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Comparison of CT and Optical Image-based Assessment of Liposome Distribution
by
Huang Huang
A thesis submitted in conformity with the requirements for the degree of Master of Science
Graduate Department of Pharmaceutical Sciences University of Toronto
© Copyright by Huang Huang 2012
ii
Comparison of CT and Optical Image-based Assessment of
Liposome Distribution
Huang Huang
Master of Science
Department of Pharmaceutical Sciences
University of Toronto
2012
Abstract
The use of multimodal imaging as a tool to assess the in vivo pharmacokinetics and
biodistribution of nanoparticles is important in drug development and imaging-guided therapy.
The current study reports the use of combined computed tomography (micro-CT) and optical
imaging to quantitatively assess the whole body (macro) and intratumoural (micro) distribution
of a nano-sized liposome-based CT/optical imaging probe carrying iohexol and Cy5.5 over a
study period of eight days. The liposomes were found to have a vascular half-life of 30.3 ± 8.9 h
in mice bearing subcutaneous H520 non-small cell lung cancer (NSCLC) tumours with the
maximum liposome accumulation in tumour achieved 48 h post injection. The liposome
accumulation in tumour was quantitatively assessed using both micro-CT and fluorescence
molecular tomography (FMT), where micro-CT was used to guide the FMT tumour delineation
and signal correction. In situ confocal laser-scanning fluorescence microscopy analysis at the
tumor site revealed that most of the liposomes remain in the vasculature at early time points (24
h) with the majority having extravasated into the tumor interstitium at later time points (eight
days). This investigation demonstrates the critical role micro-CT can play in guiding FMT-based
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quantification of distribution. As well the combination of CT and optical imaging enable
visualization of the liposomes at the whole body, tumor and cellular levels with high sensitivity
and excellent anatomical background. Such non-invasive assessment of therapeutic distribution
at the macro and micro scale is necessary for implementation of personalized medicine including
image-guided patient stratification and real-time adjustment of therapeutic dose.
iv
Acknowledgments
It is a great pleasure to thank the many people who made this thesis possible.
Foremost, I would like to express my sincere gratitude to my supervisor, Dr. Christine Allen.
With her enthusiasm, her inspiration, and her continuous support for my study and research, she
helped me enjoy the research I did. I deeply appreciate all her contributions of time and patience
to make my M.Sc. experience exciting. Through my thesis writing, she provided encouragement,
sound advice, and good teaching. It has been a pleasure to be her student.
My committee member and unofficial co-supervisor, Dr. David Jaffray, has provided a lot of
guidance to me during this research. I am especially grateful for his unique thinking and diverse
knowledge, which provided me many ideas that have been very helpful. I appreciate Drs. Allen
and Jaffray for obtaining the funding that has supported my research.
I would like to thank my other committee member, Dr. Gang Zheng, for taking his time to
critically review my work and offer constructive suggestions.
My sincere thanks to all my colleagues and friends in the Allen lab and at STTARR, past and
present. In particular, I would like to express my special gratitude toward John Lo and Michael
Dunne. John took the time to train me as an undergraduate student, and provided collaborative
help that paved the way to this research. I thank Michael for using the H520 tumour model he
developed in our lab. His generous help on tumour cell preparation and animal inoculation
described in this research is also deeply appreciated. I would also like to thank Dr. Jinzi Zheng
for providing scientific support, and Debbie Squires for technical assistance with the
administration of the contrast agents.
Finally, I would like to thank my beloved family and friends for all their love, support and
encouragement throughout my studies. Thank you all.
v
Table of Contents
Acknowledgments.......................................................................................................................... iv
Table of Contents............................................................................................................................ v
List of Tables ................................................................................................................................ vii
List of Figures .............................................................................................................................. viii
List of Appendices ......................................................................................................................... xi
CHAPTER 1 GENERAL INTRODUCTION .............................................................................. 1
1.1 Goal, Objectives and Rationale........................................................................................... 1
1.2 Liposomes ........................................................................................................................... 3
2.2.1 Physico-chemical Properties................................................................................... 3
2.2.2 Liposomes as Drug Carriers.................................................................................... 6
1.3 Multimodal Imaging ......................................................................................................... 10
2.2.1 CT Imaging ........................................................................................................... 11
2.2.2 Optical Imaging .................................................................................................... 12
2.2.3 CT/Optical Multimodal Imaging .......................................................................... 14
1.4 Image-based Assessment of Nanoparticle Distribution.................................................... 15
CHAPTER 2 Comparison of CT and Optical Image-based Assessment of Liposome Distribution .............................................................................................................................. 19
2.1 Introduction....................................................................................................................... 20
2.2 Materials and Methods...................................................................................................... 23
2.2.1 Synthesis of Cy5.5-DSPE ..................................................................................... 23
2.2.2 Liposome Preparation ........................................................................................... 24
2.2.3 Liposome Characterization ................................................................................... 24
2.2.4 In vitro Stability and Agent Release ..................................................................... 25
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2.2.5 In vivo CT/Optical Imaging .................................................................................. 26
2.2.6 Image Analysis...................................................................................................... 27
2.3 Results............................................................................................................................... 30
2.3.1 Synthesis and Characterization of Cy5.5-DSPE................................................... 30
2.3.2 Physicochemical Characterization of the Liposomes ........................................... 31
2.3.3 In vivo Pharmacokinetics, Biodistribution of the Liposomes ............................... 35
2.4 Discussion ......................................................................................................................... 46
CHAPTER 3 Conclusion............................................................................................................. 50
3.1 Conclusions....................................................................................................................... 50
3.2 Future Directions .............................................................................................................. 52
References..................................................................................................................................... 54
Appendix....................................................................................................................................... 67
vii
List of Tables
Table 2.1 Physicochemical characteristics of CT and CT/optical liposomes (n=5). Data
are represented as the mean ± standard deviation .............................................................. 32
Table 2.2 Pharmacokinetic parameters for CT and CT/optical liposomes (n=7) in non-
tumour-bearing mice and CT/optical liposomes (n=5) in tumour-bearing mice. Statistics
were analyzed between the CT and CT/optical liposomes in non-tumour-bearing mice,
and between CT/optical liposomes in non-tumour and tumour-bearing mice .................. 36
viii
List of Figures
Figure 2.1 Schematic of the CT/optical liposome-based contrast agent (not drawn to scale). The
average hydrodynamic diameter of the liposomes was found to be 86 ± 5 nm by dynamic light
scattering (DLS)............................................................................................................................ 22
Figure 2.2 The mass spectrum of Cy5.5-DSPE. The measured molecular weight of Cy5.5-DSPE
was determined to be 1645.7 Da, which is in good agreement with the theoretical molecular
weight 1646 Da. ............................................................................................................................ 30
Figure 2.3 Representative UPLC chromatograms for Cy5.5 and purified Cy5.5-DSPE. Cy5.5
elutes at ~3.5 min, Cy5.5-DSPE elutes at ~7.2 min. The distinction between the two sets of peaks
assures the purity achieved using the column chromatography method....................................... 31
Figure 2.4 Hydrodynamic diameter (obtained by DLS analysis) of the CT/optical liposomes over
a 2-week period with dialysis against a 250-fold volume excess of HBS containing 45mg/mL
BSA at 37°C. Data are represented as the mean ± standard deviation of three independent
batches of liposomes. .................................................................................................................... 33
Figure 2.5 (a) The in vitro release profile of iohexol and Cy5.5 from CT/optical liposomes (n=3)
and iohexol from CT liposomes (n=3) incubated in FBS at 37°C. (b) Ratio of iohexol to Cy5.5
(wt/wt) in the CT/optical liposomes following incubation in FBS at 37°C. The initial iohexol-to-
Cy5.5 ratio was normalized to 1. * indicates statistically significant difference (p < 0.01) from t =
0..................................................................................................................................................... 34
Figure 2.6 Micro-CT (top) and FMT (bottom) images of a non-tumour bearing mouse injected
with CT/optical liposomes. The same window and level were used for all images from each
modality. ....................................................................................................................................... 37
Figure 2.7 Micro-CT transverse (top), coronal (middle), and FMT images of a tumour bearing
mouse injected with CT/optical liposomes at various time points. The transverse micro-CT
images display the right side of the mouse. Arrow indicates the location of the tumour. The same
window and level was used for images from each modality. ....................................................... 38
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Figure 2.8 Pharmacokinetics and biodistribution profiles of CT and CT/optical liposomes in
non-tumour bearing mice as determined using micro-CT image-based assessment. (a) The blood
iodine concentration. The biodistribution in (b) liver, (c) spleen, (d) left kidney and (e) right
kidney. * indicates statistically significant differences (p<0.05) between CT and CT/optical
liposomes in %ID/cm3 tissue. ....................................................................................................... 41
Figure 2.9 (a) Pharmacokinetics of CT/optical liposomes in tumour bearing mice as determined
using micro-CT image-based assessment of the blood iodine concentration. (b) Micro-CT and
FMT image-based determination of tumor distribution profile for CT/optical liposomes (n=5). *
indicates a statistically significant difference between micro-CT and FMT data at that time point.
(c) Ratio of iohexol to Cy5.5 (wt/wt) at the tumour site. The initial iohexol-to-Cy5.5 ratio was
normalized to 1. * indicates statistically significant difference from pre-injection. (d) Iohexol-to-
Cy5.5 ratio (wt/wt) determined from various concentric cubic VOIs over the tumour region in
both FMT and micro-CT. Data include liposome accumulation in tumours 24 to 72 h post-
injection. The tumour volumes are 25 to 30 mm3. The ratios were normalized to the iohexol-to-
Cy5.5 ratio pre-injection. .............................................................................................................. 44
Figure 2.10 In vivo microscopic images of the CT/optical liposomes in the tumour area at 24 h
(a, b) and 8 days (c) post-injection................................................................................................ 45
Figure A1. (a) Inside the FMT system. The imaging cassette (as seen in (b)) is placed in the
scanning stage prior to a scan. (b) A photo of the phantom gel inside the imaging cassette. The
gel insert (1 x 1 x 1 cm3) is fused inside the large gel (3.8 x 3.8 x 1.3 cm3), and the cassette
height is adjusted to 1.3 cm to sandwich and tightly immobilize the gel. (c) A schematic of the
large gel geometry with the gel inserted either toward or away from the source laser. Both
geometries were considered In this study. .................................................................................... 70
Figure A2. Effect of background concentration (a-f) on signal detected in ROI. The gel insert
(200 nM GH680) is facing the camera with various backgrounds (a) 20 nM, (b) 50 nM, (c) 80
nM, (d) 150 nM, (e) 200 nM, and (f) 300 nM. The dotted contours (5 mm x 5 mm) represent the
ROIs used for the analysis. The positions of the ROIs were located using both the FMT intensity
as well as the reflectance image. Slices shown have been averaged over 5mm in the z-dimension
for illustration purposes. ............................................................................................................... 71
x
Figure A3. FMT GH680 concentrations vs. actual GH680 concentrations at various background
levels (n=3 per level). The dotted line represents the line of identity. The FMT concentration
increases substantially as the background level increases. At the lowest background of 20 nM,
the quantified concentrations are very close to the actual concentrations in the gel cubes.
Background below 20 nM was found to be equivalent to zero background. At any given
background, linearity is still preserved for varying GH680 concentrations in the gel cubes (R2 >
0.95 for all fits). ............................................................................................................................ 72
Figure A4. The quantification error of the FMT for the gel inserts with various background
levels before and after applying background subtraction method using the method reported here.
The signal-to-background (S/B) ratio has been used to express the strength of the FMT signal in
the gel cube compared to background (BKG). Data are expressed as mean ± standard deviation
(n=3 to 12). The dotted line indicates a S/B ratio of 1, representing that the GH680
concentrations in the gel cube and background are the same. As shown, the quantification error
before background subtraction followed an exponential growth as the S/B ratio decreased,
reaching 908 ± 89% at S/B ratio of 0.167. Overall, with background subtraction, the
quantification improved substantially (p < 0.05).......................................................................... 73
xi
List of Appendices
Appendix……………………………………………………………………………...…………67
xii
List of Abbreviations
AUC – area under the curve
BSA – bovine serum albumin
CH – cholesterol
CT – computed tomography
DICOM – digital imaging and communications in medicine
DLS – dynamic light scattering
DOPE – 1,2-dioleoyl-sn-glycero-3-phosphoethanolamine
DOTA – 1,4,7,10-tetraazacyclododecane -1,4,7,10-tetraacetic acid
DPPC – dipalmitoylphosphatidylcholine
DSPE – Distearoylphosphatidylethanolamine
DTPA – diethylene triamine pentaacetic acid
EMPA – Poly(glycidyl methacrylate)poly(2,3,-epoxypropylmethacrylate)
EPR – enhanced permeation and retention
FBS – fetal bovine serum
FDA – food and drug administration
FMT – fluorescence molecular tomography
FRV – freeze-drying rehydration vesicles
Gd – Gadolinium
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HBS – HEPES buffered saline
HDL – high density lipoprotein
HEPES – hydroxyethylpiperazine ethanesulfonic acid
HU – Hounsfield unit
ID – injected dose
ke – elimination constant
LDL – low density lipoprotein
LUV – large unilamellar vesicles
MRI – magnetic resonance imaging
MWCO – molecular weight cut off
NSCLC – non-small cell lung cancer
PC – phosphatidylcholine
PE –Phosphatidylethanolamine
PEG – polyethylene glycol
PET – positron emission tomography
PS –Phosphatidylserine
RES – reticuloendothelial system
ROI – region of interest
SPECT – single photon emission computed tomography
SUV – small unilamellar vesicles
xiv
t1/2 – half life
UPLC – ultra-performance liquid chromatography
US – ultrasound
UV – ultraviolet
VD – volume of distribution
1
CHAPTER 1 GENERAL INTRODUCTION
1.1 Goal, Objectives and Rationale
The overall goal of this research is to evaluate and compare the in vivo distribution of liposomes
using CT and optical imaging modalities. The specific objectives include:
1. Synthesis and in vitro characterization of liposomes that support CT and optical imaging.
2. Quantitative in vivo comparison of whole body distribution and tumor accumulation
profiles of the CT/optical liposomes obtained by CT and optical imaging.
3. Assessment of the CT and optical imaging modalities in their ability to quantify liposome
distribution in vivo.
Medical imaging is one of the primary tools used to evaluate structure and function non-
invasively in a living subject [1-8]. The employment of imaging to assess the distribution of
nanoparticles and drug delivery systems in vivo has provided invaluable insight into the pathway
and fate of these systems [9-15]. This unique information enables the optimization of delivery
systems and may accelerate the development of therapeutic strategies. In cancer research, by
loading a drug delivery system with therapeutic and imaging agents, one enables image-guided
drug delivery for the real-time monitoring of pharmacokinetics and whole-body distribution as
well as tumour accumulation, and intratumoural distribution. Such multifunctional delivery
systems may ultimately contribute to the development of personalized therapies and lead to
improvements in treatment outcomes.
In recent years, multimodal imaging has received increasing interest as this provides a
means to exploit the unique strengths of each modality and to gain complementary information
[16-21]. In this study, CT and optical imaging were selected to assess the in vivo distribution of
liposomes. CT imaging provides anatomic information and has been shown by our laboratory to
reliably enable quantitative assessment of the distribution of liposomes in vivo [22-24].
Therefore CT may be considered a reference that enables assessment and validation of the
2
performance of other imaging modalities in the quantitative evaluation of biodistribution of
liposomes. On the other hand, optical imaging offers high detection sensitivity and can provide
information on liposome distribution at the cellular level via microscopy or endoscopy
measurements. In addition, the development of FMT allows optical imaging to three-
dimensionally and quantitatively image the whole-body biodistribution of fluorophores in small
animals, enabling data registration and comparison with CT and other tomographical imaging
modalities [25, 26]. Therefore, the combination of CT and optical imaging, should enable
monitoring of the macro and microdistribution of liposomes in vivo, and allow for data
comparison and validation between CT and FMT.
Liposomes are the most established of the advanced drug delivery technologies. Several
drugs relying on formulation in liposomes have been approved by the FDA and used for the
treatment of cancer, infectious disease and autoimmune diseases [27-30]. As drug delivery
vehicles, liposomes have the ability to incorporate hydrophilic, hydrophobic and/or amphiphilic
molecules [16, 31]. Through the optimization of their physico-chemical properties such as size,
charge, and stability, liposomes can achieve the desired in vivo pharmacokinetic profile. In
oncology, properly designed liposomes can take advantage of the leaky tumour vasculature to
passively accumulate at tumour sites [32, 33]. The utilization of liposomes as carriers for
imaging agents has great potential in the medical imaging field as they can be easily labeled or
loaded with probes, contrast agents, or radionuclides to support imaging in single or multiple
modalities [34-37]. Image-based assessment of the in vivo distribution of liposomes can provide
unique information that is otherwise unavailable using traditional methods [38-42].
The experiments performed to accomplish the objectives outlind above are detailed in
Chapter 2 of this dissertation. Briefly, CT/optical liposomes were prepared with a CT contrast
agent encapsulated within the liposomes and a fluorophore was conjugated to the surface of the
liposomes. The liposome formulation was characterized in vitro in terms of size and size
distribution, zeta potential, agent loading levels, and agent release profiles. Quantitative micro-
CT, FMT and intravital microscopy were used at several time points to determine the
biodistribution of the liposomes for eight days following intravenous administration to mice
bearing subcutaneous H520 non-small cell lung cancer tumours.
3
1.2 Liposomes
Liposomes are vesicles with an internal aqueous volume surrounded by one or more concentric
bilayers of lipids. In an aqueous medium, lipids will be thermodynamically driven to form
liposomes, creating a hydrophilic outer surface and inner core, as well as a hydrophobic domain
within the bilayer. This property allows the encapsulation of molecules in either the hydrophobic
or hydrophilic compartments, as part of the lipid framework or free in the internal aqueous
volume, respectively. Since their discovery in 1965 [43], liposomes have been explored broadly
in the field of pharmaceutics for the encapsulation and controlled delivery of various
chemotherapeutic and anti-fungal drugs [29]. As well, liposomes have shown promises in cancer
imaging applications such as disease detection, characterization and staging (ref). Compared to
their unencapsulated counterparts, drugs administered in liposome formulations experience
longer circulation lifetimes and reduced systemic toxicities. Through the selection of their
composition and the method of preparation, one can control various parameters such as size,
drug loading and retention, in vivo circulation lifetime, as well as their in vivo distribution.
Several liposome formulations for chemotherapy have been approved by the FDA for human use
with many other formulations in preclinical and clinical evaluation. For example, Doxil, the
pegylated liposome formulation of doxorubicin, has been approved for the treatment of ovarian
cancer [44]. Another approved liposome formulation, DaunoXome, is used for treatment of
Kaposi’s scarcoma [45]. In addition to chemotherapy, Visudyne, a liposomal formulation, has
been approved by the FDA for the treatment of macular degeneration [46].
2.2.1 Physico-chemical Properties
A liposome consists of several components analogous to that of a cell membrane. The main
component of the liposome is phospholipid, which generally includes phosphatidylcholines (PC),
phosphatidylethanolamines (PE), and phosphatidylserines (PS). Phospholipids are amphiphilic
molecules consisting of a polar head group and two non-polar tail chains, which have various
lengths and degrees of saturation. These phospholipids comprise at least 40% of the liposome,
and are the main building blocks of the liposome membrane. A sterol, commonly cholesterol, is
another component in the liposome, and serves as structural support for the lipid bilayer by
increasing the fluidity of rigid membranes and rigidity of fluid membranes [47]. The presence of
4
cholesterol in liposomes also increases the encapsulation efficiency of hydrophilic agents
through decreasing the permeability of the bilayer [48]. On the other hand, cholesterol has been
shown to decrease solubilization capacity for hydrophobic drugs, possibly due to that the fact
that cholesterol is a bulky and robust molecule intercalated within the hydrophobic region of the
bilayer, thus reducing the space available for loading hydrophobic agents [49-51]. In addition,
inclusion of cholesterol in the lipid bilayer has also been shown to inhibit the transfer of lipid
components to plasma lipoproteins, resulting in improved in vivo stability [52]. In addition to
cholesterol, poly(ethylene glycol) (PEG) has been widely exploited in liposome formulations to
improve their in vivo performance. PEG is a non-ionic, water-compatible, flexible polymer chain
that is usually conjugated to a lipid such as DSPE that serves to anchor the hydrophilic polymer
to the bilayer surface. The presence of PEG provides steric stability and such liposomes are
known to have enhanced circulation lifetimes and reduced accumulation in the liver and spleen,
in comparison to conventional liposomes [53]. This may be due to the theory that PEG
discourages liposome self-aggregation, reduces the binding of serum opsonins, and increases
surface hydrophilicity. Studies have shown that 5 mol% of PEG2000-lipid is optimal in prolonging
the circulation lifetime of liposomes [54]. Such steric stabilization is critical for targeting
liposomes to solid tumors, where a longer circulation life time leads to increased tumour
accumulation.
Liposomes can be classified into four different categories depending on their size and the
method of preparation employed: small unilamellar vesicles (SUV), multilamellar vesicles
(MLV), large unilamellar vesicles (LUV), and freeze-drying rehydration vesicles (FRV). There
are several steps in liposome preparation. The first step is lipid dispersion, which relies on the
dispersion of the lipid in a solution, so that the lipids may self-assemble into various forms of
lipid spheres with aqueous internal volumes. Any soluble molecule can be encapsulated into the
internal aqueous volume of the liposomes or inserted into the bilayer (if attached to a
phospholipid) by inclusion in the aqueous solution during liposome formation. Common
methods for lipid dispersion include mechanical dispersion, solvent dispersion, emulsion
preparation, or detergent solubilization [55]. Next, the liposomes are usually extruded to result in
a well-defined size and a narrow size distribution. The extrusion step uses high pressure to force
the liposomes through membranes with pores of a certain size [56]. Extrusion typically produces
SUVs with a unimodal size distribution [57, 58].
5
The size of liposomes is critical in controlling their in vivo circulation time. It has been
shown that liposomes smaller than 100 nm have a longer circulation lifetime than liposomes
greater than 200 nm in size, since the smaller liposomes are cleared less rapidly by the RES [59].
Liposome size is also important in dictating the liposome’s ability to take advantage of the
enhanced permeability and retention (EPR) effect, which occurs in solid tumors and at sites of
inflammation. Tumour vasculature lacks a smooth muscle cell layer, and includes large
fenestrations (200-4000 nm) caused by poor alignment of endothelial cells [33]. This results in
enhanced permeation of liposomes smaller than 200 nm in diameter through the fenestrations
and into the tumour interstitial space. In addition, the poor lymphatic drainage of the tumour
tissue creates the enhanced retention of liposomes, leading to a high accumulation of liposomes
in tumour over time. It has been shown that liposomes up to 400 nm in diameter are able to take
advantage of the EPR effect, but smaller liposomes will do so to a greater extent [60]. Therefore,
size and circulation lifetime of the liposomes are crucial in determining the extent of the EPR
effect.
There are several vascular mediators that affect the EPR effect, including bradykinin,
nitric oxide (NO), prostaglandins, angiogensin-converting enzyme inhibitors, vascular
permeability factor and other cytokines. Two methods to artificially promote the EPR effect for
more efficient tumour-selective drug delivery was reported. One method is the elevation of
systemic blood pressure by iv infusion of angiotensin II [61-63]. Induction of systemic
hypertension leads to increased blood flow and vasoconstriction in normal tissues, resulting in no
change in blood flow volume. On the contrary, tumour blood vessels usually lack a smooth
muscle layer or pericytes needed for vasoconstriction. Therefore, during hypertension, tumour
blood vessels would open, leading to increased blood flow and increased drug delivery [62].
Elevated blood pressure was suggested as a strategy to increase tumor-targeted delivery of
SMANCS [64]. Another method is through the use nitroglycerin and similar nitric oxide (NO)
releasing agents. In hypoxic tumour tissue, nitroglycerin can be converted into nitrite, which can
then be reduced to NO [65]. NO is one of the potent mediators of vascular extravasation. It has
been shown that administration of nitroglycerin induced a two-fold increase in NO in tumor
tissue in a dose dependent manner, whereas normal tissues showed no significant increase [66].
In a study with tumour-bearing mice, nitroglycerin ointment was topically applied over the tumor
or on skin opposite or distal to the tumor site, at doses of 1.0 μg/mouse to 1.0 mg/mouse, the
6
increased NO level led to significant elevation in blood flow in tumour tissue, and the EPR effect
was seen to increase substantially. This led to significantly augmented accumulation of both the
Evans blue/albumin complex and the macromolecular anticancer drug PEG conjugated zinc
protoporphyrin (PZP) in all tumors studied [67].
2.2.2 Liposomes as Drug Carriers
Due to their amphiphilic nature, liposomes have been widely explored to incorporate both
hydrophilic and hydrophobic drugs, such as doxorubicin, cisplatin, paclitaxel, camptothecin, and
vincristine [68, 69]. Hydrophilic drugs can be easily encapsulated in the internal aqueous volume
of the liposome, while hydrophobic drugs may be incorporated within the hydrophobic region of
the lipid bilayer. Drugs can be passively and actively loaded into the liposomes. In passive
loading, drugs and lipids are co-dispersed in an aqueous medium, thus achieving encapsulation
while liposomes are being formed. The loading efficiencies for passive encapsulation are usually
very low [70]. Active loading involves the creation of a gradient (i.e. pH, ionic) across the lipid
membrane, which causes the drug to enter the aqueous core. For example, Doxil® is prepared by
loading doxorubicin into liposomes using an ammonium sulphate gradient [71-73].
Taking advantage of the EPR effect, liposomes can exploit passive targeting to increase
the localization of anticancer drugs at solid tumours. Passive targeting can result in increases in
drug concentrations in solid tumours of several fold compared to levels obtained following
administration of free drug [74]. Liposomes can also be actively targeted to solid tumors by
conjugating targeting moieties to their surface. Over the last few decades, significant research
has been dedicated to the development of actively targeted liposome formulations [29, 75].
Targeting moieties may include antibody, small molecular weight, naturally occurring or
synthetic ligands like carbohydrates, glycoproteins, peptides, or receptor ligands, i.e. essentially
any molecule that selectively recognizes and binds to target antigens or receptors over-expressed
or specifically expressed on cancer cells [76]. There are several advantages of employing
actively targeted liposomes. First of all, relatively few ligand molecules are required per
liposome to deliver high amounts of drugs to target cells. Compared with other delivery systems
such as immunoconjugates, which can only deliver a few drugs (<10) per antibody molecule,
liposomes have the ability to deliver thousands of drug molecules using a few tens of antibody or
7
ligand molecules on the liposomal surface [77-79]. In addition, the presence of multiple targeting
molecules on a single liposome can promote multivalent binding of monovalent antibody
fragments, thus greatly increasing their binding avidity for the targets. Another advantage of
targeted liposomes lies in the potential of enabling additive or synergistic effects between
signaling antibodies present on the liposome surface and the encapsulated drug. Antibodies have
been shown to exhibit additivity or synergy when used in combination with drugs [79-81]. For
example, clinical studies have demonstrated the additive benefits for an antibody against anti-
HER2 in combination with taxol or in combination with anthracycline drugs plus
cyclophosphamide [79].
In active targeting strategies using liposomes, the choice of the target receptor, targeting
ligand, and the drug are all important factors to be considered. The target receptor should be
either selectively expressed or over-expressed on malignant cells. These targeted cells should
possess minimum heterogeneity in their receptor expression with minimum shedding of the
receptors. The location of the receptors is also an important factor. Targets within the vasculature
or readily accessible from the vasculature should bind targeted liposomes more readily than
targets buried deep within tissues. Vascular targets are accessible to the liposomes as long as
they remain in the blood pool, while targeting to tumour cells requires the passive accumulation
of liposomes at the tumour site, followed by binding of the ligand to the target receptor on the
cell surface. Targeting ligands that have been explored most extensively include antibodies,
fragments of antibodies, peptides, aptamers, vitamins, and carbohydrates [82]. The factors
affecting ligand selection include binding affinity, avidity, immunogenicity, and ligand density
on liposomes [76]. In addition, since the ligands are surface bound, it is important to ensure that
these ligands do not seriously compromise the pharmacokinetics of the liposomes. In choosing
the drug to be encapsulated, one should consider the drug release rates and their bioavailability.
It is desired that when liposomes are at the tumour site or internalized into cells by receptor-
mediated endocytosis, the drugs are readily released. Strategies that enable remote drug release
at the tumour site include the design of pH and thermo sensitive liposomes [83, 84].
There have been several promising applications of antibody targeted liposomes in
research. For example, anti-HER2 immunoliposomes were developed to target mammary
carcinoma cells [79, 85, 86]. In these studies, anti-HER2 immunoliposomes were produced by
coupling the doxorubicin loaded liposomes with recombinant human mAb HER2-Fab’ or anti-
8
HER2 scFv C6.5. After administration in nude mice bearing HER2-over-expressing tumor
xenografts, the anti-HER2 immunoliposomes resulted in efficient tumor localization, with
doxorubicin tumour-to-blood and tumour-to-muscle ratios greater than 22-fold at 67 h post
injection. Histological samples with immunoliposomes containing colloidal gold showed that the
immunoliposomes were present in both the perivascular areas and within cellular regions of the
tumour. In contrast, non-targeted liposomes were observed mainly in extracellular and
perivascular spaces, and were not observed within individual tumour cells. In multiple HER2-
overexpressing human breast tumor xenograft models, treatment with doxorubicin loaded anti-
HER2 immunoliposomes produces significantly increased efficacy as compared to free dox or
dox-loaded non-targeted liposomes and significantly less systemic toxicity than free dox [79]. It
was later demonstrated that the effect was due to the specific targeting of the liposomes, and not
due to effects of the targeting agent or free drug alone. Therefore, it was concluded that the
therapeutic advantage associated with doxorubicin-loaded anti-HER2 immunoliposomes was due
to specific intracellular drug delivery to the target cells. Anti-HER2 immunoliposomes are
currently undergoing scale up for clinical evaluation [76].
Therapeutic efficacy was also demonstrated for anti-CD19 targeted liposomes in a
murine model of human B-cell lymphoma. Anti-CD19 targeted liposomes were shown to
efficiently bind and internalize into maliganant B cells compared to non-targeted liposomes. In
vivo survival studies performed in SCID mice xenografts demonstrated a significantly increased
life span for mice treated with anti-CD19 targeted liposomes loaded with either doxorubicin or
vincristine, compared to mice treated with non-targeted liposomes or free drugs [87].
In addition to targeting cancer cells, liposomes can also be designed to carry targeting
moieties that target the tumour vasculature. Aside from greater accessibility of the liposomes to
the tumour vasculature, there is limited variation in the vasculature between tumour types. As a
result, vascular targeting is more generally applicable than specialized tumour cell-targeted
treatment [88]. RGD liposomes loaded with doxorubicin were designed to target the αvβ3
integrins that are over-expressed on neovasculature [89]. It was demonstrated that compared to
non-targeted liposomes and non-specifically targeted RAD liposomes, the RGD liposomes
exhibited faster blood clearance and increased spleen uptake, likely due to the presence of αvβ3
integrins on MPS cells in the spleen. However, tumour accumulation at 24 h was unchanged and
animals administered the RGD-liposomes experienced markedly decreased tumour growth. In
9
another study, a novel peptide GPLPLR was attached to the liposomes to target membrane type
1-matrix metalloproteinase (MT1-MMP), which can be found on both vascular endothelial cells
and tumour cells [90]. PET imaging with liposome-encapsulated 18F-FDG demonstrated an
increase in tumour accumulation for the targeted formulation compared to the non-targeted
liposomes. Furthermore, incorporation of a cytotoxic peptide into the liposome resulted in a
significant increase in therapeutic efficacy.
Dunne M. et al attached the amino acid sequence asparagine-glycine-arginine (NGR) to
the liposome surface to target matrix metalloprotease aminopeptidase N (APN/CD13) expressed
on tumour vasculature [22]. This study showed a two fold increase in tumour accumulation of
the NGR liposomes compared with the non-targeted and control liposomes. In addition, it was
demonstrated that the inclusion of a longer PEG chain (i.e. PEG3400 versus PEG2000) at the
surface of the liposome was found to alter the shape of the tumour accumulation versus time
profile.
In addition to therapeutic applications, passively and actively targeted liposomes are
being explored for diagnostic applications. Liposomes labeled with various imaging agents have
already been shown to hold potential for detecting diseases, as well as for visualizing various
important aspects of the drug delivery process. In addition to this, liposome formulations are
being prepared with to contain both imaging and therapeutic agents. Compared to conventional
therapeutic liposomes, such imageable delivery systems take advantage of medical imaging and
can be used to noninvasively assess biodistribution and target site accumulation, to monitor and
quantify drug release, to facilitate therapeutic intervention, to predict therapeutic response, and to
longitudinally monitor the efficacy of therapeutic interventions [91]. Such image-guided therapy
will provide great potential in the development of personalized medicine.
10
1.3 Multimodal Imaging
There has been an increase in the use of non-invasive imaging techniques for clinical diagnosis,
treatment guidance, as well as drug discovery and development research [92-94]. Examples
include computed tomography (CT), optical, magnetic resonance (MR), single photon emission
CT (SPECT), positron emission tomography (PET), and ultrasound (US) [95-100]. These
imaging modalities have been successfully employed to investigate anatomical or functional
information of tissues in the body. Each imaging modality has its own inherent strengths and
limitations and differs in terms of sensitivity of detection, spatial and temporal resolution, depth
of penetration into tissues, accuracy, cost and 3D tomography. For instance, CT and MR provide
a high degree of spatial resolution with 3D tomography but are limited by low sensitivity. On the
other hand, PET has good sensitivity but provides low spatial resolution. In contrast, US has high
spatial resolution but relatively low penetration and sensitivity, whereas optical imaging also has
good sensitivity but suffers from low tissue penetration. Since modalities with the highest
sensitivity have relatively poor resolution, while those with high resolution have poor sensitivity,
there is no single modality that can provide overall structural and functional information.
Therefore, it is often through the combination of multiple modalities that complementary
information is obtained [16].
The idea of combining multiple imaging modalities moved to the mainstream with the
successful development of commercial fused instruments. PET/CT [17, 101] and SPECT/CT
[102] fused systems have been successfully developed and widely adopted in the clinic, and the
PET/MRI system was recently introduced [103, 104]. With hybrid technology clearly on the rise,
there has been significant progress made toward the development of multimodal imaging probes
as researchers and investigators look to increase the clinical benefits of such hybrid instrument
technology [105-110].Ideally, the combined imaging modalities and imaging probes work
synergistically to provide high-resolution and high-sensitivity. For example, with dual function
probes for MRI/optical [111, 112], both MRI and whole body optical imaging can be used to
assess the probe distribution in the body. Although MRI provides more biodistribution
information at the whole body level than whole body optical imaging, the presence of a
fluorophore allows confirmation of probe labeling in subsequent histology or in vivo fluorescent
11
endoscopy. Moreover, such a system could be translated into clinical settings, where MR
imaging is used for preoperative scanning to achieve disease localization, identification and to
guide the surgical procedure, while optical imaging may be used for intraoperative target
delineation.
2.2.1 CT Imaging
CT is widely used in clinical settings for diagnosis because radiography has the ability to go
through the body and provide external visualization of the internal anatomical structures [113].
In oncology, CT is used to detect or confirm the presence of a tumour, to help plan therapy or
surgery, and to assist in monitoring treatment response [114-116]. Contrast in CT is generated by
the differential attenuation of the X-ray beam in neighbouring tissues or materials. This
attenuation is dependent on the density of the tissue and is represented by the Hounsfield unit
(HU). Conventional contrast agents for CT include iodine and barium, which have high atomic
numbers and provide high X-ray attenuation [24].
Volumetric CT imaging allows for extremely fast data acquisition (a few seconds) in
submillimeter isotropic voxels, providing high spatial and temporal resolution. Moreover, CT
provides a linear response to increasing concentrations of contrast agent without being affected
by environment [34]. When combined with 3D image analysis tools, not only is the volumetric
quantification of an organ or tissue possible, but the amount of contrast agent distributed within
the organ or tissue can also be quantified. This makes CT very attractive for evaluation of
biodistribution and pharmacokinetics of contrast agents.
Micro-CT imaging has gained popularity in preclinical CT imaging involving small
animals such as mice, rats and rabbits. Over the past decade, the number of publications using
micro-CT imaging in preclinical in vivo studies has increased exponentially [117]. Micro-CT is
equipped with higher spatial and temporal resolution and allows researchers to capture
increasingly detailed anatomical images of small animals and to monitor the progression of
disease in small animal models. The first applications of micro-CT were to evaluate bone
anatomy and density [118, 119]. In the 1990s, micro-CT was further applied to study the
vasculature of small animals such as angiogenesis [120, 121] and neovascularization [122]. To
12
date, micro-CT has been popular in cardiothoracic imaging [123] and imaging of various organs
of small animals [124-126].
2.2.2 Optical Imaging
Optical imaging involves the detection of light photons transmitted through tissues. It utilizes
bioluminescent and fluorescent endogenous reporters or exogenous probes to monitor molecular
and biological processes [127]. Exogenous fluorescent probes have been developed in the near-
infrared (NIR) region (700–1000 nm) where optical imaging is optimal due to minimal tissue
absorption and low autofluorescence at these wavelengths. These probes offer a number of
advantages over radionuclides in that they are relatively inexpensive, have straightforward
synthetic and conjugation chemistries, and emit detectable, non-ionizing photons on excitation
[128]. Indocyanine green (ICG) is one such NIR probe that has been approved by the FDA and
used extensively for monitoring hepatic function, angiography, and cardiac physiology [129,
130]. Cy5.5 is another NIR fluorophore in the cyanine dye family that has been widely used in
studies to label targeting moieties [131, 132] or form activatable in vivo fluorescent probes that
utilize the concept of FRET to quench probe fluorescence upon protease cleavage [130, 133].
Studies involving fluorescent compounds include gene expression profiling, elucidation
of cellular pathways, and protein function determination [134, 135]. Currently, there is a number
of high-resolution microscopic fluorescence imaging techniques developed to study molecular
events in vivo. In particular, intravital fluorescence microscopy [136-138], confocal laser
scanning microscopy [139, 140], multi-photon laser scanning microscopy [141], and in situ
scanning force microscopy[142] have been introduced within the past years. Such techniques are
preferred for visualization at the cellular or intracellular levels and generally operate with small,
two-dimensional fields of view (~1mm2) [143].
Macroscopic fluorescence imaging systems employ photographic principles to collect
images in low light and enable whole-body imaging of small animals. Fluorescence reflectance
and tomographic fluorescence are the two main types of macroscopic imaging. Fluorescence
reflectance imaging (FRI) systems consist of an excitation source, filters, and a charge-coupled-
device (CCD) camera to obtain two-dimensional images. This is useful for imaging of surface
13
events, such as xenograft tumours and surgically exposed organs, and for intra-operative use
[144]. However, FRI has a limited depth penetration and is thus not inherently quantitative. On
the other hand, tomographic fluorescence systems are able to image the biodistribution of
fluorescence in small animals and tissues three-dimensionally and quantitatively, with a
penetration depth of up to several centimeters [133]. Fluorescence molecular tomography (FMT)
systems illuminate a tissue of interest over multiple angles and collect photons that have
propagated through tissue. Mathematical processing of the raw data yields three-dimensional
quantitative images of the fluorophore distribution in tissue [145]. Early FMT systems require
the use of index-matching fluids and the need to immerse mice or tissues in the fluid to simplify
the mathematics. Recent development has eliminated the need for the fluid [144]. While photon
scattering is the dominating limitation for fluorescent microscopy in terms of image depth, it also
reduces the image resolution for fluorescent microscopy. In FMT, photon scattering leads to
challenging image reconstruction problems and effectively limits the image resolution (1 mm - 1
cm) as well as the accuracy of quantification [143, 144]. The penetration depth of FMT is limited
by light attenuation in tissue, which depends on both tissue scattering and absorption. In muscle
or brain, the penetration depth is 3-6 cm, while in less absorbing organs such as the breast, the
penetration depth is 10-12 cm [143]. Therefore, macroscopic optical imaging can be used to
image small animals or certain tissues/organs of larger animals and humans, or used in
endoscopic and intraoperative applications
As with most optical imaging methods, FMT is highly sensitive and is able to detect
fluorophores present in the picomolar concentration range [133]. A single FMT scan usually
takes 3-5 minutes. FMT has been used in studies evaluating protease activity [133], angiogenesis
and the effects of chemotherapy on tumours [146]. In the latter study [146], Cy5.5 was
conjugated to annexin V, and the complex was used to image Lewis lung carcinomas in a mouse
model. Based on the FMT fluorescence intensity, an apoptotic index ratio was calculated and
found to be in agreement with results found using ex vivo staining of excised tumour tissues.
This demonstrates the capability of FMT to noninvasively assess the tumour accumlation of the
fluorescent probe and tumour response to chemotherapy. In addition, the multi-channel feature
allows simultaneous imaging of fluorophores emitting at different wavelengths [147, 148].
Overall, FMT’s high sensitivity, operational safety, and short image acquisition times makes it
an attractive choice for bench-top quantitative in vivo optical imaging.
14
2.2.3 CT/Optical Multimodal Imaging
The lack of a commercial multimodal scanner to incorporate whole body optical imaging makes
multimodal imaging more difficult as simultaneous imaging in the distinct modalities is not
possible. This makes image registration across modalities more difficult, especially at early time
points.. Therefore, to achieve near-simultaneous imaging across modalities, the scanners for each
modality must be in close proximity, and the scanning time for each modality must be fast. In
this case, CT imaging is the best candidate to combine with optical imaging as CT imaging
provides both high resolution and good anatomical reference, while also offering fast scanning
time (a few seconds for micro-CT). FMT as an optical imaging modality offers scanning time in
the range of 3-5 minutes, which makes near simultaneous multimodal CT/optical imaging
feasible. However, it can be argued that at early timepoints (i.e. less than 1 hr after
administration of contrast agents), image co-registration creates a larger relative temporal error.
But as the study period is prolonged (i.e. scanning a few hours post administration), this error
becomes negligible.
As mentioned before, owing to intrinsic fluorescent properties and photon scattering, the
reconstructed FMT images have a low spatial resolution (1mm) and poor anatomical reference.
For these reasons, CT [149-151] and MR [25, 26] are being used increasingly in conjunction
with FMT to provide the necessary anatomic information. Moreover, the quantitative nature of
CT enables the assessment of pharmacokinetics and biodistribution of contrast agents in vivo
[24], and can be used as a tool to evaluate the true extent that FMT can be used for quantitative
imaging. On the other hand, FMT and microscopic fluorescence imaging offers high sensitivity
and the latter enables the monitoring of the contrast agents at a microscopic level. Therefore,
combining CT and FMT should provide confirmation of the consistent transportation of the
contrast agents in vivo, as well as provide a quantitative monitoring of whole-body distribution
of the probes, and enable monitoring of the probes in a localized region. Currently, efforts are
underway to improve multimodal imaging involving optical imaging, and hybrid imaging
systems using MR or CT with FMT have already been proposed (28-32).
15
1.4 Image-based Assessment of Nanoparticle Distribution
The characterization of the pharmacokinetics and in vivo distribution of novel imaging and
therapeutic agents is crucial for understanding their in vivo performance and effectiveness. The
employment of imaging techniques to assess the in vivo distribution of the nanoparticles
provides unique insight into the in vivo pathway and fate of the nanoparticles. The noninvasive
nature of image-based assessments enables repeated in vivo data acquisition from the same
subject over multiple time points. This increases the accuracy of measurements. In addition, if
the imaging technique has high resolution, the intra-organ and tissue distribution of the agent can
be resolved. In cancer research, the ability to noninvasively quantify the nanoparticles
accumulated at tumor sites and to noninvasively map their intratumoral distribution with respect
to functional and physiological parameters of the tumor microenvironment provides the potential
to assist in the development of effective therapies.
Nanoparticle formulations are designed to improve the biodistribution and the target site
accumulation of systematically administered therapeutic agents. Many drug loaded nanoparticles
have been labeled with contrast agents in order to assess their in vivo pharmacokinetics and
biodistribution using imaging. In 2001, a preliminary clinical study evaluated the biodistribution
and pharmacokinetics of 111In-DTPA-liposomes in 17 patients with a wide range of cancers (i.e.
breast, head and neck, bronchus, glioma, cervix) [40]. Scintigraphy was used to visualize the
biodistribution of the liposomes. SPECT scans were used to help better identify the tumour.
Strong blood pool images were obtained 30 min post injection. Normal organ uptake was seen
prominently in the RES of the spleen and liver. The clearance half life of the liposomes was
determined to be 76.1 h, though it varied from 40 to 100 h from patient to patient. Overall,
through imaging, the tumour was seen in 15 out of the 17 patients. Clear visualization of the
tumours was obtained only at 48-72 h after injection due to the high blood background at earlier
time points. It was also found that there was considerable heterogeneity in the uptake of the
liposomes both between different tumor types and between different patients with the same
tumor type. For example, the levels of liposome uptake seen in the breast tumors (5.3 ± 2.6%
ID/kg) were considerably lower than those seen in the lung (18.3 ± 5.7% ID/kg) and head and
neck tumours (33.0 ± 15.8% ID/kg). In patients with head and neck tumours, the liposome
uptake in tumour varied from less than 10% ID/kg to over 50% ID/kg. This study highlights the
importance of image-guided therapy in identifying whether a patient is able to benefit from the
16
treatment. In another study, γ-scintigraphy was used to image the distribution galactosamine-
modified liver-targeted pHPMA-GFLG-doxorubicin labeled with 123I [152]. Through imaging, it
was shown that this targeted formulation effectively localized in the liver, while a comparable
formulation lacking galactosamine failed to show liver localization. Image superimposition of
the anatomical CT and functional SPECT images revealed that the majority of the targeted
formulation was associated with areas of normal liver (16.9 ± 3.9% ID, determined through
radioactivity distribution) with low accumulation in areas of hepatic tumour tissue (3.3 ± 5.6%
ID) This study highlights the importance of monitoring drug distribution, and also exemplifies
the advantages of multimodal imaging.
Visualizing drug distribution at the tumour site has been studied using gadolinium and
fluorescently labeled liposomes [153]. In this research, MRI and fluorescence microscopy were
used to monitor the intratumoural distribution of RGD-modified endothelial cell-targeted
liposomes and RAD-modified control liposomes. MRI was used for localization of the liposomes
at the whole body level, and fluorescence microscopy was used for liposome localization at the
sub-cellular level. Imaging results showed that liposomes targeted to endothelial cells
accumulated preferentially in the angiogenic rim of subcutaneously transplanted B16 tumors,
whereas control liposomes distributed non-specifically throughout the tumors. Zheng et al [24]
used quantitative CT imaging to assess the in vivo distribution of a combined liposome-based CT
and MR imaging agent This study reported the bulk organ/tissue (liver, kidneys, spleen, tumor
and blood) and intratumoral distribution of liposomes containing iohexol and gadoteridol over a
14-day period in VX2 sarcoma-bearing New Zealand White rabbits using CT imaging. It was
demonstrated that through quantitative CT based assessment, the pharmacokinetics and
biodistribution profiles of the agent were determined. It was concluded that such noninvasive,
quantitative image-guided pharmacokinetics and biodistribution assessments in the development
and preclinical testing of novel nanocarriers has the potential to greatly facilitate their clinical
translation. A later study [23] evaluated the performance of a similar CT liposome formulation to
detect tumor and inflammatory lesions in a rabbit model relative to 18F-fluorodeoxyglucose PET
(FDG-PET). This study showed that CT imaging was able to detect the differential liposome
accumulation at sites of tumor and inflammation. In addition, the use of the liposome increased
the CT contrast of neoplastic and inflammatory lesions, which was able to detect more soft-tissue
inflammatory lesions in comparison to FDG-PET.
17
Another important application of image-guided drug delivery is the potential to use
imaging to visualize drug release from the nanoparticles. As the vast majority of therapeutic
agents used in nanotechnology are inactive when conjugated to or entrapped in the delivery
system, it is important to ensure that the agents are actually being released. Conventional
methods to determine drug release require the harvesting and homogenization of target organ or
tissue, which has the problem of destabilizing carrier material as well as, leading to difficulty in
discrimination between released and retained drug. The employment of imaging can overcome
this problem and enables the noninvasive analyses of in vivo drug release through the simple co-
incorporation of the drugs and contrast agents in one delivery vehicle. MR contrast agents such
as gadolinium and manganese are highly suitable for monitoring drug release as they depend on
the interaction with surrounding water molecules to generate a signal, which varies substantially
when these agents are present within versus outside of water-impermeable vesicles such as
liposomes. In an interesting study, manganese sulfate (MnSO4) was used both to load
doxorubicin into liposomes and to generate a significant increase in MR signal upon drug and
contrast agent release [42]. This temperature sensitive liposome formulation (TSL) was shown to
have comparable relaxivity to non-temperature sensitive liposomes (NTSL) below its transition
temperature, but its relaxivity substantially increased upon heating to temperatures exceeding the
transition temperature, indicating release of Mn2+ from the liposomes [42]. A follow up study
[154] showed through MRI determineation, that the contrast agent release correlated well with
doxorubicin release, which was analyzed invasively using HPLC and fluorescence.
Employment of multimodal imaging to provide complementary information on the
distribution of nanoparticles has received increased interest among researchers. In one study,
liposomes were produced carrying rhodamine-labeled lipid on the surface and encapsulated
Ferridex particles and fluorescently labeled siRNA [155]. In this liposome formulation, Ferridex,
an MRI contrast agent, and siRNA were encapsulated during liposome formation, while 0.2%
rhodamine B-DOPE, a fluorescent dye conjugated to a phospholipid, were mixed together to
form a part of the lipid bilayer. This formulation served as a method to deliver therapeutic
siRNA to silence COX-2, a key enzyme in the inflammatory pathway that is upregulated in
several cancers. MRI was used to image the siRNA delivery to tumors in mouse models, while
ex vivo fluorescence microscopy was performed in histological samples to confirm the MRI
results and determine the siRNA delivery into cells [155]. MR/optical liposomes have also been
18
prepared through initial chelation of MRI contrast agents such as Gd with a chelator such as
DTPA or DOTA [156, 157]. For example, liposomes containing Gd-lipid and rhodamine-DOPE
were formed by sonication and studied in a T47D breast cancer mouse xenograft model [112].
The T47D cells were incubated along with the liposomes prior to inoculation. T1-weighted MRI
and whole body optical imaging (Maestro) were performed seven days after inoculation, and
clear contrast enhancement in the tumour was observed. Tumour sections showed fluorescence
consistent with rhodamine localization, confirming the in vivo imaging results. In another study
[158], 99mTc-chelates and Gd-DTPA were incorporated into perfluorocarbon-containing
liposomes. This formulation produced liposomes of 270 nm in size, and was used to target αvβ3
integrin in a tumour model. Three imaging modalities were used to monitor the liposomes, with
MRI to assess the extent of vascularization, CT for soft tissue contrast, and SPECT for probe
sensitivity detection.
19
CHAPTER 2 Comparison of CT and Optical Image-based Assessment of
Liposome Distribution
This chapter has been submitted for publication in Molecular Imaging as “Comparison of CT and
Optical Image-based Assessment of Liposome Distribution” Huang H, Dunne M, Lo J, Jaffray
DA, Allen C.
Contribution statement by the author:
In this thesis, the following studies and analyses have been performed by Huang Huang:
1. Synthesis and characterization of Cy5.5-DSPE (section 2.2.1), excluding Mass Spectrometry.
2. Preparation and characterization of CT/optical liposomes (section 2.2.2 – 2.2.4)
3. In vivo CT/optical imaging (section 2.2.5), excluding the tumour cell preparation and
inoculation, as well as the IV injection.
4. Image and data analyses (section 2.2.6).
20
2.1 Introduction
Image-based assessment of the in vivo pathway and fate of nanoparticles has received
widespread interest [92, 159-162]. This is made possible by labeling the nanoparticles with one
or more contrast agents or probes which enable visualization and measurement of the spatio-
temporal distribution of the nanoparticles in vivo using single or combined imaging modalities.
Imaging modalities that are commonly used in clinical and pre-clinical settings include computed
tomography (CT), optical, magnetic resonance (MR), positron emission tomography (PET),
single photon emission computed tomography (SPECT) and ultrasound (US) [18, 163-167].
Each of these modalities has inherent strengths and weaknesses as they differ in terms of
sensitivity, resolution, speed, and/or cost [16]. For example, optical and radionuclide imaging
(i.e. PET and SPECT) are associated with high sensitivity, however, the former presents low
tissue penetration while the latter is limited by relatively poor spatial resolution. MR and CT can
provide detailed anatomic information but are constrained by their relatively low sensitivity. US
provides high spatial resolution but it has limited field of view, and its performance is highly
user dependent. Furthermore, its application is greatly limited by its inability to access biological
targets enclosed by bony structures or air. Multimodal imaging combines the strengths of
different modalities to provide complementary information on structural, functional, and
molecular processes in vivo. Efforts to combine modalities have led to the successful
development of integrated PET/CT and SPECT/CT scanners, which have been widely adopted in
clinical settings for the detection and diagnosis of cancer [19, 168]. Prototype PET/MR systems
have also recently been developed and successfully implemented for both small animal and
human imaging [169, 170]. Furthermore, images acquired on different scanners can be registered
together to provide more complete and accurate information [1].
Multimodal imaging involving optical imaging has been studied in the past [25, 26, 149-
151, 171, 172]. The development of fluorescence molecular tomography (FMT) in optical
imaging provides the capability to three-dimensionally image the biodistribution of fluorophores
in small animals and tissues with an imaging depth of several centimeters [133, 144, 173, 174].
Hybrid imaging systems using MR or CT with FMT have also been proposed [175-179]. Owing
to intrinsic fluorescent properties and photon scattering, reconstructed FMT images present
relatively low spatial resolution (1 mm). This in turn leads to questions regarding the accuracy
21
associated with the quantification of accumulation of fluorophores in localized tissues such as
tumour using FMT. Therefore, comparison and co-registration of the quantitative data extracted
from the images in FMT with other modalities is of interest yet remains challenging.
Liposomes are the most established of the advanced drug delivery technologies and can
easily be labeled or loaded with probes, contrast agents, or radionuclides to support imaging in
single or multiple modalities [34, 37, 153, 155, 156, 180, 181]. Labeled liposomes have been
used to self-report on their fate in vivo. Image-based assessment of the distribution of drug
delivery systems can provide unique information that is otherwise unavailable using
conventional techniques [13, 182]. Such labeled delivery systems or formulations may be
employed in the design and implementation of personalized medicine. Numerous studies have
reported on the in vivo distribution of liposomes as determined using nuclear imaging techniques
[40, 183]. However, the imaging time window in PET and SPECT is limited by radioactive
decay of the radioisotopes, and thus is not ideal for long study periods [24]. Alternatively, CT
contrast agents such as iodine and barium provide strong and persistent X-ray attenuation, which
makes CT imaging an attractive tool for monitoring the fate of long-circulating nanoparticles
over prolonged periods of time. In addition, CT imaging allows for fast 3D data acquisition with
sub-millimeter resolution. This then permits sequential imaging with other fast 3D imaging tools
such as fluorescence molecular tomography (FMT), and consequently, increases the temporal
resolution of data obtained across multiple imaging modalities. Previous studies in our laboratory
have employed CT imaging to quantitatively assess the distribution of liposomes containing the
iodinated agent iohexol over prolonged periods of time in mouse and rabbit models [24, 184].
The current study extends the use of a liposome formulation to support optical imaging (Figure
2.1) and compares the information that can be gained on the macro- and microscopic distribution
of liposomes in vivo using micro-CT, FMT and confocal laser-scanning fluorescence
microscopy.
22
Figure 2.1 Schematic of the CT/optical liposome-based contrast agent (not drawn to scale).
The average hydrodynamic diameter of the liposomes was found to be 86 ± 5 nm by
dynamic light scattering (DLS).
23
2.2 Materials and Methods
Anhydrous dimethyl sulfoxide (DMSO), anhydrous chloroform, triethylamine (TEA), C8
reverse-phase silica gel, and bovine serum albumin (BSA) were purchased from Sigma-Aldrich
Canada (Oakville, Canada). 1,2-dipalmitoyl-sn-glycero-3-phosphocholine (DPPC, MW 734) and
1,2-distearoyl-sn-glycero-3-phosphoethanolamine-N-poly(ethylene glycol) 2000 (DSPE-
PEG2000, MW 2774) were purchased from Genzyme Pharmaceuticals (Cambridge, USA). 1,2-
distearoyl-sn-glycero-3-phosphoethanolamine (DSPE, MW 748.09) and cholesterol (CH, MW
387) were obtained from Avanti Lipids Inc. (Alabaster, USA). Omnipaque™, a commercially
available iodinated CT contrast agent that contains iohexol (M.W. 821.14) at 300 mg/mL iodine,
was purchased from GE Healthcare (New Jersey, USA). Cy5.5 mono-NHS ester (Cy5.5-NHS,
molar extinction coefficient 250,000 M-1cm-1, MW 1128.4) was obtained from Amersham
Sciences (Buckinghamshire, UK). NCI-H520 human non-small cell lung cancer cells and RPMI
1640 medium (2 mM L-glutamine, 10 mM HEPES, 1 mM sodium pyruvate) were supplied by
ATCC (Manassas, USA). Female nude athymic CD-1 mice were provided by Charles River
(Wilmington, USA). 4-(2-hydroxyethyl)-1-piperazineethanesulfonic acid (HEPES) was
purchased from BioShop Canada (Burlington, Canada).
2.2.1 Synthesis of Cy5.5-DSPE
The synthesis of Cy5.5-DSPE was modified from previously published studies [185, 186]. In this
method, DSPE dissolved in anhydrous chloroform mixed with TEA was added to Cy5.5-NHS
dissolved in anhydrous DMSO at a DSPE:Cy5.5 molar ratio of 1:1. The solution was stirred at
22ºC for 18 h with protection from light and was then dried and re-dissolved in methanol.
Unreacted reagents were removed by reverse phase chromatography using a column packed with
C8 reverse-phase silica gel. Deionized water, methanol and methanol/chloroform (80/20, % v/v)
were used to elute Cy5.5, Cy5.5-DSPE, and DSPE, respectively. The Cy5.5-DSPE product was
confirmed using electrospray mass spectroscopy (QStarXL mass spectrometer with electrospray
(ESI) source (MDS Sciex, Concord, Canada)) with methanol/H2O (50/50, % v/v) as solvent. The
24
purity of Cy5.5-DSPE was determined by ultra performance liquid chromatography (UPLC)
separation of Cy5.5-DSPE and Cy5.5 on a 10 cm BEH C18 column (Waters, Milford, USA). A
constant flow rate of 0.1 mL/min was employed with a gradient mobile phase of 50/50 (% v/v)
0.05M Triethylammonium acetate (TEAA) buffer (pH 7.0)/methanol to 100% methanol in 6
minutes. Both compounds were detected using a fluorescence detector (Waters, Milford, USA).
The yield of Cy5.5-DSPE was measured by ultraviolet (UV) absorbance at a wavelength of 674
nm using a Cary 50 UV-visible spectrophotometer (Varian, Palo Alto, USA).
2.2.2 Liposome Preparation
The method used for liposome preparation was adapted from a previously published report [34].
Briefly, lipid components for the CT/optical liposome formulation (i.e. DPPC, CH, DSPE-
PEG2000, and Cy5.5-DSPE) were dissolved in anhydrous ethanol at 70 °C at a molar ratio of
54.98:40:5:0.02 DPPC:CH:DSPE-PEG2000:Cy5.5-DSPE. OmnipaqueTM (300 mg/mL iodine) was
added to the solution with a lipid concentration of 100mM following ethanol removal. The
solution was kept at 70 °C for 4 h with intermittent vortexing. Unilamellar vesicles were formed
via extrusion at 70 °C using a 10-mL Lipex Extruder (Northern Lipids Inc, Vancouver, Canada)
with five passages through two stacked 200 nm pore size Track-Etch polycarbonate membranes
(Whatman Inc., Clifton, NJ) followed by ten passages through two stacked 80 nm membranes.
The unincorporated contrast agents were removed by 18 h of dialysis (MWCO 8kDa) against a
250-fold volume excess of 0.02mM HEPES-buffered saline solution (HBS, pH 7.4). The
liposome formulation was then concentrated to a final iodine concentration of approximately 40
mg/mL, with a final Cy5.5 concentration ranging between 35 μg/mL and 45 μg/mL. Liposomes
without Cy5.5-DSPE (CT liposomes) were also made using the same method with a molar lipid
ratio of 55:40:5 DPPC, CH, DSPE-PEG2000.
2.2.3 Liposome Characterization
To evaluate their hydrodynamic diameter and zeta potential, the liposomes were diluted to a lipid
concentration of 2.5 mM in deionized water. The hydrodynamic diameter was measured by
25
dynamic light scattering (DLS) at an angle of 90 degrees and a temperature of 25 °C using a
90Plus particle size analyzer (Brookhaven, Holtsville, NY). Zeta potential was measured by
photon correlation spectroscopy (PCS) using a ZetaPALS zeta potential analyzer (Brookhaven,
Holtsville, NY). The average of 10 runs consisting of 25 measurement cycles per run was
reported. For iodine and Cy5.5 concentration measurements, the liposomes were ruptured with a
10-fold volume excess of ethanol and diluted with HBS. The iodine concentration was
determined by measuring UV absorbance at a wavelength of 245 nm, and that of Cy5.5 was
determined by measuring UV absorbance at a wavelength of 674 nm. The loading efficiencies
for both iohexol and Cy5.5 were calculated using the following equation:
Agent loading efficiency (%) = (Amount of agent in final product) / (Amount of agent added
during preparation) x 100
2.2.4 In vitro Stability and Agent Release
Liposome stability was evaluated by monitoring their hydrodynamic diameter by DLS analysis
over a 14-day incubation period. Specifically, the liposomes were placed in a dialysis bag
(MWCO 8 kDa) and immersed in 250-fold volume excess of HBS containing 45 mg/mL BSA.
The sample was incubated at 37 °C. Over the course of 14 days, aliquots of the liposome solution
were taken from the dialysis bag and their hydrodynamic diameter was measured using DLS.
The release of contrast agents from the liposomes was also evaluated. Briefly, liposomes were
mixed with FBS such that the final lipid concentration was 6.5 mM and incubated at 37 °C. At 0,
2, 6, 24, 48, 72, and 96 h post-incubation, aliquots were collected and a Sepharose CL-4B
column was used to separate the liposomes from the free agents and protein. The liposome
fraction was collected, and the encapsulated amounts of iohexol and Cy5.5 were determined by
measuring the UV absorbance at 245 nm and 674 nm, respectively.
26
2.2.5 In vivo CT/Optical Imaging
The in vivo imaging studies were performed under protocols approved by the University Health
Network Animal Care and Use Committee. Athymic female CD-1 mice (6 weeks, avg. wt. 27 g)
were injected via the tail vein with a liposome formulation (CT or CT/optical) such that each
animal received 1.8 g lipid/kg body weight, 0.35 g iodine/kg body weight, and for the CT/optical
liposomes, 0.3 mg Cy5.5/kg body weight. The animals were anaesthetized using 2% isoflurane
and full body 16-second anatomical micro-CT scans (GE Locus Ultra micro-CT, GE Healthcare,
Waunakee, WI) were performed pre-injection and 0.083, 1, 4, 8, 24, 48, 72, 150, and 168 h post-
injection. CT images were acquired at 80 kVp and 70 mA with a voxel size of 0.15 mm x 0.15
mm x 0.15 mm and a field of view of 15.7 cm (transaxial) and 10.2 cm (long). Immediately after
each CT scan, the animal was fixed in the scanning chamber of the FMT system (VisEn Medical
Inc., Bedford, MA). A reflectance image of each animal was obtained and the chamber was filled
with the index matching fluid (VisEn Medical Inc., Bedford, MA) up to the animal’s upper chest.
The scanning window was adjusted to its maximum size. Each animal was scanned at excitation
and emission wavelengths of 670 nm and 700 nm, respectively. FMT images were acquired with
a nominal voxel size of 1 mm x 1 mmx 0.5 mm and a tomographic nominal field of view of 3.45
cm x 3.45 cm.
The same protocol was used for imaging tumour-bearing animals. H520 NSCLC cells
were cultured in RPMI 1640 medium supplemented with 10%vol FBS and 1%vol penicillin-
streptomycin in an atmosphere containing 5% CO2. Cells were inspected for consistent
morphology and growth rate and were passaged no more than 10 times prior to mouse
inoculation. Athymic female CD-1 mice (4 weeks, avg. wt. 23 g) were inoculated
subcutaneously with 5 x 106 H520 cells in the right hind flank. The CT/optical liposomes (0.35
g/kg iodine, 0.3 mg/kg Cy5.5, and 1.8 g/kg lipid) were injected into the animals 9 days after cell
inoculation. CT scans were performed pre-injection and 0.083, 24, 50, 73, 97, 122, 146, 169, and
193 h post-injection. Immediately after each CT scan, an FMT scan was performed, with the
scanning window adjusted to surround the right hind flank. At 24 h and 193 h post-injection, one
animal was scanned using the confocal laser-scanning fluorescence microscope (Leica
Microsystems Inc., Bannockburn, IL). Specifically, the skin above the tumour was removed, and
the microscopic probe (excitation and emission wavelengths of 650 nm and 700 nm,
27
respectively) was stabilized at various locations inside and on the surface of the tumour. Real-
time confocal videos of the regions were recorded.
2.2.6 Image Analysis
CT images were exported via DICOM and analyzed using MicroView v2.2 (GE Healthcare,
Waunakee, WI) according to a reported method [24]. Briefly, the mean iodine concentrations in
various organs were determined by contouring the region of interest (ROI) on multiple 2D slices
to generate a 3D volume, and calculating the mean voxel intensity (expressed in CT attenuation
Hounsfield Units, or HU) for each volume. For the non-tumour bearing animals, iodine
concentrations in blood (aorta), liver, spleen, and kidneys were measured. For the tumour-
bearing animals, iodine concentrations in blood, tumour, and muscle surrounding the tumour
were measured. Signal enhancement for each tissue was calculated as the difference between the
mean voxel intensity at each time point post-injection and the mean voxel intensity pre-injection.
For the tumour, the corrected mean voxel intensity was determined by subtracting the muscle
mean voxel intensity from the tumour mean voxel intensity. The intensity values were then
converted to iodine concentrations using a CT calibration curve of HU versus iodine
concentration, and the results were expressed as percent injected dose per volume tissue
(%ID/cm3). Pharmacokinetic parameters were calculated by fitting the blood iodine
concentration profiles with a one-compartment model using Scientist®3.0 (Micromath®, St.
Louis, MO). Initial parameter values were estimated through curve stripping of the blood iodine
concentration versus time plot. The fitting model used a mono-exponential decay function (C =
C0e-Ke*t, where Ke is the elimination constant) and method of weighted least squares with
weightings of 0, 1, and 2. The best fit was chosen based on highest coefficient of determination,
model selection criterion, and lowest coefficient of variation. All best fits possessed an R2 >
0.98. The vascular half-life was calculated as t1/2 = ln(2)/Ke. The area under the blood-iodine
concentration versus time curve (AUC) was calculated using the trapezoidal rule. Volume of
distribution (VD) was calculated as VD = Dose/C0. Clearance (Cl) was calculated as Cl =
Dose/AUC.
28
FMT images for non-tumour bearing animals were not analyzed due to the lack of
defined tissue boundaries, which are needed for accurate delineation of the organ and tissue
structures (Figure 4). The FMT data for the tumour-bearing animals were analyzed for the
tumour, where tissue boundaries were better defined due to isolation of the tumour from highly
perfused tissues. The mean Cy5.5 concentration in the tumour was determined using the FMT
analysis software (VisEn Medical Inc., Bedford, MA). Briefly, a 3D contour was initially laid
out to include the tumour region. An intensity threshold was adjusted to exclude zero or low
fluorescent voxels in the contour until the contour volume matched the tumour volume
determined by micro-CT analysis. A contour was also applied to the muscle region surrounding
the tumour to obtain the background signal. The corrected mean voxel intensity was determined
by subtracting the muscle mean voxel intensity from the tumour mean voxel intensity. The
corrected mean voxel intensity was converted to %ID/cm3 tumour using an FMT calibration
curve of fluorescence intensity versus Cy5.5 concentration.
The iohexol-to-Cy5.5 ratio (wt/wt) for the tumour was calculated for all time points by
dividing the concentration of iohexol (quantified by micro-CT) by the concentration of Cy5.5
(quantified by FMT) at the tumour region and normalizing to the iohexol-to-Cy5.5 ratio prior to
in vivo administration.
Results obtained by micro-CT image-based quantification of liposome accumulation in
tumour were compared to those obtained by FMT image analysis. Data selected for comparison
were between 24 to 72 h post-injection of the formulation wherein the liposome accumulation in
the tumours was high. Within this timeframe, micro-CT defined tumour volumes ranged between
25 to 30 mm3. For FMT analysis, a 2 mm3 cubic VOI was placed at the centre of the tumour, the
mean Cy5.5 concentration of the VOI was quantified, and the minimum and maximum FMT
intensity of the ROI was recorded. The VOI was then increased, without changing its centre, to
4, 6, 9, 18, 24, 32, 50 and 80 mm3. For each VOI, the Cy5.5 concentration was quantified and the
minimum and maximum FMT intensity were recorded. All FMT VOIs remained within the
boundary of the FMT observed tumour. Similarly in micro-CT, cubic VOIs were generated to
match the VOI volumes of FMT, and the mean iodine concentration was measured for each VOI.
For FMT VOIs that were larger than the micro-CT defined tumour volume, the micro-CT VOIs
remained the same as the tumour size. The iohexol-to-Cy5.5 ratio was obtained for all VOIs. For
FMT, the percentage window threshold level for each VOI was calculated as follows:
29
% FMT window threshold level = min. FMT intensity / max. FMT intensity x 100%
30
2.3 Results
2.3.1 Synthesis and Characterization of Cy5.5-DSPE
The mass spectrum for Cy5.5-DSPE is shown in Figure 2.2. The theoretical molecular
weight of Cy5.5-DSPE (1646 Da) and the molecular weight determined by MS analysis was in
good agreement. Figure 2.3 shows the UPLC chromatograms demonstrating the separation of
Cy5.5 (~3.5 min) and the more hydrophobic Cy5.5-DSPE (~7.2 min). The purity of Cy5.5-DSPE
was determined to be 99.6 ± 0.3% and the yield of Cy5.5-DSPE was 15 ± 5%.
Figure 2.2 The mass spectrum of Cy5.5-DSPE. The measured
molecular weight of Cy5.5-DSPE was determined to be 1645.7
Da, which is in good agreement with the theoretical molecular
weight 1646 Da.
31
Figure 2.3 Representative UPLC chromatograms for Cy5.5 and
purified Cy5.5-DSPE. Cy5.5 elutes at ~3.5 min, Cy5.5-DSPE
elutes at ~7.2 min. The distinction between the two sets of peaks
assures the purity achieved using the column chromatography
method.
2.3.2 Physicochemical Characterization of the Liposomes
The physicochemical properties of the CT and CT/optical liposomes are summarized in
Table 2.1. Following concentration of the liposome formulation to ~40mg/mL iodine (35 –
45μg/mL Cy5.5), the agent-to-lipid ratio (w/w) was ~0.53:1 for iohexol, and ~1.4x10-4:1 for
Cy5.5. DLS analysis of the CT/optical liposomes revealed a narrow, unimodal size distribution,
with the incorporation of Cy5.5 increasing the hydrodynamic diameter slightly. The zeta
potential of the CT/optical liposomes (-34 mV) was more negative than that of the CT liposomes
(-28 mV), which is attributed to the negatively-charged Cy5.5 molecules at the surface of the
vesicles [187, 188]. The CT/optical liposomes were shown to be stable with no change in their
size over the two week incubation period in the presence of BSA (Figure 2.4). Figure 2.5a shows
32
the in vitro release of iohexol and Cy5.5 from the CT/optical liposomes and of only iohexol from
CT liposomes in FBS at 37°C. After 96 hours, ~15% iohexol was released from both CT and
CT/optical liposomes, while ~25% Cy5.5 was released from the CT/optical liposomes.
Therefore, the ratio of iohexol to Cy5.5 (w/w) in the CT/optical liposomes increased during the
course of the incubation period such that it was significantly higher (p < 0.05) than that in the
pre-incubation formulation at all time points except that of 2 h (Figure 2.5b).
Table 2.1 Physicochemical characteristics of CT and CT/optical liposomes (n=5). Data are represented
as the mean ± standard deviation
Liposome
formulation
Hydrodynami
c diameter
(nm)
Zeta
potential
(mV)
Number of
iohexol
molecules per
liposome*
Iohexol
loading
efficiency (%)
Number of
Cy5.5
molecules per
liposome*
Cy5.5 loading
efficiency (%)
CT/optical 86 ± 5 -34 ± 3** (3.9 ± 0.8) x 104 5.8 ± 0.4 12 ± 2 68 ± 8%
CT 82 ± 2 -28 ± 3** (3.6 ± 0.4) x 104 5.9 ± 0.5 N/A N/A
* based on (8.1 ± 0.9) x 104 lipids/liposome, 4.5nm bilayer thickness [189], and a homogeneous distribution of Iohexol and Cy5.5
** Indicates statistically significant differences with p < 0.05
33
Figure 2.4 Hydrodynamic diameter (obtained by DLS analysis)
of the CT/optical liposomes over a 2-week period with dialysis
against a 250-fold volume excess of HBS containing 45mg/mL
BSA at 37°C. Data are represented as the mean ± standard
deviation of three independent batches of liposomes.
34
Figure 2.5 (a) The in vitro release profile of iohexol and Cy5.5 from CT/optical liposomes (n=3) and
iohexol from CT liposomes (n=3) incubated in FBS at 37°C. (b) Ratio of iohexol to Cy5.5 (wt/wt) in
the CT/optical liposomes following incubation in FBS at 37°C. The initial iohexol-to-Cy5.5 ratio was
normalized to 1. * indicates statistically significant difference (p < 0.01) from t = 0.
35
2.3.3 In vivo Pharmacokinetics, Biodistribution of the Liposomes
Figure 2.6 displays representative single coronal micro-CT and FMT image slices from a
non-tumour bearing mouse at various time points post-administration of the CT/optical
liposomes, illustrating the distribution of the liposomes in vivo. As shown, tissues and organs can
be distinguished in the CT images while FMT fails to enable individual tissues to be identified.
Each FMT slice contains a reflectance image of the animal and the fluorescence in the scanning
window (depicted in the square in each slice). Micro-CT and FMT image slices representing the
accumulation of liposomes in the tumour are shown in Figure 2.7. Both the transverse and
coronal CT images are displayed. The CT/optical liposomes possess a slightly increased in vivo
half-life (p < 0.05) compared to the CT liposomes in non-tumour-bearing mice (Figure 2.8a,
Table 2.2). The data obtained from analysis of biodistribution revealed a slight increase in
liposome accumulation in the liver and kidneys for the CT/optical liposomes at certain time
points, with statistically significant differences at 0.083 and 24 h post injection for the liver, and
48, 72, and 96 h post-injection for both kidneys (Figure 2.8b,d,e). Liposome accumulation in the
spleen was equivalent for both formulations (Figure 2.8c). The differential tumour-to-muscle
accumulation profile for the CT/optical liposomes over the eight-day study period is shown in
Figure 2.9b, which compares data obtained from both micro-CT and FMT.
As shown in Figure 2.9b, both analyses by micro-CT and FMT imaging reveal similar
tumour accumulation profiles with maximum liposome accumulation in tumour at 48 h post-
injection (i.e.~10 %ID iohexol/cm3 tumour and ~7 %ID Cy5.5/cm3 tumour). However, FMT-
based determination of tumor accumulation shows a higher %ID/cm3 tumour for the liposomes at
5 minutes post-injection in comparison to the accumulation observed by micro-CT. At all other
time points, micro-CT-based determination showed higher liposome accumulation than that
determined by FMT. These phenomena are partially attributed to the increased rate of release of
Cy5.5 in comparison to iohexol from the liposomes, which were demonstrated in vitro (Figure
2.5b). Figure 2.9c plots the in vivo iohexol-to-Cy5.5 ratio over the entire tumour volume wherein
the iohexol and Cy5.5 concentrations are determined by micro-CT and FMT analyses,
respectively (Figure 2.9b). Figure 2.9d shows the iohexol-to-Cy5.5 ratio over various concentric
ROIs from the observed tumour in both micro-CT and FMT. As shown, the ratio remained
constant when the ROIs were less or equal to the tumour volume, but increased drastically when
36
the ROI was greater than the true tumour volume, demonstrating a sharp decrease in the Cy5.5
concentration. The lowest FMT threshold where the iohexol-to-Cy5.5 ratio remained constant
was determined to be 66 ± 7%.
Figure 2.10 shows the images taken using the confocal microscopic probe. As shown in
Figures 2.10 a and b, most liposomes still reside within the tumour vasculature at 24 h post-
injection. However, eight days post-injection (Fig. 2.10 c), liposomes can be seen scattered
within the tumour tissue, as opposed to the tumour vasculature.
Table 2.2 Pharmacokinetic parameters for CT and CT/optical liposomes (n=7) in non-tumour-
bearing mice and CT/optical liposomes (n=5) in tumour-bearing mice. Statistics were analyzed
between the CT and CT/optical liposomes in non-tumour-bearing mice, and between
CT/optical liposomes in non-tumour and tumour-bearing mice
Formulation t1/2
(hrs)
Vd
(mL)
Blood AUC
(mg·mL-1·hr)
CL
(mL·hr-1)
CT 29.2 ± 3.7** 2.1 ± 0.3 175 ± 29** 0.039 ± 0.003*
CT/optical 36.2 ± 2.1** 2.3 ± 0.3 244 ± 46* ** 0.055 ± 0.004*
CT/optical (tumour
bearing)
30.3 ± 8.9 2.0 ± 0.2 157 ± 50* 0.054 ± 0.015
* Indicates statistically significant differences with p < 0.05
**Indicates statistically significant differences with p < 0.01
37
Figure 2.6 Micro-CT (top) and FMT (bottom) images of a non-tumour bearing mouse injected
with CT/optical liposomes. The same window and level were used for all images from each
modality.
38
Figure 2.7 Micro-CT transverse (top), coronal (middle), and FMT images of a tumour bearing
mouse injected with CT/optical liposomes at various time points. The transverse micro-CT
images display the right side of the mouse. Arrow indicates the location of the tumour. The same
window and level was used for images from each modality.
39
40
41
Figure 2.8 Pharmacokinetics and biodistribution profiles of CT and CT/optical liposomes in
non-tumour bearing mice as determined using micro-CT image-based assessment. (a) The blood
iodine concentration. The biodistribution in (b) liver, (c) spleen, (d) left kidney and (e) right
kidney. * indicates statistically significant differences (p<0.05) between CT and CT/optical
liposomes in %ID/cm3 tissue.
42
43
44
Figure 2.9 (a) Pharmacokinetics of CT/optical liposomes in tumour bearing mice as determined
using micro-CT image-based assessment of the blood iodine concentration. (b) Micro-CT and
FMT image-based determination of tumor distribution profile for CT/optical liposomes (n=5). *
indicates a statistically significant difference between micro-CT and FMT data at that time point.
(c) Ratio of iohexol to Cy5.5 (wt/wt) at the tumour site. The initial iohexol-to-Cy5.5 ratio was
normalized to 1. * indicates statistically significant difference from pre-injection. (d) Iohexol-to-
Cy5.5 ratio (wt/wt) determined from various concentric cubic VOIs over the tumour region in
both FMT and micro-CT. Data include liposome accumulation in tumours 24 to 72 h post-
injection. The tumour volumes are 25 to 30 mm3. The ratios were normalized to the iohexol-to-
Cy5.5 ratio pre-injection.
45
Figure 2.10 In vivo microscopic images of the CT/optical liposomes in the tumour area at 24 h
(a, b) and 8 days (c) post-injection.
46
2.4 Discussion
Optical imaging enables both micro- and macroscopic monitoring of nanoparticles.
Intravital microscopy provides information with cellular or intracellular resolution within a small
imageable area (~1 mm2) [143], therefore offering localized information on nanoparticle
distribution. Conventional whole-body optical imaging techniques such as fluorescence
reflectance imaging (FRI) are more limited by penetration depth and provide planar images,
preventing reporting of underlying biological processes [190]. Conversely, fluorescence
molecular tomography (FMT) provides the capability to three-dimensionally and quantitatively
image the biodistribution of fluorophores in small animals and tissues with an imaging depth of
several centimeters [133, 144, 173]. However, owing to intrinsic fluorescent properties and
photon scattering, reconstructed FMT images present low spatial resolution (1mm) and poor
anatomical reference. For these reasons, CT [149-151] and MR [25, 26] are being used
increasingly in conjunction with FMT to deliver indispensable anatomic information.
In the current study, iohexol and Cy5.5 were co-incorporated in a liposome formulation
to enable CT and FMT imaging of the biodistribution and tumour accumulation of the
nanoparticles following administration. Iohexol was passively encapsulated within the liposomes
while Cy5.5 was conjugated to the bilayer surface via insertion of a phospholipid-Cy5.5
construct. The levels of the CT and optical agents incorporated in the CT/optical liposome
formulation were significantly different due to the distinct differences between the intrinsic
sensitivity and resolution of the micro-CT and FMT imaging modalities. Micro-CT requires an
iodine concentration of at least 0.5 mg/mL [191] for detection while FMT can detect fluorescent
probe concentrations in the low picomolar range (ng/mL of Cy5.5) [133]. Our CT/optical
liposomes contain ~40000 iohexol molecules and only ~12 Cy5.5 molecules (Table 1) per
liposome, constituting a 1000-fold difference in the dose of agents administered (i.e. 0.35g/kg
iodine compared to 0.3mg/kg Cy5.5). The unique capacity of the liposomes to incorporate such
significantly different amounts of CT and optical contrast agents allows a single dose of
liposomes to be administered with the desired contrast enhancement achieved in both CT and
optical imaging modalities.
Development of a liposome formulation that is stable in vitro (Figure 2.4) with minimal
release of the incorporated contrast agents (Figure 2.5a) is shown to translate into prolonged in
47
vivo stability. This stability results in extended circulation lifetimes and provides prolonged
contrast enhancement in both the CT and FMT images (Figures 2.6–2.9). The tumour
accumulation profile (Figure 2.9b) clearly demonstrates the EPR effect, with a maximum
liposome accumulation in tumour at 48 h. This shows that prolonged circulation is important to
achieve the optimal contrast enhancement level in the tumour. The lower %ID/cm3 values from
FMT compared with that from micro-CT (Figure 2.9b) reveal that Cy5.5 is released at a more
rapid rate than iohexol in vivo, correlating well with the in vitro observations (Figure 2.5a).
Micro-CT has been shown to provide an accurate delineation of organs/tissues and a
quantitative measure of liposome distribution in vivo and is therefore useful in guiding the FMT
delineation of the tumour [24]. FMT detects the tumour through estimation of location from the
reflectance image of the animal and presence of the fluorescence intensity in that region (Figure
2.7). However, photon scattering leads to an intrinsic blur around the tumour, which increases
the observed tumour volume and makes delineation of the tumour difficult. The smaller the
actual tumour, the greater the relative increase in volume observed by FMT. In addition,
quantification error arises in FMT with inaccurate delineation (Figure 2.9d). Therefore, tumour
volumes obtained from micro-CT were employed as references to guide the accurate FMT
delineation of the tumour and subsequent measurement of liposome accumulation at the site.
Several FMT-based studies have used CT to guide FMT reconstruction (54-57). Specifically,
Garofalakis et al. (54) demonstrated that anatomical information provided by the CT data set can
be utilized during the FMT reconstruction process to improve its ability to accurately report the
concentration of fluorophores in vivo through application of geometric constrains. In the current
study, a different approach was employed where the CT data set was used as a guide for
delineation of the VOIs in the FMT data set. This method serves as a means to evaluate the
FMT’s consistency in quantification. As shown in Figure 2.9d, the iohexol-to-Cy5.5 ratio
remained constant (~1.72) only when the VOI was within 10% of the true tumour volume (as
determined through micro-CT). In order to reach the true tumour volume, the FMT VOI of the
tumour was reduced through increasing the FMT signal threshold level to exclude voxels with
lower fluorescence. The minimum FMT threshold that was needed to match the true tumour
volume as well as to keep the iohexol-to-Cy5.5 ratio constant was found to be 66 ± 7% of the
maximum intensity within the VOI. Similar adaptive thresholding methods have been proposed
and applied to PET imaging [192, 193]. In these studies, thresholds (% of the maximum
48
intensity) were applied to PET images and ROIs were contoured and compared to those
determined by CT imaging. The PET threshold that gave rise to the smallest difference in VOI
volumes was identified as optimal. This threshold was reported to be around 40% [192] and has
been adopted in several clinical studies [194-196]. However, it was noted that the threshold
depends on the volume of interest [193] as well as the background signal [197] and therefore
should not be a fixed value. Indeed, in our study, the constant FMT threshold only holds for
tumours of a certain volume (25-30 mm3), and varied based on the micro-CT-defined tumour
volume. In addition, the presence of heterogeneous background fluorescence contributes to the
quantification error of FMT [198]. It has been reported that this error can be as large as 50%.
Nevertheless, subtraction of the background fluorescence prior to image reconstruction can lead
to improved image quality and quantification accuracy. In spite of this subtraction, quantification
error was still reported to be over 30% [198]. Such quantification error leads to differences in
micro-CT and FMT-based quantification results (Figure 2.9b). In fact, as shown in Figure 7b, the
background fluorescence greatly affected the FMT signal at early time points, most notably at 5
min post injection, where the quantified Cy5.5 concentration in the tumour is abnormally high
when compared with the micro-CT result. The extent to which background fluorescence can
affect FMT quantification is currently the goal of a separate study.
The main benefit provided by multimodal imaging is the acquisition of complementary
information. In our study, micro-CT imaging enabled the clear identification of anatomical
features (Figure 2.6) with good spatial resolution and permitted the quantitative and longitudinal
assessment of the pharmacokinetics and biodistribution of the liposomes in vivo. This
compensated for the inability of the FMT to distinguish between different organs (Figure 2.6)
due to poor image resolution and considerable photon scattering. These characteristics
effectively blur the boundaries of organs and result in severely limited information regarding the
intratumoral distribution of the liposomes (Figure 2.7). In contrast, as shown in Figure 2.7,
micro-CT provides information on the intratumoural heterogeneity associated with liposome
accumulation. Moreover, FMT can only provide a reflectance image of the animal prior to
scanning as a crude and planar estimation of its anatomical features, and therefore greatly relies
on micro-CT to provide an accurate delineation of organs or tissues. In this study, each animal
was scanned in different positions in micro-CT and FMT owing to scanning limitations. When
animals are scanned in the same position in the two modalities, the images from both micro-CT
49
and FMT can be co-registered. In that case, micro-CT would not only complement FMT in
providing accurate volume information, but also offer the precise location of organs/tissues that
would otherwise only be estimated from FMT. Nonetheless, the greater sensitivity associated
with FMT, and optical imaging in general, not only enabled a very low fluorescent probe dose to
be administered, but also provided sufficient signal when only low levels of liposome were
present in the tumour. For example, the FMT image in Figure 2.7 exhibits a distinguishable
signal in the tumour region eight days after liposome administration, confirming the presence of
liposomes, whereas the micro-CT image did not visually show contrast at this time point.
Furthermore, by utilizing the confocal microscopic endoscope, the distribution of liposomes in
localized regions of the tumour can be visualized in real time (Figure 2.10). Therefore, by
exploiting CT/optical dual-modality liposomes, one can employ CT imaging to monitor and
quantify whole-body distribution and clearance of the liposomes, as well as provide adequate
anatomical background with good spatial resolution. While optical imaging modalities can be
used to confirm liposomal distribution using whole-body imaging, localized monitoring of the
distribution of the liposomes (i.e. within the tumour microenvironment) is also made possible via
microscopic imaging. Ultimately, the high sensitivity attributed to optical imaging may be
exploited in monitoring and ensuring the clearance of liposomes from the body.
50
CHAPTER 3 Conclusion
3.1 Conclusions
This thesis has demonstrated the feasibility and effectiveness of combining CT and optical
imaging to obtain qualitative and quantitative measurements of the pharmacokinetics and in vivo
biodistribution of a CT/optical liposome formulation. Through the use of multimodality FMT
and micro-CT imaging, the whole-body and tumour distribution of liposomes were monitored
and quantitatively assessed (Chapter 2). Micro-CT provided quantitative data on the distribution
of liposomes in all organs including tumour, while FMT was exclusively employed to assess the
liposome accumulation in tumour. At the tumor site, FMT demonstrated higher sensitivity than
micro-CT to detect the presence of liposomes. These findings demonstrated the complementary
nature of CT and optical imaging. Specifically, FMT and micro-CT were shown to provide
consistent assessments of liposome accumulation in tumours. A major limitation associated with
FMT is that the presence of high background fluorescence at early imaging time points due to
high systemic levels of liposomes affected the ability of FMT to provide quantitative assessment
of tumor-specific fluorescence signal. Nevertheless, it was concluded that when combined with
CT, FMT can serve as a valuable quantitative 3D optical imaging tool.
In an attempt to improve FMT quantification, the micro-CT data was used to guide the
FMT analysis of liposome accumulation in tumours The tumour volume information obtained
from the CT data set was used during the FMT analysis such that the FMT VOIs matched the
tumour volume determined using micro-CT. Results showed that the employment of micro-CT
greatly increased the delineation and quantification accuracy of FMT. This work provided
valuable insight on the use of CT to assist with FMT image analysis, which will undoubtedly
serve in the ongoing translation of 3D optical imaging techniques into the clinical setting.
Overall, this thesis provided two main innovations. The first is the development of
purification and characterization methodology for the Cy5.5-DSPE optical probe. The second is
the demonstration of the feasibility of using a registered CT data set to improve the accuracy of
quantitative FMT image analysis. This thesis provided significant contribution to the field of
51
multimodal imaging, through the development of a new CT/optical imaging agent and an
improved dual-modality imaging data quantification method.
52
3.2 Future Directions
In this report, CT was used to assist FMT-based quantification of the tumour accumulation of a
liposome imaging probe. The accuracy of FMT-based quantification alone has been reported to
vary widely (20 – 300%) for home-built FMT systems [146, 198, 199]. In these reports, the
FMT’s sensitivity and imaging performance has been shown to drop in the presence of non-
specific background fluorescence that reduces contrast [198, 199]. In vivo fluorescence
background is caused by a combination of tissue auto-fluorescence and non-specific distribution
of the administered fluorescent probe. Therefore, there is a need to study the effect of
background fluorescence on the linearity and accuracy of quantification of FMT. In an ongoing
study (see Appendix), the ability of FMT to quantify a fluorescent target embedded in a 3D
tissue-like gel phantom containing known fluorophore and background signal levels is assessed.
In addition, a simple background subtraction method is used to improve the quantification
performance of the FMT system.
Continued development of the FMT system has led to a new generation of imaging
systems, such as the FMT 1500 and FMT 2500 series by Perkin Elmer, which eliminates the
upright suspension of the mouse and the use of IMF fluid that were originally required for the
first generation FMT systems. In the new FMT systems, the anaesthetized mouse can be
immobilized in an imaging cassette in the prone position during scanning, and the imaging
cassette can be transferred to other scanners for easy multimodality image registration. In this
thesis, a first generation FMT system was employed, thus the registration of the FMT images to
the micro-CT images was challenging. Errors in image registration (i.e. location of the FMT
tumour volume within the 3D CT data set) may have contributed to the inaccuracies in
quantification. With the advent of the new generation of FMT systems, the co-registration of
53
FMT images and micro-CT images should be much more accurate, thereby also likely improving
the quantitative nature of the FMT data. In addition, accurate whole-body co-registration would
also allow CT-based contouring of various organs and tissues of interest, such as the liver, heart,
spleen, and the kidneys, and the subsequent FMT based assessment of the biodistribution of
liposomal agents. Therefore, for future studies involving FMT, it is recommended that the new
generation FMT systems be used to allow proper image registration with 3D imaging modalities
such as CT, MRI, or PET.
The work in this thesis has demonstrated great potential in the development of CT/optical
probes for clinical applications such as image-guided surgery. However, the CT/optical
liposome formulation designed in this work was not found to provide adequate retention of the
Cy5.5-DSPE lipid conjugate, in comparison to the retention of the physically entrapped agent
iohexol. Future studies should focus on the design of a CT/optical liposome formulation wherein
the optical agent is also entrapped within the internal aqueous volume of the vesicles. This will
then ensure that the ratio of the CT to optical agent remains relatively constant over time and will
facilitate more accurate in vivo data comparison between the CT and optical imaging modalities.
As a result, this will greatly increase the confidence level of quantitative optical imaging data,
which will offer significant value in the comprehensive CT-guided assessment of quantitative
optical imaging modalities such as the FMT.
54
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Appendix
Included herein are preliminary results obtained from studies examining the effect of background
fluorescence on the linearity and accuracy of quantification of FMT as previously described in
Chapter 3.2.
The gel phantoms were prepared using agarose and index matching fluid (IMF, Perkin
Elmers). Briefly, agarose was dissolved in IMF (2% w/w) at 80°C. The fluorophore GenhanceTM
680 (GH680), which has similar excitation and emission wavelengths to those of Cy5.5, was
then added into the solutions to achieve concentrations of 0, 20, 50, 80, 100, 150, 200 and 300
nM GH680. Each individual solution was then cooled inside a 1cc cubic mold (1x1x1 cm3)
resulting in 1cc cubic gels that varied in terms of GH680 concentration. Each of these small
cubic gels was placed at the centre of an 18cc mold (3.8x3.8x1.3 cm3), and the mold was filled
with the GH680 agarose solution (prepared above), which engulfed the small 1cc cubic gel. The
solution was cooled down until the larger 18cc gel formed, which was then removed from the
mold. This results in a set of gel phantoms wherein the small gel was considered the “insert” and
the larger the “background” (Figure A1c). These gel phantoms were the immobilized inside an
FMT imaging cassette (Figure A1b), which was placed on the scanning stage of the FMT 2500
system (Figure A1a) and FMT scanning was performed. As shown in Figure A1c, the phantoms
were scanned with two different orientations, with the gel insert facing toward and away from
the CCD camera.
Quantitative image analysis was performed with zero FMT window threshold. The
contoured volume of interest (VOI) was adjusted to approximately a third of the actual gel
insert’s volume and engulfed its centre. The mean FMT intensity within the contour was
measured. Similarly, the background intensity was measured. A calibration curve was
constructed by contouring the gel insert with zero background fluorescence to convert mean
68
FMT intensities to concentrations of GH680. The background subtraction method was then
applied to the gel insert as follows:
Corrected insert intensity = mean insert FMT intensity – mean background FMT intensity
Quantification error in each gel insert is determined by the following equation:
Quantification Error = (Quantified concentration (with or without background subtraction) –
Actual concentration) / Actual concentration x 100%
The obtained images (Figure A2) showed that the gel inserts can be identified visually
and accurately delineated at high signal to background ratios (S/B ratios > 1, Figure A2a, b, c,
and d), but that this worsens as the S/B ratio decreased (i.e. S/B ratios ≤ 1) (Figure A2e, f).
Therefore, a higher S/B ratio is preferred for the successful detection and delineation of the
target. The FMT quantification results also demonstrated that it is necessary to construct a
custom FMT calibration curve using phantoms with sizes and an environment that is similar to
that of the target to provide more accurate quantification. The internal FMT quantification,
which contained calibrations provided by the vendor, gave rise to ~100% quantification error at
zero background compared to our own calibration, which gave less than 5% error at zero
background.
The FMT signal increased linearly with increased GH680 concentrations in the gel insert
(R2 > 0.95 for all linear fits in Figure A3) regardless of the background level. However, the
quantification accuracy decreased with increasing background levels (Figure A3), leading to an
exponential increase in quantification error (Figure A4). As shown in Figure A4, at the lowest
69
S/B ratio of 0.167, the quantification error was 908 ± 89%. At the highest S/B ratio of 15, the
quantification error was 6 ± 4%. Background subtraction led to an overall reduction in the
quantification error. Specifically, after background subtraction, at the lowest S/B ratio of 0.167,
the quantification error was reduced to 104 ± 89%, while at the highest S/B ratio of 15, the
quantification error remained at 6 ± 4%. Such results are consistent with studies performed by
Gao and Stobiet [198], where the background fluorescence was found to significantly affect the
accuracy of quantification using FMT when the S/B ratio is below 3 with homogeneous
background fluorescence, with a quantification error of ~150% at a S/B ratio of ~2 [198]. These
results demonstrate that the FMT’s quantification results need to be carefully interpreted for
studies involving a comparison between target concentrations with different background levels.
Assuming that acceptable quantification error is ≤ 30%, it was observed that without
background subtraction, acceptable quantification error is reached only when S/B ≥ 3.75, while
with background subtraction, acceptable quantification error was reached when S/B ≥ 1.5. This
demonstrates that applying such a background subtraction when analyzing FMT images greatly
improves the accuracy of quantification. However, it is still required that the target signal be at
least 1.5 times greater than the background signal. Thus, for an intravenously administered
fluorescent probe to be quantitatively assessed in FMT, it should be required that the target of
interest be isolated from organs responsible for the agent’s distribution and clearance; the target
should be isolated from major blood vessels; and the time of imaging post administration should
be optimized to provide the optimum signal-to-background ratio.
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Figure 11. (a) Inside the FMT system. The imaging cassette (as seen in (b)) is placed in the
scanning stage prior to a scan. (b) A photo of the phantom gel inside the imaging cassette. The
gel insert (1 x 1 x 1 cm3) is fused inside the large gel (3.8 x 3.8 x 1.3 cm3), and the cassette
height is adjusted to 1.3 cm to sandwich and tightly immobilize the gel. (c) A schematic of the
large gel geometry with the gel inserted either toward or away from the source laser. Both
geometries were considered In this study.
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Figure 12. Effect of background concentration (a-f) on signal detected in ROI. The gel insert
(200 nM GH680) is facing the camera with various backgrounds (a) 20 nM, (b) 50 nM, (c) 80
nM, (d) 150 nM, (e) 200 nM, and (f) 300 nM. The dotted contours (5 mm x 5 mm) represent the
ROIs used for the analysis. The positions of the ROIs were located using both the FMT intensity
as well as the reflectance image. Slices shown have been averaged over 5mm in the z-dimension
for illustration purposes.
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Figure 13. FMT GH680 concentrations vs. actual GH680 concentrations at various background
levels (n=3 per level). The dotted line represents the line of identity. The FMT concentration
increases substantially as the background level increases. At the lowest background of 20 nM,
the quantified concentrations are very close to the actual concentrations in the gel cubes.
Background below 20 nM was found to be equivalent to zero background. At any given
background, linearity is still preserved for varying GH680 concentrations in the gel cubes (R2 >
0.95 for all fits).
73
Figure 14. The quantification error of the FMT for the gel inserts with various background levels
before and after applying background subtraction method using the method reported here. The
signal-to-background (S/B) ratio has been used to express the strength of the FMT signal in the
gel cube compared to background (BKG). Data are expressed as mean ± standard deviation (n=3
to 12). The dotted line indicates a S/B ratio of 1, representing that the GH680 concentrations in
the gel cube and background are the same. As shown, the quantification error before background
subtraction followed an exponential growth as the S/B ratio decreased, reaching 908 ± 89% at
S/B ratio of 0.167. Overall, with background subtraction, the quantification improved
substantially (p < 0.05).
