Develope of Ultrasound Elastography for Nondestructive And

DEVELOPE OF ULTRASOUND ELASTOGRAPHY FOR NONDESTRUCTIVE AND

NONINVASIVE CHARACTERIZATION OF STIFFER POLYMERIC

BIOMATERIALS

HAOYAN ZHOU

Submitted in partial fulfillment of the requirements For the degree of Doctor of Philosophy

Dissertation Adviser: Dr. Agata Exner

Department of Biomedical Engineering CASE WESTERN RESERVE UNIVERSITY

January, 2016

CASE WESTERN RESERVE UNIVERSITY SCHOOL OF GRADUATE STUDIES

We hereby approve the thesis/dissertation of

Haoyan Zhou ______candidate for the ______Doctor of Philosophy degree *.

(signed) ______Horst von Recum (chair of the committee)

______Agata A. Exner

______Stuart Rowan

______Joseph M. Mansour

______Anant Madabhushi

(date) ______03 August 2015

*We also certify that written approval has been obtained for any Proprietary material contained therein.

To my wife, father, and mother

Table of Contents Table of Contents……………………………………………………………i

List of Tables………………………………………………………………..v

List of Figure………………………………………………………………..vi

Acknowledgements…………………………………………………...... viii

List of Abbreviations……………………………………………………….ix

Abstract……………………………………………………………………..xii

Overview…………………………………………………………………....xiv

Chapter 1: Background and Introduction

1.1 Polymer degradation and erosion……………………………………….1

1.1.1 Polymer degradation………………………………………..1

1.1.2 Polymer erosion…………………………………………….4

1.1.3 Traditional and current methods……………………………5

1.2 In Situ forming drug delivery systems…………………………………..7

1.3 Ultrasound elastography………………………………………………...11

1.4 References………………………………………………………………14

Chapter 2: Biomedical Imaging in Implantable Drug Delivery ………..23

Systems (DDS)………………..……………………………….……….…...24

2.1 Introduction………………………………………………….………...... 25

2.2 Biomedical Imaging Modalities…………………………….…………...29

2.2.1 Ultrasound………………………………………………….…29

2.2.2 Magnetic Resonance Imaging (MRI)………………….……...33

2.2.3 Optical imaging and Optical Coherence Tomography (OCT)..37

2.2.4 X-ray Imaging and Computed Tomography (CT)…….……...40

2.3 Conclusion…………………………………………...………….……...43

2.4 References……………………………………………………………....46

Chapter 3: Validation of Ultrasound Elastography Imaging for

Nondestructive Characterization of Stiffer Biomaterials…..…………..60

3.1 Introduction……………………………………………………….…….61

3.2 Materials and Methods……………………………………………….....64

3.2.1 PDMS Sample Fabrication…………………………………....64

3.2.2 Phantom Preparation………………..…………………….…..65

3.2.3 Mechanical Testing…………………………………….……..66

3.2.4 Strain Imaging……………………………………….……...... 68

3.2.5 Statistics………………………………………………….…...69

3.2.6 Region of Interest (ROI) study…………………………….…70

3.3 Results………………………………………………………………….70

3.3.1 Mechanical Testing………………………………………...... 70

3.3.2 Ultrasound Elastography (UE)…………………………..…..73

3.3.3 Strain Data and Statistics Evaluation………………………..74

3.3.4 Modulus and Strain Correlation………………………..……77

3.4 Discussion……………………………………………………………..79

3.5 Conclusion…………………………………………………………….83

3.6 References……………………………………………………………..84

Chapter 4: Nondestructive Characterization of Biodegradable Polymer

Erosion in Vivo Using Ultrasound Elastography Imaging ………...... 89

4.1 Introduction…………………………………………………………...90

4.2 Materials and Methods………………………………………………..93

4.2.1 Materials…………………………………………………….93

4.2.2 In Situ Forming Implant (ISFI) Solution Preparation………93

4.2.3 Phantom Fabrication and ISFI Injection……………………94

4.2.4 In Vitro UE Scan and Image Analysis…………………...... 95

4.2.5 In Vitro Mechanical Testing………………………………..96

4.2.6 In Vitro Erosion Study…………………………………...... 97

4.2.7 Animal Preparation and Implant Injection…………………97

4.2.8 In Vivo UE Scan……………………………………………98

4.2.9 In Vivo Erosion Study……………………………………...99

4.2.10 Predication of Implant erosion in vitro………………...... 99

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4.3 Results………………………………………………….…………...100

4.3.1 Implant Erosion In Vitro………………………………….100

4.3.2 Implant Erosion In Vivo………………………………...... 103

4.3.3 Predication of Implant erosion in vitro…………………...106

4.4 Discussion………………………………………………………...... 106

4.5 Conclusion………………………………………………………….111

4.6 References………………………………………………………...... 112

Chapter 5: Conclusions and Future Directions……………………...116

5.1 Conclusions…………………………………………………………116

5.2 Limitations………………………………………………………….118

5.3 Future Directions……………………………………………………119

5.4 References………………………………………………………...... 121

Bibliography……………………………………………………………122

Appendix………………………………………………………………..154

List of Tables

Table 2.1 - Current tools for biomaterial characterization…………………….27

Table 2.2 - Properties of biomedical imaging modalities……………………..28

Table 3.1 - PDMS sample elastic modulus…………………………………....72

Table 3.2 - UE experiment statistics…………………………………………..77

Table 4.1 - Summary of erosion measurements………………………………106

List of Figures

Figure 1.1 - Schematic illustration of bulk erosion and surface erosion…………4

Figure 1.2 - Schematic illustration of phase inversion process…………………..9

Figure 1.3 - Release of fluorescein from a 29kDa PLGA ISFI formulation……..10

Figure 1.4 - Algorithm of strain estimation used in quasi-static elastography...... 12

Figure 1.5 - Working flow of UE……………………………………………...…14

Figure 2.1 - Examples of ultrasound in implantable DDS……………………...... 31

Figure 2.2 - MRI images of an intravitreal implant………………………………35

Figure 2.3 - In vitro–in vivo erosion profiles of PEG:dextran implants……….….39

Figure 2.4 - Pre- and peri-operative imaging for transarterial chemoembolization.43

Figure 2.5 - Summary of biomedical imaging in implantable DDS………………46

Figure 3.1 - PDMS sample fabrication process………………………………...…66

Figure 3.2 - Ultrasound elastography scan setup………………………………….69

Figure 3.3 - Representative strain-stress curve of PDMS samples………………..71

Figure 3.4 - Elastic modulus of polyacrylamide tissue mimicking phantom……...73

Figure 3.5 - B-mode and UE color coded strain map of PDMS samples………....74

Figure 3.6 - Strain results from UE………………………………………………..75

Figure 3.7 - Elastic modulus and 1/strain correlations in both phantoms…………78

Figure 3.8 Average strain value comparison…………………………………..79

Figure 3.9 Finite element analysis of transducer-subject interface …………...83

Figure 4.1 - Void creation and ISFI implant injection process…………………95

Figure 4.2 - Animal study design……………………………………………….98

Figure 4.3 - Young’s modulus of 34 kDa PLGA implants…………………….101

Figure 4.4 - In vitro: Color coded strain map of implant over time……………102

Figure 4.5 - In vitro strain and erosion…………………………………………103

Figure 4.6 - In vivo: Color coded strain map of implant over time…………….104

Figure 4.7 - In vivo strain and erosion…………………………………………..105

Figure 4.8 - In vitro/In vivo erosion and strain correlation…………………………...109

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Acknowledgements

I would like to take the opportunity to express my deepest appreciation and thanks to my advisor Dr. Agata Exner for her guidance and mentorship. I would like to thank you for encouraging my research and for allowing me to grow as a research scientist. Your advice on both research as well as on my career have been invaluable. I would also like to thank all the members of the Exner lab past and present: Luis Solorio, Hanping Wu,

Christopher Hernandez, Monika Goss and Reshani Perera. You have made the Exner lab an amazing environment to be a part of.

I am also grateful for the guidance and support I have received from my Ph.D committee: Dr. Horst von Recum, Dr. Stuart Rowan, Dr. Joseph Mansour and Dr. Anant

Madabhushi. Your time, academic support and input are greatly appreciated. Thank you.

For the non-scientific side, a special thanks to my family. Words cannot express how grateful I am to your love and support. Without my wife Ning, my father Yong, and my mother Li I would not have been able to accomplish any of my goals or dreams. Thank you.

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List of Abbreviations DDS Drug delivery system PDMS Polydimethylsiloxane PEVA Poly(ethylene-co-vinyl acetate) IVIVC In vitro- in vivo correlation ISFI In situ forming implant DSC Differential scanning calorimetry GPC Gel permeation chromatography US Ultrasound MRI Magnetic Resonance Imaging MR Magnetic Resonance MRF Magnetic resonance fingerprinting BT-MRI Bench top- Magnetic Resonance Imaging EPR Electron paramagnetic resonance OCT Optical coherence tomography CT Computed tomography IVUS Intravascular ultrasound SWEI Shear wave elasticity imaging UE Ultrasound elastography TACE Transarterial chemoembolization kDa kilodaltons m meter cm centimeter mm millimeter μm micrometer g gram kg kilogram mg milligram μg microgram l liter

ix ml milliliter μl microliter dl deciliters ᵒ C degree centigrade S second h hour d day keV kiloelectronvolts kPa kilopascal MPa megapascal Pa pascal N newton MHz megahertz wt weight w weight v volume E Young’s modulus Eq Equation TEMED tetramethyl ethylenediamine APS ammonium persulfate FDA Federal Drug Administration NIH National Institutes of Health IACUC Institutional Animal Care and Use Committee C drug concentration D dimensional T tesla Gd-DTPA gadolinium- diethylenetriaminepentacetate SPIO Super Paramagnetic Iron Oxide MION Monocrystalline Iron Oxide Nanocompounds GFP Green fluorescent protein

BPLPs Biodegradable photoluminescent polymers MSCs Mesenchymal stem cells TDT Tissue Doppler Tracking CDI Color Doppler Imaging ROI Region of interest ANOVA Analysis of Variance HSD Honestly significant difference MW Molecular weight NMP N-methyl pyrrolidinone PBS Phosphate buffered saline

Development of Ultrasound Elastography for Nondestructive and Noninvasive Characterization of Stiffer Polymeric Biomaterials

Abstract

HAOYAN ZHOU

Significant advancements in biodegradable polymeric materials have been made for numerous applications including tissue engineering, regenerative medicine and drug delivery. The functions of these polymers within each application often rely on controllable polymer degradation and erosion, yet the process has proven difficult to measure in vivo.

Traditional methods for investigating polymer erosion and degradation are destructive, hampering accurate longitudinal measurement of the samples in the same subject. To overcome this limitation we have explored the use of ultrasound elastography imaging as a tool to nondestructively measure strain of poly(lactic-co-glycolic acid) (PLGA) phase sensitive in situ forming implants which changes with progressive loss of structural integrity resulting from polymer erosion. In order to better employ this technology, ultrasound elastography imaging was first characterized and validated by comparing to the gold standard unconfined compression testing of PDMS samples with different Young’s moduli. The detection limit as well as detectable difference of this technique were identified. This imaging system was also optimized to scan stiffer polymeric biomaterials.

Using this tool, we investigated erosion kinetics of implants comprised of three different

PLGA molecular weights in vitro and in vivo. The in vitro environment was created using

xii a novel polyacrylamide based tissue mimicking phantom while the in vivo experiment was performed subcutaneously using a rat abdominal model. A strong linear relationship independent of polymer molecular weight was found between average strain values and erosion values in both the in vitro and in vivo environment. Results support the use of a mechanical stiffness based predicative model for longitudinal monitoring of material erosion and highlight the use of ultrasound elastography as a nondestructive tool for measuring polymer erosion kinetics.

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Overview

To meet the needs of advanced characterization techniques for monitoring and understanding the behavior of an ISFI system, the goal of this thesis work is to develop and validate the ultrasound elastography technique for the characterization of ISFI system erosion in vitro and in vivo. The thesis work is discussed in the following five chapters.

Chapter 1: Background and Introduction

This chapter focuses on providing background knowledge and rationale for the materials discussed in this thesis. Concepts include different degradation and erosion mechanisms of biodegradable polymers as well as factors which affect their degradation and erosion processes and the various technologies used traditionally to measure these processes. In addition, the ISFI system, which is the core focus of this thesis work is discussed in depth. Contents include liquid to solid transition mechanisms, examples of polymer used in this system and principles behind each phase of drug release. Finally, the ultrasound elastography working algorithm as well as categorization are discussed.

Chapter 2: Biomedical Imaging in Implantable Drug Delivery Systems

The focus of this chapter is an overview of current biomedical imaging techniques, including magnetic resonance imaging (MRI), ultrasound imaging, optical imaging, X-ray and computed tomography (CT), and their application in the evaluation of implantable drug delivery systems. The nondestructive and noninvasive nature of these imaging techniques make them ideal for characterization of implant behaviors in vivo. Versatile imaging tools with different features and capabilities can meet this need and provide quantitative data on morphological and functional aspects of implantable systems.

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Chapter 3: Validation of Ultrasound Elastography Imaging for Nondestructive

Characterization of Stiffer Biomaterials

Ultrasound elastography (UE) has been widely used as a “digital palpation” tool to characterize tissue mechanical properties in the clinic. UE benefits from the capability of noninvasively generating 2-D elasticity encoded maps. This spatial distribution of elasticity can be especially useful in the in vivo assessment of tissue engineering scaffolds and implantable drug delivery platforms. However, the detection limitations have not been fully characterized and thus its true potential has not been completely discovered.

Characterization studies have focused primarily on the range of moduli corresponding to soft tissues, 20kPa-600kPa. However, polymeric biomaterials used in biomedical applications such as tissue scaffolds, stents, and implantable drug delivery devices can be much stiffer. In order to explore UE’s potential to assess mechanical properties of biomaterials in a broader range of applications, the work in this chapter discusses an investigation on the detection limit of UE strain imaging beyond soft tissue range. To determine the detection limit, measurements using standard mechanical testing and UE on the same polydimethylsiloxane samples were compared and statistically evaluated.

Chapter 4: Nondestructive Characterization of Biodegradable Polymer

Erosion in Vivo Using Ultrasound Elastography Imaging

The focus of the work in this chapter is on investigating the feasibility of utilizing ultrasound elastography imaging as a tool to nondestructively monitor polymer erosion of the ISFI system by measuring the strain of PLGA which changes with progressive loss of structural integrity resulting from polymer erosion. Using this tool, erosion kinetics of implants comprised of three different PLGA molecular weights (18, 34 and 52 kDa) were

xv investigated in vitro and in vivo. The in vitro environment was created using a novel polyacrylamide based tissue mimicking phantom while the in vivo experiment was performed subcutaneously using a rat abdominal model. A strong linear relationship independent of polymer molecular weight was found between the average strain values and erosion values in both the in vitro and in vivo environment. Results support the use of a mechanical stiffness based predicative model for longitudinal monitoring of material erosion and highlight the use of ultrasound elastography as a nondestructive tool for measuring polymer erosion kinetics.

Chapter 5: Conclusion and Future Directions

This chapter focuses on summarizing the overall body of work, limitations of the techniques and future directions. A brief discussion of alternative approaches and expansion of the concepts are also included.

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Chapter 1: Introduction and background

Polymeric biomaterials have been widely used in various biomedical applications including synthesis of active targeting nanoparticles in molecular imaging[1, 2], orthopedic fixing devices[3, 4], manufacturing of tissue engineering scaffolds to fight organ failure[5,

6], and drug delivery systems for sustained release[7-10]. The majority of applications benefit from the degradation and erosion properties of polymeric biomaterials, because they provide programmable in vivo retention time and self-clearance after function. A physical chemical understanding of polymer degradation and erosion is critical for researchers to utilize these polymeric biomaterials rationally in different biomedical applications.

1.1 Polymer degradation and erosion

1.1.1 Polymer degradation

Polymer degradation and erosion play a role for all polymeric materials. There is no clear distinction between degradable and non-degradable polymers. It is the relation between the time-scale of degradation and the time-scale of the application that determines whether a polymer is degradable or non-degradable. Degradable polymers refer to those materials which degrade during their application, while non-degradable polymers are those that require a much longer time to degrade than the duration of the application[11].

There are different types of degradation mechanisms such as thermal-, photo-, mechanical and chemical degradation. Thermal-degradation depends on the thermal stability of weak bonds, for example chain-growth polymers like poly(methyl methacrylate) can be degraded by thermolysis at high temperatures[11-13]. Thermo-oxidation is another

1 process that occurs at the same time with increasing temperature, and it usually competes with thermal-degradation[14]. Photo-degradation is typically initiated following the absorption of photons such as UV light and γ-radiation through various photolytic mechanisms including chain breaks, aging in UV and photo-oxidation[11, 15]. However such photo effects are of minor importance for polymeric biomaterials, unless they are going through γ-sterilization, after which a significant loss of molecular weight can be observed. Mechanical degradation affects those polymers that are subjected to mechanical stress, such as biomaterials used in orthopedic fixing devices or suture applications [11,

16]. The most common degradation mechanism is chemical degradation via hydrolysis[17] or enzymatic hydrolysis[18]. Enzymatic hydrolysis is also referred to as biodegradation, meaning that the degradation is facilitated at least partially by a biological system [11, 19].

Hydrolysis is usually caused by the breakage of hydrolysable bonds within the polymer matrices.

There are several factors which affect the kinetics of hydrolysis, including the type of hydrolysable bonds, pH, copolymer composition and water uptake [11, 20]. Chemical and physical changes happen along with the degradation process, such as crystallization of oligomers and monomers [21, 22], pH changes [23] and etc. will also have significant impact on degradation rate.

It is the polymer backbone bond which determines the rate of hydrolysis. It varies tremendously. For instance, poly(anhydrides) has a half-life of 0.1 hour, poly(ortho esters) has a half-life of 4 hour, while poly(esters) can have a half-life of 3.3 years and poly(amides) can have 8300 years [11, 20]. However the reactivities of hydrolysable bonds can changes dramatically when using catalyst [24] or by altering the chemical neighborhood of

2 functional groups [25]. For example, the reaction rate constant of hydrolysis for poly(ethyl acetate) can be increased from 2.5x10-10 (s-1) to 1.1x108 (s-1) by replacing the hydrogen with chlorine in the acid α-position[26]. pH also plays a very important role to affect the hydrolysis kinetics. The reaction rate of esters may change orders of magnitude due to pH changes[26]. The effect of pH on degradation of PLGA sutures have been investigated intensively. It has been found that PLGA is most stable in neutral pH medium, while degrades the tremendously faster in both low and high pH [27]. Same catalysis was also observed in poly(bis-(p-carboxyphenoxy)propane) anhydride), the degradation rate increased by a factor of 10 when increasing the medium pH from 7.4 to 10 [28]. By introducing a second monomer into the polymer chain, many physical and chemical properties of the polymer can be altered, such as glass transition temperature and crystallinity. It has been found the degradation property of the poly(anhydrides) heavily depends upon the copolymer composition [29]. For example, the degradation of poly(l,3- bis-p-carboxyphenoxypropane-co-sebacic acid) was shown to be governed by the aromatic content [30].The last but also very important factor to affect degradation is water uptake.

Since hydrolysis is a reaction involves both water and the functional group within the polymer, the reaction rate is determined by the concentration of both reactants [31, 32]. In contrast to hydrophilic polymers, lipophilic polymers cannot absorb as much water, therefore, result in reduced hydrolysis. The water uptake is especially important for drug delivery, because certain drug delivery system may swell during water uptake, which can be a significant factor controlling drug release.

1.1.2 Polymer erosion

As a result of polymer chain scission, degradation, oligomers and monomers are formed. Thus, these oligomers and monomers leave the polymer bulk and causing the loss of materials. This process is called erosion [33]. Due to the complexity of the involved processes such as swelling, degradation, the dissolution and diffusion of oligomers and monomers and morphological changes, erosion is much more complicated than degradation [34, 35]. The erosion process can be classified into two categories: surface and bulk eroding (Figure 1.1) [11, 33]. Surface eroding refers to the process that the polymers lose material only from the surface, so that they get smaller overtime but keep original matrix integrity. For bulk eroding polymer, the erosion and degradation happens throughout the material, thus the size of the polymer does not change. However the matrix integrity decreases overtime. The predictability of surface eroding polymer makes it an idea material for drug delivery purpose, because the release of drug can be directly related to the polymer erosion [34].

Figure 1.1. Schematic illustration of bulk erosion and surface erosion

Although degradation is the most important process involves in erosion, other factors to control the erosion behavior of a polymer may also be significant. Often these factors should be considered based on specific applications and environments. For example, in drug delivery, interaction with surrounding water, swelling and matrix porosity are all critical to the erosion process[36]. In tissue engineering, surface property, porosity and cell remodeling of the polymer matrix should be taken into account when designing the scaffolds [37]. Different tissue environment can have great impact on polymeric materials.

For example, tissue with different mechanical properties can cause various degree of mechanical degradation and thus change the erosion process [11, 38].

Degradation and erosion behaviors of polymeric biomaterials do not only provide benefits but also raise problems in biomedical applications of these materials. For instance, there are concerns related to the stability and solubility of sensitive therapeutic agents [39], or the survival of living cells [40, 41] in the constantly changing chemical environment due to degradation and erosion processes. Other concerns are associated with the loss of mechanical integrity of the polymer matrix during erosion [42], which can be undesirable when happening too fast, or the toxicity of the degradation by products[43]. A powerful characterization technique with features such as good spatial resolution, nondestructiveness, noninvasiveness etc. will be key for biomaterials researchers to gain better understanding of the polymer degradation and erosion process, thus providing solutions to previous problems.

1.1.3 Traditional methods to characterize polymer degradation and erosion

Traditional analytical methods including gel permeation chromatography (GPC), osmotic pressure measurement and end group analysis have been used to investigate

5 polymer degradation process. Gravimetrical analysis is the major technique to measure erosion. The above methods provide either direct or indirect measurements of molecular weight. For example, GPC estimates polymer molecular weight indirectly by measure elution time, which is based on hydrodynamic volume of the polymer molecule. Small solutes have longer elution time compare to large solutes, because they tend to stay in porous packing material longer. When comparing the elution time profile with a standard sample, weight average molecular weight can be calculated (Mw) [44]. Osmotic pressure provides direct measurement of number average molecular weight (Mn) by measure the osmotic pressure difference between two sides of a semipermeable membrane [45]. End group analysis also provides direct measurement of number average molecular weight

(Mn), however end group labeling is required [46].

Although above methods provides different measurements of degradation and erosion, they are all destructive to the sample. Thus, sequential time analysis on a single sample is not possible. In addition, complex sample preparation may lead to artifacts. In order to overcome these limitations, efforts using biomedical imaging to monitor polymer degradation and erosion nondestructively have also been made. Mader et al. demonstrated the feasibility of usng EPR spectroscopy and MRI to investigate PLGA and poly(sebacic anhydrides) erosion[47]. However the high expense and low temporal resolution of MRI and EPR tampers the wide application of this technique. Artzi at el. have used fluorescence to monitor in vivo hydrogel erosion [48]. In their study, a fluorescently tagged polymer was implanted in an animal model. The loss of fluorescence signal with time was converted to erosion. In vitro and in vivo erosion was found to be well correlated suggesting the

6 possibility of using in vitro erosion data to predict in vivo erosion. Fluorescence labelling is required for this technology, however not all polymers are fluorescence tagged.

1.2 In situ forming implant drug delivery systems

As one example of using polymeric biomaterials for medical applications is in situ forming implant drug delivery systems (ISFI). They’ve been a focus of research in recent years. This thesis work focuses on this ISFIs. ISFI is an injectable liquid formulation consists of biodegradable polymers and active therapeutic agents, which forms a solid depot and releases drug in a controlled manner upon injection into the body[49, 50]. A releasing period of weeks to months and sometimes years can be achieved by using different polymer formulations. Such strategies are typically used clinically to increase patient compliance by replacing frequent administration of drugs such as contraceptives and hormones to maintain plasma concentration within the therapeutic window. This system has also been investigated as a means of local drug administration which favors high drug concentration at a site of interest, such as a tumor, while reducing systemic drug exposure to minimize unwanted side effects[51, 52]. Several sustained-release delivery systems based on this concept have already be commercialized and approved by FDA. For example, Eligard® provides controlled release of leuprolide acetate to treat prostate cancer

[53]. One the other hand, Atridox© offers a local sustained release of doxycycline over a week after direct injection into the periodontal pocket[54].

ISFI can be divided into five categories based on the mechanism of liquid to solid transition: 1) thermoplastic pastes, 2) thermal gelling system, 3) in situ crosslink system,

4) solidifying organogels, and 5) solvent exchange or phase inverting system.

Thermoplastic pastes are polymers that can be injected above their melting temperature

7 and then solidify as they cool down in tissue environment. These polymers typically have a low melting temperature from 25 ᵒC to 65 ᵒC, and have an intrinsic viscosity from 0.05 to 0.8 dl/g [55]. Common thermoplastic pastes are comprised of PLA, PGA[56] and poly(trimethylene carbonate)[57]. In contrast, thermal gelling systems are liquid at room temperature, and form a gel at temperatures above the low critical solution temperature

(LCST). This process is controlled by the phase separation of the hydrophobic and hydrophilic moieties on the polymer chain. When temperature increases above the LCST, the hydrogen bonds between polymer and water becomes energetically unfavorable, thus a transition happens as the polymer quickly dehydrates and turn into a more hydrophobic structure[58]. Examples of this system include poly-(N-isopropyl acrylamide)[59], PEO-

PPO-PEO copolymers (Pluronics ®) [60, 61], and PEG-PLA[58]. Another mechanism to trigger liquid to solid transition is in situ crosslinking, which is initiated by heat, photon absorption [62], or ionic mediated interactions[63]. Upon entering the injection site, the crosslinking reaction is initiated and the solution formulation can then form a solid polymer or gel in situ. PCL, PEG-oligoglycolylacrylates[62], alginate, and 1,2-bis(myristoyl)- glycero-3-phophocoline (DMPC)[64] are all polymer examples used in this type of systems.

Solidifying organogels are amphiphilic organogel waxes at room temperature and transition into a cubic liquid crystal phase upon interacting with aqueous environment.

Cubic liquid crystal phases are unique structures formed by amphiphilic molecules which organize into a tortuous array of hydrophilic and hydrophobic domains. Amphiphilic lipids, oils such as peanut oil, waxes, and glycerol esters of fatty acids are among the most commonly used organogels for drug delivery applications[50].

Finally, phase inverting systems consist of a water insoluble biodegradable polymer

(typically PLGA) dissolved in a biocompatible organic solvent such as NMP. Drugs are typically suspended by mechanical agitation or directly dissolved in formulation solution.

Upon injection into an aqueous tissue environment, the solvent/nonsolvent exchange results in the precipitation of the polymer into a solid depot and releases drug in a controlled manner[65]. This transition from liquid to solid is referred to as phase inversion process, illustrated in Figure 1.2.

Figure 1.2. Schematic illustration of the phase inversion process [67].

A typical tri-phasic release profile can usually been observed from the ISFI system, which is governed by 3 different release mechanisms: phase inversion controlled, diffusion controlled and degradation and erosion controlled [66]. Each of these mechanisms is related to certain behavior of the ISFI system. For example, phase inversion controlled release is related to the phase inversion process, in which solvent exchange takes place.

During this process, water enters the system while organic solvent leaves the system, bringing out drug at the same time[67]. Because large amounts of drug are released in

9 relatively short periods of time in this phase, this is referred to as the burst release phase[68].

Due to dangerous toxicity issues that can be associated with the large burst, much effort has been dedicated to eliminating burst release and facilitating consistent sustained release over a long period of time[69]. In the diffusion controlled phase, the mass transport of the drug is mainly governed by the concentration gradient between the inside and outside of the implant system[70, 71]. In the final degradation controlled phase, the polymer matrix starts to break down and lose structural integrity, thus the release is facilitated during this process[72]. Figure 1.3 demonstrates the typical tri-phasic release profile from in situ forming implant.

Figure 1.3. Cumulative release of fluorescein from a 29kDa PLGA ISFI formulation

1.3 Ultrasound Elastography

As one of the advancements in clinical ultrasound, ultrasound elastography has been widely used as a “digital palpation” tool for diagnostic purpose. In this thesis work

UE will be developed then employed as biomedical imaging tool for polymer degradation and erosion characterization. UE is a dynamic technique that uses ultrasound to assess the mechanical stiffness of materials noninvasively and nondestructively by measuring material distortion in response to external or internal stimulation. It was first introduced in

1970’s [73]. After the further development of UE to map tissue stiffness by Ophir et al. in

1990’s [74], it was used in even wider range of pathological conditions such as vascular diseases [75], cancer [76, 77] and chronic liver diseases[78]. This is due to the fact that soft tissue elasticity depends on tissue composition such as collagen, fat, etc. and the macroscopic and microscopic structure of these compositions [79].

Based on different stimulation type and generated distortion, commercialized UE can be categorized into quasi-static elastography and transient elastography [73]. In quasi- static elastography, also called strain imaging, a mechanical compression is applied to the testing material. Internal organ vibration was also used as one type of mechanical stimulation in some situations. The displacement or strain is measured based on speckle tracking of two ultrasound B-mode frames before and after compression. In order to estimate the speckle displacement, either an A-line cross-correlation method[74] or a tissue

Doppler method[80, 81] is used. A-line cross-correlation is more widely applied due to its high sensitivity. The strain is calculated accumulatively over frames throughout the entire compression process and plotted as a color coded map. Figure 1.4 demonstrates the algorithm used to estimate strain. The ultrasound image is divided into multiple pixels,

11 which is also called “Derivative Pitch” (Figure 1.4 A). During compression, the length of

(L−L0) each pixel will change from L0 to L, so that strain can be calculated by 푎푖푛 = . The L0 compression process can be characterized using a velocity profile, acquired using ultrasound tissue Doppler (Figure 1.4 B). In a time frame from A to C, strain can be calculated, because the biggest strain occurs at time C[81]. Figure 1.4 C summarizes the general steps for the strain calculation. Briefly, velocity of each pixel is measured using ultrasound tissue Doppler. After an integration of velocity on certain time frame, displacement can be calculated, then strain can also be estimated.

However strain is not an internal property of materials, it depends on external conditions such as stress, material geometry and boundary situation. So it’s not the best indicator of mechanical property[73].

Figure 1.4. Algorithm of strain estimation used in quasi-static elastography

Due to the fast development of ultrasound elastography technology, Young’s modulus can now be estimated using transient elastography or shear wave elasticity imaging (SWEI). In contrast to quasi-static elastography SWEI uses a time-varying stimulation, the acoustic radiation force[82], which produces the shear waves that propagates throughout the testing material. Shear waves, which are only produced at very low frequency (10Hz to 2000Hz) due to strong absorption at high frequencies, travel slowly, and their velocity (1-50m/s) is directly related to the medium shear modulus[73]. Then

Young’s modulus can be estimated using shear modulus. In order to capture and map out the shear wave velocity in 2 dimensions, ultrafast imaging technique[83] was employed to acquire the transmission of shear waves. With integration of acoustic radiation force generator to the ultrasound transducer, the entire imaging process can be done with one ultrasound probe in an acquisition time less than 30 milliseconds[84]. Thus, real-time imaging is still possible.

Although SWEI provides technological improvement of direct measurement of material modulus, it also suffers from various limitations. It requires relatively more complex system, which able to generate acoustic radiation force and to image much small displacement induced by the shear waves. The presence of both compression and shear waves in the studied material produces potential overlapping waves, which reduces the quality of modulus estimation. The following figure 1.5 summarizes the typical working flow of both types of ultrasound elastography technologies[73].

Figure 1.5. Working flow of A) Quasi-static elastography; B) Transient elastography.

1.4 References

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Chapter 2: Biomedical Imaging in Implantable Drug Delivery Systems

Modified with permission from Current Drug Targets, 2015, Vol 16, Number 6, June 2015, pp. 672-682(11) ; Copyright© Bentham Science Group Haoyan Zhou, Christopher Hernandez, Monika Goss, Anna Gawlik and Agata Exner

2.1 Introduction

Traditional routes of administration, which include oral, topical or intravenous injections, result in immediate or expedited release and bioavailability of the active agent.

This bolus of drug often causes a spike in plasma concentration of drug temporarily above the therapeutic window and requires frequent administration in order to maintain therapeutic efficacy. This frequent administration of drug not only causes variable drug concentrations, but can make it difficult for patients to maintain the proper regimen.

Ideally, the concentration of drug should remain constant within the therapeutic window while requiring minimal administration in order to achieve the desired treatment efficacy.

Implantable polymeric drug delivery systems (DDS) have been studied extensively over the last few decades as a means to achieve extended therapeutic concentrations of drugs for various diseases [1-5]. The motivation behind the development of these systems can be attributed to benefits such as longevity, predictable steady state pharmacokinetics, decreased frequency of administration and improved patient compliance [6-8]. One of the earliest examples of an implantable drug delivery device was Norplant®, a subcutaneous implant made from nondegradable crosslinked polydimethylsiloxane (PDMS), for the extended release of a contraceptive steroid [9]. In 1990, Norplant® received FDA approval and by 1992 was utilized by over 600,000 women in the United States [10, 11]. At the time,

Norplant® was considered one of the most effective in preventing pregnancy for a longer period of time than any other available contraceptive. Although relatively successful, complications sometimes arose due to the long implantation period, which allowed for fibrous encapsulation of the implants and made them difficult to remove. Incomplete

25 removal was common due to implant fracture and often involved ultrasound imaging to locate the residual implant[12].

Other early implantable drug delivery vehicles similar to Norplant® all involved incorporating a drug in a nondegradable polymer matrix. Polymers such as poly(ethylene- co-vinyl acetate) (polyEVA) or crosslinked PDMS, were initially used because they were relatively biologically inert and biocompatible. However, as with Norplant®, these implants required invasive and sometime difficult removal procedures [7, 12]. This resulted in a shift to the use of biodegradable polymers, such as polyanhydrides or polyesters, for implantable DDS. Although there has been some clinical success with these types of materials - Lupron Depot [13], Eligard [14], or Gliadel wafer [15] are good examples - the majority of innovative implantable technologies have remained in the preclinical phase due to challenges with clinical translation. Notably, while these devices are able to concentrate therapeutic agents at the target site (e.g. a tumor), their eventual in vivo behavior is unpredictable because it is typically governed by uncontrollable physiological, biological and chemical processes of the surrounding tissue. These processes result in poorly understood in vivo implant behavior and, subsequently, a deficient in vitro-in vivo correlation (IVIVC) hampering implant research, development and clinical translation.

Reliable tools that are able to gather the necessary information about implant behavior in vivo are not readily available, yet quantifying factors that impact implant formation, degradation, and local drug distribution is essential to improving their performance.

Because the FDA requires characterization of implant properties such as biodegradation, interface characteristics with tissue, and release kinetics etc., prior to clinical trials, translational bottleneck has been created, especially in strategies incorporating various

26 biological, chemical and mechanical components that can increase the cost and time required to complete the product development pipeline. It is currently estimated that the entire FDA approval process takes between 10-15 years and costs about 1 billion dollars

[16-20]. Currently, high throughput methodologies are not commonplace in the fabrication and evaluation of implantable DDS, particularly in vivo. Instead, studies to investigate and comprehensively determine in situ forming implant (ISFI) properties are costly and inefficient and utilize tools and techniques (summarized in Table 2.1) that are destructive and highly operator dependent.

Table 2.1. Comparison of current techniques and proposed tools in characterization of biomaterials Benefits of new nondestructive imaging Current tools for biomaterial characterization tools Light microscopy Scanning electron microscopy Atomic force microscopy Destructive Nondestructive Costly Noninvasive (can be carried out in Mechanical testing Differential scanning Time consuming vivo in living animal) calorimetry (DSC) Small sampling of High throughput Gel permeation bulk High sensitivity and resolution chromatography (GPC) material (temporal and spatial)

Advancements Surface electron spectroscopy Operator Inexpensive Contact angle dependent Simple to use (operator independent)

Image Guided Biomaterials Nuclear magnetic resonance spectroscopy

It thus stands to reason that the streamlined, efficient evaluation of material or medical device performance in vivo is now more than ever of paramount importance to the advancement of the drug delivery field. In vivo animal work is expensive, time consuming, cumbersome (as current strategies are typically combined with ex vivo analysis), and ethically questioned in many circles. In vitro and mathematical models of in vivo systems are potentially intriguing, but it is unlikely that any one of such systems can account for

27 the multitude of complex processes taking place at any given time in vivo. In contrast, medical imaging technology can provide the tools to replace traditional characterization techniques and revolutionize the way we advance the development of biodegradable implant technologies. Biomedical imaging techniques offer the possibility of high throughput, longitudinal measurements and analysis of devices in their intended implanted state. The great variability in the energy sources of different imaging modalities offers a diverse set of imaging depths (0.3mm to fully body) and contrast and spatial resolutions

(0.2-200um) that can be applied to the characterization of implants regardless of their composition or site of placement (Table 2.2). Of the many imaging modalities available, four have been specifically and extensively used to characterize implantable DDS and include ultrasound imaging, MRI, optical imaging, and radiographic imaging. This review paper serves to provide insights on the biomedical imaging modalities available based on the advantages and limitations associated with their ability to characterize the various aspects implantable DDS.

Table 2.2. Properties of biomedical imaging modalities [21-27] Drug Imaging Spatial Real Penetration tracking Portability Cost modalities resolution time depth Capability Micro-US 20-100μm Yes 10mm No High Low Micro-MRI 200μm No Full body Yes Low High Fluorescence 0.2-1μm Yes 0.3-1.0mm Yes High Low microscopy OCT 1-15μm Yes 1-3mm No High Low Mediu Micro-CT 5μm No Full body Yes Low m US= ultrasound; MRI= magnetic resonance imaging; OCT= optical coherence tomography; CT= computed tomography

2.2 Biomedical Imaging Modalities

2.2.1 Ultrasound

In medical imaging, ultrasound utilizes the propagation of sound waves ranging from 2-15 MHz in organs and tissues of different acoustic impedance to produce anatomical images. This is implemented by using an ultrasound transducer, which is capable of both producing and receiving sound waves. When these sound waves travel through a medium, such as tissue, a portion of the sound waves is reflected back to the transducer as they interact at the interfaces of materials with different acoustic impedances.

The depth of the imaging structure is determined by the time between firing the ultrasound signal and receiving the echo. The amplitude of the echo, or portion of reflected sound waves, is encoded and displayed in a gray-scale image. Acoustic impedance, governed by the density and bulk modulus of a material, determines the ultrasound image contrast. This mechanism allows ultrasound to provide structural/mechanical information about an implantable DDS. In combination with its noninvasive nature and high temporal resolution

(up to 500 frames per second), ultrasound has been used to continuously monitor implant properties such as positioning within the body [28-30], morphology [28, 31-33], and associated tissue response in vivo [31, 34]. In early work, ultrasound was extensively used for the placement, extraction and characterization of implants for the sustained delivery of contraceptive steroids [35, 36]. In cases where intrauterine devices have been left in the uterus for long periods of time, ultrasound was also used as a method to detect fragmented implants that can cause complications [34]. In another study, ultrasound was used in prostate brachytherapy to guide the placement of 125I containing implants and visualize the implants distribution in real-time [37].

Upon implantation of a foreign biomaterial, it is also important to be able to noninvasively monitor the host response to the material over time. The reaction can lead to implant damage and hampering of the long term performance. Changes in tissue architecture surrounding implants inserted in both the eye [31] and the vasculature [38, 39] have been monitored using ultrasound as a measurement of biocompatibility or treatment outcome. Serruys et al. used quantitative intravascular ultrasound (IVUS) to evaluate the treatment outcomes of their anti-proliferative drug-eluting vascular scaffolds by quantifying the area of obstruction in the lumen [38]. In another study by Reibaldi et al, a

50 MHz ultrasound biomicroscope was used to evaluate a hyaluronan based intravitreal implant for sustained release of antiproliferative drugs. Here, the internal ultrasound reflectivity of the implant and the surrounding tissue response, which correlates to implant biodegradation and biocompatibility respectively, were monitored for 150 days in vivo [31].

In the context of cell delivery using polymeric scaffolds, ultrasound has also been used to quantitatively evaluate bone marrow stromal cell numbers in a β-Tricalcium phosphate composites by correlating the cell density to ultrasound amplitude [40].

With the advent of polymer implants that form in situ rather than traditional preformed implants, there has been a strong need for ways to noninvasively characterize their behavior in vivo. The transition from a liquid to solid depot requires an imaging technique that has both high spatial and temporal resolution, as this transition step is critical to predicting drug release kinetics [33, 41, 42]. In 2009, Solorio et al. developed a technique using ultrasound b-mode imaging and grayscale analysis to monitor the phase inversion kinetics of in situ forming PLGA implants and correlated the process with in vivo drug release [32]. The phase transition process from liquid to solid causes a dramatic change in

30 acoustic impedance which can be detected by ultrasound in real time. A validation study was performed in our lab by comparing ultrasound b-mode images to digital photos of the same implant (Figure 2.1. A, B).

Figure 2.1. Examples of ultrasound in implantable DDS. (A) Representative gray-scale images of the implants over time below photos of the actual implant. Scale bar is 5mm (B) Quantification of phase inversion calculated using photos of actual implants and ultrasound gray-scale analysis. (C) Representative ultrasound elastography images of implants over time indicating implants becoming soft. (Unpublished data). Information on in vivo implant erosion and biodegradation is critical when designing degradable DDS. Methods for measuring degradation and erosion such as gravimetrical analysis and gel permeation chromatography (GPC) are destructive and time- consuming, leaving a need for a less invasive way to monitor polymer behavior. Many have investigated using ultrasound to assess polymer degradation and erosion of tissue scaffolds through the use of a special technique called ultrasound elastography (UE) [43,

44]. Ultrasound elastography is a dynamic technique that uses ultrasound to assess the mechanical stiffness of materials noninvasively by measuring material distortion or strain in response to external compression. In our lab, the feasibility of using implant mechanical

31 properties measured by UE to estimate its rate of polymer erosion have been tested. In this study, in situ forming PLGA implants with various molecular weights (17kDa, 34kDa and

52kDa) were injected into a tissue mimicking phantom. UE scans were performed daily and compared to erosion measurements gathered by gravimetrical analysis. By comparing the UE data to the erosion data, it was found that the rate of change in mechanical properties correlates well with the rate of implant erosion. Representative ultrasound elastography images shown in Figure 2.1. C, indicate the implant softening process overtime due to polymer degradation and erosion.

Applications of ultrasound for characterization of implantable DDS have not been limited to imaging of their physical structures. Increasing the power of ultrasound well beyond diagnostic levels has been investigated as an external way to modulate drug release from an implantable DDS. This capability of ultrasound was attributed to the cavitation and acoustic streaming associated with the increased power [45, 46], but the exact mechanism has not yet been fully understood. The first attempt was done in 1985 by

Miyazaki et al. using ultrasound to increase drug release from an ethylene-vinyl alcohol copolymer [47]. Then Kost et al. tested the feasibility of ultrasound to modulate drug release from both biodegradable [46] and non-degradable [48] polymeric delivery systems.

In this study, up to a 5-fold reversible increase in degradation rate and up to a 20-fold reversible increase in release rate was observed in implants made from polyanhydrides, polyglycolides and polylactides. In addition to modifying the drug release from implantable systems, ultrasound at various frequencies has also been applied to enhance transdermal transport of low molecular weight drugs and proteins [49].

2.2.2 Magnetic Resonance Imaging (MRI)

Magnetic resonance imaging (MRI) is a non-invasive imaging technique that depends on both the local proton density and the molecular environment to generate 2- dimensional (2D) anatomical images. MR images are produced by applying a strong magnetic field (1.5-11.7 T) to a sample, roughly aligning a number of protons with the direction of the field, which can be considered as a magnetization vector. These protons experience a torque and precess around the field axis at the Larmor frequency specific to the magnetic field of the scanner [50]. Radiofrequency pulses are applied to modify the magnetization vector, and as the vector relaxes back to its alignment, it emits this energy which can then be measured using coils. Different molecular environments influence the behavior of the magnetization, which in turn influences the MR signal. Various pulse sequences that cause differences in the magnetization due to T1 and T2 relaxation time and proton density can be used to selectively create MR images with varying contrast [50].

Unlike with other imaging modalities, the measurement of the time constants, T1 and T2, in MR allows us to not only investigate the physical properties, but also the chemical properties of materials. For this reason MRI has been increasingly used for characterization of implantable DDS both in vitro and in vivo [51-53]. Implant properties, such as water uptake [54], spatial variation in matrix degradation [55], and their correlation to release kinetics [56] have been obtained with MRI. In one study, MRI was used to examine the buffer uptake kinetics into a PLGA implant after incorporating a high molecular weight peptide by correlating liquid concentration to T1/T2 contrast maps. A strong peptide-polymer interaction was observed which affected the polymer chain configuration and mobility, thus changing the water uptake kinetics [54]. All of these

33 examples however, rely on relative changes in the contrast generated by MRI scanners and thus have been limited by the mostly qualitative nature of this imaging modality. Ma et al. have recently developed a new approach in the acquisition and post-processing of magnetic resonance signals, termed magnetic resonance fingerprinting (MRF), which performs a quantitative analysis on the MR signal that is based on physical and chemical changes within materials and tissues [57]. This new technique has the potential to allow for MRF to noninvasively monitor complex changes in implantable DDS such as degradation, chemical activity, tissue response, etc. Although current MRI systems have several advantages over other imaging modalities, the high installation and running cost of clinical

MRI has prevented its further use in the drug delivery community. At the cost of a smaller bore, a bench-top MRI (BT-MRI), which has similar capabilities as clinical MRI, is a cost effective alternative and is more ideal for preclinical imaging of implantable devices.

Mader et al. used BT-MRI and electron paramagnetic resonance (EPR) spectroscopy to non-invasively monitor injectable ISFI behaviors including solvent exchange, precipitation and degradation in vitro and in vivo [41, 57].

The use of MR contrast agents, such as gadolinium or iron oxide-based agents, enables MRI to measure drug distributions throughout the body. Gadolinium based contrast agents have been used as mock drugs to study the distribution of drug away from implant systems in targeted tissues such as the eye [58, 59], brain [60], bone [61-63] and muscle

[63, 64]. In one study by Csaky et al., the mass transport and distribution of an ocular drug released from a polyvinyl alcohol (PVA) polymeric implant was investigated by using its surrogate, Gd-DTPA (Figure 2.2.) [58]. In another study with Gd-DTPA, which is similar in size and diffusion coefficient to the antimicrobials gentamicin and vancomycin, was also

34 used to visualize drug transport from orthopedic implants [65]. In this study, implants of different materials were implanted in either the intramedullary canal or the quadriceps and the local drug distubtions away from the implants were assessed using MRI. The implantation site was shown to have a dramatic effect on the distribution and movement of drug [63].

Figure 2.2. Representative MRI images of an intravitreal implant. Concentration of Gd-DTPA is correlated to a color scale bar on the right of each image. (A, B) In vivo images of 4 and 7 hours after implantation respectively, indicating the distribution of Gd-DTPA in the vitreous cavity. The boxed area of vitreous is magnified, and the concentration of Gd-DTPA over distance is quantified on the right. (C, D) Ex vivo image of 4 and 7 hours after implantation respectively, indicating the distribution of Gd-DTPA in the vitreous cavity. The boxed area of vitreous is magnified, and the concentration of Gd-DTPA over distance is quantified on the right. (Adapted from Ref. [58], with permission).

In contrast to gadolinium, iron oxide based contrast agents have mainly been used in the context of cell delivery using polymeric scaffolds. Contrast agents including Super

Paramagnetic Iron Oxide (SPIO), and Monocrystalline Iron Oxide Nanocompounds

(MION) produce low intensity in MR images. These contrast agents are more biocompatible than gadolinium allowing their use as cell labeling agents [66]. They are typically incorporated into a delivered cell enabling MRI tracking of the distribution and concentration of these cells [67]. Bouzier-Sore et al have used MRI to track the SPIO labeled human Adipose Derived Stem Cells from a porous polysaccharide based scaffold for bone regeneration [66]. SPIO has also been used by Hoehn et al. to monitor the embryonic stem cell migration after implantation into brain and was validated by using green fluorescent protein (GFP) registration [68].

The power of magnetic fields is not limited to the imaging of polymeric materials, but can be used to modulate drug release from DDS. By exciting magnetic microspheres with applied magnetic fields, drug release from an implantable DDS can be controlled. It has been shown that the drug release from a magnetite embedded polymer matrix can be triggered or increased by applying oscillating magnetic fields [69-72]. This finding was attributed to the movement of the iron beads due to the oscillating magnetic field, which produced “micro-cracks” in the matrix, thus facilitating the influx of liquid and efflux of drug [70]. In vivo studies by Kost et al. showed that subcutaneously implanted polymer matrix containing insulin and magnetic beads can repeatedly reduce glucose levels on demand by applying oscillating magnetic field [71]. The effect of various magnetic field amplitudes [70] and frequencies [72] on drug release from the magnetically excitable DDS has also been investigated.

2.2.3 Optical imaging and Optical Coherence Tomography (OCT)

Optical imaging techniques such as microscopy, fluorescence imaging, and optical coherence tomography have been used extensively in the development and characterization of DDS. Optical imaging measures the interactions of light with matter to produce 2- dimensional images with high spatial and temporal resolution. Advancements in detection of photons and highly specific biomarkers have allowed optical imaging to be at the forefront of molecular imaging at the cellular and subcellular level. However, the low energy source limits this imaging modality to mostly surface or thin sample characterization. For this reason the majority of optical imaging applications in implantable

DDS involve invasive procedures such as the use of ex vivo resection of samples and cryosectioning. Nevertheless, the development of in vivo optical imaging techniques, such as OCT, have allowed scientists to non-invasively study implant characteristics in vivo in the preclinical setting.

In the development of implantable DDS for localized drug delivery, it is essential to have the capability of measuring both drug release kinetics, and drug distribution away from implants. These characteristics are essential to achieving effective drug concentrations within the appropriate treatment volume. Fluorescence imaging is widely used for this purpose on extracted tissues [73-75]. In this process, tissue is removed and cryosectioned into thin pieces followed by fluorescence imaging for drug detection. This technique benefits from high spatial resolution and low detection limit while suffering from the fact that very few drugs are fluorescent. Weinberg et al. used fluorescence imaging to measure the distribution of drug away from their polymer implants while implanted in a rabbit liver tumor model. In this study, doxorubicin, which has intrinsic fluorescence, was

37 delivered using preformed PLGA millirods after a radiofrequency ablation. This study revealed that the drug distribution was highly dependent on tissue architecture (ablated or non-ablated tissue) [75]. As stated earlier, many drugs are not intrinsically fluorescent. To circumvent this drawback, drugs without natural fluorescent can be labeled with a fluorescent tag. However this labeling process may also change the drug structure, thus resulting in different therapeutic efficacy and transport properties.

In addition to the direct measurement of drug release and distribution, an understanding of the interactions between implants and their local physiological environment is equally important. Processes including polyester degradation and erosion through hydrolysis when implanted in the aqueous tissue environment[76], pH changes inside the in situ forming implants [77, 78] and infections as a result of tissue response to the implant system have all been studied using fluorescence imaging [79]. In order to use fluorescence as a measure of degradation of polymeric implants, a new type material called aliphatic biodegradable photoluminescent polymers (BPLPs) was developed. This polymer demonstrated great cytocompatibility in vitro, minimal chronic inflammatory response in vivo and tunable fluorescence emission [80]. Artzi at el. have also used fluorescence to monitor in vivo hydrogel erosion. In their study, a fluorescently tagged polymer was implanted in an animal model. The loss of fluorescence signal with time was converted to erosion. In vitro and in vivo erosion was found to be well correlated suggesting the possibility of using in vitro erosion data to predict in vivo erosion (Figure 2.3.) [76].

Figure 2.3. In vitro–in vivo erosion profiles of PEG:dextran correlate and vary with material surface area. In vitro and in vivo erosion profiles of PEG:dextran cast in a series of shapes are depicted by tracking the loss of fluorescence intensity with time. (A)The effect of material shape on degradation profile was followed in vivo non-invasively in the dorsal subcutaneous space of mice (disk shaped materials are presented). B–D, The loss of fluorescence signal with time in vitro (B) and in vivo (C) was converted to weight loss and correlated (D). Correlations found between mean values of in vitro and in vivo erosions are indicated. (Adapted from Ref. [76] with permission). Optical coherence tomography (OCT) is an optical method analogous to ultrasound imaging, which uses the coherence between backscattered light from an object and reference light to synthesize 2D and 3D images. Owing to the precise measurement of distance using this coherence method and employment of long wavelength light (typically near-infrared light), OCT provides superb spatial resolution (<10 μm) and moderate penetration (1-2 mm), offering advantages over other imaging modalities. In the clinical setting, OCT has been primarily used to distinguish normal from pathological tissues in vivo and ex vivo. For this reason, OCT has been used to evaluate the treatment outcomes in studies involving drug eluting stents [81-84]. In a study by Guagliumi et al., OCT was used to study the vascular tissue response (neointimal tissue thickness and stent cover rate) from anti-proliferative agent eluting stents implanted in patients with long coronary stenosis.

In addition to measuring tissue response, OCT has also been used for other purposes such as visualizing the microstructure of the implant matrix [85, 86], quantifying the diffusion of drug in the eye [87] and guiding the placement of implants [88]. In one study,

Patterson et al. used 3D OCT to characterize the in situ degradation of PLGA microspheres contained within a hyaluronic acid hydrogel gel after implantation into a rat skull defect model. The quantitative degradation was estimated by monitoring the change in average microsphere diameter. Furthermore, the obtained degradation rate was found to be correlated to the drug release from the implant [85]. OCT can also be used for quantitative measurements such as investigating the permeability of drug transport through tissues. A study by Ghosn et al. showed the feasibility of using OCT to nondestructively estimate the permeability coefficient of different small molecular weight drugs through rabbit cornea and sclera [87].

2.2.4 X-ray Imaging and Computed Tomography (CT)

When highly energetic electrons interact with matter, their kinetic energy is converted into electromagnetic radiation, producing X-rays. An X-ray beam interacts variably with tissues, being highly attenuated by bone compared to soft tissue, for example.

This interaction produces variable X-ray transmission and results in a ‘shadow’ of the anatomical structures. The transmitted radiation is captured by an analog or, more frequently, a digital detector which then converts the X-rays into a 2D film or digital image.

During the last 40 years, computed tomography (CT) has revolutionized X-ray imaging by using linear detector arrays for acquisition of multiple beam projections which are then converted into tomographic images which can later be reconstructed into 3D images,

40 providing quantitative volumetric information. In medical diagnostic applications, high- energy photons (greater than 15 keV) are utilized because they are not easily absorbed through tissue, allowing for whole body imaging. The high use cost and relatively low resolution of diagnostic CT scanners (range from 0.5-1 mm) have limited their use in preclinical small animal studies. Advances in detector technology and computer processing has allowed for high-resolution micro-CT scanners that can achieve isometric voxel sizes as small as 5 μm [27]. This improved spatial resolution has allowed scientists to noninvasively look at microstructural changes in polymeric DDS.

Micro-CT, with the exception of a few examples, has been used in drug delivery for imaging and analysis of the 3D structure and morphology of polymeric implants.

Specifically, micro-CT has been used to look at in vivo structural properties of implants such as tortuosity [89, 90], porosity, average pore size [91-93] and their correlation to release kinetics [94, 95]. The resolution of CT has even allowed for 3D visualization of porosity changes in PLGA scaffolds with different concentrations of porogens [93]. Due to the high absorbing properties of bone, the use of radiographic imaging in the preclinical setting has been primarily used in the study of implants associated with bone tissue engineering. Micro-CT has been used to evaluate bone ingrowth into [96-98], or changes in bone morphology [99, 100] around drug/peptide eluting scaffolds. Although the majority of clinically available drugs provide poor contrast under X-ray, heavy metal drugs like cisplatin, a platinum based anticancer drug, have high X-ray attenuation and thus provide inherent contrast. Exner et al., have exploited this property and developed a non-invasive method using CT to measure both the drug release kinetics from and local drug concentrations surrounding their PLGA millirods [101-103]. Unlike optical fluorescent

41 techniques, the use of CT to measure drug concentration allows for deep penetration distances with high spatial resolution allowing for monitoring in vivo in large animals.

Although the contrast between most biomaterials and tissue under radiographic imaging is relatively low, CT has been clinically useful in the treatment of hepatocellular carcinoma with transarterial chemoembolization (TACE). While not an implantable system,

TACE is a loco-regional technique that relies solely on image guidance for its administration. During a TACE treatment, CT is used to guide a catheter while it is inserted into the local blood supply of a tumor. Once at the tumors feeding blood vessel, an embolic agent is released to block the blood flow, initiating ischemic tumor necrosis. Lipiodol, a commonly used embolic agent, made from an iodinated ethyl ester of fatty acids in poppy seed oil, has also been investigated for the controlled release chemotherapeutic drugs such as doxorubicin [104]. Nakamura et al. has shown that by adjusting the ratio of Lipiodol to doxorubicin, pharmacokinetic outcomes can be better than direct drug infusion. Also, because it is iodinated, Lipiodol acts as a CT contrast agent, allowing for non-invasive monitoring of tumor accumulation and distribution (Figure 2.4) [105]. The degree of distribution within the tumor has been strongly correlated, through histopathology, to tumor necrosis [106].

Figure 2.4. Pre- and peri-operative imaging for transarterial chemoembolization (A) Digital subtraction angiography image of hepatocellular carcinoma tumor and its corresponding feeding vessel branches of the hepatic artery. Noncontrast CT image showing tumor prior to TACE treatment (B) and post treatment accumulation of Lipiodol in tumor (C). All tumor regions are encircled by the dashed line. Images reproduced with permission from Dr. Yong Wang, Wu Han Union Hospital, China.

Even with benefits such has superior penetration and spatial resolution to other imaging modalities, the use of X-ray imaging also has several drawbacks. For one, X-ray radiation is ionizing, and thus can be harmful to patients if used frequently. The ionizing radiation is often a limiting factor when performing long term implant characterization studies that require frequent exposure in order to achieve high temporal resolution. In addition, X-ray imaging does not typically provide great contrast between different soft tissues and polymeric implants, making it necessary to coat the implants with contrast agents, such as Hexabrix, to distinguish the boundaries [107]. These coatings, if too thick, can limit CTs ability to look at surface structures.

2.3 Conclusion

Understanding implantable DDS in vivo behavior is essential for ensuring a smooth translation into the clinical setting. To accomplish this task, noninvasive, nondestructive analysis tools are quickly becoming an essential component of preclinical research in this

43 field. Most important, as described above, are the various biomedical imaging modalities that can be applied to monitor specific aspects of implantable DDS including morphology, physiochemical properties, tissue response to the implant and therapeutic agent and drug distribution. Certainly, each imaging modality has its unique advantages and limitations;

Figure 2.5 summarizes the capabilities of each modality in applications for DDS. For example, optical imaging provides high spatial resolution and functional information through the use of various biomarkers while suffering from the lack of penetration depth, making it ideal for the in vitro or ex vivo applications. In contrast, CT offers a wide field of view, high temporal and spatial resolution and deeper penetration, yet it lacks contrast between different soft tissues. In order to obtain more comprehensive information, researchers are not limited to the use of a single imaging modality. Rather, the use of multimodal imaging can overcome the limitations of individual techniques while combing desired advantages. It is also important to look for ways to exploit the diverse energy sources associated with the different imaging modalities in order to not only image implantable systems but manipulate their behavior on demand. A constant rate of drug delivery may not be ideal in all clinical settings. For instance, patients with conditions such as diabetes, heart rhythm disorders, angina pectoris, etc. may need a pulsed or even programmable delivery pattern based on the stages of biological conditions. Magnetic and ultrasonic fields have already been used to externally manipulate drug release kinetics at will, but are still in their infantile stage. With proper development, they may be used to control drug release profile in a more precise manner. The beauty of nondestructive imaging analysis is that each implantable technology can be investigated with several techniques all in the same animal over the lifetime of the single device. This can

44 significantly reduce inter-animal variability and can ultimately provide the most clinically relevant data that cannot be obtained by any other means. The broad range of parameters that needs to be investigated in preclinical exams of these devices leave much room for innovation and advancement of this relatively young field. As implantable technologies mature and become ever more complex, new imaging strategies will continue to be critical to their advancement. In particular, imaging tools that can provide functional information about local implant performance and therapeutic efficacy combined are and will continue to be in great demand in drug delivery research and have the potential to speed up the transition into the clinical setting. In order to employ these biomedical imaging technologies properly, better understanding of their capabilities as well as functionalities for polymeric biomaterials is significant. Since biomaterials have very different properties in contrast to soft tissue, specific characterization and validation studies of imaging tools for biomaterials will be one critical step before real applications.

Figure 2.5. Summary of the various applications of biomedical imaging advancing research and clinical translation of implantable drug delivery systems. 2.4 References

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Chapter 3: Validation of Ultrasound Elastography Imaging for Nondestructive

Characterization of Stiffer Biomaterials

Note: Parts of this chapter have been submitted for publication as Haoyan Zhou, Monika Goss, Christopher Hernandez, Joseph Mansour and Agata Exner. Validation of Ultrasound Elastography Imaging for Nondestructive Characterization of Stiffer Biomaterials, submitted to Annals of Biomedical Engineering.

3.1 Introduction

As discussed in the previous chapter, biomedical imaging has started to play a more important role in the characterization and modulation of implantable drug delivery systems.They have also been used in other polymer associated biomedical applications such tissue engineering scaffold, and cardiac polymeric grafts. For instance, near-infrared fluorescence imaging has been used to track scaffold matrix disintegration and cell invasion [34]. The remodeling of cardiac elastomeric tissue on graft scaffold has been evaluated using MRI [35]. As discussed in the end of Chapter 2, specific characterization and validation studies of the imaging technologies is critical to ensure proper employment and best outcomes.

Mechanical properties are one of the physical properties of biomaterials that have gained growing recognition for their capability of regulating cell and tissue response [1, 2].

Such responses include mesenchymal stem cells (MSCs) demonstrating different morphologies when cultured on substrates with varying moduli [3], muscle cells, epithelial cells, and endothelial cells showing increased proliferation on stiffer substrates [4-6], and neurons showing greater proliferation on softer substrates [6, 7]. In addition, varying elasticity of a cell substrate was found to improve the non-viral gene delivery in murine pre-osteoblasts [8]. These above examples suggest that the capability to measure the mechanical properties of biomaterials is essential for development of drug delivery platforms and tissue engineering scaffolds. Currently available methods for mechanical property evaluation involve direct mechanical measurements, which require animal sacrifice and sample destruction, hence many animals and samples need to be prepared for a time course experiment. Ideally, a non-invasive tool with the capability of measuring the

61 mechanical properties of the same sample over time needs to be developed. Ultrasound elastography has been found to be a valuable non-invasive and, importantly, nondestructive tool to characterize tissue mechanical properties, the changes of which may be related to various pathological conditions such as vascular disease[9, 10] , cancer [11-15]and chronic liver diseases [16]. This is due to the fact that soft tissue elasticity depends on tissue composition such as collagen, fat, etc. and the macroscopic and microscopic structure of these compositions.

The elasticity estimation using the UE method is based on certain simplifications and assumptions [17]. Strictly speaking, tissue is inelastic. It does not meet the definition of an elastic material, which requires a linear relationship between stress and strain. Soft tissues exhibit viscoelastic properties such as hysteresis, stress relaxation, and creep [18].

For time scales shorter than the characteristic time of fluid motion, simplifications and assumptions such as uniaxial applied stress and linearity between acting force and tissue deformation can be applied to approximate this biomechanical problem [17-21]. The above simplifications and assumptions may also be applied when assessing the mechanical properties of biomaterials such as hydrogels, cell containing tissue scaffolds [22, 23], drug delivery implants[24], etc. Because the majority of these biomaterials absorb water or body fluid when interacting with human tissue, the materials exhibit viscoelastic properties [25-

27]. Different methods have been developed to assess the mechanical properties of biological tissue. Among them, transient elastography has advantages such as non- dependence on the boundary conditions and providing direct shear modulus measurements.

However, the high speed shear wave reflected from harder samples make it difficult to

62 differentiate the shear wave from the bulk wave, thus limiting the capability of transient elastography to detect stiff samples [28].

In this study, a quasi-static method (strain imaging) was used. In strain imaging, the sample is subject to an external compressive stress, typically freehand compression by clinicians, and the ultrasound system tracks and measures the local deformation induced by this stress. Due to the quasi-static nature of the applied stress, the generalized viscoelastic equation of motion (1) can be a simplified Hookean equation (2). Since ω=0 and x is a constant, the velocity and accelerations vanish [18].

푑2푥 푑푥 푀 + 푅 + 퐾푥 = 퐹 푒푗휔푡 (1) 푑푡2 푑푡 0

Where M, R and K are inertial, viscous, and stiffness controlled terms respectively, x is displacement, F0 is amplitude of force, and ω is vibrational frequency.

퐹0 = 퐾푥 (2)

In order to track and measure the local responsive deformation of the tissue, either an A-line cross-correlation method [29] or a tissue Doppler method can be used [30].

To the best of the authors’ knowledge, the only characterization studies [28, 31] done with strain imaging focused on moduli within the range of soft tissues from 20kPa, a typical value of soft tissues such as liver or breast[32], to 600kPa, a typical value for harder tissues such as arteries[33]. In the study conducted by Fromageau et al. [28], a modulus to modulus comparison was made between the strain imaging method and a standard mechanical tension test. Young’s modulus estimation from strain imaging was obtained by calculating the slope of stress-strain curve in which the strain distribution was acquired

63 using strain imaging, and stress was found from the recorded force applied over a known area [28].

In this study, the Young’s modulus of PDMS samples was measured using unconfined compression mechanical testing as a gold-standard reference. In order to test strain imaging’s capability of differentiating samples with various moduli within and beyond the soft tissue range, PDMS samples from 47kPa to 4MPa were prepared and tested using both standard mechanical testing and strain imaging. A custom made stage was used to introduce uniform compression conditions to samples at the same time during strain imaging. The detection limit was determined by statistically evaluating a direct strain to modulus comparison between strain imaging and the unconfined compression test. The applied compressive and the mechanical properties of the material surrounding the sample were varied and found to affect the detection limit.

3.2 Materials and Methods

3.2.1 PDMS Sample Fabrication

PDMS samples of varying elastic moduli were made using the Sylgard 184 Silicone

Elastomer Kit (Fisher Scientific, Waltham MA) by varying the concentration of curing agent and curing time. Samples ranged between 2.5-12% wt % of curing agent and 1 h to

1.5 h curing time (Table 3.1). 3% of Carbon black (Fisher Scientific, Waltham MA) was added to the elastomer solutions as a scattering agent. PDMS solutions were mixed and degassed in vacuo for 10 minutes before being poured into a petri dish to a thickness of 2.5 mm and cured in an oven at 60°C for the designated time. Once cured, a 3 mm biopsy punch was used to cut out samples in a disc shape for use in mechanical and UE testing. In

64 order to reduce the effect of sample shape, and therefore boundary conditions, on the scan result of strain imaging, three samples were manufactured within each modulus group.

Both strain imaging and standard mechanical test were performed on the same samples.

3.2.2 Phantom Preparation

Stock solutions with a ratio of acrylamide to bis-acrylamide of 37.5:1 (Fisher

Scientific, Waltham MA) and acrylamide concentration from 4% to 32% (w/v) in 1x PBS solution were prepared to fabricate tissue mimicking phantoms of different moduli.

Titanium dioxide was added as a scattering agent to more clearly define the boundaries between the phantom and samples when performing ultrasound elastography. The polyacrylamide solutions were crosslinked by free radical polymerization using 1.7% and

0.1% (v/v) of tetramethyl ethylenediamine (TEMED, Fisher Scientific, Waltham MA) and ammonium persulfate (APS, Fisher Scientific, Waltham MA), respectively. The phantoms were manufactured in plastic molds with two polyacrylamide gel layers. 100 ml of the above mixture was stirred and poured into the plastic phantom mold to form the first layer.

After the first layer gelled, the PDMS samples were placed and lined up on the gelled polyacrylamide. The second 100 ml acrylamide mixture was then poured on top of the first layer, embedding the PDMS samples. The complete PDMS embedded phantom fabrication process can be seen in Figure 3.1.

Figure 3.1. PDMS sample fabrication process. PDMS elastomer and curing agent were combined with carbon black. The mixture was poured into a petri dish to obtain a 2.5mm thickness layer. The mixture was then cured in the oven at designated temperature. The PDMS discs were made using a 5mm biopsy punches. The disc shape PDMS samples were embedded in the middle of the tissue mimicking phantom for strain imaging scans and mechanical testing. The tissue mimicking phantoms were about 2 cm thick.

3.2.3 Mechanical Testing

PDMS samples

The Young’s modulus of the PDMS samples was determined by unconfined compressive mechanical testing using a rheometer (Rheometrics RSAII, NJ). Samples were loaded with increasing compressive nominal strain. Thereby, nominal strain ɛ and nominal stress ơ are defined as

ɛ = (퐿 − 퐿0)/퐿0 (4)

ơ = 퐹/퐴 (5) with L and L0 being the heights in the loading direction of the deformed and the un- deformed sample, respectively, and with F and A being the force at the interface in the loading direction and the cross-sectional area of the interface perpendicular to the loading direction, respectively. A strain rate of 0.01/s was used. Testing time was optimized for each sample in order to obtain at least 30% strain. The Young’s modulus was determined by the strain-stress curve generated from the instrument. To assure consistent contact between the compressing probe and PDMS sample, a threshold of 0.1 N was used to generate the stress-strain measurement. Measurements with associated force less than 0.1

N were discarded. The measurements with strain greater than 20% were also removed due to high deformation of the PDMS samples. A first order polynomial fit was used to determine the linear region of the stress strain curve for modulus calculation. The Young’s modulus (E) was determined by finding the slope of the linear region of the stress strain curve (Eq (6) ).

ơ = 퐸ɛ (6)

Polyacrylamide Hydrogels

To find the optimal phantom composition and therefore modulus, acrylamide samples were made using the method listed previously. Samples were made in various percentages ranging from 4% to 32% acrylamide (v/v). The Young’s modulus of these polyacrylamide samples was then measured using the punch out and mechanical testing procedure described previously.

3.2.4 Strain imaging

Strain imaging was performed using a Toshiba clinical ultrasound system,

AplioXG SSA-790A (Toshiba Medical Imaging Systems, Otawara-Shi, Japan), with a linear array transducer centered at 12 MHz. To perform the measurements the ultrasound transducer was fixed to a laboratory designed stage, and a linear actuator was used to induce uniform strain. This computer programmable linear actuator provided repeatable displacement. Since the ultrasound transducer was fixed, samples between transducer and linear actuator were compressed by pushing up from below. Figure 3.2 illustrates the UE scan setup. In order to ensure contact between transducer and polyacrylamide phantom, a

10% pre-strain in reference to thickness of the phantom was applied. During the scan, a compression cycle composed of 3 compressions and 3 dilations was induced. A time frame

Tissue Doppler Tracking (TDT) algorithm was used to calculate axial strain[30]. Color

Doppler Imaging (CDI) frequency was set at 8.8 while velocity scale was 0.3 cm/s to accommodate the maximum value of the velocity profile. Velocity smoothing was used to optimize the strain calculation. After the scan, a color coded strain map was generated and superimposed on top of the B-mode image. The regions of interest (ROI) were chosen based solely on the B-mode images to remove potential operator dependent bias. The color map range was chosen to be the same for all recorded data. The size of the ultrasound transducer allowed for testing of 4 PDMS samples per scan. Therefore a total of 12 samples were separated into 3 groups. PDMS samples embedded in two tissue mimicking phantoms of different moduli (stiff and soft) were scanned with UE to examine the effect of the surrounding environment on the detection limit of the strain imaging technique. Different

68 degrees of compression (1 mm and 3 mm) were applied by the distance controlled linear actuator to also investigate its role in the detection limit of this UE technique.

Figure 3.2. Ultrasound elastography scan setup.

3.2.5 Statistics

Unless otherwise noted, all data is presented as mean ± standard deviation. The strain output from strain scans in response to uniform compression conditions was evaluated using one way analysis of variance (ANOVA) then subjected to Tukey’s HSD

(honestly significant difference) test. The alpha value was set at 0.05 with significance defined as P<0.05. The P value for each individual comparison was calculated using a t- test with Bonferroni's correction. In order to avoid batch effect, the statistical analysis was performed on the group of four samples, which were scanned simultaneously.

3.2.6 Region of Interest (ROI) study

To test the objectiveness and accuracy of the data analysis method, four 302 kPa

PDMS samples were scanned using UE. Upon data acquisition, region of interests (ROI) were drawn by five operators independently. The strain values averaged among the ROIs chosen by five operators were compared to the average value based on one ROI. The percent differences were calculated for each sample. In addition, Lin’s concordance correlation coefficient analysis was also performed to test the agreement and reproducibility between above two analysis methods.

3.3 Results

3.3.1 Mechanical tests

Acoustic scattering PDMS samples

As described above, twelve groups of PDMS discs (n=3, 5.0 mm diameter, 2.5 mm height) were fabricated for use in both standard compressive mechanical testing and strain imaging. For samples tested in unconfined compression a nonlinear relationship between stress and strain was found (Figure 3.3). Therefore Young’s moduli of the samples were calculated using the linear region of the stress-strain curve as earlier described. The mechanical testing results are summarized in Table 3.1. The reproducibility of the mechanical properties of PDMS samples was also evaluated and found to be satisfactory, with an average standard deviation of 3.44 %.

Figure 3.3. Representative strain-stress curve of PDMS samples. Curing agent ratio: 2.5%, 3%, 4%, 6% and 9%. (Detail formulation see Table 3.1)

Table 3.1. Measured elastic modulus of PDMS samples with different curing agent ratio and curing time. Curing agent ratio= curing agent/elastomer base. (n=3 for each curing agent ratio)

Sample Curing Curing Elastic agent Time Modulus Number Ratio (h) (kPa) 1 2.50% 1.5 47±3 2 3.00% 1.5 302±9 3 3.50% 1.5 459±23 4 4.00% 1 667±71 5 4.00% 1.5 854±16 6 4.50% 1.5 1211±35 7 6.00% 1.5 1645±40 8 9.00% 1.5 2229±22 9 9.50% 1.5 2935±196 10 10% 1.5 3297±237 11 11% 1.5 3722±114 12 12% 1.5 4224±314

Polyacrylamide tissue mimicking phantoms

The same analysis was done on the strain-stress curves of the polyacrylamide phantom materials. The elastic moduli of phantoms with varying acrylamide concentration are shown in Figure 3.4. The range of moduli was found to be 4 to 650 kPa using mechanical testing. 10% and 15% acrylamide concentrations were chosen to manufacture phantoms with tissue relevant Young’s moduli, namely normal breast tissue (50 kPa) and cancerous breast tissue (100 kPa), respectively.

Figure 3.4. Elastic modulus of polyacrylamide tissue mimicking phantom tested by unconfined compressive mechanical testing.

3.3.2 Ultrasound Elastography (UE)

The PDMS samples were scanned using both strain imaging and B-mode imaging simultaneously. The accumulated strain in reference to the original geometry of each sample was imaged together with B-mode scan. The resulting color-coded strain maps were superimposed onto the B-mode scan image (Figure 3.5). In this figure, the top row is a pure B-mode image and the bottom row is a strain map superimposed on the B-mode image.

The color scale bar on the right of Figure 3.5 indicates the accumulated strain; deep blue to dark red color is indicating strain from low to high. A general decrease of strain was observed on PDMS samples from sample 1 to sample 12 (with increasing modulus) while an increased strain was observed on the surrounding phantom highlighted in the white box in Figure 3.5.

Figure 3.5. B-mode image and UE color coded strain map of 12 PDMS samples.

3.3.3 Strain data and statistics evaluation

The strain data is presented in Figure 3.6. As expected with increasing sample modulus, a decreased strain was observed with strain imaging. The average strain was greater under 3 mm displacement as compared to 1 mm displacement. In Figure 3.6, the boxes depict the soft (47-667 kPa), medium (854-2229 kPa), and stiff (2935-422 4kPa) groups of PDMS samples that were scanned simultaneously. The Young’s modulus range of each group is also highlighted. A one way ANOVA followed by Tukey’s HSD (honest significant difference) test was used to compare average strain of PDMS samples within each group. The statistically significant differences decreased with increasing PDMS

74 samples stiffness. For example, three, one and one comparison was found to be statistically significant in soft, medium and hard PDMS sample group, respectively, when performing strain imaging in soft phantom with 1mm compression (Figure 3.6A). The statistically

Figure 3.6. Strain results from UE. A) Soft phantom (E=50kPa); B) Stiff phantom (E=100kPa). The boxes are illustrating three groups in which four PDMS samples are scanned together. The elastic modulus range is highlighted in each box. Statistically significant differences between successive samples (i.e. Sample 2 compared to Sample 1) were evaluated using a Tukey HSD test and are annotated with stars.

75 significant differences were found to be different when using different phantom stiffness and compressions. Within soft phantoms, 5/9 and 6/9 comparisons were found to be statistically significant with 1 mm and 3 mm compression, respectively (Figure 3.6A). In contrast 8/9 and 9/9 statistically significant differences were found in the stiff phantom with 1 mm and 3 mm compression, respectively (Figure 3.6B).

The comparison P-values were calculated via a one way ANOVA with Bonferroni’s correction and are presented in Table 3.2. More statistically significant differences were seen from scans done in stiff phantoms when compared to soft phantoms. Interestingly, P values were found to be smaller when comparing 3 mm to 1 mm compression in both phantom environments. The modulus differences between successive pairs of samples is summarized in Table 3.2. The average modulus differences were calculated to be 206 ± 49 kPa, 458 ± 115kPa and 430 ± 70kPa for modulus range 47-667 kPa, 854-2229kPa and

2935-4224kPa, respectively.

Table 3.2. P values when comparing between successive pairs of PDMS samples. ΔE is indicating the elastic modulus difference between the two comparing samples and E range is indicating the highest and lowest elastic modulus value of the each scan group. Total number of comparisons with P<0.05 is summarized at bottom of the table. P value is calculated using t-test with Bonferroni's correction. Soft phantom (50kPa) Stiff phantom (100kPa) E range (kPa) Comparison 1mm 3mm ΔE (kPa) 1mm 3mm ΔE (kPa) 47-667 1-2 <0.0001 <0.0001 255 <0.0001 <0.0001 255 2-3 <0.0001 <0.0001 157 <0.0001 <0.0001 157 3-4 <0.0001 <0.0001 208 <0.0001 <0.0001 208 854-2229 5-6 0.0531 <0.0001 357 <0.0001 <0.0001 357 6-7 0.8975 0.0505 434 <0.0001 <0.0001 434 7-8 0.001 0.0005 584 <0.0001 <0.0001 584 2935-4224 9-10 0.4713 0.3560 362 <0.0001 <0.0001 362 10-11 0.9999 0.9998 425 0.1733 0.0022 425 11-12 0.0010 0.0017 502 0.0026 0.0011 502 Sum(p<0.05) 5 6 8 9

3.3.4 Modulus and Strain Correlation

To evaluate the capability of strain imaging for differentiating samples with various

moduli, Young’s modulus and 1/strain were correlated using a linear regression model

fitted using the least squares approach. The correlation result is shown in Figure 3.7. A

linear relationship between elastic modulus and 1/strain was observed. Scan results taken

in stiff phantoms showed a stronger correlation with R2=0.9844 and R2=0.9644 for 1 mm

and 3 mm compression condition respectively. A weaker linear relationship was seen in

soft phantoms with R2= 0.8276 and R2=0.8186 for 1 mm and 3 mm compression

respectively.

Figure 3.7. Elastic modulus and 1/strain correlations in both phantoms. A) Soft phantom; B) Stiff phantom. 3.3.5 ROI study

The percent difference between the average of five ROIs and one ROI was found to range from 0% to 3.03% (Figure 3.8). The average difference was 1.65%. In addition, a concordance correlation coefficient of 0.8256 was observed using Lin’s concordance correlation coefficient analysis, suggesting a substantial strength of agreement between

78 five ROIs method and one ROI method. Thus, the above results suggest that the analysis method used in this chapter was sufficient to achieve objectiveness and accuracy.

Figure 3.8. Average strain value comparison between 5 ROI and 1 ROI. 3.4 Discussion As discussed in the introduction, strain estimation in this study is based on the assumption that the viscoelastic properties of PDMS samples can be neglected due to the fact that the characteristic time in strain imaging is shorter than the relaxation time of

PDMS. The prepared PDMS samples have a modulus range between 47kPa-4MPa, which covers most soft tissues and also exceeds soft tissue stiffness range. By statistically comparing strain imaging data to standard mechanical testing data, it was found that the detection limit of strain imaging can exceed the soft tissue range, up to 4MPa, with detectable difference as low as 157kPa depending on sample stiffness and experimental setup. Phantom stiffness and compression conditions were both found to effect the

79 detection limit. A concentration-dependent stiffness change of the polyacrylamide tissue mimicking phantom was also observed. 50 kPa and 100 kPa were selected to examine the effect of environment mechanical properties on the detection limit of the strain imaging technique. 50 kPa and 100 kPa were also relevant to soft tissue modulus, mimicking breast tissue and cancerous breast tissue respectively (Figure 3.4).

In strain imaging scans, a decreased strain was observed from sample 1 to 12 in both types of tissue mimicking phantom setups as well as under both compression conditions due to the fact that stiffer PDMS materials have a smaller degree of deformation response when experiencing the external compression force. It is important to notice that the statistical analysis was performed on four samples as a group, which were scanned simultaneously by strain imaging. This is to avoid the batch effect of the samples that were not scanned at the same time. The PDMS samples with lower modulus, ranging from 47 kPa to 667 kPa, were more clearly differentiated by strain imaging, as all three comparing differences were statistically significant. However, stiffer samples in the range of 854-2229 kPa and 2935-4224 kPa were more difficult to differentiate, as only one difference was found to be statistically significant in these two groups. Nonetheless, phantom stiffness and compression conditions were both found to affect the detection limit. When comparing strain results between the two tissue mimicking phantom setups, more statistically significant differences were found in the stiffer phantom environment (Figure 3.6 and

Table 3.2), which suggests a better capability to differentiate sample stiffness, thus an improved detection limit. This finding can be explained by a Hookean model of three springs, phantom, PDMS sample and phantom, in series. The PDMS samples will experience a greater deformation under the same total displacement when the spring

80 constant of the phantom increases. This increased deformation produces a higher strain in the PDMS sample that can be picked up by ultrasound strain imaging, thus improving the detection limit. However, very similar strain values were observed when comparing stiff phantom data to soft phantom data (Figure 3.6). We attribute this to the fact that most of the PDMS samples were much stiffer than both soft and stiff phantom material, and the thickness of PDMS samples was much smaller than the phantom thickness. Thus, most of the strain was still taken by the compliant phantom materials. An improved detection limit was also found by applying a greater compression. This is because the total displacement of both phantom and PDMS sample was increased by the higher compression. And thus, the deformation of PDMS samples can be increased and more easily detected by strain imaging. Combining both methods in the above discussion, the detection limit of ultrasound strain imaging can reach the range of 47 kPa - 4 MPa with optimized scan setup.

This detection limit exceeds the range of the soft tissue, suggesting the possibility of using this technique for the characterization of mechanical properties of stiffer biomaterials.

A linear relationship was observed between Young’s modulus and 1/strain with a high correlation coefficient in both soft phantom (R2=0.8186 R2=0.8277) and stiff phantom

(R2=0.9928 R2=0.9592), suggesting the possibility of using strain to quantitatively reflect the mechanical properties of test samples by controlling the testing conditions. The correlations with the stiff phantom were found to be better than the soft phantom. This suggests that environment mechanical property affects the capability of strain imaging to detect stiffness differences. Surrounding environment with higher modulus seems to be preferable. One of the challenges associated with detecting very stiff materials is the possible high stress that needs to be exerted on the tissue surface in order to produce

81 detectable strain in the sample. This could lead to significant patient discomfort and thus prevent the technique from being applied clinically. A finite element analysis was performed on a cylindrical shaped phantom containing a smaller cylinder sample under assumptions including non-linear geometry and modulus homogeneity using COMSOL. It was found that a surface stress of 2.6 kPa was required to produce a detectable strain of 5% on the sample (Figure 3.9). The positive strain value demonstrated a displacement in the tensile direction. Although this estimated result is comparable to a typical palpation stress of 90kPa for breast cancer examination, further experiments still need to be carried out. In future study, an in depth investigation of the stress generated on the transducer-subject interface during the scan needs to be carried out to generate conclusive results.

Figure 3.9 Finite element analysis of transducer-subject interface during compression

3.5 Conclusion

The capabilities of applying ultrasound strain imaging to the evaluation of biomaterials with a wide range of stiffness has been evaluated by comparing strain imaging results to standard mechanical test results. The detection limit of this technique was found to be as high as 4 MPa, which exceeds the soft tissue stiffness, suggesting the possibility that this technique can indeed be appropriate for assessing mechanical properties of biomaterials. The detectable difference was found to be as low as 157 kPa depending on sample stiffness and experimental setup. Testing setup such as phantom stiffness as well

83 as compression conditions were examined and recommend to improve the detection limit strain imaging.

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402.

Chapter 4: Nondestructive Characterization of Biodegradable Polymer Erosion in Vivo Using Ultrasound Elastography Imaging

Note: Parts of this chapter have been submitted for publication as Haoyan Zhou, Anna Gawlik, Christopher Hernandez, Monika Goss and Agata Exner. Nondestructive Characterization of Biodegradable Polymer Erosion in Vivo Using Ultrasound Elastography Imaging, submitted to Biomaterials.

4.1 Introduction

In Chapter 2, the characterization and validation study of ultrasound elastography demonstrated the possibility of using this technique for stiffer biomaterials beyond the soft tissue range. In this Chapter, the feasibility of using UE for characterization of in situ forming implant erosion both in vitro and in vivo will be discussed.

Among many of the biomaterials’ behaviors, in vivo retention time resulting from polymer degradation and subsequent erosion is one of the most important parameters, which the biomaterials’ function in different applications is relying on. Continued rapid development of new biomaterials for applications in tissue engineering[1, 2], regenerative medicine[3, 4], gene therapy[5, 6] and controlled drug delivery[7-9] has stimulated the ongoing need for new tools facilitating high throughput in vitro characterization and streamlined evaluation of in vivo degradation and erosion [10, 11]. Significant advances in the fields using polymeric biomaterials have led to increasingly sophisticated technology with new challenges including quantification of local and systemic pharmacokinetics associated with a polymeric implant drug delivery system [13-15] or the process of cells migrating into a degrading tissue scaffold [16, 17]. Many of these sought-after parameters are, in turn, highly dependent on the degradation and erosion processes in vivo, which involve very specific and complex interactions with local tissue at the implantation site.

Studies done in vitro have shown poor correlations with in vivo behavior, suggesting that in vitro models can rarely represent an in vivo system adequately. Therefore new nondestructive technologies that can be used to study polymer degradation and erosion in situ are highly desirable.

Traditionally, gel permeation chromatography (GPC) and gravimetric analysis have been used as tools for determination of molecular weight changes during polymer degradation and mass loss during the erosion process, respectively. However these methods are destructive, making longitudinal studies of a single sample over time impossible, leading to an increased number of experimental animals required for a particular study.

This non-sequential analysis of materials also leads to inaccurate conclusions as there can be broad batch to batch and animal to animal variations. Also, the necessity to surgically remove individual samples increases the likelihood of specimen damage prior to analysis.

In an attempt to circumvent the issues associated with these invasive and destructive techniques, predictive degradation and erosion models have been developed. Models for degradation have used random theory to predict the random scission process of polymer chains[18], which is assumed to be a first- or second- order kinetic process[19]. Erosion models have been especially complex due to the multitude of involved parameters such as swelling, dissolution of oligomers or monomers, and morphological changes. Even though very comprehensive models have been developed including the Monte Carlo model and the diffusion theory based model [16, 20], they have proven to be only somewhat predictive.

Recently, research efforts have been shifting to the use of biomedical imaging to non-invasively monitor the degradation and erosion process of implants in vivo [12, 21-

25]. These imaging techniques allow for high throughput, serial (ie. longitudinal) studies of biodegradable materials in their intended implanted state. Mader et al. have already demonstrated the feasibility of using MR imaging to monitor polymer tablet behaviors such as size, water content and erosion in vivo [21]. Most recently, Artzi et al. demonstrated noninvasive assessment of implanted hydrogel erosion using in vivo fluorescence

91 imaging[12]. Although these techniques have shown to be successful in preclinical animals, the high cost of MRI and the translatability of fluorescence imaging to human subjects have hindered these techniques from achieving widespread use.

Ultrasound elastography (UE) is a dynamic technique that uses ultrasound to assess the mechanical stiffness of materials noninvasively and nondestructively by measuring material distortion or strain in response to external compression. The distortion is measured based on speckle tracking of two ultrasound B-mode frames before and after compression.

In order to estimate the speckle displacement, either an A-line cross-correlation method or a tissue Doppler method is used. A-line cross-correlation is more widely applied due to its high sensitivity. The strain is calculated accumulatively over frames throughout the entire compression process and plotted as a color coded map. UE has already been widely used as a “digital palpation” tool to characterize tissue mechanical properties for diagnostic purposes such as early detection of breast and prostate cancer [26, 27]. Previous studies done by Kim et al. have shown the feasibility of using ultrasound elastography for monitoring tissue scaffold degradation[28] and tissue in-growth[29]. In Chapter 2, UE was characterized for its application in imaging stiffer materials by comparing it to the gold standard mechanical test using polydimethylsiloxane (PDMS). The detection range was found to be between 47 kPa and 4 MPa, with a detectable difference as low as 157 kPa based on the optimized scan setup during experimentation[30].

For this study, UE technique was applied to a PLGA based in situ forming implant

(ISFI) drug delivery system for characterization of implant erosion in vitro and in vivo.

Implant solutions prepared with three different molecular weights of PLGA were examined.

For the in vitro study, implants were injected into a novel polyacrylamide based tissue

92 mimicking phantom, and then performed UE sequentially at designated time points. For the in vivo study, the PLGA was implanted subcutaneously on the abdominal side of

Sprague Dawley rats, and then performed UE. In vitro and in vivo erosion of the implants was measured simultaneously using gravimetric analysis. Finally, the relationship between strain and erosion was analyzed by comparing both data sets. The goal of this study was to demonstrate the feasibility of establishing a mechanical stiffness based predictive model using ultrasound elastography for noninvasive and nondestructive characterization of implant erosion in an example of ISFI drug delivery system.

4.2 Materials and Methods

4.2.1 Materials

All materials were used as received with no further purification. Poly(DL-lactide- co-glycolide) (PLGA 50:50: MW 18kDa, inherent viscosity 0.19 dl/g; MW 34kDa, inherent viscosity 0.29 dl/g; MW 52kDa, inherent viscosity 0.41 dl/g) was purchased from

Evonik, Birmingham, AL. N-methyl pyrrolidinone (NMP) was purchased from Fisher

Scientific, Waltham, MA, and sodium fluorescein (MW 376.28) was purchased from

Sigma Aldrich, St. Louis, MO. Acrylamide, bis-acrylamide, ammonium persulfate (APS),

N,N,N’,N’-Tetramethylethylenediamine (TEMED), titanium dioxide and phosphate buffered saline were purchased from Fisher Scientific, Waltham, MA.

4.2.2 In situ forming implant (ISFI) solution preparation

ISFI solutions of PLGA in NMP were prepared with 18 kDa, 34 kDa, or 52 kDa polymers. Sodium fluorescein was used as a mock drug because it has a similar chemical

93 structure to the clinically used chemotherapeutic, doxorubicin. The polymer and mock drug were added to NMP in glass scintillation vials and allowed to mix overnight in an incubator shaker at 37 °C until the polymer had completely dissolved. The final ISFI solution had a

60:39:1 mass ratio of solvent: polymer: drug. The solution was used immediately after incubation.

4.2.3 Tissue mimicking phantom fabrication and ISFI injection

50 kPa tissue mimicking phantoms were made from 10% (w/v) solutions of acrylamide and bis-acrylamide (37.5:1 ratio, respectively) in 1X PBS solution. Titanium dioxide was added as a scattering agent to more clearly define the boundaries between the phantoms and implant samples when performing ultrasound elastography. The acrylamide solutions were crosslinked by free radical polymerization using 1.7% and 0.1% (v/v) of

TEMED and APS, respectively. The phantoms were polymerized in plastic molds with two polyacrylamide gel layers. 100 ml of the above mixture was stirred and poured into the plastic phantom mold to form the first layer. After the first layer gelled, custom made hemispheres of sucrose, 7mm in diameter, were placed on the gelled polyacrylamide. The second 100 ml acrylamide mixture was then poured on top of the first layer, embedding the sucrose hemispheres. Phantoms were allowed to swell in PBS for 6 days until the phantom volume reached plateau and the embedded sucrose hemispheres had fully dissolved. This resulted in voids in the phantom in the same shape as the sucrose hemispheres. These voids were flushed 3 times using PBS before implant injection. Then

150μl of polymer solution was injected into the voids to form solid implants, which had the same dimensions and shape as the voids. PLGA implants embedded in tissue mimicking phantoms were kept in 300ml of PBS at 37˚C using an incubator shaker (80

RPM, 37 ˚C) for 25 days. PBS was replaced daily. The injections were performed in triplicates. The void creation and ISFI implant injection process is summarized in Figure

4.1.

Figure 4.1. Void creation and ISFI implant injection process.

4.2.4 In vitro UE scan and image analysis

Implants were scanned daily using a clinical ultrasound system (Toshiba Aplio500) with a linear array transducer centered at 12MHz to evaluate their mechanical stiffness change over time. A laboratory-designed stage and a linear actuator were used to induce uniform compression for all samples. The UE scans were carried out as described previously in Chapter 2. Briefly, the ultrasound transducer was fixed to the laboratory designed stage, and the programmable linear actuator was used to provide uniform displacement. Since the ultrasound transducer was fixed, samples between the transducer and linear actuator were compressed by pushing up from below. An optimized 10% pre- strain was applied to ensure contact between the transducer and polyacrylamide phantom.

During the scan, a compression cycle composed of 3 compressions and 3 dilations was induced. The displacement of each compression and dilation equaled 1mm. A time frame

Tissue Doppler Tracking (TDT) algorithm was used to calculate axial strain [31].Color

Doppler Imaging (CDI) frequency was set at 8.8 while the velocity scale was 0.3 cm/s to accommodate the maximum value of the velocity profile. Velocity smoothing was used to optimize the strain calculation. After the scan, a color coded strain map was generated and superimposed on top of the B-mode image. The regions of interest (ROI) were manually selected based solely on the B-mode images to remove potential operator dependent bias.

The mean strain value of each implant was obtained by spatially averaging over the entire implant ROI.

4.2.5 In vitro mechanical testing

A sheet shaped implant was created by injecting polymer solution into a sheet shaped void within the tissue mimicking phantom. A disc shaped (3mm in diameter) PLGA implant was then obtained from the PLGA sheet using a biopsy punch prior to mechanical testing. The Young’s modulus of the implants was determined using unconfined compressive mechanical testing with a rheometer (Rheometrics RSAII, NJ). A strain rate of 0.01/s was used. Testing time was optimized for each sample to ensure at least 30% strain. After the stress-strain curve was generated, a threshold of 0.1 N was used to ensure consistent contact between the compressing probe and PLGA sample. Thus, measurements with associated force less than 0.1 N were discarded. A first order polynomial fit was used to determine the linear region of the stress-strain curve for modulus calculation. The

Young’s modulus (E) was determined by finding the slope of the linear region with strain no greater than 20% on the stress-strain curve.

4.2.6 In vitro erosion study

At designated time points (days 1, 3, 6, 9, 12, 16 and 21), samples were removed from the phantoms. The procedure was carefully performed to avoid any damage to the samples to ensure 100% sample recovery. A gravimetrical analysis was performed after freeze drying the harvested samples to obtain the implant dry mass. To ensure complete removal of water from the implants, the freeze drying process was allowed to run for 72

푃표푙푦푚푒푟 푑푟푦 푚푎푠푠 hours. Then, erosion was calculated using the equation 퐸푟표푠푖표푛 = , 퐼푛푖푡푖푎푙 푝표푙푦푚푒푟 푚푎푠푠 where polymer dry mass was obtained after freeze drying and initial polymer mass was known from the initial formulation.

4.2.7 Animal preparation and implant injection

The animal study was performed using 6-10 week old male Sprague Dawley rats

(body weight 200-300g, Charles River Laboratories Inc, Wilmington, MA). The protocol followed the National Institutes of Health (NIH) guidelines for animal care and was approved by the Case Western Reserve University Institutional Animal Care and Use

Committee. The anesthesia was induced using 3% isoflurane inhalation and maintained at

2% isoflurane inhalation with an oxygen flow rate of 1 l/min (EZ150 Isoflurane Vaporizer,

EZ Anesthesia™). The rat was placed in the supine position on a 37˚C heating pad, and once completely anesthetized, the hair on the abdomen region was removed. Implant solutions were prepared as described earlier. 100μl of each polymer solution was injected subcutaneously at one of three locations on the abdominal side of the rat using a 21-gauge hypodermic needle. The injection location of each polymer type was alternated between

97 animals to account for variability between tissue locations (Figure 4.2). Three animals were used for each time point.

Figure 4.2. Animal study design. 100μl of each polymer solution (18kDa, 34kDa and 52kDa) was injected subcutaneously at one of three locations on the abdominal side of the rat. The injection location of each polymer type was alternated between animals to account for variability between tissue locations. UE scan was performed every other day to assess the mechanical stiffness of the implants. To measure the erosion, implants were removed at day 1, 4, 8, 12, 16 and 22 for gravimetrical analysis.

4.2.8 In vivo UE scan

In vivo UE scans were performed using the same experiment set up as described earlier. The rats were anesthetized on a custom made platform that was attached to the linear actuator. The ultrasound transducer was fixed to a customized stage, hence the

98 animal was compressed by pushing up from below using the linear actuator. This set up was used to induce uniform strain on each animal. A standard B-mode image was used to determine the starting compression point, where the rat’s abdominal skin was just barely in contact with the ultrasound transducer. During the scan, unlike in the in vitro study, a displacement of 2mm was used in the compression and dilation cycle to produce greater strain for detection. Other parameters were the same as those used in the in vitro experiment.

The UE scan was performed every other day from day 1 to day 22 (Figure 4.2).

4.2.9 In vivo erosion study

At designated time points (days 1, 4, 8, 12, 16 and 22), the animals were sacrificed

(Figure 4.2). The implants were excised and freeze dried. The dry sample mass was obtained using gravimetrical analysis. The erosion was calculated using the equation described in the in vitro section.

4.2.10 Predication of Implant erosion in vitro

Combining findings in both in vitro and in vivo experiments, a predictive model based on strain-erosion relationship was built. In order to test this model, an in vitro single blind prediction study was performed. Polyacrylamide phantom fabrication and PLGA implant injection were performed as described earlier. 18 kDa, 34 kDa and 52 kDa PLGA were used to prepare the implant solution. 5, 10 and 15 day time points were chosen for the erosion examination (n=3). UE was performed without knowing the date and the identity of the implant. Erosion was measured using the gravimetrical analysis described earlier. The estimation of implant erosion was implemented by combing the experimental

99 strain data and the predictive model built earlier. The percent error were calculated by comparing the estimation erosion to the actual erosion measured.

4.3 Results

4.3.1 Implant erosion in vitro

The mechanical test was performed on 34 kDa PLGA implants. As shown in Figure

4.3, the Young’s modulus of the PLGA implants was found to decrease over time after injection into the tissue mimicking phantom. The highest modulus was found to be 878 kPa at day 1, while the lowest modulus was 7 kPa found at day 22. The decrease in modulus continued until day 8, then no clear trend was observed, and modulus values ranged from

7 to 99 kPa from day 9 to day 23. It is worth noting that the sample size was n= 3 for most days but n=2 for days 8, 11, 12, 16, 19, 21, 22 and 23. This is due to sample damage during the harvesting and preparation process at the later time points.

100

Figure 4.3. Young’s modulus of 34 kDa PLGA implants

The accumulated strain in reference to the original geometry of each sample was color coded and superimposed onto the B-mode image (Figure 4.4). The ultrasound transducer was located at the top of the image, while the linear actuator induced the compression cycle from the bottom. In this figure, the color scale bar on the right indicates the accumulated strain, deep blue to dark red was indicating strain from low to high.

Reduction in strain was observed over time for all PLGA molecular weights: 18kDa,

34kDa and 52kDa, except for the period from day 0 to day 1. The implants were found to stiffen in the first day (Figure 4.4) due to the phase inversion process. Then, the mean strain value for each time point was calculated over 3 samples and plotted in Figure 4.5 A. In this plot, the same implant stiffening from day 0 to day 1 was observed. In addition, strain was found to change at different rates for implants with different molecular weights. 18kDa implants had the highest rate of increasing strain, followed by 34kDa then 52kDa. These

101 implants also reached their plateau stage at different time points: 18kDa PLGA at 14 days,

34kDa PLGA at 17 days and 52kDa PLGA at 21 days. In Figure 4.5 B, the rate of implant erosion was also found to be different depending upon the molecular weight of the implant.

18kDa implants had the highest rate of erosion followed by 34kDa then 52kDa.

Figure 4.4. In vitro: Color coded strain map of implant over time

102

Figure 4.5. In vitro: A) Strain of implants obtained from ultrasound elastography scan, the dashed line is indicating the water strain value under the same scan condition as explained in the discussion; B) Implant erosion in phantoms.

4.3.2 Implant erosion in vivo

The color coded strain map and B-mode ultrasound images for 18 kDa, 34 kDa and 52 kDa implant at day 1, day 10 and day 20 are summarized in Figure 4.6. For each implant type, the top row represents the color coded strain map and the bottom row represents B-mode images. The abdominal skin is located at the top of the image, with the ultrasound transducer fixed above and linear actuator pushing up from the bottom. The region of interest (white dashed circle) was selected based on B-mode images. As animal experiments progressed from day 1 to day 20, the implants became softer and the regions

103 of interest became smaller (Figure 4.6). The mean strain values of each implant type were calculated for all 5 animals and plotted in Figure 4.7A. Again, the strain was found to change at different rates based on the molecular weight of the implants. 18 kDa implants had the highest rate of increasing strain, followed by 34 kDa then 52 kDa. In addition, the erosion of each implant type is plotted in Figure 4.7B. The erosion rate is also molecular weight dependent. 18 kDa PLGA eroded the fastest, followed by 34 kDa then 52 kDa.

Figure 4.6. In vivo: Color coded strain map of implant over time

104

Figure 4.7. In vivo: A) Strain of implants obtained from ultrasound elastography scan, B) Implant erosion

105

4.3.3 Predication of Implant erosion in vitro

By comparing the erosion estimation using the predictive model and the actual measured erosion for 9 samples up to 15 days, an average percent difference of 17.3% was found.

The percent difference for samples at day 5, 10 and 15was found to be 9.23%, 16.59% and

26.14%, respectively. At each day, both the actual measured erosion and predicted erosion showed the same expected trend related to polymer molecular weight with 18 kDa PLGA implants eroding was having the fastest erosion followed by 34 kDa and then 52 kDa implants. The summary of this prediction study is listed in Table 4.1.

Table 4.1 Summary of erosion measurements (Dry mass/Initial mass) and estimation from in vitro prediction study (n=3)

Percent difference Actual measurements Erosion prediction (%) Day 5, 18 kDa 1.0696 1.0689 0.067 Day 5, 34 kDa 1.0920 1.2259 10.923 Day 5, 52 kDa 1.1471 1.3771 16.702 Day 10, 18 kDa 0.6883 0.5404 21.484 Day 10, 34 kDa 0.7435 0.6283 15.498 Day 10, 52 kDa 0.8245 0.7190 12.797 Day 15, 18 kDa 0.5389 0.6931 22.239 Day 15, 34 kDa 0.5651 0.7248 22.026 Day 15, 52 kDa 0.6713 1.0200 34.180

4.4 Discussion

As stated earlier, some of the 34 kDa samples became fragile and broke, leading to difficulty in mechanical testing. Because of the challenges faced with this first set of PLGA implants, mechanical testing was not performed on the remaining two molecular weight implants. From mechanical testing of 34 kDa PLGA, a decreasing modulus trend was observed within the first 8 days, which indicates the loss of structural integrity during the degradation and erosion process. We anticipated the same decreasing modulus trend would

106 be seen in the 18 kDa and 52 kDa PLGA implants when preforming mechanical tests on them. After 8 days, an inconsistent fluctuation of implant modulus was observed. This was attributed to the fact that some of the samples became very fragile and easy to break at this stage, which made the implant harvesting and preparation extremely difficult and thus led to inconsistent testing results. The challenges in the mechanical testing also reflects the drawback of traditional methods as they are destructive which may lead to inaccurate testing results.

In the in vitro study section, the morphology and strain distribution of PLGA ISFI implants at every designated time point can be clearly observed in Figure 4.4. Based on the ultrasound B-mode images, the size of the implant did not change over the course of the entire experiment. This is because of the empty voids, which support the shape of the implants. This was confirmed when the later time point samples were removed. It was observed that the PLGA implants became attached to the inside of the phantom voids, causing them to maintain their shape and size while becoming hollow in the center. Based on the UE images, the stiffness of the implants was found to increase from day 0 to day 1 and decrease afterwards. The early stiffening process was due to the liquid to solid precipitation process of the implant. After day 1, the stiffness change of the implant was governed by the erosion of the implant and therefore became softer overtime. The same trend was also observed in quantification (Figure 4.5A). The rate of strain increase was found to be dependent on the molecular weight of the implants which corresponds very well with the rate of implant erosion (Figure 4.5B). This indicates that the softening process is a result of erosion of the implants. Due to the different hydrophilicities associated with different molecular weights of polymer used, the implants erode at different rates through

107 hydrolysis. After increasing in strain, all implant types seemed to reach a plateau phase, at different rates. For example, 18 kDa implants reached plateau at about 14 days while 34 kDa and 52 kDa implants reached this phase at about 17 days and 21 days, respectively

(Figure 4.5A). This plateau is believed to be where the implants have eroded to the point at which their mechanical stiffness has decreased below the sensitivity of our technique and thus changes in strain could no longer be detected. This was confirmed by scanning water under the same condition. The strain value of water was about 7% (the dashed line in Figure 4.5A), which equaled the plateau strain of all implant types. The correlation between strain and implant erosion was also analyzed and plotted in Figure 4.8A. A strong linear strain-erosion relationship was observed, with R2= 0.9245. This relationship was also found to be independent of polymer molecular weight. Based on this finding, change in implant integrity, and thus mechanical stiffness, appears reflect the polymer erosion with a simple linear relationship.

108

Figure 4.8 A) In vitro erosion and strain correlation, B) In vivo erosion and strain correlation

In the in vivo study section, the observations were very similar to what was observed in vitro. Implants of all types became softer overtime after injection. The rate of implant softening was again based on the different molecular weight of polymer used.

18kDa had the highest softening rate followed by 34 kDa then 52 kDa (Figure 4.7A). This corresponds well with the implant erosion rate (Figure 4.7B). Unlike in the in vitro studies,

109 the implants, which were injected subcutaneously into the rats’ abdominal regions became smaller in size overtime (Figure 4.6), and this change seemed to also be dependent upon the molecular weight. For example, the 18 kDa implants were almost gone while 34 kDa and 52 kDa implants were still about half of their original size at 20 days after injection

(Figure 4.6). It was hypothesized that the surrounding tissue was compressing the implant while the tissue mimicking phantom was not because the implant filling space was pre- created. This injection site compressive force has also been reported to alter the drug release profile of the ISFIs [7]. In addition, the strain values of the implants in vivo were smaller than in vitro. Furthermore the plataeu phase oberved in the in vitro studies was not found in the in vivo studies. This was anticipated because the stiffness of the surrounding tissue environment mainly consists of muscle and internal organs [32], and is not as high as the tissue mimiking phantom (50kPa). Thus, most of the strain applied was absorbed by the compliant surrounding tissue, resulting in a lower strain from implants when compared to the in vitro study. The correlation between strain and erosion was also analyzed and plotted in Figure 4.8. The findings were very similar to what observed in vitro; a strong linear relationship between strain and erosion was found, suggesting the possibility of using the mechanical stiffness of implants to predict implant erosion independent of molecular weight.

In the prediction study in vitro, by comparing the erosion estimation using the predictive model and the actual measured erosion for 9 samples up to 15 days, an average percent error of 17.3% was found. The percent error for 5 days, 10 days and 15 days estimation was found to be 9.23%, 16.59% and 26.14%, respectively. The smaller

110 differences were found for the earlier time points was anticipated due to the loss of structural integrity of the PLGA implants which created difficulty in the harvesting process.

Biodegradable polymeric materials have been widely used for various biomedical applications. A nondestructive and high throughput imaging technique for the longitudinal assessment of biomaterial erosion will help researchers to refine their designs in biomedical devices with erosive properties. UE has shown high potential for clinical use, but limited applications in pre-clinic. By examining the relationship between polymer erosion and strain in this presented study, I was able to build a predicative model for longitudinal nondestructive characterization of polymer erosion. Its accuracy for erosion estimation was also tested in a single blind study, showing only 17.3% difference from direct measurements. Although this model is material and condition specific, the simple mechanical stiffness and erosion relationship can be adapted to build other models for prediction in various situations. When combining this model with the use of UE integrated diagnostic ultrasound, it is now possible to track and even predict the fate of erosive polymeric materials in a nondestructive and noninvasive manner.

4.5 Conclusion

The mechanical stiffness of in situ forming implants was monitored using ultrasound elastography imaging. The softening process of implants over time was found to be dependent on the PLGA molecular weight used, which corresponds well with the implant erosion rate. By analyzing the correlation between implant mechanical stiffness and erosion, a strong linear relationship was observed, which is independent of PLGA

111 molecular weight. This finding was observed in both in vitro and in vivo studies, demonstrating the possibility of building a mechanical stiffness based predictive model using UE for longitudinal assessment of implant erosion.

4.6 References

[1] Anseth KS, Shastri VR, Langer R. Photopolymerizable degradable polyanhydrides with osteocompatibility. Nat Biotechnol 1999;17:156-9.

[2] Falco EE, Patel M, Fisher JP. Recent developments in cyclic acetal biomaterials for tissue engineering applications. Pharm Res 2008;25:2348-56.

[3] Mano JF, Silva GA, Azevedo HS, Malafaya PB, Sousa RA, Silva SS, et al. Natural origin biodegradable systems in tissue engineering and regenerative medicine: present status and some moving trends. J R Soc Interface 2007;4:999-1030.

[4] Shi C, Zhu Y, Ran X, Wang M, Su Y, Cheng T. Therapeutic potential of chitosan and its derivatives in regenerative medicine. J Surg Res 2006;133:185-92.

[5] Han S, Mahato RI, Sung YK, Kim SW. Development of biomaterials for gene therapy. Mol Ther 2000;2:302-17.

[6] Lim YB, Kim CH, Kim K, Kim SW, Park JS. Development of a safe gene delivery system using biodegradable polymer, poly[alpha-(4-aminobutyl)-L-glycolic acid]. J Am

Chem Soc 2000;122:6524-5.

[7] Patel RB, Solorio L, Wu HP, Krupka T, Exner AA. Effect of injection site on in situ implant formation and drug release in vivo. J Control Release 2010;147:350-8.

112

[8] Solorio L, Olear AM, Hamilton JI, Patel RB, Beiswenger AC, Wallace JE, et al.

Noninvasive characterization of the effect of varying PLGA molecular weight blends on in situ forming implant behavior using ultrasound imaging. Theranostics 2012;2:1064-77.

[9] Grayson ACR, Voskerician G, Lynn A, Anderson JM, Cima MJ, Langer R.

Differential degradation rates in vivo and in vitro of biocompatible poly(lactic acid) and poly(glycolic acid) homo- and co-polymers for a polymeric drug-delivery microchip. J

Biomat Sci-Polym E 2004;15:1281-304.

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[11] Nair LS, Laurencin CT. Biodegradable polymers as biomaterials. Prog Polym Sci

2007;32:762-98.

[12] Artzi N, Oliva N, Puron C, Shitreet S, Artzi S, bon Ramos A, et al. In vivo and in vitro tracking of erosion in biodegradable materials using non-invasive fluorescence imaging. Nat Mater 2011;10:704-9.

[13] Plourde F, Motulsky A, Couffin-Hoarau AC, Hoarau D, Ong H, Leroux JC. First report on the efficacy of l-alanine-based in situ-forming implants for the long-term parenteral delivery of drugs. J Control Release 2005;108:433-41.

[14] Cao YX, Zhang C, Shen WB, Cheng ZH, Yu LL, Ping QN. Poly(N- isopropylacrylamide)-chitosan as thermosensitive in situ gel-forming system for ocular drug delivery. J Control Release 2007;120:186-94.

[15] Solorio L, Olear AM, Zhou H, Beiswenger AC, Exner AA. Effect of cargo properties on in situ forming implant behavior determined by noninvasive ultrasound imaging. Drug Deliv Transl Res 2012;2:45-55.

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[16] Gopferich A. Mechanisms of polymer degradation and erosion. Biomaterials

1996;17:103-14.

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Microstructured templates for directed growth and vascularization of soft tissue in vivo.

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Invivo. Biomaterials 1981;2:215-20.

[19] Maniar ML, Kalonia DS, Simonelli AP. Determination of specific rate constants of specific oligomers during polyester hydrolysis. J Pharm Sci 1991;80:778-82.

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[21] Mader K, Bacic G, Domb A, Elmalak O, Langer R, Swartz HM. Noninvasive in vivo monitoring of drug release and polymer erosion from biodegradable polymers by

EPR spectroscopy and NMR imaging. J Pharm Sci-Us 1997;86:126-34.

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2013;3:1198.

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Consistent quasistatic and acoustic elasticity determination of poly-L-lactide-based rapid- prototyped tissue engineering scaffolds. J Biomed Mater Res A 2013;101:138-44.

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115

Chapter 5: Conclusions and Future Directions

5.1 Conclusions

Advancements in biomedical imaging techniques have led to a growing number of applications for characterization of polymeric biomaterials behavior in vivo. This noninvasive approach provides a comprehensive understanding of the variety of events that happen to the materials, resulting in solutions to many challenges including the kinetics of cell invasion into a tissue engineering scaffold, quantification of local and systemic pharmacokinetics and implant-body interactions on an implantable drug delivery devices.

In the meantime, sequential analysis of the same sample is possible due to the nondestructive nature of the imaging technology.

The goal of the thesis work was to develop and validate the ultrasound elastography technology for the characterization of the polymer erosion in in situ forming drug delivery system. In Chapter 1, basic background knowledge about degradation/erosion process of polymeric materials, formation and releasing mechanisms of ISFIs as well as principles behind UE technology was discussed. In Chapter 2, general physics principles as well as advantages and disadvantages of different imaging modalities were discussed. As described above, there are various biomedical imaging modalities that can be applied to monitor specific aspects of implantable DDS including morphology, physiochemical properties, tissue response to the implant and therapeutic agent and drug distribution.

Certainly, each imaging modality has its unique advantages and limitations. In order to obtain more comprehensive information, researchers are not limited to the use of a single imaging modality. Rather, the use of multimodal imaging can overcome the limitations of individual techniques while combing desired advantages. It is also important to look for

116 ways to exploit the diverse energy sources associated with the different imaging modalities in order to not only image implantable systems but manipulate their behavior on demand.

On the other hand, a gap may form between biomaterials researchers and these powerful imaging tools, since majority of the biomedical imaging technologies were designed for soft tissue specimens. Certain characterization and validation studies to tune and develop these imaging technologies for polymeric biomaterials will provide very valuable information, so that biomaterial researchers can use these imaging tools properly.

Chapter 3 focused on the development and validation work of the UE technology.

A computer aided linear movement platform has been developed to produce uniform compression during UE scans, which provides a solution to operator dependent results generated from this technology. In addition, the scan parameters including phantom stiffness and compression conditions were optimized to improve the UE strain detection limit. A validation study by comparing UE scan results of PDMS samples with various moduli to golden standard, unconfined mechanical compression test, has also been done.

From the study, the detection range was found to be between 47 kPa and 4 MPa, with a detectable difference as low as 157 kPa, P <0.05. The possibility of using the UE technology for assessing mechanical properties of stiffer biomaterials is supported, since the detection limit was found to be as high as 4MPa, which exceeds the soft tissue stiffness.

This characterization work of UE will provide useful background knowledge of the technique such as detection limit and detectable difference to biomaterial researchers, so that they can employ UE properly in a wider range of applications.

In continuation of the work in Chapter 3, Chapter 4 focused on the applications for the developed UE technology to real biomedical challenges using polymeric biomaterials.

117

As discussed previously, each release stage of the ISFI system is governed by the implant behaviors within the in vitro and in vivo environments. It is important that we have the capability to monitor these behaviors, and thus relate to drug release profiles. The work in

Chapter 3 aims to characterize the erosion process using ultrasound elastography. UE scans were performed on ISFI formulations with three different molecular weight PLGA in novel tissue mimicking phantoms (in vitro) and on the back of a Sprague Dawley rats (in vivo).

The gathered strain results were compared to the erosion data using gravimetrical methods.

A strong linear strain-erosion relationship was found in both in vitro and in vivo, with R2=

0.9245 and R2= 0.8806, respectively. Since this relationship was found to be independent of PLGA molecular weight, a mathematical prediction model was established for all molecular weight PLGA. In order to test the prediction model, a single blind in vitro study was conducted and showed a 17.3% average error from the gold standard measurements.

5.2 Limitations

This thesis work showed very positive result demonstrating the feasibility of using ultrasound elastography to characterize polymer erosion nondestructively and noninvasively. The strong correlation between mechanical properties and erosion of the polymeric biomaterials suggest the possibility of erosion prediction using a stiffness based model. However, the technology has some limitations. For example, for translation of the technique to the clinic, high stress built up on the interface between the ultrasound probe and skin may cause patient discomfort. Also the dependence of strain upon environmental conditions makes it not the best indicator of material mechanical property. In addition, this work does not include the characterization of polymer degradation, because it has been shown that the degradation of PLGA does not necessarily occur within the same time

118 period as the loss of mechanical stiffness [1]. However mechanical property based predictive model may still be very useful for other polymer types.

5.3 Future directions

Ultrasound elastography provides a nondestructive and noninvasive means to assess mechanical properties of polymeric biomaterials. When combining the technique with the mathematical predictive model we discussed in Chapter 3, it becomes a powerful technology to assess polymer erosion. However, one of the challenges associated with this technology is the dependence of strain on external factors such as tissue boundary conditions and applied stress during the scan. The fast development of shear wave elasticity imaging (SWEI) may provide a solution due to its capability of direct modulus measurement. This new technique is implemented by using the acoustic radiation force [2] and ultrafast imaging [3] techniques, which produces shear waves and acquires the propagation of shear waves, respectively. The modulus distribution of the material can then be reconstructed by estimating the speed of the shear wave. The entire process can be done with one integrated ultrasound transducer in an acquisition time of less than 30 milliseconds [3], thus real time imaging using this technology is possible.

Since the relationship between mechanical properties and erosion has been found to be independent of polymer molecular weight, the mathematical model which correlates polymer stiffness to polymer erosion may be universal to one polymer type. However the relationship may not necessarily be linear. It is anticipated to be governed by the erosion/degradation mechanism of the polymer. In order to improve the characterization system and overcome the limitations discussed above, three steps can be carried out in the future.

119

Step 1: Characterize and validate the SWEI system in an in vitro tissue mimicking phantom. 3 parameters including, 1. Detection limit, 2. Sensitivity, 3. Spatial resolution will be investigated in the characterization study. The obtained knowledge in the characterization study will be used to determine whether the technique can be used for materials with certain modulus.

Step 2: Library building: establish relationship between Young’s moduli and degradation/erosion of popular polymeric biomaterials. Perform calibration studies for polymers that are commonly used in biomedical applications such as PLGA, PEG hydrogels, PU, etc. Calibration studies will be performed in the in vitro tissue mimicking phantom. UE will be used to measure Young’s modulus while standard traditional methods such as gravimetrical analysis and GPC will be used to measure erosion and degradation, respectively. Results will be compared to find the mathematical relationships.

Relationships in this library will be grouped based on their erosion and degradation mechanism (hydrolysis, enzymatic, etc.). The models gathered in the library will help to determine the different mechanisms of degradation.

Step 3: In vivo predication. Based on the library built in Aim2, UE will be performed in two single blind animal studies. 1. Implantable drug delivery system:

PLGA based in situ forming implant (ISFI) will be injected in a Sprague-Dawley rat model.

2. PU based tissue engineering scaffold will be implanted. In both studies, Young’s modulus of the samples will be measured using UE. Erosion and degradation will be estimated using the obtained Young’s modulus and predicative model in the library. The estimation result will be compare to the standard measurement of erosion and degradation.

120

5.4 References

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[3] Sarvazyan AP, Rudenko OV, Swanson SD, Fowlkes JB, Emelianov SY. Shear wave elasticity imaging: a new ultrasonic technology of medical diagnostics. Ultrasound Med

Biol 1998;24:1419-35.

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Appendix

A.1 Polyacrylamide hydrogel for ultrasound elastography phantom

Materials:

1. 40% acrylamide solution (Purchased pre-made from BioRad: 161-0148EDU)

2. Tris-base

3. APS (Ammonium Persulfate)

4. H2O

5. TEMED. TEMED is toxic (and odorous). Use disposable pipette. Clean area with

water.

Procedures:

1. Making 1.5M Tris HCl (pH8.8)

a. Dissolve 18.2 grams of solid tris base in 20mL distilled water.

b. Check the pH of the solution using a calibrated pH meter.

c. Slowly add HCl to the solution to adjust the pH to 8.8.

d. Pour the solution into a graduated cylinder to check the volume.

e. Add distilled water to bring the final volume to 100mL

2. Making Polyacrylamide gel (w/wo carbon black scattering)

Polyacrylamide gel 100ml

Acrylamide(40%) 37.5ml

Tris 1.5M 10ml

H2O 50ml

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Carbon black 1g

APS(10%) 1.7ml

TEMED 0.1ml

Mix well all ingredients but Carbon black, APS and TEMED in a flask. Add APS,

Carbon black and mix again. Finally, add TEMED, mix and pour into the mold.

Polymerization starts within around 5 minutes. Wash area with water after making acylamide gels.

NOTES:

a. The APS is a 10% (by weight) solution of Ammonium Persulfate, and is

the cross linker (others can be used). Increasing the amount will harden

the gel.

b. TEMED is a free radical generator and is used to initiate the linking

process.

c. TEMED and temperature to get different results. Warm water will speed

polymerization, which is rather exothermic anyway. Using cold water

does slow the onset of the reaction, and may result in a clearer mix.

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A.2 Manufacturing of PDMS with different modulus

Materials: 1. Sylgard 184 Silicone Elastomer Kit (Silicone base and crosslinking agent)

2. Carbon black

Procedures:

1. To make a 3mm thickness layer on a 100*20mm petri dish, 16.42g of silicone

base and crosslinking agent are needed.

2. Weigh 20g of silicone base in the cup. (Not all of the silicone can be poured into

the petri dish in the end)

3. Add 0.6g of carbon black(3%) and determined amount of crosslinking agent for

different crosslinking density:

Crosslink agent amount:

5%-> 1g

10%-> 2g

20%-> 4g

4. Well mix using a stir rod and degas in the desiccator to remove the bubbles.

(Around 5 mins)

5. Pour 16.42g of PDMS into the 100*20 petri dish.

6. Put the prepared sample into the freezer for 1-2 hours for further removing of

bubbles.

7. Incubate the samples in the conventional oven at 150F for 1.5 hours.

Notes: PDMS is very viscous and hard to be clear off. Handle with caution.

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A.3 Ultrasound elastography scan protocol using Toshiba ultrasound scanner Instruments: Ultrasound probe stand Toshiba AplioXG SSA-790A clinic ultrasound system Linear actuator Steps for performing UE scan 1. Turn on the ultrasound system and linear actuator.

2. Set up a “Patient” scan profile.

3. Fix the transducer on the probe stand.

4. Choose the operating transducer (8 or 12 MHz).

5. “Preset” -> Luis PLGA.

6. Press “Elast” and choose the region of interest in which UE is performed.

7. Make sure the “2D focus” is at the same level as the ROI chosen in the step6.

8. Color Doppler Imaging “(CDI) frequency” -> 8.8 and “velocity scale” -> 0.3

cm/s.

9. Press “Freeze”-> unpress “Freeze” and start the linear actuator compression

program->press “Freeze” again after the program finished.

10. Press “ElastQ” to start analysis.

11. Adjust “color fusion” to 0 to see B-mode image.

12. Choose ROI in this mode.

13. Adjust “color fusion” to 0.4 to see UE color map.

14. Analysis need to be performed before quit the “ElastQ” program.

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A.4 Implant Erosion Study Protocol Protocol for Erosion: Materials: PLGA NMP (N-methyl pyrrilidone) 1% Agarose Fluorescein Phosphate Buffered Saline 2M NaOH

Steps for making Sheets 1. Prepare PLGA solution in NMP (60/39/1, NMP, PLGA, Fluorescien)

2. Label 1.5ml centrifuge tubes for time points cutting off lid locks and poking a

hole into the lid.

3. Mass and record each of the centrifuge tubes.

4. Load syringes with polymer solution, then label and weigh the syringes.

5. Prepare agarose phantoms

6. Fill 20 ml scintillation tubes with 10 ml of PBS and put in incubator

7. Incubate polymer and PBS over night at 37oC in the incubated shaker

8. Tare the scale with the PBS filled scintillation tube

9. Inject the polymer solution into the agarose phantom.

10. Place phantom into the warm PBS and put in the shaker.

11. Repeat steps 8-10 for each time point and replicate in the study.

12. After 5 h, remove the 5 ml of buffer, and replace with 5 ml of fresh warm buffer.

13. At each time point completely remove the buffer and transfer the implants into the

pre-massed centrifuge tubes and record wet mass.

14. Freeze implants overnight and lyophilize for 4 d.

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15. After lyophilization, remass tubes with dried mass of implant

16. Record the loss of mass over time and normalize by the initial implant mass

159

A.5 Implant Degradation Study Protocol Protocol for Degradation: Materials: PLGA NMP (N-methyl pyrrilidone) Fluorescein Phosphate Buffered Saline 2M NaOH

Steps for making Sheets 1. Take implants used for erosion study after the dry mass has been recorded.

2. Dissolve in THF for at least 2 h into a 10 mg/ml concentration.

3. After the implants have dissolved, syringe filter at least 1 ml into GPC vials using

a 0.45 µm filter

4. Label vials and run on GPC at 25°C for 40 minutes and compare output spectra

peaks with standard curve of known Mw.

5. Plot log(Mw) over time, and fit to find the 1st order degradation kinetics.

160