove the 5 validity of a microfluidic BBB platform for use by both the scientific community, to study BBB physiological functions and responses to various chemical or physical stimuli in basic research, and the industry community, for prescreening of BBB clearance of novel pharmaceuticals as a predictive tool. It is our educated opinion that microfluidic systems will inevitably be commercialized for heavy use in these applications in the coming years, and the work in this dissertation is intended as the launching point. 1.2 Summary of Innovation This dissertation reports the first published chip-based microfluidic cell culture model for the BBB [28], the first published microfluidic platform allowing simultaneous testing of endothelial cell trans-membrane and morphological properties under multiple distinct shear stresses [29], and the first multidrug (>3) correlation of a microfluidic BBB model with in vivo brain penetration results [30]. The multilayered microfluidic device (MMD) comprises two base polydimethylsiloxane (PDMS) substrates, two glass layers, and a free-standing porous membrane [31] fixed between the PDMS layers. It houses two perpendicularly-crossing channels to introduce dynamic flows, and the functional barrier area is located on free-standing membrane at the channel junction, enabling the ability to conduct flow-based permeability assays. This is particularly advantageous because it allows steady-state concentrations to be maintained in both the luminal and abluminal chambers, whereas static transwell concentrations gradually change over time as they reach equilibrium, reducing the accuracy of permeability calculations. Commercial electrode sticks have been used to measure TEER in conventional microfluidic systems [32]; in contrast, the μBBB system is alternately designed with integrated fully-fabricated 6 thin-film electrodes, fixing the distance between electrodes for measurement repeatability. Nondestructive microscopy of the system is also possible due to transparency of the PDMS substrate. In addition, we developed a novel experimental design employing a parallel array of the luminal channels containing endothelial cells, allowing unprecedented simultaneous measurement of endothelial cell trans-membrane and morphological properties under varying magnitudes of shear stress. In summary, these engineered innovations of the novel platform enable scientific advantages, by allowing mimicry of the dynamic environment found in vivo, as well as practical advantages, by providing greater experimental control, high tunability of model conditions, better measurement of BBB functionality, material conservation, and cost. 1.3 Research Objectives The objective of this dissertation is to introduce, characterize, and validate the first microfluidic BBB, was largely accomplished through three distinct research phases, which are respectively reported in Chapters 3-5. The forthcoming chapters will be structured as follows, describing the complete scope of this dissertation. Chapter 2 provides the background necessary for understanding the studies described in the following chapters, including (1) underlying biology of the BBB, (2) review of previous models of the BBB, (3) review of microfluidics systems and fabrication methods used in this dissertation, (4) characteristics of the BBB model used in this dissertation, and (5) methods of model validation used in this dissertation. Chapter 3 describes initial development and characterization of the μBBB model using the techniques and methods described in Chapter 2, establishing the μBBB system 7 as a low-cost, polymeric alternative to previous dynamic hollow-fiber based systems, with particular discussion comparing these systems. The foundational concepts of the μBBB are covered in this chapter, and the methods of observing trans-membrane properties were developed. Cell lines bEnd.3 and C8-D1A were used in this study, and the effects of chemical modulation (histamine, pH elevation) on trans-membrane barrier properties (TEER, tracer permeability) were observed. Chapter 4 describes the development of a modified version of the device designed for observation of quantitatively-dependent effects of shear stress stimulation on endothelial cell physiological properties, in an unprecedently high-throughput manner. This was done to test for any flow-rate limitations for the BBB model with the bEnd.3 cell line, and to observe mechanical modulation effects on both trans-membrane barrier properties (TEER, tracer permeability) and morphometric properties (cell alignment, shape), as well as on BBB protein expression (zonal occludin-1, P-glycoprotein). Chapter 5 describes a proof-of-concept study of the system as a predictive tool for drug clearance, by running 7 CNS-targeting drugs currently under development through the BBB model prepared with bEnd.3 cells in monoculture and in co-culture with C6 astrocytes. Concentration-specific cytotoxicity of these compounds were measured to establish acceptable permeability assay concentrations, and permeated concentrations of the drugs were measured with chromatographic methods. Permeability results were compared in vivo data from literature to confirm in vivo correlation. Chapter 6 summarizes the project's impact, and stature within the current, new body of microfluidic BBB platforms, presents some unpublished results relating to P-glycoprotein and glial fibrillary acidic protein expression by the selected cell lines bEnd.3 8 and C6, and discusses several future directions and applications for the model characterized in this dissertation. 1.4 References [1] Ferri, C. P., M. Prince, C. 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[7] Reichel, A. Addressing central nervous system (cns) penetration in drug discovery: Basics and implications of the evolving new concept. Chem Biodivers. 6(11):2030-2049, 2009. [8] Pathan, S. A., Z. Iqbal, S. M. Zaidi, S. Talegaonkar, D. Vohra, G. K. Jain, A. Azeem, N. Jain, J. R. Lalani, R. K. Khar, and F. J. Ahmad. Cns drug delivery systems: Novel approaches. Recent Pat Drug Deliv Formul. 3(1):71-89, 2009. [9] Cucullo, L., B. Aumayr, E. Rapp, and D. Janigro. Drug delivery and in vitro models of the blood-brain barrier. Curr Opin Drug Discov Devel. 8(1):89-99, 2005. [10] Frampton, J. P., M. L. Shuler, W. Shain, and M. R. Hynd. Biomedical technologies for in vitro screening and controlled delivery of neuroactive compounds. Cent Nerv Syst Agents Med Chem. 8(3):203-219, 2008. [11] Wolburg, H. and A. Lippoldt. Tight junctions of the blood-brain barrier: 9 Development, composition and regulation. Vascul Pharmacol. 38(6):323-337, 2002. [12] Haseloff, R. F., I. E. Blasig, H. C. Bauer, and H. Bauer. In search of the astrocytic factor(s) modulating blood-brain barrier functions in brain capillary endothelial cells in vitro. Cell Mol Neurobiol. 25(1):25-39, 2005. [13] Taylor, E. M. The impact of efflux transporters in the brain on the development of drugs for cns disorders. Clin Pharmacokinet. 41(2):81-92, 2002. [14] Cucullo, L., M. Hossain, V. Puvenna, N. Marchi, and D. Janigro. The role of shear stress in blood-brain barrier endothelial physiology. BMC Neurosci. 12(40, 2011. [15] Galbraith, C. G., R. Skalak, and S. Chien. Shear stress induces spatial reorganization of the endothelial cell cytoskeleton. Cell Motil Cytoskeleton. 40(4):317-330, 1998. [16] Rubin, L. L., D. E. Hall, S. Porter, K. Barbu, C. Cannon, H. C. Horner, M. Janatpour, C. W. Liaw, K. Manning, J. Morales, and et al. A cell culture model of the blood-brain barrier. J Cell Biol. 115(6):1725-1735, 1991. [17] Chien, S., S. Li, and Y. J. Shyy. Effects of mechanical forces on signal transduction and gene expression in endothelial cells. Hypertension. 31(1 Pt 2):162-169, 1998. [18] Acevedo, A. D., S. S. Bowser, M. E. Gerritsen, and R. Bizios. Morphological and proliferative responses of endothelial cells to hydrostatic pressure: Role of fibroblast growth factor. J Cell Physiol. 157(3):603-614, 1993. [19] Chien, S. Molecular basis of rheological modulation of endothelial functions: Importance of stress direction. Biorheology. 43(2):95-116, 2006. [20] Siddharthan, V., Y. V. Kim, S. Liu, and K. S. Kim. Human astrocytes/astrocyte-conditioned medium and shear stress enhance the barrier properties of human brain microvascular endothelial cells. Brain Res. 1147(39-50, 2007. [21] Chien, S. Effects of disturbed flow on endothelial cells. Ann Biomed Eng. 36(4):554-562, 2008. [22] Santaguida, S., D. Janigro, M. Hossain, E. Oby, E. Rapp, and L. Cucullo. Side by side comparison between dynamic versus static models of blood-brain barrier in vitro: A permeability study. Brain Res. 1109(1):1-13, 2006. [23] Cucullo, L., M. S. McAllister, K. Kight, L. Krizanac-Bengez, M. Marroni, M. R. Mayberg, K. A. Stanness, and D. Janigro. A new dynamic in vitro model for the multidimensional study of astrocyte-endothelial cell interactions at the blood- 10 brain barrier. Brain Res. 951(2):243-254, 2002. [24] Neuhaus, W., R. Lauer, S. Oelzant, U. P. Fringeli, G. F. Ecker, and C. R. Noe. A novel flow based hollow-fiber blood-brain barrier in vitro model with immortalised cell line pbmec/c1-2. J Biotechnol. 125(1):127-141, 2006. [25] Booth, R. and H. Kim. A multi-layered microfluidic device for in vitro blood-brain barrier permeability studies. International Conference on Miniaturized Systems for Chemistry and Life Sciences. 15(1388-1390, 2011. [26] Stamatovic, S. M., R. F. Keep, and A. V. Andjelkovic. Brain endothelial cell-cell junctions: How to "open" the blood brain barrier. Current Neuropharmacology. 6(179-192, 2008. [27] Krizanac-Bengez, L., M. R. Mayberg, E. Cunningham, M. Hossain, S. Ponnampalam, F. E. Parkinson, and D. Janigro. Loss of shear stress induces leukocyte-mediated cytokine release and blood-brain barrier failure in dynamic in vitro blood-brain barrier model. Journal of Cellular Physiology. 206(1):68-77, 2006. [28] Booth, R. and H. Kim. Characterization of a microfluidic in vitro model of the blood-brain barrier ([small mu ]bbb). Lab on a Chip. 2012. [29] Booth, R., S. Noh, and H. Kim. A multiple-channel, multiple-assay platform for characterization of full-range shear stress effects on vascular endothelial cells. Lab on a Chip. 14(11):1880-1890, 2014. [30] Booth, R. and H. Kim. Permeability analysis of neuroactive drugs through a dynamic microfluidic in vitro blood-brain barrier model. Annals of biomedical engineering. 1-13, 2014. [31] Chueh, B. H., D. Huh, C. R. Kyrtsos, T. Houssin, N. Futai, and S. Takayama. Leakage-free bonding of porous membranes into layered microfluidic array systems. Anal Chem. 79(9):3504-3508, 2007. [32] Douville, N. J., Y. C. Tung, R. Li, J. D. Wang, M. E. El-Sayed, and S. Takayama. Fabrication of two-layered channel system with embedded electrodes to measure resistance across epithelial and endothelial barriers. Anal Chem. 82(6):2505-2511, 2010. CHAPTER 2 BACKGROUND 2.1 Structure and Function of the Blood-Brain Barrier The distinguishing characteristic in the process of drug delivery to the central nervous system (CNS), consisting of the brain and spinal cord, is the blood-brain barrier (BBB). Around the end of the 19th century, it was first noted by Paul Ehrlich that intravenous injections of dye elucidate a clear lack of staining in the CNS [1]. A few years later, the term BBB was first coined by Lewandowski et al. when studying the limitations of perfusion of potassium ferrocyanate into the CNS [2]. The invention of the electron microscope in the 1960s allowed the anatomical structure of the BBB to be observed and described using intravascular horseradish peroxidase injections [3], rapidly progressing our understanding of the structure and function of the BBB. 2.1.1 Introduction to the Neurovascular Unit The BBB effectively restricts virtually all molecules except small and lipophilic ones - only small lipophilic molecules with molecular weights below ~500 Daltons typically cross the BBB freely [4]. The presence of this uniquely restrictive barrier to compounds in the CNS exists primarily for 4 physiological reasons: (1) Maintenance of homeostasis of the brain, (2) protection of brain tissue from exogenous compounds, (3) 12 controlling nutrient supply in the brain, and (4) directing inflammatory responses according to changes in the local environment [5]. 2.1.2 BBB Physiological Features The physical characteristics of the BBB are dictated by a dynamic interaction between multiple cell types, primarily the brain endothelial cells lining the capillaries in the brain. Brain endothelial cells are distinct from peripheral endothelial cells in several ways, including reduced pinocytic activity [6], lack of fenestrations [7], higher mitochondrial density [8], and higher expression of membrane transporters [9]. These distinct characteristics are highly dependent on the interactions with surrounding glial cells, thus the neurovascular unit is considered to consist of multiple types of cells. Anatomically, the neurovascular unit is comprised of both the endothelial cells and the surrounding pericytes and astrocytes [10], though there is evidence that neurons may also play a role in endothelial phenotype as well [11] (Figure 2.1). The BBB's barrier properties are primarily governed by a combination of the physical barrier provided by the tight junctions, the transport barrier provided by the membrane transport efflux mechanisms including ATP-binding Casette transporter proteins such as P-gp or other multidrug resistance proteins (MRPs) such as breast cancer resistance protein (BCRP) [12], as well as a metabolic barrier component. Additionally, the BBB maintains the ionic composition of the brain for optimal synaptic functions of neurons, largely via specific ion channels and transporters [13]. Thus, the BBB is important for protecting the CNS from neurotoxic and xenobiotic compounds, in addition to the homeostasis necessary for CNS function and nutrient supply. 13 Figure 2.1 Structure of brain capillaries. Structurally, brain capillaries are made up of brain microvascular endothelial cells the endfeet of astrocytes, and pericytes within the basement membrane. In addition to tight junctions between endothelial cells, the blood-brain barrier is functionally controlled by ATP-binding cassette (ABC) transporters such as p-gp, or other multidrug resistance proteins (MRPs) such as breast cancer resistance protein (Bcrp). Figure from Fricker [12]. 2.1.2.1 Role of Endothelial Cell Tight Junctions The paracellular route for compounds to pass the endothelial cell layer is primarily regulated by tight junctions, a highly complex structural assembly of proteins [14] which make up the extracellular space between adjacent endothelial cells, effectively abolishing aqueous diffusional pathways between the blood and brain [15,16]. Endothelial cell tight junctions consist of transmembrane proteins [17,18], largely occludins [19], claudins [20], and junctional adhesion molecules (JAMs). These compounds are directly linked to cytoplasmic proteins known as zonal occludins, which are further linked to the actin cytoskeleton [21]. Thus, the zonal occludins (ZO-1, ZO-2, ZO-3) regulate the Structure of Brain Capillaries 14 effectiveness of tight junctions in barrier function, and are most commonly studied (particularly ZO-1) for validation of BBB properties because they are specific markers for tight junctions and act as an intermediate molecule in the tight junction complex. In addition to their role as structural barriers, tight junctions have also been observed to be dynamic signaling complexes, involving control of gene expression, cell proliferation, and differentiation in a bi-directional manner [22]. For example, with this mechanism, tight junctions coordinately receive and transmit signal molecules of the Rho class with intracellular mechanisms [23], signaling routes mainly involving protein kinases activated through phosphorylation cascades [24]. Thus, the role of tight junctions in BBB function likely extends beyond guarding the paracellular route for compounds. 2.1.2.2 Role of Membrane Transporters P-glycoprotein (P-gp), often referred to as the primary multidrug resistance (MDR) protein, is an efflux transporter found on luminal endothelial membranes as well as at astrocyte processes in the brain [25]. While the paracellular route for compounds is guarded by tight junctions, the transcellular route is guarded largely by ABC transporters such as P-gp [26] or Bcrp, effectively expelling a large variety of compounds into the luminal space as a key component in BBB homeostasis. For this reason, P-gp expression is considered to be an essential measure for evaluating cell constituents in in vitro BBB models [27]. Also relevant to drug delivery through the BBB is the presence of transporters on endothelium which move the opposite direction from P-gp. These are particularly receptor-mediated systems which make it possible for particular macromolecules which 15 cannot enter paracellular routes to enter the brain through transcellular routes [10]. Among the best known and characterized are transferrin receptor [28], glucose carrier GLUT-1 [29], and amino acid transporter L1 [30], which exist in higher concentrations than peripheral endothelial cells, providing potential delivery routes to the brain for tailored macromolecules [31]. To date, such macromolecules have not proven to reach the CNS in effective pharmacological concentrations, though such routes are promising for future clinical application, and future study testing the effectiveness of such macromolecules for BBB passage in in vitro models should include characterization of the presence of the target receptor in the BBB model in use. 2.1.2.3 Role of Astrocytes As early as 1967, it has been predicted that astrocytes play a major role in inducing BBB phenotype and specialization [32]. Astrocyte endfeet have been observed to cover the majority of the abluminal surface of brain endothelial cells [33], secreting a number of inducing factors, such as transforming growth factor-β, glial-derived neurotrophic factor (GDNF), basic fibroblast growth factor, and angiopoetin 1 [34]. These processes influence a number of mediating compounds in BBB function [35], including effects on both the paracellular compound pathway, tight junction expression [36], and effects on the transcellular pathway, membrane-bound transporters such as P-gp [37] and GLUT-1 [33]. Astrocytes have been seen to produce factors inducing development of tight junctions through these processes, leading to induction of transcytotic mechanisms such as transferrin receptor [38]. BBB characteristics have even been induced in non-brain endothelial cells such human vein endothelial cells by co-culturing with astrocytes [39]. 16 Furthermore, endothelial cells have been shown to produce factors to facilitate astrocyte differentiation [40]. It is clear that the interaction between these two cell types are highly important to BBB physiology, therefore they are included in most in vitro co-culture models. 2.1.2.4 Role of Shear Stress The exposure to physiological shear stress plays a critical role in modulating endothelial cell morphology [41]. Endothelial cells cultured under shear stress show a number of physiological characteristics more representative of in situ [42], such as an abundance of endocytic vesicles, microfilaments, and clathrin-coated pits [43]. A number of membrane-bound proteins, including integrins [44], caveolae [45], G proteins [46], and ion channels [47,48], have been shown to be involved in mechanotransduction of shear stress into pleiotropic physiological responses, initiated by downstream signal-regulated kinases [49]. Among the physiological functions affected are: (1) production of substances related to vasoactivity and cell adhesion [50,51], (2) increased expression of tight junctions [52], (3) increased cell survival [53], (4) energy metabolism [54], and (5) membrane transport systems [42]. Accordingly, these physiological responses have an effect on barrier activity; therefore, reconstituting a high-shear stress environment is essential for a truly representative BBB model. 2.2 Traditional Models of the Blood-Brain Barrier The two primary classifications of BBB models are in vivo, studies with complete animal models, and in vitro, studies with reconstituted cell-based platforms in the 17 laboratory. While all aspects of the in vivo system are yet to be reproduced in an in vitro model [55], they have contributed significantly to our current understanding of endothelial transport and regulation. Indeed, the principle advantages of in vitro models include (1) higher capacity and higher throughput, (2) lower costs and reagents required, (3) the ability to quantify compounds directly in physiological buffers, (4) feasible identification of cell toxicity, (5) and lesser ethical constraints [56]. Nevertheless, to increase their experimental advantage, in vitro models must be developed to mimic the in vivo microenvironment as closely as possible to ensure their predictive accuracy. 2.2.1 In Vivo Models Direct in vivo brain uptake techniques provide reliable characterization of drug BBB penetration [57]. Studies of in vivo brain penetration look at both the permeability surface area product PS, as well as brain uptake ratio Kp, which includes equilibration of the compound in neural tissue over time [58]. Kp is based on the ratio of brain and plasma concentrations under steady-state biodistribution [59]. PS is particularly advantageous in terms of permeability information, because it is not compromised by drug metabolism, protein binding, or nonspecific brain binding [60]. PS is measured by perfusing the brain directly with the tracer via the carotid artery, allowing short-term measurement of permeability. However, PS is a highly technically demanding product to measure, and is considerably lower-throughput than Kp, often only measured in late stages of compound development. Though these methodologies are comparably low-throughput compared to in vitro measurements, their results are particularly important for validating in vitro models. 18 In addition to full-animal in vivo experiments, isolated brain capillaries from various sources, including human and bovine, rat, rabbit, and pig [61], have been studied ex vivo for studying drug accumulation, transporter activity , and gene expression [62,63]. However, such ex vivo studies are technically demanding and ethically limiting, the preparation procedures tend to modify barrier functions [64], while access to the luminal surface of microvessels is nearly impossible [65]. Thus, the development of in vitro models was deemed necessary. 2.2.2 In Vitro Models Cells used in in vitro-based BBB models are commonly derived from one of many mammalian species, including bovine, porcine, rat, murine, or human [66]. High costs and laborious, time-consuming isolation procedures lead to the necessity for use of immortalized cell lines. These cell lines have the advantage of undergoing a large number of passages without any change in phenotype, and enable ultra-high yield and homogeneity [67]. However, these cell lines have a disadvantage in that they typically show higher leakiness and lower expression levels of tight junction and transporter proteins than primary cells [55]. Overall, the utilization of immortalized cell lines have been widely accepted for in vitro models due to high consistency from passage to passage as well as in intralaboratory-comparison, highly repeatable physiological behaviors and rapid, low-cost model characterization. 19 2.2.2.1 Transwell Systems The current state-of-the-art for BBB models, and epithelial/endothelial cell culture models in general, is the simple transwell system (Figure 2.2A). Transwells are comprised of porous inserts for well plates, available in many sizes (6-, 12-, 24-, or 48-wells). These systems are unique over the universal polystyrene-surface cell culture vessels in that they allow interaction between multiple chambers through a porous barrier which restricts migration of cells of a certain size, while allowing all components of the cellular media [69]. Another, perhaps more significant way these models transcend basic Figure 2.2 Traditional in vitro BBB models. (A) The majority of in vitro BBB models use transwells, in static condition with a porous insert in a multi-well setup. (B) Hollow fiber bundled systems represent a dynamic in vitro for introducing flow-based environments while allowing co-culture with astrocytes. Figure from Cucullo [68]. ABTraditional BBB modelsTranswellsDIV-BBB 20 cell culture vessels is that they allow modeling of cooperative interaction between the multiple cell types, while keeping the cells of different type isolated from each other physically [70]. However, the overly simplistic transwell platform lacks the exposure to intraluminal shear stress. This is critical for the vascular endothelium to develop and/or maintain intrinsic BBB properties observed in vivo; therefore, integration of a shear stress component to the system is crucial. In addition, experimental control over delivered permeability compounds is insufficient for accurate permeability measurement, as concentrations in the luminal and abluminal chambers change over time, contradicting assumptions of linearity of membrane flux during permeability assays. 2.2.2.2 Dynamic In Vitro BBB Models To enable the introduction of flow into BBB cell culture models, several types of microfluidic systems have been utilized. The cone-plate apparatus represents the first attempt [71], comprising a rotating cone opposite an endothelial cell monolayer. The angular motion of the cone translates to shear stress exerted on the cell monolayer; however, the shear stress is not entirely uniform along the radius of the cone, resulting in an uneven magnitude of shear stress applied to the cells. In addition, such systems did not contain porous substrates for permeability studies or compartmentalized co-cultures. Nevertheless, many early studies of shear stress effects on cells were conducted with these systems [72]. Artificial capillary-like structures known as hollow fibers, which are made from thermoplastic polymers such as polypropylene or polysulfone, have been adapted to model cell-based vascular systems under flow and have been coined dynamic in vitro 21 BBB (DIV-BBB) systems (Figure 2.2B) [73]. In macroscopic terms, these hollow fiber bundles resemble vessel structures, though at a 10x larger scale. Bundles are typically seeded in the interior with brain microvascular cells, with astrocytes in the exterior space of the bundles, allowing study of physiological response of cells under tunable levels of shear stress [74]. However, due to the 3D structure of these systems, cell growth cannot be evaluated directly with microscopy, and they take significantly longer to reach full confluence than 2D systems. The thicknesses (200 μm) of the hollow fiber walls are far higher than track-etched porous membranes, by more than a factor of 10x. This increase in distance makes the presence of cell-cell interaction via migration of astrocyte processes through the porous substrate far less likely [75]. Finally, in comparison to these hollow fiber systems, much smaller systems with lesser cell and reagent consumption and faster turn-around times can be achieved with microfluidic systems containing tailored 2D culture surfaces and integrated sensors. 2.3 MEMS and Microfluidics Microfabrication technology, particularly microelectromechanical systems (MEMS) allow the development of a more innovative BBB model. MEMS technologies have stemmed from the methodological foundations provided by the integrated circuit industry, allowing development of mechanical systems at an increasingly smaller scale. Thus far, microfluidic systems have been employed primarily in industry for the applications of analytic devices, miniaturized sensors, flow cytometry, and disposable HPLC chips [76]. More recently, these techniques have garnered increased interest in the pharmaceutical industry due to advantages over simple systems, such as (1) smaller 22 dimensions with high resolution/sensitivity, (2) incorporation of sensing and actuating function, (3) ease to study interaction of molecules with cells, (4) minimal invasiveness, (5) high portability, (6) shorter analysis time, and (7) high-throughput experimentation [77]. 2.3.1 Previous Microfluidic Cell Culture Systems In terms of flow-based cell culture systems, microfluidics hold several practical advantages over macroscale flow systems, including efficient exploitation of mechanical forces, dominance of viscous and diffusional forces, rapid turn-around times, and low costs [78]. Microfluidic systems permit spatial confinements resulting in biochemical gradients and diffusive profiles more representative of the in vivo microenvironment. The advent of microfluidics have generated unprecedented opportunity to study and use biological cells in new, uniquely tailorable microenvironments in a highly parallel experimental manner [79], enabling generation of abundant information. Microfluidic platforms have seen considerable progress for the application of liver cell culture and study [80,81]. Because of the small dimensions and resultantly laminar flows inherent to microfluidic platforms, they are particularly well suited for modeling vascular systems in a high-throughput and environmentally relevant manner; therefore, such systems have been utilized for reconstituting vascular systems [82] and probing artery function [83], proving useful for studying blood circulation dynamics [84], behaviors of vascular endothelial cells under shear stress [85], angiogenesis dynamics [86], and vascularization of tailored scaffolds [87]. However, the system described in this dissertation represents the first reported microfluidic system for the application of BBB modelling [88]. 23 2.3.2 Fabrication Methods Techniques used in this dissertation involve both hard (silicon, glass) and soft (polymer) micromachining methods. While hard micromachining has its roots in the integrated circuits industry [89], soft lithography methods have been developed more recently, based on fabrication of polymeric substrates [77]. 2.3.2.1 Hard Micromachining Methods The primary thin-film deposition method used in this dissertation is sputtering [90]. Sputtering has some advantages over other thin-film deposition methods, such as evaporation and chemical vapor deposition (CVD): A wide variety of materials are feasible for deposition, and the Denton Discovery 18 system available in our fabrication facility has multiple targets, allowing three metals to be deposited in a single pump-down process. Second, there is relatively low energy and temperature of the sputtered atoms compared with evaporation, which is particularly advantageous for the lift-off process, in which the films are laid directly onto photoresist, which tends to be distorted at high temperatures. However, even with this low-temperature process, distorting does occur at high enough power; therefore, sputtering processes in this dissertation were conducted at no higher than 50W power. Lithography is used to transfer a master pattern onto the substrate surface. Photolithography is the predominant method for microfabrication, where UV light passing through a mask is used to define the etching pattern on the substrate. The lithography method used for thin-film patterning in this dissertation is the lift-off process, which differs from the etching process in that the photoresist is deposited prior to thin- 24 film deposition, used the following steps: (1) Generation of the 2D pattern on computer-aided drafting (CAD) software; (2) Fabrication of the mask for these processes were generated on a quartz plate with a chromium absorber metal by e-beam lithography, which has a higher resolution than photolithography; (3) Deposition of the photoresist on a cleaned, prepared glass substrate. A lift-off photoresist (LOR-10B) in addition to patterning positive photoresist (S1813) was used, in that the material is dissolved by the developer when exposed to UV light. Spin-coating of the viscous photoresist solutions was used to uniformly deposit the materials on the substrate, with speeds ranging from 1500-4000 RPM; (4) Soft-baking of the photoresist on a hotplate (190°C for LOR-10B, 110°C for S1813) to ensure success of the pattern transfer; (5) UV exposure through the aligned photomask. A UV lamp projects light through the mask to the substrate in hard contact at the correct dose to achieve proper pattern development; (6) Wet development of the exposed photoresist using liquid developer (MIF-300); (7) Thin-film layer deposition. The sputtering process is low-temperature enough to prevent distortion of the photoresist pattern; (8) Lift-off using acetone under sonication. This process results in the final patterned layers of metal films on the glass substrate. 2.3.2.2 Soft Micromachining Methods In contrast with traditional hard materials, polymers are inexpensive, easy to handle, have highly tunable mechanical properties, and are largely biocompatible. The silicone-based elastomer polydimethylsiloxane (PDMS) has been adopted as the most popular bioMEMS polymer substrate due to its highly suitable combination of physical and chemical properties [91]. These properties allow (1) high fidelity in pattern transfer; 25 (2) superior conformability to other surfaces for large-scale integration [92]; (3) good sealing with itself and other surfaces, both reversibly and irreversibly [93]; (4) highly transparent (5) highly permeable to gases [94], facilitating cell-based applications. The PDMS material used in this dissertation was the Sylgard 184 (Dow-Corning), which cures quickly at a mixed ratio of 10:1 at any temperature higher than about 60°C. Due to the malleable characteristics of uncured PDMS, an enormous variety of shapes can be defined by use of replica molding, enabling high-fidelity 3D pattern generation with exceptionally high aspect ratios [89]. The replica molds for this dissertation were generated using photolithography. In contrast with the lift-off process used for hard micromachining in this project, the replica mold was generated from SU-8, which is a negative photoresist, in that the material is solidified by the developer when exposed to UV light. SU-8 is composed of EPON SU-8 resin, and a photosensitizer triaryl sulfonium salt [95]. Due to the epoxy resin's high stability and high cross-linking density, extremely thick coatings can be achieved. One of the thickest varieties (SU-8 2075) was used in this project to enable the 200 μm thick microfluidic structures. 2.3.2.3 Bonding Methods Soft substrate bonding is one of the most critical steps for generation of multi-layered microfluidic device. Hard substrate bonding (silicon-silicon, silicon-glass, or glass-glass) were not required for the structures used in this dissertation. The particular soft-substrate bonding methods under concern in this dissertation were PDMS-PDMS, PDMS-glass, and PDMS-polycarbonate membrane. Indeed, errors with bonding are perhaps the most prominent source of leaking in microfluidic devices [96]. Thus, an 26 important design consideration for microfluidic devices is the method of bonding. The use of oxygen plasma allows simple oxidation of the surface of both PDMS and glass substrates to generate irreversible siloxane bonds (Si-O-Si) for a strong, reliable bond [97]. However, the need to bond the polycarbonate porous membrane between the PDMS substrates limits the feasibility of the plasma oxidation method. More recently, a method was devised where PDMS prepolymer is used as a mortar to enable bonding of porous membranes into layers of PDMS [98], and this method was used for the study presented in Chapter 3. The PDMS prepolymer mixture is diluted in toluene to significantly reduce the viscosity of the mixture in a tunable manner, and is spin-coated onto a Si wafer, which is then pressed to the channel substrate to generate a sufficiently thin layer of PDMS prepolymer to allow bonding of the membrane without clogging the channels. The assembly is pressed together and cured in an oven, generating a strong bond. However, this method requires both heat and pressure, making sufficiently uniform application of pressure to obtain a complete seal around the channel structures, while preventing deformation of the channel structure or the membrane, quite tedious for larger structures leading to frequent failures; therefore, a different method was adopted for the subsequent studies. This method utilizes 3-amino-propyltriethoxysilane (APTES), a biocompatible surface treatment, to modify the surface of the membrane, allowing plasma activation [99]. Following ~20 min of surface activation in a 5% aqueous solution at 80°C, the membrane and PDMS substrates are activated with oxygen plasma, and a strong, irreversible bond is achieved at room temperature in the same manner as conventional plasma bonding [97], after which residual APTES on the free-standing 27 membrane is dissolved in ethanol. Indeed, this method yielded a more reliable bond than the prepolymer mortar method, as indicated by a significant reduction in the occurrence of leaks. 2.3.2.4 Packaging and Preparation Packaging methods used in this dissertation pertain primarily to postassembly preparation. A Disco DAD641 dicing machine was used to cut the glass wafers used for electrode inserts into rectangular shapes to be embedded into the channel layers. Prior to bonding, a biopsy punch with 2mm diameter was used to core holes for the inlets and outlets. Following bonding, inlets and outlets were prepared for connection to tubing assemblies. Needles with the sharp edge cut flat were used to connect tubing to the chips due to their low volume, and were embedded inside 25 μL pipette tips to ensure secure connection to the inlet holes. Dow Corning 734 flowable sealant was used for securing inlet connectors due to its high compatibility with PDMS, and lack of any chemical reaction to ethanol, which is used heavily during the sterilization process. 2.4 In Vitro Model Characteristics As it is well known that no existing in vitro BBB model has mimicked all BBB functionalities [100], researchers in development of BBB in vitro models aim to achieve the most relevant features of the BBB for the particular aim of investigation [101]. Nevertheless, the ultimate goal in the field is to achieve as many BBB characteristics as possible, as is our goal in this dissertation. Inclusion of the right model characteristics are key to generating the best possible model. 28 2.4.1 Constituent Cell Types The current standard is co-culture systems with endothelial and glial cells. A third cell type, the pericyte, is present in vivo and covers approximately a quarter of the abluminal endothelial surface, playing a role in endothelial proliferation and inflammatory processes [102]. Difficulties in isolating this cell type have limited their use in in vitro models. For this dissertation, we have focused on the two standard cell types, brain endothelial and glial cells. Though advantageous in initial cell phenotype, primary cells are difficult and costly to obtain, and generally lose BBB characteristics after only a few passages, while also being subject to ethical limitations. Conversely, immortalized cell lines retain consistent physiological and morphological characteristics over as many passages as needed. This is due to the immortalization process through viral transformation. These provide advantages in experimental consistency, despite their drawbacks in cell phenotype. Many immortalized cell lines have been used in BBB models, including brain endothelial cells of bovine, porcine, murine, or rat source, MDCK cells, and CACO-2 cells [103]. Popular endothelial cell lines have been used, including the RBE4 from rat origin [104] or the hCMEC/D3 cell line derived from human origin [105]. In addition, the highly characterized human epithelial cell line Caco-2 has been used in BBB models [106], despite heavy differences from brain endothelial cells in terms of cell phenotype [107]. For this dissertation, we opted to use the bEnd.3 immortalized murine cell line because it has been shown to express levels of ZO-1, claudin-5, and occludin comparative to primary brain microvascular endotheliacl cells (BMECs) [108,109], and exhibits rapid 29 proliferation. For co-culture, the immortalized rat glial cell line C8-D1A was used in the initial characterization study. However, its proliferative properties were inferior to bEnd.3, thus subsequently the C6 glial cell line was used because it was commonly used in previous co-culture BBB models [110,111] and to generate astrocyte-conditioned medium [112], and because its proliferative properties were comparable to bEnd.3. 2.4.2 Porous Membrane The most common materials for track-etched porous membranes used in BBB models are polycarbonate and polyethylene terephthalate (PET), each with their own advantages. Polycarbonate membranes have lower nonspecific binding properties and thus less interference with testing compounds, whereas PET membranes are transparent, allowing light-based microscopic observation of cells adhered to them [113]. Another influential property is pore size, where pores of 0.4μm have been seen to produce highest trans-endothelial electrical resistance (TEER) with otherwise similar conditions [114]. Furthermore, 0.4 μm pore size has been shown to restrict astrocyte cell bodies from migrating through the membrane, while allowing end-feet to pass through the pores to interact with the adjacent endothelial cells [115]. Conversely, 3.0 μm pores prompt migration of astrocyte growth on both sides of the membrane, and clogging of pores, preventing passage of astrocytic soluble factors [116,117]. For these reasons, polycarbonate membranes of 0.4 μm pore size (provided by Corning) were used in this dissertation, with a 10 μm membrane thickness, and nominal pore density of 1x108 pores/cm2, which is significantly higher than the primary alternative PET (4x106 pores/cm2). 30 2.4.3 Adhesion-Promoting Treatments Coating of the membrane culture surface is a key model condition for achieving optimal model performance, particularly in microfluidic models where adhesion to the substrate is a potential issue. These protein coatings are intended to mimic the basal lamina, a key extracellular component in the BBB [118]. The major components of the basal lamina include type IV collagen and fibronectin [119], which are commonly used in previous BBB models as well as in this dissertation. What concentrations should be used for these coatings have not been clearly established [120]; therefore, some trial-and-error experimentation was required for this study. For example, in Chapter 3, lower concentrations were used (10 μg/mL) during the coating step, and was sufficient for the low flows used in the study. However, increasing concentrations of each protein to 100 μg/mL for coating in Chapter 4 enabled optimal adhesion of endothelial cells at comparatively higher flows (several orders of magntitude), and this coating scheme was also used in Chapter 5. 2.4.4 Cellular Media An advantage of immortalized cell lines is that they perform consistently well without media supplementation, and the same media formulation was used for all studies in this dissertation: A 50/50 mixture of Dulbecco's Modified Eagle Medium (DMEM) and Ham's Nutrient Mixture F12, supplemented with 0.365 g/L L-glutamine and 5% fetal bovine serum (FBS). In addition, penicillin/streptomycin solution and amphotericin B were added to the media formulation to help prevent bacterial and fungal contamination, respectively. 31 Many BBB models have opted to replace the presence of astrocytes with astrocyte-conditioned medium, or media bathing astrocyte cell cultures to allow it to contain astrocyte-derived soluble factors secreted by the cells [121]. These astrocyte-conditioned media have been seen to modulate barrier properties relating to expression of tight junction [122] and efflux transporter proteins [123]. Other supplementation includes the addition of hydrocortisone to the cell culture media following seeding, which has been seen to benefit the formation of barrier properties, though the exact mechanism by which hydrocortisone does this remains largely unclear [124]. Another additive commonly used to purify primary cells is puromycin, which eliminates nonendothelial cells from the cultured cells [125]. 2.4.5 Microfluidic Structures The use of microfluidics in this dissertation enabled the application of shear stress to the cells in a highly controllable manner. The microfluidic structure itself went through a number of changes depending on the aims of the study itself. All mask designs were generated using SolidWorks software. For consistency, the depth of the microfluidic structure was kept at a constant 200 μm thickness. Slight variations of SU-8 film thicknesses occur due to imperfect levelling of the hot-plate and occasional presence of bubbles during the sot-bake process [126]. To reduce the occurences of these bubbles, a glass dish was placed over the hot-plate during the soft-bake process following initial spin-coating, and the film was deposited in two 100 μm layers instead of a single 200 μm layer. The degree of error allowed during fabrication of the silicon replica molds was 10 μm; therefore, for quality control, SU-8 thicknesses were measured with a Tencor 32 Profilometer, and only molds were used where all measurements were between 190-210 μm. All flow in all versions of the microfluidic structures used in this dissertation are completely laminar [127] and thus viscous-dominant, where dissolved particle motion is dominated by diffusion, as in capillaries: The Reynolds number for a rectangular channel is calculated as: 𝑅𝑅=2𝜌𝑉𝑎𝑎𝑎(𝑤ℎ)𝜇(𝑤+ℎ) (2.1) where ρ and μ are density and viscosity, respectively, and w and h are channel width and height, respectively. Based on this equation, the lowest aspect ratio channel used in this dissertation (and highest Reynolds number) of dimensions 100μmX400μm, has a Reynolds number of ~2.3s/cm*Vave, where Vave is the average velocity. Thus, the minimum velocity for transition to turbulent flow is ~1000 cm/s, several orders of magnitude higher than practical flow-rates used in this dissertation; therefore, it is a valid assumption that flow is completely laminar for all experiments. 2.5 Methods of Model Characterization This dissertation uses previously established methods of characterization to validate the model in each of the studies described in the forthcoming chapters. The primary methods of model characterization are TEER and compound permeability, as they are direct representations of monolayer tightness and compound diffusion, respectively. Additionally, to look at cell morphologies and specific expression of BBB protein constituents, imaging and protein analysis methods are also used, though they are 33 more indirect measures of barrier function. Finally, to characterize flow characteristics of the microfluidic component of the model, computational simulations are used. The specific methodological procedures of each of these techniques for the studies in Chapters 3-6 will be described in greater detail as it pertains to the specific study. 2.5.1 TEER Measurement TEER of endothelial cell monolayers is feasible because the cells may be considered to have a level of resistance to ionic movement through the paracellular pathways (tight junctions), which can be considered as equivalent to a combination of resistors and capacitors in a circuit [128], though the capacitors are typically neglected from the model circuit for simplicity, leaving a series of resistors. Thus, the placement of two electrodes opposite each chamber representing the luminal and abluminal compartments allows measurement of this resistance. According to consensus in the field of in vitro BBB models, a generally accepted level of TEER above 150Ωcm2 is characteristic of a good in vitro model [129-131], while in vivo microvessels commonly reach 1800 Ωcm2 [132]. In contrast, peripheral TEER in vivo is typically measured to be less than 100 Ωcm2. It is worthy of note that TEER results are difficult to compare or repeat across separate laboratories [129]. For example, one group has published TEER values of 400-700 Ωcm2 [133] with commercial Endohm chambers, significantly lower than data measured with custom equipment in the same laboratory between 1200-1800 Ωcm2 [134] under the same conditions. Thus, TEER is most useful when used as a quality control measure for experimental consistency, as a comparison of intralaboratory culture conditions, or for monitoring toxicity or barrier 34 modulation. 2.5.2 Trans-BBB Permeability Methods The most direct measurement of BBB function of an in vitro model is measurement of compound tracers. The relationship between TEER and solute transport is not necessarily linear, since transport depends on a combination of transport through all paracellular pathways (tight junctions), while TEER depends on areas with lowest electrical resistance between cells [135]. Indeed it was shown that at TEER values higher than 130 Ωcm2, paracellular permeability was independent of TEER status [136]; therefore, paracellular permeability should be monitored with tracer compounds. To be used as a marker of paracellular transport, tracer compounds should not be compounds which work as ligands for transcellular transporters [129]. There are many convenient, fluorescent compounds which fit this category, such as sodium fluorescein [137], lucifer yellow [138], propidium iodide, or fluorescein isothiocyanate (FITC)-labeled dextrans [139]. FITC-Dextran is particularly convenient, and was used in each of the proposed studies, because it comes in many sizes, allowing monitoring of permeability according to size difference with physicochemical consistency. The flow-based microfluidic permeability assay platform provides diffusive conditions much closer to that of in vivo than in large-scale static systems. Because at such sub-mm scales, compound transport is dominated by the convective effect [140] due to the laminar flow profile in capillaries or microchannels, test compounds provided by the steady laminar flow, permeability rates are dependent on the supplied concentration, and not on the time-dependent compound motion that occurs in static diffusion systems. 35 2.5.3 Imaging Methods In addition to barrier function measurement with TEER and permeability, cellular function can be monitored using methods of microscopy. Fluorescence microscopy of immobilized, fixed cultures of BBB cells allows monitoring of the localized expression of specific proteins. One of the most important of these compounds is considered to be ZO-1, one of the key components of tight junctions [52]. Expression of this compound is essential to barrier function; therefore, an endothelial monolayer lacking clear expression of ZO-1 is expected to be lacking in barrier function. Second, the use of microscopy allows monitoring of morphological characteristics, such as cell shape and orientation. Cellular morphometry is particularly relevant as it relates to the response to shear stress [141]. 2.5.4 Protein Expression Techniques Direct assays of protein expression is useful for characterizing constituent cells used in the model. An analytical technique useful for monitoring the expression of specific proteins in a cell population is western blot. Total protein extracts from the population of cells are separated by gel electrophoresis according to size or charge, and are transferred to a nitrocellulose or polyvinylidene fluoride (PVDF) membrane, where they can be stained with specific antibodies [142]. The expression of this antibody is proportional to the total protein expressed, and can be quantitated using band densitometry. Any compound can be used for this technique, as long as there are antibodies available. Also, the expression, hence activity, of P-gp can be directly assayed using MDR biochemical assays [143]. These methods are particularly useful for 36 comprehensive BBB functional characterization, because TEER and permeability are focused on the paracellular pathway, while assays observing P-gp activity and expression focuses on the transcellular pathway, both of which are constituents of BBB function. 2.5.5 Microfluidics Simulations Flow characteristics of the microfluidic structures used in this dissertation were elucidated early with the use of computational simulations. These simulations were conducted using Comsol 4.0, with the laminar flow multiphysics module. Drafted CAD files of the microfluidic structures can be exported into the Comsol model, or drawn within the software itself. 3D models with geometric meshes with approximately 3-5,000,000 element number were used to maximize model precision, while staying within the memory limits of the computers used (16 GB RAM). 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The elongation and orientation of cultured endothelial cells in response to shear stress. Journal of biomechanical engineering. 107(4):341-347, 1985. 49 [142] Towbin, H., T. Staehelin, and J. Gordon. Electrophoretic transfer of proteins from polyacrylamide gels to nitrocellulose sheets: Procedure and some applications. Proceedings of the National Academy of Sciences. 76(9):4350-4354, 1979. [143] Fruttero, R., M. Crosetti, K. Chegaev, S. Guglielmo, A. Gasco, F. Berardi, M. Niso, R. Perrone, M. A. Panaro, and N. A. Colabufo. Phenylsulfonylfuroxans as modulators of multidrug-resistance-associated protein-1 and p-glycoprotein. Journal of medicinal chemistry. 53(15):5467-5475, 2010. CHAPTER 3 CHARACTERIZATION OF A MICROFLUIDIC IN VITRO MODEL OF THE BLOOD-BRAIN BARRIER (μBBB)1 3.1 Abstract The blood-brain barrier (BBB), a unique selective barrier for the central nervous system (CNS), hinders the passage of most compounds to the CNS, complicating drug development. Innovative in vitro models of the BBB can provide useful insights into its role in CNS disease progression and drug delivery. Static transwell models lack fluidic shear stress, while the conventional dynamic in vitro BBB lacks a thin dual cell layer interface. To address both limitations, we developed a microfluidic blood-brain barrier (μBBB) which closely mimics the in vivo BBB with a dynamic environment and a comparatively thin culture membrane (10μm). To test validity of the fabricated BBB model, μBBBs were cultured with b.End3 endothelial cells, both with and without co-cultured C8-D1A astrocytes, and their key properties were tested with optical imaging, trans-endothelial electrical resistance (TEER), and permeability assays. The resultant imaging of ZO-1 revealed clearly expressed tight junctions in b.End3 cells, Live/Dead assays indicated high cell viability, and astrocytic morphology of C8-D1A cells were confirmed by ESEM and GFAP immunostains. By day 3 of endothelial culture, TEER 1 Reproduced by permission of The Royal Society of Chemistry. Published: Lab on a Chip, 2012, Vol 12, p 1784-1792. http://pubs.rsc.org/en/content/articlelanding/2012/lc/c2lc40094d 51 levels typically exceeded 250Ωcm2 in μBBB co-cultures, and 25Ωcm2 for transwell co-cultures. Instantaneous transient drop in TEER in response to histamine exposure was observed in real-time, followed by recovery, implying stability of the fabricated μBBB model. Resultant permeability coefficients were comparable to previous BBB models, and were significantly increased at higher pH (>10). These results demonstrate that the developed μBBB system is a valid model for some studies of BBB function and drug delivery. 3.2 Introduction Diseases of the central nervous system (CNS) present a prevalent and ever-increasing burden for the world healthcare industry. For example, Alzheimer's disease is diagnosed in an estimated 24 million people, a number projected to double every 20 years [1]. Despite such emerging demands for treatment of CNS diseases, only 7% of CNS drugs in clinical development reach the marketplace (Figure 3.1A), compared to the 12% average across all therapeutic areas, or 20% for cardiovascular drugs [2,3]. This low success rate is attributed primarily to a unique CNS structure coined as the blood-brain barrier (BBB) [3], which introduces a pharmacokinetic hurdle by blocking compounds from entering brain tissues from capillaries [4]. Only compounds smaller than about 500Da easily cross the BBB, but few CNS diseases consistently respond to this category of molecules [5]. Because the BBB blocks nearly all polar or large compounds, new drug treatments for the CNS of higher molecular weight must take BBB function into account, requiring more extensive preclinical studies. The use of in vitro models of the BBB would augment 52 Figure 3.1 Motivation and background for μBBB development. (A) Probability of success is lower for new CNS drugs than those in other healthcare areas due to the unique architecture of brain capillaries [2]. (B) The CNS is unique due to the extraordinary selectivity of the BBB [3]. Better model systems of the BBB will contribute to development of CNS disease treatments. Effective in vitro BBB models should successfully include key properties: (1) endothelial cells with tight junction expression; (2) co-culture with astrocytes; (3) presence of shear stress; (4) selective permeability to compounds; (5) high electrical resistance across tight junctions. ooHigh Electrical ResistanceTight JunctionsDemand for BBB StudiesBlood-Brain Barrier (BBB) Key PropertiesCNS Drugs have low probability of success2BrainEndothelial CellsAstrocytesSelective PermeabilityCapillary (Fluid Flow)BrainShear StressAlong CellsRBBB models should exhibit key properties05101520Cardio-vascularArthritisInfectious DiseaseOphtalmolMetabolic DiseaseUrologyCNSOncologyWomen's HealthAverage% Probability of Success For New Drug TrialsIndustry Average spinal cordAll tissues perfused except CNS3.ABCNSooCl-Cl-Cl-Cl-Cl-oooCl-Cl-Cl-Cl-oooSmaller solutes pass more easilyDirect contact between cell typeso(2)(1)(3)(4)(5)brainRadio-Histogram of a Mouse Fetus 53 the conventional pharmaceutical approach focusing on drug design, help predict the penetration of drug candidates across the BBB [24], and allow prescreening and optimization of new treatments prior to animal and clinical studies [25]. BBB models can also be used to study the role of barrier function on CNS disease progression [26], and test innovative methods of delivery [27]. BBB studies have been performed largely in two platforms: in vivo and in vitro models (Table 3.1). In vivo models directly utilize entire living organisms, typically rats or mice, while in vitro models construct artificial environments with cultured cells to mimic in vivo structures. In vitro models are a valuable precursor to animal models due to lower cost, time, and ethical constraints. More specific to the BBB, unlike in animal studies, in vitro models enable controlled, repeatable, and noninvasive tests: permeability assays, resistance measurements, and microscopy. Table 3.1 Qualitative comparison of standard BBB models with the μBBB proposed in this article. Experimental system In vivo models In vitro models System type [Citations] Animals [6-8] Transwells [9-18] DIV-BBB [19-22] μBBB [23] Relative cost High Very Low Low Low Massively-parallel, controlled, and repeatedly identical No Yes Yes Yes Shear stress/dynamic flow (Quantitative analysis) Yes (No) No - Yes (Yes) Yes (Yes) Space between co-cultures Immediate <10 μm >150 μm <10 μm Functional media volumes N/A 0.5-2 ml 1.4 ml 12 μl Time to steady-state TEER N/A 3-4 days 9-12 days 3-4 days TEER electrodes - Ion flow profile (Gap size) (Fixed position) Invasive Uniform (<2mm) (No) Non-uniform (>1cm) (Yes) Uniform (<400μm) (Yes) Nondestructive microscopy No Yes No Yes Fabrication N/A Simple Complex Moderate 54 Although traditional in vivo models provide environments closer to the human phenotype, they cannot provide massively-parallel, controlled, and repeatedly identical environments for reliable and quantitative studies (Table 3.1). More importantly in terms of practicality, in vivo models require extraordinary amounts of cost, time, and man-hours per test, while increasingly facing ethical issues as well. In vitro models are able to significantly reduce such issues by offering identical environments in numerous arrays, as well as lower cost, time, and ethical constraints. Thus, the development of valid in vitro models can facilitate the overall drug development process by acting as a precursor, or even a replacement, for animal studies. The validity of an in vitro model is dependent on how well it reproduces the key physiological and biological characteristics of its in vivo archetype (Figure 3.1B). The key characteristics of the BBB include: (1) the primary structure, consisting of strongly expressed tight junctions between endothelial cells which directly control compound permeability [28]; (2) co-culture of endothelial cells with astrocytes including endfoot contact, which plays an important role in modulating barrier function through cell-cell signaling [29]; (3) mechanotransductive effects of shear stress from fluid flow on endothelial cells, which is known to critically influence cell differentiation and tight junction formation [30,31]; (4) selective permeability from the constituted structures to dissolved compounds; (5) maintenance of high electrical resistance representing the maturity and soundness of the structures. To mimic such key characteristics, various in vitro models have been developed to date [9-22] and can be mainly divided into two groups: static and dynamic models, defined by the inclusion of fluid flow, resulting in shear stress over the surface of the 55 cells. Static models have been the most widely used since the first transwell setup in 1991 [11]. Recently, dynamic in vitro BBB (DIV-BBB) [20-22] models have been developed which utilize hollow fibers to mimic the BBB architecture and flow conditions, providing adequate shear stress. However, wall thickness (150μm) is significantly higher than transwell thickness (10μm), discouraging cell-cell interaction, and DIV-BBBs take significantly longer to reach steady-state TEER values [20,32] than static transwell models. To our knowledge, no existing BBB systems have addressed each of these shortcomings yet. In order to address the issue, we have developed a microfluidic BBB (μBBB) [23] that includes each of the following advantages over existing in vivo and in vitro static and dynamic BBB Models (Table 1): (1) significantly lower costs and timescales than in vivo studies; (2) massively-parallel, controlled, and repeated environments not available in in vivo models; (3) dynamic microenvironment providing shear stress stimulation to the cells, and allowing the improved analysis of test compounds and controlled delivery compared to static models; (4) much thinner culture membrane, decreasing the distance between co-cultured cells from DIV-BBB models. In addition, the developed μBBB model uses smaller functional volumes for quicker media exchange and material conservation. Shorter times to steady-state TEER levels allow a more rapid turn-around time, shortening experiments and allowing a more high-throughput approach to experimentation. The developed μBBB also enables installation of high-density electrodes with tiny (200 μm) gaps between either electrode and the cell layers, with uniform ion flow density, minimizing background resistance and error. Nondestructive microscopy of the system is possible by carefully designing electrode l