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.
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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.
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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
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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
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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
0
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Free Gad 43C n=2
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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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Ad
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Free Gad 43C n=2
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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%.