Magnetic Resonance - Based Evaluation of Small Molecule Release from a Thermosensitive Drug Delivery

System

by

Amanda Aleong

A thesis submitted in conformity with the requirements for the degree of Master of Applied Science

Institute of Biomaterials and Biomedical Engineering University of Toronto

© Copyright by Amanda Aleong 2017

ii

Magnetic Resonance-based Evaluation of Small Molecule

Release from a Thermosensitive Drug Delivery System

Amanda Aleong

Master of Applied Science

Institute of Biomaterials and Biomedical Engineering

University of Toronto

2017

Abstract

There is an unmet need for clinically-implementable imaging toolsets to evaluate spatio-temporal

drug release from thermosensitive nanocarriers in response to hyperthermia. This thesis presents a

magnetic resonance (MR)-based platform for (1) evaluating hyperthermia-induced destabilization

of thermosensitive drug carriers and (2) quantifying subsequent small molecule diffusion. The

platform consists of a custom designed agar phantom, a temperature-controlled T1-weighted

imaging workflow and a MATLAB analysis algorithm for semi-automated quantification of small

molecule kinetics. Using this platform, thermosensitive liposomes (TSL) encapsulating

gadoteridol were assessed at 22°C, 37°C, and 43°C and compared to free imaging agent and non-

thermosensitive liposomes. In addition, the physiological relevance of the gel phantom was

benchmarked against muscle and tumor tissue. Results demonstrated complete destabilization of

TSL at 43°C in 1.5% agar, with a measured diffusion coefficient of (2.90 ± 0.52)×10-4 mm2/s which

was not statistically different from free small molecule diffusion at 43°C, (2.72 ± 0.87)×10-4

mm2/s.

iii

Acknowledgments

‘I am never really satisfied that I understand anything, for understand it as well as I may, my

comprehension can only be an infinitesimal fraction of all I want to understand’

- Ada Lovelace

This work is dedicated to the family and friends who have made this journey possible. Thank

you for your unending love and support and for always adding that much needed spice to my life.

To my supervisor, Dr. Jinzi Zheng, you represent all that I aspire to be. Thank you for being such

a wonderful role model and giving so much of your time and patience. ‘What would Jinzi do?’

has become a natural part of my everyday thought process. It’s been four years already but the

time has gone too fast. Thank you for the many opportunities you have created for me to learn

and grow. All the conferences and meetings, presentations and workshops. Few are so lucky in

their Master’s and I will never forget any of it.

To my committee members, Dr. David Jaffray and Dr. Christine Allen, thank you for all the

excellent questions and for sharing your expertise. Your guidance has truly pushed me to do

better science and think on deeper levels.

To my labmates, Aditya, Linyu, Manuela, Nick, Inga, and others at the STTARR Imaging

Centre, thank you for keeping it lively at lab. Thank you all for the support with experiments and

presentation preps and for the lab outings and new experiences.

To the friends, peers and students I have met and shared cheers and tears with along the way,

thank you for shaping me into the person I am today.

To my boyfriend, Francis, thank you for maintaining my sanity and reminding me what is

important in life. Thank you for always expecting more of me and helping me to be my best self.

To my family, Mom, Dad and my sisters Caity and Heidi, thank you for always believing in me

for better or for worse.

I would like to extend a special thanks to all those who have contributed to this work through

experiments or by providing materials: my summer student, Gabriel, for his excellent work on k-

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mean clustering and code testing. Nancy, Mike and others at the Allen Lab for preparation of

thermosensitive liposomes. Linyu for the preparation of non-thermosensitive liposomes. Aditya

and Nick for help with cell work and inoculations.

v

Table of Contents

Acknowledgments.......................................................................................................................... iii

Table of Contents .............................................................................................................................v

List of Figures ............................................................................................................................... vii

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

Chapter 1 ..........................................................................................................................................1

Introduction .................................................................................................................................1

1.1 Rationale for Imaging Drug Delivery ..................................................................................2

1.2 Thermosensitive Drug Delivery ...........................................................................................2

1.2.1 Preclinical Assessment of Thermosensitive Drug Delivery ....................................3

1.2.2 Challenges in Clinical Translation ...........................................................................4

1.3 Magnetic Resonance and Thermosensitive Drug Delivery .................................................5

1.3.1 MR-based Assessment of Thermosensitive Drug Delivery .....................................6

1.3.2 Real-time MRI under Hyperthermia ........................................................................7

1.4 Diffusion of Drug Carriers and Small Molecules ................................................................8

1.4.1 Theory of Diffusion .................................................................................................8

1.4.2 Measuring Diffusion In Vivo ...................................................................................9

1.4.3 Comparison to the Apparent Diffusion Coefficient ...............................................10

1.5 Thesis Overview ................................................................................................................11

Chapter 2 ........................................................................................................................................13

Methods .....................................................................................................................................13

2.1 Aim 1: Visualization of Drug Release in a Gel Phantom ..................................................13

2.1.1 Phantom Preparation ..............................................................................................13

2.1.2 Imaging Workflow .................................................................................................14

vi

2.2 Aim 2: Quantification of Drug Release in MATLAB .......................................................16

2.2.1 Validation of Signal Linearity ...............................................................................16

2.2.2 Image Analysis Algorithm .....................................................................................16

2.3 Aim 3: Evaluation of Physiological Relevance .................................................................20

2.3.1 Diffusion Through Muscle Tissue .........................................................................20

2.3.2 Diffusion Through Tumor Tissue ..........................................................................21

2.3.3 Tuning the Gel Phantom Diffusivity......................................................................22

Chapter 3 ........................................................................................................................................23

Results .......................................................................................................................................23

3.1 Aim 1: Visualization of Drug Release in a Gel Phantom ..................................................23

3.2 Aim 2: Quantification of Drug Release in MATLAB .......................................................27

3.3 Aim 3: Evaluation of the Physiological Relevance of the Gel Phantom ...........................31

Chapter 4 ........................................................................................................................................39

Discussion .................................................................................................................................39

4.1 Aim 1: Visualization of Drug Release in a Gel Phantom ..................................................39

4.2 Aim 2: Quantification of Thermosensitive Drug Release..................................................42

4.3 Aim 3: Evaluation of Physiological Relevance of the Gel Phantom .................................45

4.4 Summary and Future Directions ........................................................................................50

References ......................................................................................................................................51

Appendix A: Heterogeneous Diffusion Through Tumor ...............................................................61

Appendix B: Analysis of Direct Injection into Muscle .................................................................63

Appendix C: Inspection of Signal Along a Contour ......................................................................65

Appendix D: Diffusion Limit in Hydrogels ...................................................................................66

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List of Figures

Figure 1 Illustration of phantom preparation: showing formation of the spherical void using a

balloon mold. ................................................................................................................................ 14

Figure 2 Schematic of the experimental set up in MR.................................................................. 15

Figure 3 Example of contours generated using the MATLAB script ........................................... 18

Figure 4 Sample graph displayed after calculation of the diffusion coefficient showing the signal

curve used to approximate the slope and the Laplacian over time of the same curve .................. 19

Figure 5 Representative scout images showing axial (left), coronal (middle) and sagittal (right)

sections through the agar phantom. .............................................................................................. 23

Figure 6 Representative temperature profiles for the duration of imaging measured via an optical

fibre probe ..................................................................................................................................... 24

Figure 7 Signal intensity vs. flip angle. ........................................................................................ 25

Figure 8 Axial images showing the cross section through the spherical void in the agar phantom

before, immediately after agent injection, and at the end of the imaging period for Free Gad at

22°C .............................................................................................................................................. 25

Figure 9 Images obtained at t = 30min for free Gad (left panel), non-thermosenstive liposomes

(NTSL; center panel) and thermosensitive liposomes (TSL; right panel) at 22°C, 37°C and 43°C

....................................................................................................................................................... 26

Figure 10 Change in signal intensity versus gadolinium concentration before (a) and after

adjustment using Equation 7 (b). .................................................................................................. 28

Figure 11 Mean signal intensity vs time with increasing distance from the edge of the spherical

void for free Gad in agar at 22°C. ................................................................................................. 29

Figure 12 Sample scout images for muscle (top panel) and SKOV-3 tumor (lower panel). ........ 31

Figure 13 Adjusted signal vs. time at 0.7mm from the edge of the spherical void for runs

performed in agar (a) and muscle (b) ............................................................................................ 32

viii

Figure 14 Rate constant and time delay for agar and muscle as determined by curve fitting with

Equation 7. .................................................................................................................................... 33

Figure 15 Signal vs. time for two concentrations of Gd at 43°C. ................................................. 34

Figure 16 Diffusion coefficients calculated in agar (shaded) and muscle (solid) for free small

molecules (blue), NTSL (green) and TSL (red) ............................................................................ 35

Figure 17 Showing clusters generated by K-means segmentation in an ROI indicated by the

white dotted line and the corresponding signal vs. time curves ................................................... 36

Figure 18 Diffusion coefficients at 22°C, 37°C and 43°C for 1.5% Agar, 7.5% BactoTM Agar,

Muscle and SKOV-3 ..................................................................................................................... 37

Figure 19 SKOV-3 tumor embedded in agar adjacent to a spherical void. .................................. 61

Figure 20 Mean signal intensity along a contour at 0.7mm from the contrast pool for SKOV-3. 61

Figure 21 Mean signal versus time for select clusters derived from k-means segmentation for

SKOV-3 at three temperatures. ..................................................................................................... 62

Figure 22 Illustration of direct bolus injection into muscle tissue ................................................ 63

Figure 23 Plot of signal along a MATLAB generated contour at two distances .......................... 65

Figure 24 Graph showing small molecule diffusion coefficients at 22°C for agar gels of

increasing concentration compared to diffusion coefficients through tissue. ............................... 66

ix

List of Abbreviations

ADC Apparent diffusion coefficient

AU Arbitrary unit

C Concentration

CDDP Cisplatin

CT Computed tomography

DOX Doxorubicin

D0 Diffusion coefficient in water

Deff Effective diffusion coefficient

DCE Dynamic contrast enhanced

DWI Diffusion weighted imaging

∇^2 (∆S) Spatial laplacian

ΔSI Change in signal intensity

EPR Enhanced permeability and retention

FA Flip angle

FDA Food and drug administration

FOV Field of view

FRAP Fluorescence recovery after photo bleaching

Gad Gadoteridol

x

Gd Gadolinium

[Gd] Concentration of gadolinium

GRE Gradient recalled echo

HBS HEPES buffer solution

HEPES 4-(2-hydroxyethyl)-1-piperazineethanesulfonic acid

HIFU High intensity focused ultrasound

HPLC High-performance liquid chromatography

k Proportionality constant

kB Boltzmann constant

K Rate constant

m Linear slope

Mn Manganese

MR Magnetic resonance

MW Molecular weight

NTSL Non-thermosensitive liposomes

r1 Longitudinal relaxivity

R2 Coefficient of determination

RH Hydrodynamic radius

RF Radiofrequency

ROI Region of interest

xi

Sadj Adjusted signal intensity

Sbase Averaged signal intensity before contrast agent injection

Sx,y Signal at pixel position (x,y)

Slope22 Slope of signal vs. concentration at 22°C

SlopeT Slope of signal vs. concentration at T

t Time

T Temperature

Tm Melting temperature

T1 Longitudinal relaxation time

TE Echo time

ThermoDox® Thermosensitive liposomal doxorubicin

TR Recovery time

TSL Thermosensitive liposomes

1

Chapter 1

Introduction

Temperature-sensitive drug delivery systems have shown improved therapeutic efficacy,

in animal models of cancer, compared to conventional non-thermosensitive drug delivery systems,

while maintaining the benefit of reduced systemic toxicity compared to free drug therapies [1]–

[6]. The preclinical success of temperature-sensitive drug delivery systems has led to several

promising applications and increasing interest from the scientific community, particularly for

thermosensitive liposomal (TSL) formulations. However, considering recent challenges observed

during clinical trials, there is a dire need for a clinically implementable imaging toolset that enables

real-time, non-invasive evaluation of heat induced small molecule release [7]–[9]. Despite

advancements in magnetic resonance thermometry (MRT) for guiding hyperthermia in real-time,

studies have shown that ensuring conformal hyperthermia does not guarantee treatment efficacy.

New clinical trials are underway to investigate the use of MR guided hyperthermia applicators

such as high intensity focused ultrasound (HIFU) in combination with thermosensitive drug

delivery systems [10]. The development of a toolset that enables in situ quantification of small

molecules released from thermosensitive delivery systems, in real-time, will aid in the strategic

development of hyperthermia treatment protocols that can improve the efficacy of these novel drug

delivery systems.

This thesis will focus on the development of an MR-based toolset to non-invasively

quantify small molecule release from temperature-sensitive drug delivery systems in a custom

designed phantom. The platform, described herein, aims to support the effective translation of

thermosensitive drug delivery systems in the clinic by providing non-invasive pre-treatment

assessment of drug release in response to hyperthermia protocols. It incorporates three

components: 1) a biologically relevant test phantom, 2) a MR-based protocol for monitoring

kinetics of drug surrogate small molecules in real time, and 3) a software package for image

analysis and quantification of temperature-induced release. The current chapter sets the stage for

this work and provides background and insights into the present state of technology for spatio-

temporal assessment of activatable drug delivery systems.

2

1.1 Rationale for Imaging Drug Delivery

Recent insights into the heterogeneity of cancer biology have led to a paradigm shift in the

search for new treatment strategies with an emerging emphasis on personalized medicine i.e. the

right treatment for the right patient at the right time [11]. Chemotherapy has been identified as an

avenue with considerable untapped potential for tailoring treatments to the needs of the patient [6],

[11], [12]. Traditional free drug therapies are severely limited in their applications, typically being

employed as an adjuvant or second line therapy to surgery or radiation. However, the systemic

distribution of the drug leads to low accumulation in the tumor, limiting the efficacy of the drug,

and creating undesired side effects that impact the patient’s quality of life. In spite of a plethora of

new strategies for overcoming these limitations, including new engineered molecules and drug

carriers, there remain very few success stories [7], [12]. Upon investigation of the barriers to

clinical translation there is a notable lack of integrated imaging platforms for providing feedback

on the success of drug delivery. Medical imaging has played a crucial role in combatting cancer

by enabling clinicians to tailor treatments to the individual in an accurate and timely manner. To

date, they primarily aid in cancer diagnosis and staging while assisting in treatment planning and

guidance. For example, MR imaging is used to guide surgical and radio-therapeutic procedures. It

follows that drug delivery systems would benefit greatly from the implementation of

complementary imaging techniques that afford comprehensive spatio-temporal quantification of

drug distribution.

1.2 Thermosensitive Drug Delivery

Temperature-sensitive drug delivery systems are of particular interest as they have shown

extensive benefit in animal models of cancer and were the first of its kind to enter clinical trials

[9], [10], [13]. They belong to a subset of activatable drug delivery systems which encapsulate the

drug in a nanocarrier and release the therapeutic payload in response to external triggers or

endogenous cues. The specific size of the drug carrier (30 – 100nm) contributes several key

advantages to the drug delivery process. These delivery systems have been identified as the optimal

size to bypass the body’s clearance mechanisms, thereby increasing the lifetime of the drug in the

body. They remain in circulation avoiding distribution to healthy tissues and organs [14]. By

exploiting the leaky vasculature and poor lymphatic drainage characteristic to the tumor

3

morphology, known as the enhanced permeability and retention (EPR) effect, the particles

accumulate preferentially at the tumor site [15]. In addition, through destabilisation of the drug

carrier membrane at the target site in response to hyperthermia, a burst release of drug is directly

administered to the tumor, thereby delivering greater concentrations compared to free drug

therapies and conventional drug carriers [6]. Recent studies have shown that the timing with which

hyperthermia is applied can drastically affect the treatment outcome. It was established that pre-

heating the tumor provided greater efficacy in the case of thermosensitive drug delivery over

waiting for peak tumor accumulation of the nanoparticles via EPR effect [16].

With the introduction of low transition temperature delivery systems, thermal activation

can be achieved within physiologically acceptable temperatures with minimal damage to

surrounding tissue [5], [17]. Temperatures in the mild hyperthermia range (39 – 43˚C) have also

been shown to increase blood flow and induce sensitization to radiation and chemotherapy [18],

[19]. ThermoDox® (Celsion Corporation, NJ), a thermosensitive liposome-based system carrying

the chemotherapeutic drug, doxorubicin (DOX), was the first activatable system to enter clinical

trials [2]–[5]. The formulation showed increased tumor growth delay in mice implanted with FaDu

human xenografts when combined with hyperthermia over both non-thermosensitive liposomal

doxorubicin (Doxil®) and the corresponding free drug therapy, DOX with and without heat [20].

The improved efficacy of the system was attributed to an increase in therapeutic index associated

with encapsulation and rapid controlled release resulting in 4 to 15-fold greater drug dose

depending on the tumor model compared to the treatment groups without heat [4].

1.2.1 Preclinical Assessment of Thermosensitive Drug Delivery

Over the years, imaging has contributed many key insights that have shaped the field of

thermosensitive drug delivery. The non-invasive nature of molecular imaging allows in situ

evaluation of the biodistribution of intact nanocarriers and released small molecules [1], [21]. A

number of techniques have been developed for assessing the spatio-temporal distribution of drug

carriers and the subsequent release of their payload. Optical imaging, in particular, played a key

role in the early characterization of thermosensitive delivery systems. Direct imaging of the drug

molecule may be achieved by exploiting its autofluorescence, as in the case of DOX. It was found

that encapsulation of the drug molecule in a nanocarrier quenched this fluorescence. As such, the

return of fluorescent signal following destabilization of the nanocarrier provided a suitable method

4

to identify drug release. Alternatively, molecules and carriers may be modified with a fluorophore

to achieve visualization using optical imaging. This method has been used by many researchers to

illustrate the benefits of thermosensitive delivery systems. For example, Kong et al. demonstrated

the benefit of hyperthermia in facilitating the extravasation of nanoparticles at the tumor site using

a tumor window chamber model [22]. Koning et al. used fluorescence imaging to confirm the

importance of rapid drug release (on the order of seconds) at the tumor site and to demonstrate the

strong correlation between tumor drug concentration and therapeutic efficacy [23]. Furthermore,

advances in real time imaging and improvements in spatio-temporal resolution have highlighted

the impact of tumor heterogeneity on therapeutic efficacy of drug delivery systems [24]. While

fluorescence imaging has proven its value as a tool for preclinical assessment of thermosensitive

drug delivery, the depth limitation of fluorescence imaging techniques has severely narrowed its

practicality in the clinic.

1.2.2 Challenges in Clinical Translation

To date, the specific assessment of hyperthermia-induced drug release in the clinic is

primarily performed via blood sampling during and following administration of the treatment [25],

[26]. In other cases, the therapeutic efficacy may be evaluated by monitoring tumor volume over

time. Early phase I and II trials comparing radiofrequency ablation (RFA) alone and RFA +

ThermoDox® failed to meet the desired clinical endpoint of progression free survival. However,

post-hoc analysis revealed improvements in overall survival in a subset of patients receiving

hyperthermia for more than 45 minutes, thus supporting further studies with improved treatment

protocols [27]. Following this observation the failure was attributed to insufficient heating to

maintain heightened intra-vascular drug concentration long enough to allow penetration of the

drug [16]. It was also noted that there was a severe lack of implementation of clinically available

tools for ensuring conformal heat delivery to the target site and measuring effective drug release.

Since then, two new studies have been proposed with longer and more standardized heating, but

protocols continue to operate with minimal real-time feedback on drug release achieved [9], [10],

[25].

In parallel to the development of new temperature-sensitive delivery systems, advances in

non-invasive heating platforms for the clinic such as high intensity focused ultrasound (HIFU)

transducers and radiofrequency (RF) arrays have added to the need for comprehensive spatio-

5

temporal evaluation [28], [29]. HIFU, in particular, offers the means to achieve conformal heating

of complex geometries in deep seated tumors using heat deposited at multiple focal spots [30]–

[33]. Preclinical studies have shown that the method used to achieve hyperthermia and the spatial

distribution of the temperature profile can have a drastic effect on the therapeutic efficacy of the

drug delivery system due to the sensitivity of the release mechanism to the specific activation

temperature [34]. Heating methods commonly used in preclinical studies, such as water bath and

laser-based heating, are less susceptible to heterogeneous spatial heating profiles. The spatial

flexibility of clinically preferred heating systems then calls for an imaging platform capable of

monitoring the spatio-temporal temperature profile and the effective small molecule release from

temperature-sensitive drug delivery systems.

To overcome the challenges associated with temperature-sensitive drug delivery in the

clinic there is a need for a clinically implementable imaging platform capable of spatio-temporal

quantification of drug release under hyperthermic conditions. Ideally, this method will be non-

invasive, robust to temperature fluctuations and can clearly delineate released from encapsulated

small molecules. The strategy described in this thesis employs MR to non-invasively visualize

hyperthermia-induced drug release from thermosensitive carriers via a small molecule MR-

imageable drug surrogate in a reproducible phantom environment.

1.3 Magnetic Resonance and Thermosensitive Drug Delivery

Since its introduction to the clinic in 1977, magnetic resonance imaging has played a vital

role in diagnosis and treatment planning for many diseases. Due to its safe and non-invasive nature,

this technology has found major applications in oncology for treatment planning of surgery and

radiotherapy. T1-weighted MR techniques measure longitudinal proton relaxation times when RF

pulse sequences are applied to tissue under a high magnetic field. MR exploits the natural

differences in proton densities within the body to produce high resolution images of soft tissue

structures. In addition, protons in the presence of MR contrast agents, such as manganese (Mn) or

gadolinium (Gd), experience a T1 shortening effect reflected by an increase in MR signal. This is

termed the relaxivity associated with the MR contrast agent. The suitability of MR for evaluating

drug release via such small molecule imaging agents arises from the difference in relaxivity of

encapsulated versus free contrast agent molecules. The result is anatomical imaging and

6

quantifiable relaxivity changes that enables monitoring of drug release upon activation when a

drug surrogate imaging agent is present [35], [36].

1.3.1 MR-based Assessment of Thermosensitive Drug Delivery

Several studies have been conducted using MR to determine dose delivered upon thermo-

activated release, beginning in 2004 with Viglianti et al. [36]. In this example, tumors were treated

with 15 – 20 minutes of hyperthermia followed by systemic administration of a temperature-

sensitive liposomal (TSL) formulation containing both DOX and Mn. T1-weighted images were

acquired before hyperthermia and after the tumor had cooled to room temperature. MR-based Mn

concentration measurements were evaluated against tissue sample DOX concentrations (measured

via high-performance liquid chromatography (HPLC) and fluorescence imaging of histological

slices). The study showed that the relaxivity of the contrast agent trapped within the liposomal

compartment was greatly reduced by the limited mobility of the encapsulated protons.

Destabilization of the liposomal membrane allows a drastic increase in the mobility of the

encapsulated protons and a subsequent rise in measured relaxation rate, R1 (=1/T1). Since then,

the primary focus of MRI-based evaluation has remained on the acquisition and comparison of

pre- and post-treatment images to determine the in vivo drug-surrogate distribution achieved

through thermo-activation [30], [37]–[39].

It is important to note that co-encapsulation of an imaging agent with a drug only allows

visualization of release and is often not a perfect representation of the fate of the drug as transport

and clearance mechanisms can vary significantly. This is especially true if the drug must bind to a

particular substrate to take effect. Gd as a drug surrogate is therefore limited to indicating

immediate release as the pharmacokinetics and clearance differ from the drug. Despite this known

limitation, the similarity in size and hydrophilicity between Gadoteridol and the drug incorporated

in this study, cisplatin (CDDP), suggests that on shorter time scales, the drug and the imaging

agent behave in a very similar manner. Results have been published previously, showing a strong

correlation between the %CDDP released compared to the %gadoteridol released in vitro,

determined by HPLC [1].

7

1.3.2 Real-time MRI under Hyperthermia

When employing magnetic resonance to evaluate contrast agent release under

hyperthermia, the temperature dependence of the longitudinal relaxation time must be accounted

for [40]–[43]. The relationship between temperature and T1 may be summarized by the following

equation.

𝑇1 = 𝑇10 + 𝑚(𝑇 − 𝑇𝑜) (1)

Where T10 is the baseline reference T1 at temperature T0. The temperature dependence of T1-

weighted signal has important implications for assessing drug release via T1-shortening contrast

agents [44], [45]. The linear relationship between T1 and temperature is reflected by a decrease in

measured signal intensity with increasing temperature. Previously, Hey et al. investigated the use

of T1 mapping to detect release of contrast agent from TSL in an agarose phantom [46]. Results

confirmed a reduction in T1 after heating to the activation temperature of the liposomes. While

this was sufficient for qualitative indication of release, the inherent temperature dependence of T1

was not taken into account. Future studies aiming to quantify drug release using T1-based

measurements would benefit from a better understanding of the extent of temperature contributions

which counteract the effect of release.

In 2011, a proof-of-concept study proposed the use of a MR-HIFU combined strategy for

guiding not only thermal dose through feedback loops, but also drug release [47]. The group

speculated that, through monitoring of triggered release in correlation with thermal dose,

adjustments could be made to better adapt treatment protocols to suit the inhomogeneity within

and between indications. Implementation of such a strategy would benefit from further studies

with greater statistical strength and more in depth spatial analysis of small molecule release. Later,

Dou et al. reported the use of rapid T1-weighted imaging i.e. dynamic contrast enhanced (DCE-)

MRI to monitor real time changes in tumor signal in vivo [1]. Imaging was performed before,

during and 20 min following administration of their novel TSL formulation containing Cisplatin

(CDDP) and ProHance® (FDA-approved Gd-based MR contrast agent) to monitor hyperthermia-

induced release. The steep difference in the signal-time profile of heated versus unheated tumor is

indicative of burst release from the TSL. However, further work is required to fully isolate

hyperthermia-induced enhancement of the EPR effect and changes to liposomal membrane

8

permeability allowing faster exchange of water molecules which may have contributed to the

signal increases observed [48].

A viable solution for facilitating the translation of heat-activated drug delivery is the use

of a non-invasive temperature measurement tool together with imageable drug surrogates. This

will enable controlled and verifiable heat induction and image-guided quantification of drug

release at the tumor site [49], [50]. The current work provides a method for the quantification of

spatio-temporal data acquired using MR. The next section will explore how the known difference

in molecular size between the drug carrier and the molecular therapeutic can be exploited to

quantify the drug release achieved in space and time in response to hyperthermia.

1.4 Diffusion of Drug Carriers and Small Molecules

The transport of molecules through the tumor interstitium is governed mainly by diffusive

and convective forces. Following extravasation, pressure gradients have been shown to minimize

the effective convective transport of molecules. While this effect is observed heterogeneously

throughout the tumor interstitium, further transport is mainly diffusion-limited at this stage [51]–

[53]. As such, it is desirable to mimic the diffusive kinetics of small and macromolecules through

the tumor interstitium when investigating drug release from thermosensitive carriers. A phantom

with physiologically relevant diffusion characteristics will enable improved evaluation of

successful in situ drug delivery.

1.4.1 Theory of Diffusion

Herein, diffusion is defined as the net movement of molecules from an area of high

concentration to an area of low concentration via random molecular interactions i.e. Brownian

motion. Diffusion of molecules in an isotropic medium is described by Fick’s Law which states

that:

𝜕𝐶

𝜕𝑡= 𝐷 ∇2𝐶 (2)

Where C is the concentration of the diffusing molecule and D is the diffusion coefficient [54]. The

diffusion coefficient may be approximated as constant in a dilute, isotropic medium. Major factors

9

affecting diffusivity are summarized by the Stokes-Einstein relationship which assumes a spherical

molecule diffusing through a liquid medium:

𝐷0 =𝑘𝐵𝑇

6𝜋𝜂𝑅𝐻 (3)

𝑅𝐻 = 0.0332 ∗ 𝑀𝑊0.463 (4)

Where D0 is the theoretical ‘free’ diffusion coefficient, kB is the Boltzmann constant, T is the

temperature in Kelvin, 𝜂 is the viscosity of the medium at the given temperature and RH is the

hydrodynamic radius of the molecule (related to its molecular weight, MW). In practice, measured

diffusion coefficients tend to be much lower than this theoretical value due to interactions with

complex microenvironments [55]–[59].

1.4.2 Measuring Diffusion In Vivo

In addition to the dependence of diffusivity on the molecular characteristics of the system,

there are a number of factors affecting diffusion of small molecules and macromolecules which

have been investigated extensively for applications in drug delivery and image contrast

enhancement. For example, Pluen et al. investigated the impact of extracellular matrix components

and tumor cellularity on molecular diffusion [60]. They found that fibrillary collagen was a major

contributing factor to the hindrance of macromolecular (>10 nm) diffusion and that tumors with

higher stromal density tended to demonstrate lower diffusivity [51]. This hindrance effect was

termed, tortuosity (τ; Deff = 1/τ2 D0) and has since been the primary explanation for the effective

diffusion coefficient measured in non-aqueous media at a given temperature [61], [62]. It is

important to note that tortuosity is separate from the η incorporated in the Stokes-Einstein equation

as it describes the lengthening of the diffusion pathway due to obstructing obstacles, leading to

lower measured effective diffusivity. In contrast, solvent viscosity relates to a more fundamental

change in the interaction of the molecule with the solvent particles.

The heterogeneity of tumor tissue poses a significant challenge to the quantification of

molecular kinetics during drug delivery. A number of tumor models have been tested with a wide

variety of diffusing molecules ranging from 5 – 100 nm. Traditionally, molecular diffusion

coefficients were measured by fluorescence recovery after photobleaching (FRAP) [60], [63], [64].

The study by Pluen et al., mentioned previously, investigated diffusion coefficients in

10

subcutaneous and orthotopic models of glioblastoma and melanoma [60]. Their experiments found

that nanoparticles diffused with a coefficient on the order of 10-6 to 10-7 mm2/s in the tumor

interstitium. However, diffusion of liposomes with an RH of 100 nm could not be measured for

subcutaneous tumors due to the extremely slow time scale and inhomogeneity of particle

distribution. Recently, diffusion of a small molecule Gd-based contrast agent (gadoterate

melaglumine) has also been measured using dynamic T1-weighted MR imaging by Koh et al. in

the necrotic fraction of a variety of tumors xenografts in vivo [55], [65]. Using an approximation

of Fick’s Law, a diffusion coefficient was estimated from voxels with linear signal uptake. It was

found that the diffusion coefficient varied depending on the tumor model within the range of (0.9

– 3.1) x 10-4 mm2/s but did not vary drastically with tumor size. The consistent difference in

diffusion coefficient between small molecule contrast agents and nanoparticles supports further

investigation of a diffusion based separation technique to assess successful small molecule release

from thermosensitive delivery systems.

1.4.3 Comparison to the Apparent Diffusion Coefficient

With a limited number of studies quoting diffusion coefficients of small molecules in

tumors, it was hypothesized that the apparent diffusion coefficient (ADC) obtained by MR-based

diffusion weighted imaging (DWI) can be used to estimate small molecule diffusion coefficient.

While ADC measures the diffusion of water molecules, it is expected that the higher molecular

mobility will be reflected in the diffusion of small molecule contrast agents as well. A few factors

have been identified in literature which consistently influence the ADC values observed in tumors;

namely cell density, volume of extracellular space, and high concentrations of macromolecular

proteins [66]–[68]. Since these factors are also primary factors influencing the tortuosity of the

diffusion environment, it is hypothesized that the ratio of observed ADC values will match the

ratio of small molecule diffusion in those specimens.

𝐴𝐷𝐶𝑡𝑖𝑠𝑠𝑢𝑒 𝐴

𝐴𝐷𝐶𝑡𝑖𝑠𝑠𝑢𝑒 𝐵≈

𝐷𝑒𝑓𝑓,𝑡𝑖𝑠𝑠𝑢𝑒 𝐴

𝐷𝑒𝑓𝑓,𝑡𝑖𝑠𝑠𝑢𝑒 𝐵 (5)

As a first step, phantom experiments offer a reproducible environment for investigating

hyperthermia-induced drug release. They have the potential to provide definitive insight into the

efficacy of the procedure applied to achieve hyperthermia-induced activation of TSL. The methods

described in this thesis provide insight into diffusion-limited transport of small molecules

11

following release from thermosensitive carrier. Furthermore, the efficiency of hyperthermia-

induced release is evaluated by exploiting the difference in diffusion kinetics between small and

macromolecules.

1.5 Thesis Overview

There is need for a clinically-available toolset assess in situ hyperthermia-induced small

molecule release from temperature-sensitive drug delivery systems, thus enabling optimization of

hyperthermia protocols to maximize in situ drug release. MR provides a viable solution through

real-time imaging of drug release via co-encapsulation of an MR contrast agent with the drug [1],

[5], [38], [69]. Successful implementation of this platform will enable user-based protocol

optimization to ensure the highest performance of the drug delivery system with the given

equipment. The results provided in this thesis support the use of MR to quantify small molecule

release from TSL in response to hyperthermia. Furthermore, it validates the physiological

relevance of a custom designed agar phantom for treatment planning purposes.

The goal of this thesis is to support effective application of temperature-sensitive drug

delivery systems through the implementation of an MR-based toolset for quantifying small

molecule release. This was achieved through the following specific aims:

Aim 1: Prototyping of an MR-compatible gel phantom for diffusion-based assessment of small

molecule release from TSL under hyperthermia.

Aim 2: Development of MR image analysis algorithm for semi-automated quantification of small

and macromolecule diffusion.

Aim 3: Evaluation of the physiological relevance of diffusion measurements obtained in the gel

phantom against those found in normal and tumor tissue using the quantification tool from Aim 2.

Chapter 2 will describe the methods developed to evaluate thermosensitive drug delivery

systems using MR. Each experiment may be broken down into 3 stages: phantom preparation, MR

imaging and image analysis. Experiments were performed iteratively to optimize each stage based

on the results of the next. In accordance with Aim 1, an agar phantom was designed that allowed

injection of an imaging agent during scanning. Imaging parameters were optimized to best

visualize small molecule diffusion profiles and aid with subsequent image analysis. In addition, a

custom-made recirculating water chamber was implemented to maintain desired temperatures

during scanning and to minimize heating of the magnet. The semi-automated image analysis script

12

was created to extract relevant features of the image such as signal vs. time graphs along contours

radiating from the injection site and to calculate the diffusion coefficient of the agent in the tested

medium. To assess the physiological relevance of the diffusion coefficients obtained in the

developed agar phantom, experiments were repeated with ex vivo muscle (TSL, non-

thermosensitive liposomes i.e. NTSL, and free Gadoteridol; each at 22, 37 and 43°C), a MDA-

MB-231 orthotopic breast tumor model (free Gad at 22°C) and a SKOV-3 subcutaneous ovarian

tumor model (free Gad at 22, 37 and 43°C). Experiments in the MDA-MB-231 tumors were not

repeated at higher temperatures due to slow time scale of diffusion which resulted in signal

limitations during analysis.

Chapter 3 provides the results obtained using the methods described in Chapter 2. Briefly,

an MR-compatible agar phantom was developed to visualize drug release, employing diffusion

based separation of small molecules from the nano-sized carriers to assess release. Agar was found

to be a reproducible medium for diffusion studies. The water bath and temperature probe set-up

was confirmed to provide a stable temperature throughout imaging at all three temperatures

investigated. Imaging parameters were established which maximized contrast between the gel

medium and the diffusing contrast agent. Signal versus time graphs generated using the semi-

automated MATLAB script provided quantitative evidence of small molecule release through

comparison against positive and negative control groups. Stable nanocarriers did not diffuse

through either agar or muscle at any temperature while small molecules showed significant signal

enhancement in both mediums at each temperature. Diffusion in tissue was observed to be slower

than in 1.5% agar overall. Therefore, 7.5% BactoTM agar was investigated as a substitute and led

to a lower diffusion coefficient at all three temperatures, closer to the range observed in tissue.

Chapter 4 inspects the results obtained and landmarks these findings within the current field.

13

Chapter 2

Methods

2.1 Aim 1: Visualization of Drug Release in a Gel Phantom

2.1.1 Phantom Preparation

A gel phantom was designed to provide a reproducible environment for evaluation of

hyperthermia-induced small molecule release in MR. As such, it was necessary to employ a

material that was MR compatible and remained mechanically stable under hyperthermia.

Powdered agar (Sigma Aldrich, St. Louis, Missouri) was selected with melting temperature, Tm,

of 89°C and a gelling temperature of approximately 40°C. In addition, a spherical air void was

incorporated in the phantom to allow injection of a liquid agent during scanning. This prevented

damage to the gel during injection and minimized convective flow into the gel.

Gel phantoms were prepared by placing 70 mL of water in a 100 mL beaker on a hot plate.

Powdered agar was added at 1.5% (1.05 g) and stirred using a magnetic stir bar (medium speed)

at room temperature for five minutes. The beaker was placed in a water bath on a hot plate and the

temperature of the bath was monitored with a thermometer (Figure 1). The water bath was heated

to 90°C and the mixture was stirred for approximately 10 minutes until it became transparent and

began to bubble. While the agar mixture was heating up, the mold was prepared by filling and

tying a balloon with 1 mL of water. The balloon tail was threaded through a syringe with the

plunger removed and the nozzle cut off (Figure 1). This provided a spherical mold with a diameter

of approximately 1 cm. The heat was lowered and the mixture was stirred for another 2-5 min to

ensure all agar had dissolved. Room temperature water was added to the water bath to provide

rapid cooling of the agar gel and expulsion of minute air bubbles.

The warm agar solution was placed into a 20 mL syringe, pouring slowly down the side of

the tube to avoid formation of air pockets. The spherical mold was placed gently into the solution,

allowing the gel to adhere to the balloon surface as it was inserted. The angle of the syringe was

used to ensure that no agent flowed out of the injection site during imaging. The gel was then

allowed to cool completely to room temperature before removing the mold. The balloon was then

14

deflated by draining the water with a syringe and the mold was removed. The gel phantom was

then capped using parafilm to prevent dehydration of the gel.

Figure 1 Illustration of phantom preparation: showing formation of the spherical void using

a balloon mold.

2.1.2 Imaging Workflow

TSL encapsulating gadoteridol (Gad; ProHance®; (Bracco Diagnostics, Princeton, NJ)),

used in this section, were prepared as outlined by Dou et al. [1]. NTSL also encapsulating (Gad)

were prepared using the protocol described by Zheng et al. [21]. Following production, NTSL was

diluted in HEPES (4-(2-hydroxyethyl)-1-piperazineethanesulfonic acid) buffer solution (HBS) to

match the gadolinium (Gd) concentration measured in TSL. Un-encapsulated (free) Gad was also

diluted in HBS to match the Gd concentration ([Gd]) in TSL. Each phantom was imaged in a

closed-bore 1 Tesla (1T) preclinical magnet (Aspect M3, Tel Aviv, Isreal) inside a recirculating

15

water chamber connected to an external water bath pump (Figure 2). Additional insulation was

placed around the chamber to protect the magnet from temperature changes. An optical fiber

temperature probe was used to monitor and maintain temperature through manual feedback control

with the water bath.

Phantoms were imaged for 30 minutes using a T1-weighted dynamic contrast enhanced

gradient-recalled echo (DCE-GRE) sequence with the following parameters: TE/TR = 3.2/54 ms;

FA = 90°; FOV = 30x30 mm; Matrix = 128x128; slice thickness = 1 mm; inter-slice distance =

0.5 mm; temporal resolution = 15 s; 5 slices; 120 repetitions. The agent was administered over 20

seconds as a bolus injection, via a catheter line after 2 baseline scans at 30 s post scan initiation.

Scanning was performed for each of three agents (n = 3; [Gd] = 1.64 mg/ml): free Gad, NTSL,

and TSL. Each agent was further tested at 3 temperatures: 22°C (room temperature), 37°C (body

temperature), and 43°C (hyperthermia). For heated cases, the agar phantom was pre-heated in an

external water bath for 20 minutes prior to imaging and then transferred to the scanner.

Figure 2 Schematic of the experimental set up in MR: showing phantom placement using an optical

probe for temperature feed-back monitoring and a recirculating water-bath system for temperature

control.

16

2.2 Aim 2: Quantification of Drug Release in MATLAB

2.2.1 Validation of Signal Linearity

The linear relationship between MR signal intensity and low contrast agent concentrations

is an essential assumption in the method employed to calculate the diffusion coefficient as

described below. To verify this assumption, a serial dilution of Gad in the gel medium (1.5% agar)

was performed. Dilutions were performed by mixing ProHance® in warm liquid agar to achieve a

Gd concentration of 1.64mg/ml. 20 mL of the gel containing Gad was then transferred to a clean

beaker and mixed evenly with 20 mL of gel containing no Gad. 15ml of the diluted gel was

transferred to a 20 mL syringe and the dilution was repeated with the gel remaining in the beaker.

The gels were imaged with the same imaging parameters used for evaluating TSL. Each gel was

imaged three times and at 3 temperatures: 22°C, 37°C and 43°C. The mean signal intensity in a

reference gel with no Gad was subtracted from the mean signal intensity from a region of interest

(ROI) in each gel and the difference in signal intensity was plotted against Gd concentrations. The

plots were then fitted with linear regression and the goodness of fit was assessed based on the

adjusted R2 value.

2.2.2 Image Analysis Algorithm

A semi-automated image analysis script was written in MATLAB (Mathworks, Natick,

MA) to quantify small molecule kinetics in the gel phantom. Images were imported for each data

set and analysis outlined below was performed. The MATLAB code loads the images, requests

user input for specific parameters as outlined below, and computes mean signal over time for

various distances as well as a diffusion coefficient for the specified data set.

Histogram Matching

For a given concentration of contrast agent, the change in relaxation rate is inversely

dependent on (T1 + m*ΔT). As such, it is not feasible to model the relationship between signal

and temperature as linear. However, the linear signal vs. time curve at each temperature can be

approximated as linear and signal can be adjusted for temperature effects by mapping values at

each temperature to the dynamic range of room temperature data. This enables the quantitative

comparison of signal data at different temperatures, provided that the temperature was kept stable

throughout imaging. To allow quantitative comparison of curves obtained at different

17

temperatures, images were standardized for both image to image fluctuations and temperature [70],

[71].

𝑆𝑎𝑑𝑗 = (𝑆𝑥,𝑦 − 𝑆𝑏𝑎𝑠𝑒) 𝑀𝑎𝑥

𝑆𝑚𝑎𝑥 𝑆𝑙𝑜𝑝𝑒22

𝑆𝑙𝑜𝑝𝑒𝑇 (6)

The maximum signal in each image was adjusted to match the maximum of the triplicate

data set, following which a scaling factor was applied to adjust for temperature effects on signal

data. Sbase was calculated as the average of the two baseline images acquired prior to injection and

then subtracted from all post-injection images to obtain the signal relative to baseline for each

voxel. Slope22 refers to the slope obtained from the signal vs. concentration graph for 1.5 % agar

at 22°C. SlopeT is the slope of the signal vs. concentration graph corresponding to the temperature

of the data set being analyzed. This approach utilizes the slopes obtained from linear fitting of the

signal vs. concentration graphs in section 2.2.1. The adjustment factor described in Equation 6

was applied to each of the signal vs. concentration curves obtained in the previous section to

confirm that linearity was maintained after the adjustment.

Radial Contour Generation

The MATLAB script was designed to allow user input of (x, y) coordinates for a reference

baseline ROI, a reference ROI in the contrast pool, coordinates bounds for signal intensity analysis

and selection of the slice of interest, from a set of reference images displayed after running the

code. From each set of images (120 time points x 5 slices), the central slice was selected for further

analysis. A ROI mask was created using the reference time point t = 1 min, immediately after

injection with a lower threshold of 400AU (midway between the mean for the contrast pool and

the baseline gel). Subsequently, curved line segments in the gel medium were generated,

equidistant to the masked region. A set of 20 line segments were generated at distances from -0.7

to 3.5 mm using the boundary between the contrast pool and the gel medium as the reference 0

mm line through steps of 1 pixel width (0.23 mm). Segments were limited to avoid edge effects at

either corner of the contrast pool via the user-defined boundaries as illustrated in Figure 3. Signal

versus time curves were generated by calculating the mean signal along the line segment for each

time point. The signal curve at 0.7 mm for each test case was plotted together for comparison.

18

Curve Fitting

The signal vs. time curve at 0.7 mm was further characterized using the following fit:

𝑌 = {𝑌0

𝑌0 + (𝐴 − 𝑌0) ∗ (1 − 𝑒(−𝐾∗(𝑡 −𝑡0)))

, 𝑡 < 𝑡0

, 𝑡 ≥ 𝑡0 (7)

where Y0 is the baseline signal in gel/tissue normalized to 0, A is the maximum signal achieved as

time (t) goes to infinity, t0 is the time taken for contrast agent to diffuse 0.7 mm (expected to

decrease with increasing D) and K is the exponential rate constant of signal accumulation

(expected to increase with increasing D). It should be noted that this approach is not meant to

represent a physical model of the system but rather allows the characterization of key features of

the graph using an empirical model.

Fick’s Law: The Diffusion Coefficient

Given that the signal intensity varies linearly with Gd concentration for the range observed

in the gel, the diffusion coefficient can be estimated directly from signal-time curves using the

following simplification of Fick’s second law.

Figure 3 Example of contours generated using the MATLAB script: The contrast agent region

identified via thresholding is shown in yellow immediately after injection (a) and line segments

determined by Euclidean distance from the edge of the masked region, bounded by user defined limits

indicated by the yellow dotted line. Example shown in muscle at 22°C for free Gad at t = 1min.

19

∆𝑆 = 𝑘∆𝑅1 = 𝑘𝑟1[𝐺𝑑] (8)

Allowing Equation 2 to be reduced to:

𝜕∆𝑆

𝜕𝑡= 𝐷 ∇2(∆𝑆) (9)

Where R1 is the longitudinal proton relaxation rate, k is a proportionality constant relating ΔS to

ΔR1, r1 is the longitudinal relaxivity of Gd, D is the diffusion coefficient and S is the MR signal

intensity.

Figure 4 Sample graph displayed after calculation of the diffusion coefficient showing the signal curve

used to approximate the slope and the Laplacian over time of the same curve: Blue dots shows the

signal intensity over time at a contour ‘far’ from the gel boundary. Example shown for d = 2.1mm for free

Gad at 22°C in agar. Note that the max signal over the time course is well within the limits for linear signal

vs. [Gd]. Open circles show the median Laplacian, the red line shows the linear fit over a subset of the

imaging time and the red dashed line represents the median Laplacian for the points show on the same time

interval used for the fit.

A contour with a linear signal vs. time profile was used to estimate the slope of the signal

time graph, 𝜕∆𝑆

𝜕𝑡, and the median Laplacian, ∇2(∆𝑆), along the selected contour. The contour was

selected as the one closest to the contrast pool-gel boundary that allowed a good linear fit and a

steady median Laplacian. This ensured sufficient signal to achieve an estimate providing a ‘good’

approximation using Fick’s law. An isotropic spatial Laplacian mask, incorporating equal

weighting of diagonal contributions, was used to estimate the Laplacian for each pixel [55], [72].

20

𝑀 = [1 1 11 −8 11 1 1

] (10)

This method is equivalent to calculating the diffusion coefficient across a single in-plane voxel

while exploiting the geometric symmetry of the system to minimize error. As a precaution, for

every run, the signal and the Laplacian were displayed for visual assessment of the following

assumptions: (1) linearity of signal vs. time and (2) constant Laplacian over time along the selected

contour (Figure 4). Optimal signal vs. time profiles for calculation of the diffusion coefficient may

then be determined by iteration to provide the highest signal while satisfying these assumptions.

2.3 Aim 3: Evaluation of Physiological Relevance

2.3.1 Diffusion Through Muscle Tissue

The diffusivity of the small molecule contrast agent in 1.5% agar was compared to that in

ex vivo muscle to determine the physiological relevance of the molecular kinetics in the phantom.

Chicken muscle was purchased at the supermarket and stored at 4°C until specimen preparation.

Tissue fragments were cut to approximately 1x1x0.5 cm and set in agar gel adjacent to a spherical

void. The gel was prepared as described previously, and allowed to cool to 45°C prior to pouring

around the balloon mold and tissue. The agar gel was used as a support medium, to hold the tissue

in place during imaging, while the spherical void provided a reproducible contact surface area for

diffusion. Muscle was imaged over a series of angles, ranging from 10 to 90°, to ensure that

maximal contrast relative to the muscle baseline was maintained. DCE-GRE imaging was

performed using the same parameters as those used for agar (outlined in Section 2.1.2). All runs

were repeated to assess release from TSL in muscle: Free Gad, NTSL and TSL each at 22°C, 37°C

and 43°C. Imaging data sets were analyzed using the script described in Section 2.2.2. To verify

whether the linear relationship holds in tissue, signal vs. Gd concentration curves were investigated

using 7.5% BactoTM Agar1 (Becton Dickinson, Franklin Lakes, NJ) as a tissue mimic. Scaling

1 7.5% BactoTM Agar was selected based on similarity in diffusion properties suggesting similar kinetic properties of

the material.

21

factors were determined from a linear fit applied to signal vs. concentration graphs in 7.5%

BactoTM Agar and substituted into Equation 6 to enable comparison between signal intensities

measured in muscle and that in agar. It is important to note that diffusion coefficients are

independent of this scaling factor and can therefore be calculated from data prior to adjustment

with Equation 6. Thus, the diffusion coefficient of free Gad in muscle was used to determine a

suitable tissue mimic for validation of the signal vs. concentration linearity as described below in

Section 2.3.3.

2.3.2 Diffusion Through Tumor Tissue

Animal studies were performed using protocols approved by the University Health

Network Animal Care Committee. Two tumor models were investigated: an orthotopic MDA-MB-

231 breast cancer model and a subcutaneous SKOV-3 ovarian cancer model. Models were selected

based on an expected difference in tumor characteristics and ADCs obtained by diffusion weighted

imaging [73]. MDA-MB-231 human breast cancer cells were cultured in T-175 flasks in

Dulbecco's Modified Eagle's Medium supplemented with 10% Fetal Bovine Serum and 1%

penicillin/streptomycin (Gibco, Waltham, MA). 5×106 MDA-MB-231 cells suspended in 50μl of

medium were injected into the right mammary fat pad of the female SCID mice (aged 6 – 8 weeks;

n=5), purchased from an in-house breeding facility. SKOV-3 cells were similarly grown using

McCoy medium (Gibco, Waltham, MA) in place of DMEM. SKOV-3 cells (2×106 cells suspended

in 50μl; n=5) were inoculated subcutaneously in both the right and left hind flanks [73]. Tumor

volumes were monitored during growth by measurement with Vernier calipers. Mice were

sacrificed and tumors resected when tumor volumes reached the desired endpoints of 500mm3 and

400mm3 for MDA-MB-231 and SKOV-3, respectively. Endpoints were selected within the ethical

guidelines to achieve the largest tumors possible with minimal risk of necrotic tumor cores as

larger tumors provided greater depth for diffusion studies. Tumors were prepared for imaging by

slicing off the outer layer on each side with a scalpel and setting the tumor in 1.5% agar adjacent

to a spherical void as with muscle.

Imaging was performed as described in Section 2.1.2 to determine the diffusion coefficient

of free Gad in each tissue at 22°C, 37°C and 43°C for SKOV-3 tumors and at 22°C for MDA-MB-

231 tumors (n = 3 for all cases). For runs at 37°C and 43°C, tissue phantoms were heated in-bore

to the target temperature using the recirculating water chamber while monitoring temperature (as

22

a precaution against thermal damage to the tissue). Initially, images were analyzed using the

algorithm described in section 2.2.2. However, the uneven surface of the tumor after cutting away

the outer layer led to inhomogeneities that the described algorithm was not suited for. A second

segmentation algorithm was developed to overcome the inhomogeneity of the solid tumor samples.

A k-means clustering algorithm was written, similar to that described by Koh et al. for sectioning

tumor tissue based on contrast accumulation profiles [55], [65]. Briefly, for each voxel in an image

time-series the following equation was applied:

∑ (𝑆𝑥,𝑦,𝑡 + 𝑠𝑐𝑎𝑙𝑖𝑛𝑔 𝑓𝑎𝑐𝑡𝑜𝑟 ∗ (𝑑𝑆𝑥,𝑦,𝑡

𝑑𝑡+

𝑑2𝑆𝑥,𝑦,𝑡

𝑑𝑡2))

120

𝑡=1

(11)

The discrete approximations of the first and second derivative with respect to time were

obtained by taking the difference in signal across imaging time steps (Δt = 15s). The scaling factor

was used to emphasize differences in slope for subsequent k-means clustering. MATLAB’s k-

means clustering function was then applied to the resultant matrix [72]. Voxels were assigned to

clusters based on the output of the function and mean signal vs. time profiles for voxels in the same

cluster were generate. A diffusion coefficient was calculated for each run using a cluster with linear

signal vs. time profile as described previously.

2.3.3 Tuning the Gel Phantom Diffusivity

The diffusivity of Gad in 1.5% agar was evaluated against that in ex vivo muscle, breast

tumor model and ovarian tumor model. Following this comparison, higher concentrations of agar

(7.5, 10 and 15%) were tested to determine if diffusion coefficients in the physiologically relevant

range could be achieved. Subsequently, a diffusivity matched gel was used to verify the

concentration range in which signal linearity held in tissue. Serial dilutions were performed, as

described in Section 2.2.1, using 7.5% BactoTM Agar and adjustment factors were determined for

substitution into Equation 6 during image analysis. Diffusion coefficients were calculated using

the image analysis algorithm described in Section 2.2.2.

23

Chapter 3

Results

3.1 Aim 1: Visualization of Drug Release in a Gel Phantom

Agar Provides a Reproducible Phantom Medium

Agar at 1.5% w/v concentration provided a suitable phantom material which did not deform after

removal of the balloon mold. Airtight sealing of the phantom allowed it to be stored for up to one

week with no change in color or pooling of liquid in the spherical void. The balloon mold method

provided a reproducible spherical void. Furthermore, the smooth surface of the balloon ensured an

even surface for diffusion of small molecules. The developed method also provided a uniform,

transparent gel with no bubbles in the body of the gel which would hinder diffusion. Figure 5

shows a typical scout imaging set used to position the imaging plane.

Figure 5 Representative scout images showing axial (left), coronal (middle) and sagittal (right)

sections through the agar phantom: The catheter used for remote injection can be seen, as indicated, as

well as the tip of the temperature probe in the gel medium, adjacent to the spherical void.

TSL with a Gd concentration of 1.64 mg/mL and size of 94.3 nm were used for all 1.5%

agar studies. The concentration for free Gad was matched by diluting ProHance® and NTSL with

HBS. NTSL was produced in house as described previously by Zheng et al. with a batch

concentration of 4.24 mg/ml Gd and a mean diameter of 94.0 nm [21]. The similarity in size

allowed NTSL to serve as a suitable negative control. NTSL was diluted in HBS to match the

concentration of Gd in TSL.

24

Steady Temperature Profiles were Maintained During Iimaging

Hyperthermia was achieved using an in-bore recirculating water chamber connected to an

external water bath. Temperature was maintained within 1°C above the target temperature for each

scan. A sample of the profile obtained at each temperature for the duration of the 30-minute scan

is shown in Figure 6. The dip in temperature following injection under hyperthermia may be

attributed to heat transfer from the gel medium to the room temperature agent. This effect is less

evident at body temperature due to the smaller difference in temperature between the agent and

the gel medium.

90° Flip Angle Provided Highest Signal to Baseline Contrast

To achieve high image contrast between the agent and the gel medium, a trade-off was

made in the selection of imaging parameters. Figure 7 shows the signal intensity for agar, free

Gad, NTSL and TSL at 22°C using a series of flip angles between 10 and 90°. Signal intensity was

Figure 6 Representative temperature profiles for the duration of imaging measured via an optical

fibre probe: Temperature for three independent runs is shown each at room temperature (blue), body

temperature (gray) at under hyperthermia (red) with the target temperature indicated in brackets in the

legend.

25

found to increase for free Gad over the range of angle tested. On the other hand, both NTSL and

TSL exhibited a peak in signal intensity below the maximum flip angle. A flip angle of 90°

provided the largest signal contrast relative to baseline intensity in the gel medium for a Gd

concentration of 1.64mg/ml. As such, this flip angle was selected for the DCE-GRE imaging

sequence.

Figure 8 Axial images showing the cross section through the spherical void in the agar phantom

before, immediately after agent injection, and at the end of the imaging period for Free Gad at 22°C: The agar phantom is outlined in yellow and the interface between the agent and the gel at the 30min time

point is shown in red. At t = 1min, no contrast agent has entered the gel while at t = 30min contrast

enhancement is observed as a diffusive aura permeating the gel medium.

Figure 7 Signal intensity vs. flip angle: Signal for air is shown in black, the baseline 1.5% agar gel

in gray, free Gad in blue, NTSL in green and TSL in red. Signal increases with flip angle for free

Gad but shows a peak for liposomal agent. Signal from agar decreases with increasing flip angle and

remains constant for air.

26

Figure 9 Images obtained at t = 30min for free Gad (left panel), non-thermosenstive liposomes

(NTSL; center panel) and thermosensitive liposomes (TSL; right panel) at 22°C, 37°C and 43°C: The

black dotted line in each image indicates the contact surface between the contrast pool and the gel medium

as identified immediately after injection of the agent. Left and bottom scale is shown in pixels and the color

bar indicates raw MR signal intensity in arbitrary units.

Small Molecules Diffuse Visibly Through Agar While Stable Nanoparticles Do Not

Figure 8 shows representative images for free Gad at 22°C at before injection (t = 0 min),

immediately after injection (t = 1 min) and at the end of the imaging period (t = 30 min).

Immediately after injection, a contrast pool formed in the lower half of the spherical void and a

distinct line was seen where the injected contrast agent came into contact with the gel medium.

Over the time-course of imaging, signal enhancement can be seen beyond this contact surface as

contrast agent molecules diffuse into the gel medium. Comparison of the final imaging time frame

(t = 30 min), for each case investigated, reveals a visible difference in the behavior of nanoparticles

and free small molecules (Figure 9). No discernable signal enhancement in the gel was observed

for NTSL at any temperature indicating no diffusion of the intact liposomes away from the

27

injection site. Thus, the signal enhancement measured for TSL at 43°C was confirmed to be the

diffusion of the small molecule Gad into the gel following release from the encapsulating TSL.

Free Gad showed a visible diffusive spread at all three temperatures, comparable to the diffusive

spread observed for TSL at 43°C.

As expected, the signal at the injection site increases with temperature for both NTSL and

TSL. Despite the increased signal at the injection site for NTSL, no diffusion was observed in the

gel medium. This confirmed that the NTSL remained stable under hyperthermia, preventing the

release of their small molecule contrast agent load. These results demonstrated the ability of the

designed phantom to distinguish between MR-based contrast enhancement due to increased proton

exchange across the encapsulating membrane and contrast enhancement due to release of the

contrast agent molecules.

3.2 Aim 2: Quantification of Drug Release in MATLAB

MR Signal Decreases with Increasing Temperature for a Given Gd Concentration

Upon quantification of signal vs. time curves, it was evident that temperature effects were

not negligible. To compare T1-weighted intensity data acquired at various temperatures, it was

necessary to adjust signal data against changes in temperature. The relationship between signal

intensity and temperature at a given Gd concentration is non-linear. As such, signal at a given

temperature can be mapped to its corresponding value at room temperature for a specific contrast

concentration through a simple ratio. Figure 10a shows that the signal vs. concentration for each

temperature can be approximated as linear for up to 0.4mg/ml [Gd] which corresponded to a signal

increase of approximately 600 – 800 AU (arbitrary units) using the same imaging parameters and

conditions as those used in diffusion studies. By fitting the data at each temperature with linear

regression, the slopes of the graphs were obtained and substituted into Equation 6 to map the data

acquired at body temperature and hyperthermia to the scale at room temperature. The result of this

mapping on the calibration data is shown in Figure 10b.

In addition to accounting for temperature effects, it is also important to account for image

to image fluctuation. MR signal intensity is typically measured in arbitrary units where the value

28

corresponding to a specific concentration of contrast agent may vary with respect to time, system

temperature as well as other external factors. The complexity of this noise dependence is well

documented and is the reason that MR remains semi-quantitative in most practical clinical

situations [74], [75]. To reduce this effect image voxels are adjusted to the maximum signal

intensity for a given test case.

Figure 10 Change in signal intensity versus gadolinium concentration before (a) and after adjustment

using Equation 7 (b): Points show signal intensity measured with the T1-weighted DCE-GRE sequence

for agar (solid symbols) and 7.5% BactoTM Agar (open symbols) at 22°C (blue), 37°C (green) and 43°C

(red). Without adjustment, higher temperatures lead to lower signal for each Gd concentration.

Shape of Signal vs. Time Curves Provide Quantitative Evidence of Diffusion

Segmentation of the gel medium into contours radiating outwards from the injection site

resulted in curves with high reproducibility between gel phantoms. Figure 11 shows the signal

versus time curves at select distances from the contrast pool-gel boundary. It should be noted that

while there is an overall loss in signal in the contrast pool (not shown), the signal at the boundary

remains constant for the imaging duration. Progressing through contours at increasing distances,

there is a trend of increasing delay to signal accumulation that is characteristic of diffusion.

Furthermore, contours that are further away from the contrast pool-gel boundary accumulate signal

at a slower rate. This is a consequence of the decreasing concentration gradient across each contour

with increasing distance. The signal vs. time curve at 0.7 mm was selected for further

characterization as it provided the highest signal while ensuring minimal convective contributions.

29

In addition, curves that were far enough from the boundary exhibited linear behavior, allowing

approximation of the diffusion coefficient as a ratio of the linear slope to the spatial Laplacian of

the corresponding voxels.

Signal Profiles Confirm Stability of TSL Up to 37°C and Complete Destabilization at 43°C

Adjusted signal intensity vs. time curves at a distance of 0.7 mm from the injection site

provided quantitative evidence that stable nanoparticles do not diffuse into the gel medium within

the imaging period (Figure 13). NTSL at 22°C, 37°C and 43°C showed no significant signal

accumulation at this distance. On the other hand, free Gad, used as a positive control representing

100% release, showed significant signal enhancement at all three temperatures. TSL at 22°C and

37°C show only a slight increase in signal, comparable to the behavior of NTSL. The signal vs.

time curve for TSL at 43°C matches closely with the free Gad curve at 43°C. This suggests

complete destabilization of the liposomal membrane and free diffusion of the small molecule

contrast agent following release.

Characterization of key curve features revealed increasing rate constants with respect to

temperature. This was expected as diffusion relies on the kinetic energy of the diffusing particle.

Increasing kinetic energy increases the frequency of collisions and therefore the overall net transfer

of molecules. This is similarly reflected in the decreasing time taken for contrast molecules to

Figure 11 Mean signal intensity vs time with increasing distance from the edge of the spherical

void for free Gad in agar at 22°C.

30

diffuse to the selected distance of 0.7 mm from the contrast pool-gel boundary. Slower diffusion

experienced by molecules at a lower temperature results in longer time delays to detectable signal

accumulation. Both K and t0 exhibit statistically significant dependence with respect to temperature

with p-values less than 0.0001. A Student’s t-test comparing data sets in agar to the positive control

free Gad at 43°C revealed that the time delay for TSL at 43°C was statistically different from that

of free Gad at 43°C (p < 0.001). These results suggest that there is a time delay associated with

release of small molecules following destabilization of the TSL. In spite of this delay complete

release of Gad was achieved as shown by the similarity in rate constants for free Gad and TSL at

43°C.

Diffusion Coefficient of Gad Released from TSL is Equivalent to That of Free Gad in Agar at 43°C

Small molecule kinetics quantified as diffusion coefficients confirmed complete

destabilization of the heated TSL liposomes Figure 16. In fact, the diffusion coefficient measured

for TSL at 43°C was (2.90 ± 0.52) ×10-4 mm2/s, which is not statistically different from that

measured for the free Gad in the gel phantom (2.72 ± 0.87) ×10-4 mm2/s. As expected, diffusion

coefficients measured for NTSL were two orders of magnitude lower. However, the accuracy of

this measurement was limited by the low signal accumulation (less than 50 AU), which could not

reliably be distinguished from image noise. Diffusion coefficients measured for TSL at 22°C and

37°C are statistically equivalent to NTSL, supporting the stability of the liposomes at these

temperatures, but are similarly affected by limited signal.

31

3.3 Aim 3: Evaluation of the Physiological Relevance of the Gel Phantom

Ex Vivo Tissue Specimen Fixed in Agar Enabled Evaluation of Small Molecule Diffusion in Tissue

Figure 12 shows representative scout images of chicken muscle and tumor tissue (SKOV-

3) fixed in 1.5% agar, adjacent to a spherical void. From the scout images, a film of agar can be

seen separating the tissue from the injection site. This is more apparent for tumor tissue than for

muscle, where the uneven surface of the tumor disproves the assumption of isotropic diffusion

from the injection site. Time lapse images over the course of the scan period highlight the

inhomogeneity of diffusion in the tumor tissue (example provided in Appendix A). To overcome

this limitation, an alternative segmentation algorithm was developed as described in Section 2.3.2.

Figure 12 Sample scout images for muscle (top panel) and SKOV-3 tumor (lower panel): Tissue can

be clearly identified from the support material, agar.

Inclusion of a spherical void ensured a constant concentration at the boundary of the tissue.

Furthermore, the spherical void served an injection site rather than direct injection into the tissue

which would result in variable distribution of the contrast agent and potential damage to the tissue

(see Appendix B).

32

Figure 13 Adjusted signal vs. time at 0.7mm from the edge of the spherical void for runs performed in agar (a) and muscle (b): Free Gad are is

indicated in blue, NTSL in green and TSL in red. Open symbols denote runs performed at 22°C, half-filled denote 37°C and filled denote 43 °C. NTSL

at all temperatures as well as TSL at 22 and 37°C show minimal signal enhancement while TSL at 43°C matches closely to free small molecule Gad

under hyperthermia. Mean and standard deviation across runs are plotted for each time point with n=3 for all cases.

33

Signal vs. Time Curves Reveal Slower Diffusion in Muscle Compared to 1.5% Agar

Signal vs. time curves measured in ex vivo chicken muscle showed slower diffusion of free

small molecules (Gd = 1.64 mg/ml; n = 3 at all three temperatures) than in agar. Figure 13 shows

the mean signal along contours at 0.7 mm from the contrast pool gel-boundary for each test case.

Signal curves did not plateau during the 30-minute scan period, unlike in 1.5% agar. In addition,

the higher variability between the muscle samples led to slightly larger standard deviations for

triplicate data sets. Despite this, the signal versus time curves were highly reproducible owing to

the homogeneity of the muscle tissue used for this study. Signal data for muscle was adjusted based

on calibration curves obtained in a diffusivity matched 7.5% BactoTM Agar tissue mimic.

Following signal adjustment, dependence on temperature remains apparent for free small molecule

diffusion. Due to batch to batch variabilities, the TSL agent administered to muscle was at a lower

concentration of 1.3mg/ml. As a result, there was a notable difference in diffusion for TSL at 43°C

in muscle compared to the free small molecule control.

Figure 14 Rate constant and time delay for agar and muscle as determined by curve fitting with

Equation 7: Rate constant (left) and time delay (right) are shown for cases where a good fit with R2 >0.9

was achieved: Free Gad (blue) and TSL at 43°C (red). × indicates a significance of p < 0.0001 for muscle

to the corresponding agar group, * indicates p < 0.0001 significance of a group relative to free Gad at 43°C

in the same medium as determined by independent t-tests.

Characterization of curve features using Equation 7 allowed quantitative comparison

between data sets. As with agar, increasing temperature decreases the time taken for Gad molecules

to accumulate at 0.7 mm in the tissue. Similarly, the rate of signal accumulation increases with

34

temperature in muscle, supporting the temperature dependence of diffusion through agar as a tissue

mimicking property. The values derived from curve fitting of TSL at 43°C were found to be

significantly different from those of small molecules at 43°C based on a Student’s t-test performed

on the fitted parameters in muscle. Furthermore, the difference was larger than that observed in

agar. Follow-up tests were performed, to better explain the difference in the case of TSL at 43°C.

Figure 15 Signal vs. time for two concentrations of Gd at 43°C: 1.64 mg/ml Gd as a reference point for

other studies (blue solid circles) and 1.30 mg/ml Gd (blue dotted circles). TSL at 1.3mg/ml injected into a

phantom heated to 43°C is denoted by red dotted squares while TSL pre-heated to 43°C and subsequently

injected into a phantom maintained at 43°C is denoted by black dotted squares. Mean and standard deviation

across runs are plotted for each time point with n=3 for all cases.

Signal Profiles Reveal Partial Release from TSL in Muscle at 43°C

Signal vs. time profiles for TSL at 43°C in muscle was lower than that observed for small

molecules. Subsequent analysis of TSL pre-heated to 43°C prior to injection in the spherical void

supported the hypothesis of partial release. Pre-heating of TSL for 20 min, prior to injection, was

assumed to achieve and maintain 100% release from the liposomes. As expected, pre-heated TSL

mimicked the free small molecule diffusion at the corresponding injected concentration in muscle.

Evaluation of small molecule diffusion at a lower injected concentration (1.3 mg/ml) revealed that

signal profiles were heavily dependent on the concentration at the boundary of diffusion.

Comparison of signal vs. time profiles demonstrated lower plateau values in both agar and muscle

35

tissue for lower injected concentrations. The sensitivity of the signal profiles to differences in

concentration provided a means to quantify partial release from TSL.

Figure 16 Diffusion coefficients calculated in agar (shaded) and muscle (solid) for free small

molecules (blue), NTSL (green) and TSL (red): × indicates p < 0.0001 for muscle compared to the

corresponding agar group. * indicates a significance of p < 0.0001 for TSL compared to free Gad at the

same temperature and + indicates p < 0.0001 for TSL against NTSL at the same temperature.

Diffusion Coefficients in Agar Mimic Trends Observed in Muscle

Calculation of diffusion coefficients for free Gad, NTSL and TSL in muscle allowed

further quantitative comparison showing slower small molecule diffusion in muscle compared to

agar. The temperature dependence observed in agar is also seen for muscle. NTSL at all three

temperatures and TSL at 22°C and 37°C did not diffuse through muscle within the imaging time

frame, again limiting the accuracy with which the diffusion coefficient can be estimated for these

groups. In these cases, no significant difference was found between values estimated in muscle

and those in agar when a multiple comparisons test was performed comparing data in muscle to

the corresponding group in agar. At 43°C, however, the diffusion coefficient of (1.06 ± 0.18) ×10-

4 mm2/s for TSL was significantly to different that calculated for both NTSL, (4.61 ± 3.50) ×10-6

mm2/s (p<0.0001) and free small molecules in muscle, (1.54 ± 0.09) ×10-4 mm2/s (p=0.007) based

on independent t-tests.

36

Diffusion Through Tumor Tissue is Slower than in Muscle and Agar

The result of the k-means clustering algorithm is illustrated in Figure 17, where regions

are color-coded based on the shape of the signal vs. time curve. The signal vs. time curves obtained

in SKOV-3 could not be directly compared to the signal curves obtained for agar and muscle due

to the difference in segmentation algorithm. Applying the contouring algorithm to tumor data

resulted in low signal which may be attributed to (1) the heterogeneity of the tissue along the line

segment and (2) the roughness of the tissue surface, both leading to an inhomogeneous diffusion

pattern (data not shown). Instead, k-means clustering delineated regions with similar contrast

uptake patterns. Inspection of the signal vs. time profiles associated with each region revealed a

similarity to that observed in the homogeneous cases of agar and muscle. Through this method, a

region with an approximately linear signal vs. time trend may be used to estimate a diffusion

coefficient for the tissue. An example of such a curve is shown in Figure 17, indicated by the black

arrow.

Figure 17 Showing clusters generated by K-means segmentation in an ROI indicated by the white

dotted line and the corresponding signal vs. time curves: Clusters are color coded to correspond with

the mean signal vs. time graph on the right. Scale is shown in pixels and color bar indicates voxels assigned

to the same cluster.

37

Small Molecule Diffusion in 1.5% Agar is Significantly Higher than that in Tissue

Small molecule diffusion coefficients calculated for 1.5% agar were consistently higher

than that found in muscle and SKOV-3 tumors for all temperatures tested (Figure 18). The

estimated diffusion coefficient for MDA-MB-231 tumors at room temperature is also shown,

however due to limitations in the signal observed over the imaging period, further tests were not

performed at higher temperatures. The trend of increasing diffusion coefficient with increasing

temperature that was observed in agar was maintained for muscle but not for tumor tissue. In

addition, the diffusion coefficient of (7.13 ± 0.77) ×10-5 mm2/s found for 7.5% BactoTM Agar at

22°C was statistically equivalent to the diffusion coefficient of (5.96 ± 0.73) ×10-5 mm2/s measured

in muscle at 22°C and that measured in SKOV-3, (3.62 ± 0.40) ×10-5 mm2/s. While this

equivalence was similarly maintained at both 37°C and 43°C for muscle, diffusion in 7.5%

BactoTM Agar was found to be significantly higher than in SKOV-3 tumor tissue at 37°C and 43°C,

with p-values of 0.02 and 0.003 respectively determined by a multiple comparisons test comparing

pairs values within each subgroup.

Figure 18 Diffusion coefficients at 22°C, 37°C and 43°C for 1.5% Agar, 7.5% BactoTM Agar, Muscle

and SKOV-3. × denotes significance to all other data sets with p < 0.05. * indicates significance (p < 0.05)

between two data sets as shown.

38

BactoTM Agar 7.5% Provides Diffusion Coefficients Closer to the Physiological Range

To compare signal curves obtained at different temperatures in muscle it was necessary to

confirm linearity of Gd-induced signal. Calculation of diffusion coefficients in various gel media

revealed that 7.5% BactoTM Agar provided diffusion coefficients that were not significantly

different from muscle tissue (results obtained at higher concentrations provided in Appendix D).

Based on the similarity in small molecule diffusion coefficient, 7.5% BactoTM Agar was used as a

tissue mimic to assess signal linearity of Gd relaxivity and generate calibration curves (Figure 10).

Serial dilution of Gad in 7.5% BactoTM Agar showed that linearity of signal intensity was

maintained within the range of concentrations employed for calculation of diffusion coefficients.

Results confirmed the suitability of the agar phantom for assessment of temperature-

dependent small molecule kinetics in MR. The current approach enables MR-based quantification

of small molecule diffusion. This method may be used in conjunction with complementary

techniques such as MR thermometry to achieve comprehensive spatio-temporal in situ evaluation

of non-invasive heat-induced (e.g. HIFU, RF) drug release from thermosensitive carriers. In

summary, the MR-based quantification platform described here enabled effective determination

of macromolecule retention and small molecule diffusion in agar gel phantom and ex vivo

biological tissues at physiological and hyperthermia temperatures. The next chapter will discuss

the benefits and limitations of this platform in relation to current practices in both preclinical and

clinical settings.

39

Chapter 4

Discussion

4.1 Aim 1: Visualization of Drug Release in a Gel Phantom

Tumor type, heterogeneity and vascularity, timing and flow rate of injection, timing and

duration of hyperthermia are just a few examples of the factors that affect the chemotherapeutic

dose delivered to the target site. While it is not possible to directly control the tumor type, several

strategies exist to control the other factors. In fact, it is now also possible to stratify patients for

tumor types that are more likely to respond. Point-based heat applicators add an extra layer of

complexity due to the need to control the heat deposition to ensure conformal heat delivery and

subsequent drug release. Imageable drug delivery systems have been used to answer many of these

questions at the in vivo stage of assessment. However, it is well-established that the optimal

parameters for preclinical studies do not translate directly to the clinic. While, in some cases, it

may be possible to apply adjustment factors to provide an educated guess at the optimal parameters

for patients this is far from ideal. FDA approval of drug delivery systems incorporating contrast

agents is a long and costly process. In the interim, pre-treatment assessment platforms that utilize

non-invasive imaging systems offer a means to determine optimal working parameters for each

institution to meet quality of care standards.

The standard approach for assessing thermosensitive drug delivery systems in phantoms

employs the difference in relaxivity between encapsulated and released small molecule imaging

agents [37], [47]. Typically, this involves doping a low gelling temperature hydrogel such as agar

with the liposomal formulation under investigation. The phantom is subsequently imaged before

and after hyperthermic heating and the corresponding T1 relaxivity maps are used to illustrate drug

release. While this method is sufficient to demonstrate that some form of small molecule release

has been achieved, it does not offer any insight into the percentage dose released or the spatial

response to the hyperthermia applied. It is possible that release is only achieved in the central

region of heating where the temperature is closest to the transition temperature of the liposomes

and the width of signal observed is actually due to subsequent diffusion of the small molecule from

that central region. This leads to the question of whether this matching of the heating zone with

the observed signal will be reflected in tissue or tumor specimen. In fact, our studies show that low

40

percentages of agar (1.5%) do not match the diffusion profiles of most tissues and that low

temperatures observed in the periphery are insufficient to result in drug release. We expect that the

true region of drug release will be smaller in tumor tissue based on the observation that diffusion

is far slower in tissue than in 1.5% agar. This may explain why ensuring uniform heat distribution

above the liposomal transition temperature is essential for positive patient outcomes. It is important

to heat the regions of the tumor with the highest vascularity and to deliver a high enough dose so

that the diffused drug is over the threshold needed for therapeutic efficacy.

Agar and agarose have served as a base phantom material for many applications

investigating hyperthermia and drug release [76]–[78]. Agar’s major advantage over alternative

phantom materials lies in the simplicity of preparation. Typical preparation times for a batch of

pure agar phantoms, in this study, falls under 30 minutes. Incorporation of a tissue specimen raises

that time to approximately 1 hour and 30 minutes to allow time for the agar to cool to temperatures

in the mild hyperthermia range, in order to guard against thermal damage to the tissue. In

comparison, polymer based tissue mimics can take on the order of hours to days to manufacture a

batch of phantoms and typically involves more expensive equipment and starting materials or by-

products that pose a greater risk to the user[56].

In our setup, a spherical void was incorporated in the body of the gel phantom to allow

injection of the agent during scanning. This is the first example of a phantom system for assessment

of hyperthermia-induced drug delivery that models an input function during imaging. Studies have

shown that temperature-sensitive drug delivery systems typically achieve the greatest efficacy

through intra-vascular release of the therapeutic [76], [77]. Pre-heating of the target region allows

drug to be released intravascularly, maintaining a positive and relatively constant concentration

gradient to the extracellular space. Previous phantom studies which mix the liposomal agent into

the body of the gel are unable to model the effect of pre-heating the tumor and therefore do not

provide a representative time response of release in such cases [18], [81]. In addition to the benefit

of observing the rapid release in response to pre-heating, the spherical void incorporated in our

phantom retained the stable nanoparticles over the imaging duration. As a result, we can

definitively distinguish released small molecules which are able to diffuse freely through the gel

and provide a visible contrast gradient in the gel.

41

A recirculating water chamber was incorporated to maintain a steady temperature

throughout imaging. Coupled with a real time temperature feedback, the use of this system enabled

manual temperature control. Temperatures were maintained within 1°C above the target value of

interest. In the case of hyperthermia, this meant that complete destabilization of the liposomal

membrane was expected for TSL. To heat to 43°C in a closed bore magnet, it was necessary to

insulate the recirculating water chamber to protect the RF coil from overheating. Exclusion of the

insulating layer led to poor image quality due to frequency shifts in the coil or triggering of fail-

safes which caused the scan to fail and all data for that imaging set to be lost. The recirculating

water-chamber is assumed to provide a spatially uniform temperature throughout the phantom.

The method used for heating in this study provides a means to characterize the temperature

response of MR signal intensity. Investigating drug release under these conditions provides the

groundwork for quantitative assessments of drug release in response to more complex spatio-

temporal heating patterns. Currently, MR-based strategies for simultaneously guiding

hyperthermia and monitoring of drug release are under investigation. To fully harness the power

of such a tool, understanding the kinetics of drug transport and release under hyperthermia and

optimizing the hyperthermia protocol to achieve the highest concentration of the drug continues to

be a primary concern within the field of temperature-sensitive drug delivery.

Imaging parameters were optimized to achieve the largest dynamic range for the diffusing

contrast agent. To achieve this, a larger flip angle of 90° was used. While it is unusual to use such

a large flip angle for dynamic T1-weighted imaging, it was necessary to minimize the signal

contribution from the gel medium. While the selection of the Ernst angle would have increased the

signal from the contrast agent, it would similarly have increased the signal from the gel, resulting

in a net lower dynamic range. As the selected temporal resolution (15s) was higher than the time

for a single scan (7s), the selection of a larger flip angle did not have adverse effects. The minimum

TE and TR were used for the desired field of view and the spatial resolution was minimized to aid

in discrete analysis of the diffusion coefficient. Further reducing the special resolution would lead

to increased image noise making it harder to calculate the diffusion coefficient from linear regions

where signal tented to be low (150 – 300 AU).

The phantom described in this thesis enabled visual confirmation of hyperthermia-induced

small molecule release from TSL, seen as a diffusive spread of signal enhancement in the gel

medium. Comparison to control cases confirmed that stable nanoparticles did not diffuse visibly

42

through the gel. While it is clear that release is achieved, based on the diffusive spread seen for

TSL at 43°C, the phantom alone does not provide insight into the extent of release achieved in

response to hyperthermia. Hence, a software package was developed to analyze spatio-temporal

signal data and alow quantitative comparison between TSL and control data sets.

4.2 Aim 2: Quantification of Thermosensitive Drug Release

It is a generally accepted fact that MR-based imaging techniques are, at best, semi-

quantitative. Inherent fluctuations in the measured parameters prevent exact estimation of the

number of contrast molecules in a single voxel or direct comparison of the number of released vs.

encapsulated molecules. Previous approaches use the mean over a number of voxels to observe a

rapid rise in signal intensity attributed to the release of the small molecule contrast agent. However,

several other factors may contribute to this net rise in signal. For example, increased blood flow

under hyperthermia, temperature effects on T1, and inherent inhomogeneity of baseline T1 values

all affect the measured signal intensity. Together, these factors limit the accuracy of estimation of

dose distribution in the tumor under hyperthermia conditions. As such, quantification defined in

this thesis refers not to MR’s ability to measure a specific concentration of contrast agent but rather

to enumerate the extent of small molecule release via molecular kinetics that are independent of

the imaging parameters. The diffusion coefficient was selected as a metric to assess the

physiological relevance of the agar phantom because imaging parameters do not directly affect it.

To assess hyperthermia-induced release from TSL using MR, it was necessary to maintain

a constant temperature throughout imaging. A major limitation associated with MR-based

chemodosimetry lies in the temperature sensitivity of the signal measurement [45]. As such,

previous studies opted to perform imaging before and after heating as a work-around [38]. This

study demonstrated that by maintaining a steady temperature during imaging, relevant scaling

factors can be applied during analysis to enable comparison of data sets at different temperatures.

This approach provides several advantages as it allows real time visualization of small molecule

release under hyperthermia conditions, rather than a final dose distribution. Furthermore, this

method simplifies analysis by directly assessing drug release based on temperature adjusted T1-

weighted signal intensity without the need for back-calculation of T1 or concentration maps.

43

As expected, increasing temperature led to increased relaxation times, reflected as

decreased signal. Linear regression of the signal vs. concentration data points resulted in an

adjusted R2 over 0.95 for all three temperatures, supporting this method as a sufficient

approximation for subsequent calculations. The slopes derived from the linear fit provided a means

to map data sets to a chosen dynamic range. To retain as much information as possible, the largest

dynamic range among the data set was selected, which was free Gad at 22°C in 1.5% agar. A

similar technique has been previously investigated by Collewett et al. to normalize image

intensities across data sets by assuming purely multiplicative changes between images [70]. In our

case, the multiplicative change arises from the difference in temperature of the specimen being

imaged. As seen in Figure 10b the mapping provides an excellent correlation with the target

dynamic range when applied to calibration curves at other temperatures.

NTSL encapsulating small molecule contrast agents have been well characterized over the

decades since their development. They have also played a significant role in the advancement of

thermosensitive drug delivery systems by providing a baseline for assessment of liposomal drug

delivery, in many cases acting as a negative control. In comparative studies between NTSL with

and without hyperthermia, increased extravasation has been observed for cases with hyperthermia.

Paramagnetic liposomes investigated by Fossheim et al. showed an increase in relaxivity with

temperature that was associated with increased fluidity of the liposomal membrane [82]. However,

this increase in relaxivity is considered negligible in comparison to the difference observed for

TSL under hyperthermia. While both NTSL and TSL show a moderate increase in relaxivity below

the transition temperature of the liposomal membrane, TSL is higher [48]. The greater permeability

of the thermosensitive lipid membrane allows higher exchange of water molecules across the

membrane surface and therefore greater relaxivity. This effect may be seen in the contrast pool

that remains in the spherical void for NTSL and TSL. This contrasts with the decreasing relaxivity

of the free contrast agent.

Typically, contrast enhancement in the clinic is assessed visually or as the mean/area under

the curve over a specified ROI. Studies reporting on the efficacy of drug delivery using

thermosensitive drug carriers use a wide variety of metrics to measure drug release that are often

not directly comparable. To expand the use of these drug delivery systems across institutions which

may use different instrumentation to achieve activated release, it may be beneficial to establish a

standard protocol via pre-treatment quality assurance. Phantom-based quantification of spatio-

44

temporal drug release provides a reproducible means to ensure that the platform is functioning as

expected and can help raise the bar to a new gold standard for activatable drug delivery.

Furthermore, with continuous improvements in the MR resolution in space and time there is a need

for better metrics which make use of this new information. To fully exploit spatial data, our

approach sections the region of interest i.e. the phantom or tissue into curved line segments that

are equidistant from the edge of the diffusive medium. The geometric symmetry of the phantom

ensures that the mean signal will have a lower error associated with it than using single voxels

along a radial path to represent signal versus time over distance (sample figure shown in Appendix

C).

Comparison of signal vs. time along a contour at a given distance from the injection site

provides a suitable means to quantitatively evaluate differences between imaging data sets. The

signal-time profile at a distance 0.7 mm from the gel-contrast boundary was selected as an optimal

distance for assessing whether or not small molecule release had been achieved. This distance was

far enough from the boundary to reduce the risk of measuring convective flow into the gel, as seen

by the delay in signal accumulation following injection at that distance. In addition, it was close

enough to provide sufficient signal to reliably observe trends between data sets. Using this strategy,

the small differences between free Gad diffusion as well as between TSL and NTSL can be seen,

which was not immediately apparent upon visual inspection. In particular, it was interesting to see

the similarity in TSL at 43°C and free Gad at 43°C which shows an almost perfect overlap in signal

over time in agar. The similarity in the signal curves supports an instantaneous burst release of

small molecules upon thermo-activation. Following release, the small molecules are able to diffuse

at a rate comparable to the positive control at that temperature. This is further confirmed by curve

fitting with Equation 7 which showed no significant difference between TSL at 43°C and free

Gad at 43°C for both time delay and rate constant in agar.

Although the comparison of signal data showed proved to be a viable means of quantifying

the extent of release, it was desirable to provide a metric which would not be dependent on the

units of the selected MR system. The diffusion coefficient was employed to provide a quantitative

measure of the efficacy of release. The suitability of the diffusion coefficient for this purpose relies

on (1) the difference in diffusion between small molecules and stable nanoparticles, (2) the

sensitivity to differences in concentration for dense media and (3) the intrinsic nature of the

45

parameter to the system under investigation. The reproducibility of the diffusion coefficients

measured in this study (n = 3 for all cases) further supports its use as a relevant metric.

As expected, due to the difference in molecular size, diffusivity of stable nanoparticles is

significantly lower than that of free small molecules [51], [52]. Both NTSL, which are not expected

to provide a burst release at any of the three tested temperatures, and TSL, which are expected to

be stable at 22°C and 37°C, demonstrate a diffusivity two orders of magnitude lower than that of

free small molecules. While diffusion coefficients of the 100nm nanoparticles are on the order of

those found in literature, these values cannot be reliably estimated within the time period used for

scanning [60]. The maximum signal accumulation observed is typically less than 3 standard

deviations from the image baseline and is therefore statistically indistinguishable from noise.

Importantly, a significant difference in diffusion coefficient of free small molecules was observed

with respect to temperature in agar. As such, it is necessary to account for the difference in small

molecule kinetics under hyperthermia when assessing the success of release from TSL under

hyperthermia. It is suspected that the diffusion coefficient of small molecules is a major limiting

factor in the transport of small molecules within the tumor environment when delivery is achieved

via thermosensitive drug delivery systems. Therefore, a means to quantify physiologically relevant

diffusion coefficients for each TSL batch would be highly beneficial.

4.3 Aim 3: Evaluation of Physiological Relevance of the Gel Phantom

Investigation of diffusion in the context of drug transport has very different meaning

depending on the size and chemistry of the diffusing molecule or particle. Traditional free drug

therapies exhibit a rapid wash-in and wash-out from the tumor environment, with concentrations

typically lower than the desired threshold for maximal efficacy. For nanoparticles, diffusion based

transport has been shown to dominate following extravasation at the tumor site. The size of the

nanoparticle plays a significant role in the diffusion through the extracellular space [83]. In the

case of TSL, the timing of activation can drastically affect the transport of drug through the tumor.

Manzoor et al. showed the importance of intravascular release on maintaining the concentration of

drug over time [80]. This confirmed a concentration gradient driven movement of drug molecules

through the extra-vascular space. Our approach allows diffusion of small molecules to be observed

46

through ex vivo muscle and tumor by fixing a section of tissue an agar based support material. The

tissue was placed adjacent to a spherical void, to enable diffusion analysis in tissue under

reproducible conditions. Direct injection into the tissue was also investigated but the uncontrolled

distribution and consequent dilution of contrast agent proved to complicate analysis extensively

(see Appendix B). By fixing the position of the tissue adjacent to a spherical void we can apply a

similar strategy for analysis of diffusion as that used for agar.

Compartmental models, such as two-compartment or Modified Toft’s models, are

commonly used to extract kinetic parameters that characterize transport in a physiological system

[84]. Often, this type of modeling requires a complete conversion from signal intensity to contrast

agent concentration which has yet to be achieved for high temporal resolution dynamic imaging.

Furthermore, introduction of hyperthermia, greatly complicates this calculation as the relaxivity

of the contrast agent decreases, the relaxivity of the paramagnetic carriers increases and

temperature dependent susceptibility effects are non-negligible [45]. In addition, such kinetic

parameters provide an overall measure across tumors and are insufficient to describe the

heterogeneity observed within the tumor. To simplify the analysis of diffusion in the controlled

phantom environment, we performed calculations directly on the signal intensity data generated

by the MR. The validity of this approach is based on the demonstrated fact that the signal varies

linearly for low concentration of contrast agents, and that this linearity holds under the

temperatures investigated herein. To compare signal intensities in agar versus tissue, it was

important to further account for the difference in proton density of the aqueous agar media and

tissue. This was achieved by evaluating signal vs. Gd concentration in 7.5% BactoTM Agar as a

tissue mimic which was selected based on similarity in free Gad diffusivity.

In this thesis, diffusion of small molecules was used to indicate successful release from

TSL. The temperature dependence of the diffusion coefficient then plays an important role in

evaluating small molecule kinetics following heat induced destabilization of the nanocarriers. In

aqueous media, the relationship is commonly assumed to be governed by the Stokes-Einstein

equation. In general, it is expected that diffusivity will increase with increasing temperature.

However, the extent to which this occurs in tissue has yet to be fully characterized due to

conglomeration with other effects such as increased blood flow. The trend of increasing

concentration with increasing temperature in agar showed a positive correlation to that seen in

muscle tissue.

47

After adjusting for MR-induced differences in signal, comparison of signal versus time

data obtained in 1.5% agar to those in muscle reveal overall higher small molecule diffusion in the

agar phantom. Muscle showed strong temperature dependence for small molecule diffusion which

supported the use of agar as a tissue mimic with temperature dependent small molecule diffusion

properties. As expected nanoparticles do not diffuse into the tissue within the duration of imaging

due to the larger size which is agreement with previous studies investigating nanoparticle transport

in the tumor [85]. Dreher et al. showed that it can take hours to days for nanoparticles to extravasate

a few millimeters depending on the size of the nanoparticle [83]. This may be true for the

nanoparticles as well but differences observed were not found to be significantly different.

The lower signal accumulation observed in muscle for TSL at 43°C compared to free Gad

at 43°C was likely due to the lower concentration of Gd in that batch of TSL. However, other

possible contributors included partial activation of TSL, excess lipid hindering diffusion or delayed

release. To further investigate the reason for the noticeable difference, a concentration matched

run was performed using free Gad at 1.3mg/ml [Gd]. These runs (TSL and free Gad; 43°C;

1.3mg/ml Gd) were also repeated in 1.5% agar to determine if the difference was specific to

muscle. Results showed that while TSL at 43°C again matched closely with the free Gad at the

same concentration, the TSL in muscle remained lower than the corresponding free Gad in muscle.

Subsequently, TSL was heated to 43°C prior to injection for 20 minutes to ensure that complete

release and diffusion through muscle was observed. This workflow resulted in a signal vs. time

profile that matched free Gad at 43°C in muscle at the concentration. Two conclusions can be

drawn from this: (1) the presence of lipids in solution, following destabilization of the liposomal

membrane did not hinder the diffusion of free small molecules and (2) the lower signal observed

in muscle was most likely due to partial release immediately after injection and therefore a lower

concentration of free small molecules.

A possible explanation for partial release lies in the fact that the temperature probe was

placed adjacent to the tissue sample at the edge of the spherical void rather than in the body of the

tissue. Placement of the probe in the tissue would have caused the tissue to separate from the

supporting gel medium and therefore was not feasible with this setup. Given the difference in

specific heat capacity between agar and tissue it is possible that the tissue was not at the precise

temperature needed to induce complete small molecule release from TSL. Ultimately, this was

similar to the problem suspected in the clinic where there is a difficulty achieving the target

48

temperature in all regions of the tumor. Overall, this emphasizes the need for spatial temperature

maps to guide hyperthermia regimens in the clinic, especially given that diffusion is a limiting

factor and it is desirable to maximize drug delivery to the target site.

A comparison of the diffusion coefficient obtained for small molecule Gad in 1.5% agar at

room temperature, (1.23 ± 0.08) ×10-4 mm2/s, to values found in literature shows a similarity to

the diffusion coefficient of free Gad observed in calf cartilage which has been quoted at (1.55 ±

0.22) ×10-4 mm2/s [57], [59]. Cartilage provides a relevant reference value for agar as it is primarily

a matrix of collagen fibers with no cellular barriers. In comparison, diffusivity of Gad in muscle

at room temperature, (5.96 ± 0.73) ×10-5 mm2/s, is significantly lower than that observed in

cartilage. This was in accordance with observations made by Djelveh et al. who showed that

diffusion across muscle fibers experience significant tortuosity effects leading to a Deff/D0 of 0.13

for glucose in bovine muscle while our study yielded 0.15 for Gad in galline muscle [58].

K-means clustering was employed as an alternative strategy for generation of signal versus

time curves in tumor tissue. By grouping voxels with similar uptake curves, lines of iso-

concentration in space and time may be identified (assuming one to one relationship between

signal and concentration). This approach was similarly employed by Koh et al. to segment tumor

tissue based on the shape of the contrast enhancement curve over time [55], [65]. In this work, the

number of clusters was increased until the program could not identify any new regions within the

gel and began segmenting the background noise. The final number of clusters used was seven in

total which showed a maximum of two overlapping signal curves at background. Clusters revealed

signal profiles with similar shapes to those seen using the contour method for muscle tissue. Voxels

in the tumor tissue were identified with similar profiles to that expected for diffusion. The jagged

nature of the clustered voxels more closely followed the surface of the tissue that can be identified

in scout images suggesting that it is a better estimate of signal uptake in tissue than that measured

using the contouring algorithm which instead relies on the edge of the contrast pool in contact with

the surface layer of gel. In addition, the drop signal drop-off with increasing distance was very

rapid, supporting the relatively low values measured for the diffusion coefficients.

Subsequent evaluation of the physiological relevance of the release kinetics against muscle

and tumor tissue showed that the 1.5% agar phantom overestimates the rate at which the small

molecules diffuse in the tissue environment. However, key features such as temperature

49

dependence of small molecule diffusion and the difference in diffusion coefficient between small

molecules and nanoparticles was preserved. As a result, a higher concentration of 7.5% w/v

BactoTM Agar was investigated which provided a diffusion coefficient closer to the physiological

range observed. A slight temperature dependence was observed as for muscle but a multiple

comparisons test of subgroups determined by temperature showed that the diffusion coefficient

measured was statistically higher than that observed for either tumor model. While this was

expected for MDA-MB-231 tumors, the diffusion coefficient for SKOV-3 was expected to be in a

similar range or even higher than that observed in muscle.

The apparent diffusion coefficient of protons measured using diffusion weighted imaging

has been investigated as a metric for staging cancer due to the observed differences during tumor

progression. The MDA-MB-231 murine breast cancer model used in this study exhibits one of the

lowest recorded ADC values at an average value of 0.49×10-3 mm2/s [86]. Based on the values

found in literature for apparent diffusion coefficient, it was expected that diffusion would show an

increase in the following order: MDA-MB-231, muscle, SKOV-3, with 1.5% agar showing the

highest diffusivity [86]–[89]. While diffusion coefficients in MDA-MB-231 was observed to be

the slowest, SKOV-3 showed no significant difference compared to muscle except at 43°C. Given

that the ADC ratio predicts a diffusion coefficient of approximately 8×10-5 mm2/s for SKOV-3

compared to 6×10-5 mm2/s in muscle, a higher number of samples may be needed to gain the

statistical power to distinguish such a small difference. This is further supported by the

heterogeneity observed for SKOV-3 diffusion, which compromises the accuracy of estimation in

the case of the tumor. In addition, subcutaneous tumors are typically characterized by lower

apparent diffusion coefficients than orthotopic models and as such may underestimate the small

molecule diffusion coefficient [57], [90]. The acquisition of proton density maps may provide

greater insight into the heterogeneity of the agar vs. muscle and tumor tissue. This will allow

modelling of the expected heat distribution and give a directional sense for expected ADC and

small molecule diffusion.

50

4.4 Summary and Future Directions

The platform presented in this thesis enables quantification of hyperthermia induced small

molecule release from temperature-sensitive drug delivery systems using MR. The platform has

been developed as a strategy for pre-treatment evaluation of drug release in response to non-

invasive heating systems such as RF and HIFU. Advances in the spatio-temporal capabilities of

MR has enabled real-time assessment of drug release via imaging of a small molecule drug

surrogate encapsulated in the delivery system. An agar phantom was developed which allowed

diffusion-based separation of released small molecules from stable nanoparticles. Diffusion of

small molecules, following destabilization of the liposomal carriers, was found to be consistent

with the diffusion of small molecules in the absence of liposomal carriers. Quantitative comparison

of signal versus time curves provided concrete evidence of a complete and immediate burst release

of the imaging drug surrogate at the liposomal transition temperature, resulting in profiles that

perfectly mimicked that of free small molecules under hyperthermia. Overall, the developed agar

phantom was shown to be a suitable strategy for detecting and quantifying thermosensitive release

kinetics in response to hyperthermia using MR. Assessment of the physiological relevance of the

phantom revealed that small molecule diffusion in muscle and tumor tissue was significantly

slower than in 1.5% agar. As an alternative, 7.5% agar maintained the benefits of the agar phantom

reproducibility while providing a medium with more tissue relevant diffusion rates.

This platform has the potential to be used in conjunction with complementary techniques

such as MR thermometry to provide comprehensive spatio-temporal feedback on release achieved

using non-invasive heating platforms such as HIFU and RF. One such application may be as pre-

treatment quality assurance toolset in a clinical setting. Prior to implementation in the clinic,

further work is needed to integrate the current platform for quantification of thermosensitive drug

release with MR thermometry techniques. The accuracy of current techniques for evaluating

hyperthermia with MR thermometry is severely compromised by contrast agent-induced

susceptibility effects, thus limiting its application in the context of imaging drug release [45]. As

such, decoupling the temperature effect on magnitude and the susceptibility effect on temperature

measurements would facilitate the quantification of drug release in response to guided

hyperthermia applications. The current platform may be combined with spatial temperature maps

to correlate the drug release achieved in response to in situ hyperthermia and thus enable treatment

planning for temperature sensitive drug delivery systems.

51

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Appendix A: Heterogeneous Diffusion Through Tumor

Figure 19 SKOV-3 tumor embedded in agar adjacent to a spherical void: The contrast pool is masked

in red and the tumor is contoured in yellow. The red line indicates the contour at 0.7mm generated by the

MATLAB algorithm.

Figure 20 Mean signal intensity along a contour at 0.7mm from the contrast pool for SKOV-3.

The heterogeneity of diffusion is apparent for the SKOV-3 tumor. Alternatively, by

selecting visually similar signal profiles derived from K-means clustering it may be possible to

locate pixels exhibiting a similar diffusive profile to that observed in agar and muscle as shown

in the profiles below. However, further work is needed to confirm this analogy. A possible

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strategy would be to measure the distance between the edge of the tumor and each pixel in the

cluster used to generate these contours. The average distance may provide insight into the

physical distance that the molecules have diffused and explain the similarity in signal profiles.

Figure 21 Mean signal versus time for select clusters derived from k-means segmentation for

SKOV-3 at three temperatures.

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Appendix B: Analysis of Direct Injection into Muscle

Diffusion was further investigated in a muscle phantom to determine the relevance of

trends observed in agar. This was performed by direct bolus injection into a wedge of chicken

breast. However, several limitations were encountered with this method. A differential baseline

immediately after injection was observed for free vs. liposomal Gad. This was potentially

attributed to a difference in bolus pocket equilibration time due to pressure and viscosity

differences. Signal accumulation in the muscle was much lower than in agar and could not reliably

be attributed to the lower measured diffusion coefficient. Possible sources of low signal included

dilution of contrast due to uncontrolled distribution during injection, greater surface area of

diffusion for the same volume of contrast and limitations of contouring injection sites with high

boundary tortuosity. Suggestions for future included pooling contrast agent in a well on the surface

of the chicken and verifying linearity of signal vs. gadolinium concentration in muscle, for the

given contrast range.

Figure 22 Illustration of direct bolus injection into muscle tissue: The top panel shows the injection

site immediately after injection. Regions of interest (ROIs) obtained for 2 threshold cutoffs are shown in

red. The lower panel shows the agent distribution at the end of the imaging period (29.5 min). Contours

at 0.7 mm from each ROI edge are delineated in yellow. Sample image shown is for non-thermosensitive

liposomes at 22°C following direct bolus injection into muscle.

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To investigate the reason for differential post-contrast baseline in contours 0.7mm from the

contrast-gel boundary raw images were revisited. It was noted that partial volume effects limit the

precision of the gel-contrast boundary definition for liposomal agent. Previously, a threshold of

400 was used to obtain masks of the injection site. This value was selected as midway between the

peak background and free contrast signal distributions. While this provided highly reproducible

contrast pool contouring, there was notable increase in blurring at the boundary in the case of the

liposomal agent due to the reduced relaxivity of Gad. Subsequently, the threshold value was

adjusted to ensure a consistent boundary definition for all contrast pools. Liposomal agents were

typically contoured with a threshold of 250.

In the case of bolus injection into chicken breast, the threshold dependent boundary

definition described above is further complicated by the introduction of additional parameters such

as the difference in viscosity of free Gad and liposomal Gad. Although consistent injection

volumes are expected to be subjected to the same reactive forces regardless of agent type, the time

taken to equilibrate these forces is different, due to the greater resistance to flow for the more

viscous liquid (i.e. for liposomal agents). This means that liposomal Gad will continue to dissipate

the pressure build up long after free Gad. As seen in Figure 22, contouring from the same reference

time point leads to the measured signal incorporating an increase due to flow (indicated by the

yellow arrow) which does not change significantly thereafter due to the constant concentration

across the boundary. Furthermore, there is a visible decrease in signal and change in shape of the

injection site, which is likely due to agent finding pathways of least resistance outside of the

imaging frame.

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Appendix C: Inspection of Signal Along a Contour

To determine if signal mean provided a better estimate of signal at each distance over time the

trend in signal along the contour was observed for each data set. A sample data set is shown below.

Figure 23 Plot of signal along a MATLAB generated contour at two distances: Top panel shows

contour of interest in red at d = 0.2mm from the contact surface (a) and 1.4mm from the contact surface.

Lower plots show the corresponding signal measured at each pixel along the contour from left to right.

The central region below the spherical void was found to consistently provide stable signal

with a drop-off over approximately 10 pixels on either side. Subsequently, analyses were

performed using only pixels below the contrast pool. Similarly, for analysis in tissue, an ROI was

selected with 10 pixels omitted at both ends of the tissue.

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Appendix D: Diffusion Limit in Hydrogels

Figure 24 Graph showing small molecule diffusion coefficients at 22°C for agar gels of increasing

concentration compared to diffusion coefficients through tissue.

Diffusion was tested in range of concentrations to determine a suitable tissue mimic for

signal vs. Gd concentration studies. Increasing the concentration of BactoTM Agar beyond 7.5%

was not found to significantly affect the diffusion coefficient. As 7.5% provided a statistically

equivalent diffusion coefficient to muscle tissue, this concentration of agar was selected for to

determine adjustment factors. Higher concentrations used more material and took longer to prepare

without contributing any significant improvement over 7.5%.