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Targeted drug delivery to breast cancer using polymeric nanoparticle micelles
by
Karyn Susana Ho
A thesis submitted in conformity with the requirements for the degree of Doctor of Philosophy
Department of Chemical Engineering and Applied Chemistry Institute of Biomaterials and Biomedical Engineering
University of Toronto
© Copyright by Karyn Susana Ho, 2012
ii
Targeted drug delivery to breast cancer using polymeric nanoparticle micelles
Karyn Susana Ho
Doctor of Philosophy
Department of Chemical Engineering and Applied Chemistry Institute of Biomaterials and Biomedical Engineering
University of Toronto
2012
Abstract
Broad distribution and activity limit the utility of anti-cancer compounds by causing
unacceptable systemic toxicity and narrow therapeutic indices. To improve tumour
accumulation, drug-loaded macromolecular assemblies have been designed to replace
conventional surfactant-based formulations. Their nanoscale size enhances tumour accumulation
via hyperpermeable vasculature and reduced lymphatic drainage. Incorporating targeting ligands
introduces cell specificity through receptor-specific binding and uptake, enabling drugs to reach
intracellular targets. In this work, the targeting properties of polymer nanoparticle micelles of
poly(2-methyl-2-carboxytrimethylene carbonate-co-D,L-lactide)-graft-poly(ethylene glycol)-
furan (poly(TMCC-co-LA)-g-PEG) were verified using in vitro and in vivo models of breast
cancer.
To select a relevant mouse model, the vascular and lymphovascular properties of two tumour
xenograft models were compared. Greater accumulation of a model nanocarrier was observed in
orthotopic mammary fat pad (MFP) tumours than size matched ectopic subcutaneous tumours,
suggesting that the organ environment influenced the underlying pathophysiology.
Immunostaining revealed greater vascular thickness, density and size, and thinner basement
iii
membranes in MFP tumours, likely contributing to greater blood perfusion and vascular
permeability.
Based on these observations, MFP tumour-bearing mice were used to characterize the
pharmacokinetics and biodistribution of a taxol drug, docetaxel, encapsulated in poly(TMCC-co-
LA)-g-PEG nanoparticles. The nanoparticle formulation demonstrated longer docetaxel
circulation in plasma compared to the conventional surfactant-based formulation. As a result,
greater docetaxel retention was uniquely measured in tumour tissue, extending exposure of
tumour cells to the active compound and suggesting potential for increased anti-cancer efficacy.
Furthermore, active targeting of antibody-modified nanoparticles to live cells was shown to be
selective and receptor-specific. Binding isotherms were used to quantify the impact of antibody
density on binding strength. The equilibrium binding constant increased linearly with the
average number of antibodies per particle, which is consistent with a single antibody-antigen
interaction per particle. This mechanistic understanding enables binding behaviour to be
adjusted in a predictive manner and guides rational nanoparticle design.
These studies validate poly(TMCC-co-LA)-g-PEG nanoparticles as a platform for targeted
delivery to cancer on both a tissue and cellular level, forming a compelling justification for
further pre-clinical evaluation of this system for safety and efficacy in vivo.
iv
Acknowledgements
I would first like to thank my closest collaborators and co-authors: Peter Poon, Ahmed Aman, Yoko Kosaka, Meng Shi, Yakov Lapitsky, Shawn Owen, Armand Keating, Rima Al-awar, Simone Helke, Xinghua Wang, and Yusuke Katayama. You not only greatly accelerated my progress, but without you this work would not have been possible. I would also like to thank the following people for always being generous with their time and insight: Warren Chan, Carol Lee, Ralph DaCosta, Brian Wilson, Tracy Liu, Ian Corbin, Gang Zheng, Ray Reilly, Robert Prud’homme, James Jonkman, Miria Bartolini, and Alicia Viloria-Petit. To the remaining people who served on my various committees, thank you for driving me to think about my project more deeply: Vladimir Torchilin, Shirley Wu, Milica Radisic, and Brad Saville.
A special thank you to my supervisor, Molly Shoichet, for the initial vision and opportunity to pursue this project: Molly, you gave me advice, resources, and manpower to ensure I had the best chance of success in my degree. Above all, you were flexible and encouraging of special skills that will be invaluable to my future professional development. I am grateful that you share my excitement in all my endeavours.
My family has always motivated me to make the most of my potential. Mom, your unconditional support and pride give me confidence to go after my biggest dreams. You have sacrificed so much to provide for all of us, and I can’t imagine where we would be without you. Dad, it goes without saying that your enthusiasm for education inspires me to love learning. Karen, your nearly daily presence makes me feel like a piece of home is always with me. I will never forget that my life in science would never have taken off without your insistence. Karmen, from sharing a room to sharing an apartment, I am thankful for your constant companionship. You show us all that perseverance leads to great things.
To my labmates, thank you for making my time here so enjoyable, and for showing me every day how to be a better scientist. I am especially thankful for the friends I made here who did not stop at making a mark on me professionally. You were a sounding board for anything I needed, and you made my days in Toronto brighter: Ryan Wylie, Douglas Baumann, Michael Conrad, Howard Kim, Nic Leipzig, Katarina Vulic, and Malgosia Pakulska. To my best friend, Catherine Kang, when we met I could not have imagined how full my life would become by having you in it. Our friendship has touched and illuminated every aspect of my life and broadened my ambitions in countless ways. To Crissa Koh, thank you for pushing me to meet your high standards and work ethic in our final year at UBC. Your energy helped me get here and encouraged me to see more of the world.
Karyn Susana Ho
v
Table of Contents
Acknowledgements ........................................................................................................................ iv
Table of Contents ............................................................................................................................. v
List of Tables ................................................................................................................................... x
List of Figures ................................................................................................................................. xi
1 Rationale ..................................................................................................................................... 1
1.1 Hypothesis and objectives ................................................................................................... 2
2 Introduction ................................................................................................................................. 4
2.1 Barriers to conventional anti-cancer drug delivery strategies ............................................. 4
2.2 Targeting the solid tumour microenvironment .................................................................... 6
2.2.1 Hyperpermeable tumour vasculature and tissue level accumulation ....................... 6
2.2.2 Overexpressed cancer genes and receptor mediated cell uptake ............................. 8
2.3 Polymeric nanoparticles and design elements in nanomedicine .......................................... 9
2.3.1 Controlling self-assembly and particle size ........................................................... 10
2.3.2 Extending circulation time and drug activity ......................................................... 11
2.3.3 Increasing drug loading and micelle stability ........................................................ 12
2.3.4 Achieving bioactive surface modification ............................................................. 14
2.4 Poly(TMCC-co-LA)-g-PEG graft copolymer for nanoparticle drug delivery .................. 15
2.4.1 Material properties ................................................................................................. 15
2.4.2 Chemical modification .......................................................................................... 17
2.5 Antibody-modified nanoparticles demonstrate antigen-specific activity .......................... 18
2.5.1 Herceptin immunonanoparticle live cell equilibrium binding ............................... 18
2.5.2 Herceptin immunonanoparticle cell cytotoxicity ................................................... 20
2.6 Conclusions ....................................................................................................................... 24
vi
2.7 Thesis scope ....................................................................................................................... 25
2.8 References ......................................................................................................................... 26
3 Pathophysiological assessment of human tumour xenografts as models of EPR in breast cancer ........................................................................................................................................ 33
3.1 Abstract .............................................................................................................................. 33
3.2 Background ........................................................................................................................ 34
3.3 Methods ............................................................................................................................. 36
3.3.1 Materials ................................................................................................................ 36
3.3.2 Cell maintenance and preparation ......................................................................... 36
3.3.3 Tumour xenograft models ..................................................................................... 37
3.3.4 Dye injections and tissue collection ...................................................................... 37
3.3.5 Immunostaining ..................................................................................................... 38
3.3.6 Image acquisition and analysis .............................................................................. 38
3.4 Results and discussion ....................................................................................................... 39
3.4.1 Orthotopic cell transplantation influences tumour growth rate and size variation ................................................................................................................. 39
3.4.2 MFP tumours exceed SC tumours in model nanocarrier accumulation ................ 41
3.4.3 Elements of tumour vascular pathophysiology observed in tumour models ........ 42
3.5 Conclusions ....................................................................................................................... 49
3.6 List of abbreviations used .................................................................................................. 49
3.7 Authors’ contributions ....................................................................................................... 50
3.8 Acknowledgements ........................................................................................................... 50
3.9 References ......................................................................................................................... 50
4 Drug-loaded nanoparticles for targeted delivery to a mouse model of breast cancer ............... 54
4.1 Abstract .............................................................................................................................. 54
4.2 Introduction ....................................................................................................................... 54
4.3 Experimental ...................................................................................................................... 57
vii
4.3.1 Materials ................................................................................................................ 57
4.3.2 DTX concentration measurement .......................................................................... 57
4.3.3 Free DTX and DTX-NP formulation ..................................................................... 58
4.3.4 Cell maintenance and preparation ......................................................................... 58
4.3.5 Tumour xenograft model ....................................................................................... 59
4.3.6 Pharmacokinetics and biodistribution ................................................................... 59
4.3.7 Plasma preparation ................................................................................................ 60
4.3.8 Tissue preparation .................................................................................................. 60
4.4 Results ............................................................................................................................... 60
4.4.1 Pharmacokinetics ................................................................................................... 60
4.4.2 Biodistribution ....................................................................................................... 63
4.5 Discussion .......................................................................................................................... 65
4.6 Conclusions ....................................................................................................................... 68
4.7 Acknowledgements ........................................................................................................... 68
4.8 References ......................................................................................................................... 69
5 Antibody-modified nanoparticles for active and tunable binding to cancer cells .................... 73
5.1 Abstract .............................................................................................................................. 73
5.2 Introduction ....................................................................................................................... 74
5.3 Experimental ...................................................................................................................... 76
5.3.1 Materials ................................................................................................................ 76
5.3.2 Nanoparticle synthesis ........................................................................................... 77
5.3.3 Cell lines and maintenance .................................................................................... 77
5.3.4 Flow cytometric analysis ....................................................................................... 78
5.4 Theory ................................................................................................................................ 78
5.4.1 Multivalent Binding ............................................................................................... 79
5.4.2 Monovalent Binding .............................................................................................. 80
viii
5.5 Results and Discussion ...................................................................................................... 82
5.6 Conclusions ....................................................................................................................... 87
5.7 Acknowledgements ........................................................................................................... 87
5.8 References ......................................................................................................................... 87
5.9 Supplementary information ............................................................................................... 91
6 Discussion ................................................................................................................................. 92
6.1 Validated pre-clinical tumour models ............................................................................... 92
6.2 Quantitative pharmacokinetics and biodistribution ........................................................... 94
6.3 Long circulating polymer nanoparticles ............................................................................ 95
6.4 Successful passive tumour targeting .................................................................................. 96
6.5 Quantitative live cell binding ............................................................................................ 96
6.6 Mechanistic predictions of binding behaviour .................................................................. 97
6.7 Conclusions ....................................................................................................................... 98
6.8 Achievement of objectives ................................................................................................ 99
6.9 References ....................................................................................................................... 100
7 Limitations and recommendations for future work ................................................................ 101
7.1 Mixed cell populations in tumor xenograft models ......................................................... 101
7.2 Cellular uptake as a function of antibody density ........................................................... 102
7.3 Tumour penetration as a function of antibody density .................................................... 103
7.4 Evaluating alternative targeting ligands .......................................................................... 104
7.5 Safety and efficacy .......................................................................................................... 104
7.6 References ....................................................................................................................... 105
Copyright Acknowledgements .................................................................................................... 109
8 Appendices .............................................................................................................................. 111
8.1 List of abbreviations ........................................................................................................ 111
8.2 List of parameters and mathematical notation ................................................................. 112
ix
8.3 Additional data ................................................................................................................ 114
8.4 References ....................................................................................................................... 116
x
List of Tables
Table 2.1 Summary of polymer and micelle properties. .............................................................. 15
Table 2.2 Flow cytometry data showing apoptotic mechanism of cell death for SKBR-3 cells
treated with 1.75 µg/mL DOX or DOX equivalent after 24 hours of incubation. ......................... 24
Table 3.1 Immunostaining protocol details listed by antigen ........................................................ 38
Table 4.1 Pharmacokinetic parameters calculated for DTX formulations after bolus IV
administration of 1.5 mg/kg DTX to tumour-bearing mice ........................................................... 62
xi
List of Figures
Figure 2.1 Broad drug distribution and activity can cause severe toxicity in healthy tissues.
Resulting side effects are commonly seen in (A) blood (anemia, immune suppression), (B) the
digestive tract (nausea, vomiting), and (C) hair follicles (hair loss). .............................................. 5
Figure 2.2 Solid tumour pathophysiology is permissive to active and passive targeting of
nanoscale carriers. Features include disorganized blood vessel architecture, leading to gaps in
the blood vessel wall that allow unregulated transport of macromolecules and other nanoscale
materials into tumour tissue. Nanoparticles modified with antibodies are depicted here
selectively extravasating across these gaps. Certain oncogenes may also be overexpressed as cell
surface markers, providing a target for antibody binding and a means for cellular uptake. ........... 8
Figure 2.3 Long plasma circulation times promote passive targeting of nanocarriers via EPR by
increasing the number of passes through hyperpermeable tumour vasculature. PEGylated
nanoparticles are depicted here passing through gaps between disorganized endothelial cells in a
tumour blood vessel. ...................................................................................................................... 12
Figure 2.4 Active recognition and binding of cancer cells can be achieved using targeting
ligands against an overexpressed oncogene on the cell surface to provide a means of selective
cellular uptake. As an example, an antibody-modified nanoparticle is depicted here binding a
cell through an antigen-specific interaction. .................................................................................. 14
Figure 2.5 Synthesis of poly(TMCC-co-LA)-g-PEG through ring opening polymerization.
Highlighted: hydrophobic backbone (green); PEG graft (grey); furan functional group (red).
(Reprinted with permission from John Wiley and Sons [78]) ....................................................... 16
Figure 2.6 Covalent antibody attachment to prepared nanoparticles via Diels-Alder chemistry.
Diels-Alder coupling was selected to preserve antibody bioactivity because it proceeds under
mild reaction conditions and in aqueous media. (Reprinted with permission from John Wiley and
Sons [78]) ...................................................................................................................................... 18
xii
Figure 2.7 Flow cytometry analysis of Herceptin immunonanoparticle binding to cell lines at
equilibrium. SKBR-3 (HER2 overexpressing), MCF-7 (normal HER2 expression), and MDA-
MB-468 (HER2 negative) cell lines were incubated with fluorescently labeled nanoparticles to
confirm selectivity of Herceptin immunonanoparticles for HER2 overexpressing cells, and
specific antibody-antigen interactions versus a series of controls for non-specific adsorption. ... 20
Figure 2.8 Confocal images showing cellular uptake of various nanoparticle formulations in
SKBR-3 cells after 6 h of incubation at 37 °C. Blue indicates cell nuclei (A-D), green indicates
fluorescently labeled polymer (A-B), and red indicates DOX autofluorescence (C-D). (A) shows
unmodified nanoparticles, where no uptake or binding is observed. (B) depicts Herceptin
nanoparticles, where binding and receptor-mediated uptake are both observed. (C) shows that
DOX nanoparticles are also internalized and accumulate intracellularly via hydrophobic
interactions between DOX and the cell membrane. (D) illustrates that even greater uptake is
observed with Herceptin-DOX-nanoparticles, as both uptake mechanisms are present. .............. 21
Figure 2.9 Average fluorescence measurements from confocal images of DOX-conjugated
nanoparticles inside SKBR-3 cells after 6 h of incubation at 37 °C. These data show that while
both formulations undergo rapid internalization, Herceptin enhances cell uptake. ...................... 22
Figure 2.10 Cell growth relative to controls for DOX treated SKBR-3 breast cancer cells versus
HMEC-1 healthy endothelial cells after 72 h of treatment at 5.0 µg/mL DOX or DOX equivalent
(IC50 of free DOX against SKBR-3 cells). This measure of effective cytotoxicity reflects a
combination of cytotoxic and cytostatic effects. ........................................................................... 23
Figure 3.1 MFP and SC tumour sizes. Tumour volumes were calculated based on caliper
measurements post-dissection of the major and minor axes and thickness (n = 4-6). SC tumours
required longer development times to become size matched to MFP tumours. Greater variability
was also observed at longer times, particularly in SC tumours, where several animals had smaller
tumours than the cohort examined the week before. ..................................................................... 40
Figure 3.2 FITC-Dextran accumulation in tumour tissue normalized to liver tissue control. High
molecular weight dextran (2 MDa, ~80 nm) was injected IV into tumour animals as a model
nanocarrier and allowed to distribute prior to sacrifice. 3 week old MFP tumours showed higher
accumulation of the nanocarrier than 5 week old SC tumours at a 90% confidence interval. All
xiii
data are shown as the mean of n = 4 animals ± SD. Lines connecting bars denote statistical
significance, P < 0.10. ................................................................................................................... 42
Figure 3.3 CD31 and collagen IV immunostaining. Mean blood vessel wall thickness visualized
through (A) CD31 (endothelial cells) and (B) collagen IV (basement membrane). Both are
abnormally thick as compared to healthy liver control tissue, which is denoted by the dashed line.
(C) shows that mean blood vessel density assayed using CD31 staining is greatest in 3 week old
MFP tumours. (D) indicates mean vascular area as a measure of blood vessel size and capacity.
Their small size categorizes them as microvasculature. All data are shown as the mean of n = 4
animals ± SD. Starred lines connecting bars denote statistical significance, P < 0.05. ............... 44
Figure 3.4 CD31 and αSMA co-staining. Representative images of pericytes (αSMA, violet) that
are detached from blood vessels (CD31, brown) in: (A) 3 week MFP, (B) 4 week MFP, and (C) 5
week SC tumours. Several blood vessels are highlighted with black arrows; blue staining
represents cell nuclei. (D) shows that pericytes are exclusively associated with blood vessels in
healthy liver control tissue. Scale bars represent 200 µm. ........................................................... 46
Figure 3.5 LYVE-1 immunostaining. (A) shows mean lymphatic vessel density, and (B) shows
mean vessel area, both of which are indicators of lymphovascular capacity. Both measures were
found to have unequal variance between groups, and therefore although the groups were not
equivalent, ANOVA could not be used to verify their differences. While 3 week old MFP
tumours had the highest mean lymphatic vessel density, 5 week old SC tumours had greater
mean vessel size, both of which contribute to overall lymphatic drainage capacity. All data are
shown as the mean of n = 4 animals ± SD. Representative images of collagen (violet) positive
but CD31 (brown) negative fluid filled spaces are shown in: (C) 3 week MFP and (D) 5 week SC
tumours. Several of these spaces are highlighted with black arrows; blue staining represents cell
nuclei. Scale bars represent 200 µm. ............................................................................................ 48
Figure 4.1 Poly(TMCC-co-LA)-g-PEG, shown here with a furan group at the PEG terminus, is
an amphiphilic co-polymer that self assembles into polymeric nanoparticle micelles with a core-
shell structure on dialysis against water. DTX and the polymer are first co-dissolved in organic
solvent before dialysis. During dialysis, DTX partitions into the hydrophobic core, thereby
encapsulating it. The polymeric nanoparticles have functional groups available for further
xiv
modification: carboxylic acid groups on the poly(TMCC-co-LA) backbone and furan moieties on
the PEG corona. ............................................................................................................................. 56
Figure 4.2 Pharmacokinetic profiles of free DTX (o) and DTX-NP (�) in tumour-bearing mice.
The plasma profiles differ significantly by 2 h post injection. The DTX-NP formulation reached
its terminal elimination phase earlier, and coupled with a slower terminal elimination rate, the
enhanced plasma retention continued to amplify over time. Points shown are the mean of n=5
animals, with error bars representing their standard deviation. Starred points represent
statistically different group means (p < 0.05). ............................................................................... 61
Figure 4.3 Biodistribution profiles of free DTX (o) and DTX-NP (�) in (A) liver, (B) spleen,
(C) lung, (D) kidney, (E) heart, and (F) tumour tissue. Points shown are the mean of n=5
animals, with error bars representing their standard deviation. Starred points represent
statistically different group means (p < 0.05). ............................................................................... 64
Figure 5.1 Functionalizing immunonanoparticles with greater numbers of targeting antibodies
enhances their ability to associate with target cells. This effect can result from (A) increases in
binding events per particle (multivalent binding) or (B) increases in possible binding
configurations with a single interaction (monovalent binding). Illustrated here are
immunonanoparticles with Ω = 3 attached antibodies. In (A), the number of antibodies bound to
cell receptors, α, is shown as α = 3 (left) and α = 2 (right). In (B) the number of antibodies
bound to cell receptors, α, is shown as α =1 for all nanoparticles. The mechanism is an important
consideration in immunonanoparticle design, as it dictates how binding strength will increase as
the antibody conjugation density increases. .................................................................................. 75
Figure 5.2 Fractional coverage of Herceptin immunonanoparticles bound to HER2
overexpressing SKBR-3 cells as a function of immunonanoparticle concentration. The arrow
indicates ascending order of antibody conjugation density: Herceptin immunonanoparticles
bearing 1.9 (�), 3.2 (r), 5.9 (�), and 9.4 (n) antibodies; inset shows fractional coverage for
free Herceptin (p). ........................................................................................................................ 83
Figure 5.3 (A) eqK and (B) GΔ increase in absolute value as the number of Herceptin
antibodies per nanoparticle increases, thereby indicating greater binding affinity. The open
symbols (¯) represent the values calculated for free Herceptin, which denotes a monovalent
xv
case, and the closed symbols (u) represent Herceptin immunonanoparticles. The trends in (A)
and (B) follow the theoretical behaviour of monovalent immunonanoparticle binding. .............. 84
Figure 5.4 Comparison of the experimental and theoretical fractional coverages (θ) of SKBR-3
cells by free Herceptin (p) and Herceptin immunonanoparticles bearing 1.9 (�), 3.2 (r), 5.9
(�), and 9.4 (n) antibodies exhibiting monovalent binding. The experimentally derived θ values
closely match the theoretically predicted θ values, with R2 = 0.99. .............................................. 86
Figure 5.5 In the case of monovalent binding, increasing the number of antibodies per particle,
Ω, results in an increase in the number of possible binding configurations where only one
interaction occurs. (A) The number of possible combinations of monovalent binding events
increases with the number of conjugated antibodies due to an amplified number of possible
rotational binding orientations for N bound nanoparticles (first term of Equation 5). (B) Also,
given a lattice of M potential binding sites, the number of distinct lattice configurations in which
the particles can bind is given by a binomial coefficient (second term of Equation 5). ............... 91
Figure 8.1 Plasma concentration profile for docetaxel in tumour-bearing mice. Orange symbols
show Herceptin-docetaxel-nanoparticles, black symbols show docetaxel nanoparticles, white
symbols show free docetaxel. ...................................................................................................... 114
Figure 8.2 Tissue concentration profile for docetaxel in tumour-bearing mice. Orange symbols
show Herceptin-docetaxel-nanoparticles, black symbols show docetaxel nanoparticles, white
symbols show free docetaxel. ...................................................................................................... 115
1
1 Rationale Broad distribution and activity underlie the dose-limiting systemic toxicity associated with anti-
cancer drug therapy. Further compounding the problem, promising drug candidates are often
bulky and polycyclic hydrophobic compounds with poor aqueous solubility. As a result, they are
formulated in mixtures of surfactants and organic solvents which exert their own non-specific
toxicity. Therefore, targeting strategies that replace surfactant-based formulations and deliver a
greater portion of the injected dose to cancer cells have the potential to significantly enhance
treatment safety and efficacy.
To improve selectivity, several unique cancer features have been identified as potential targets,
including abnormal vascular structure and pathological overexpression of cell surface receptors.
Drug-loaded nanoscale assemblies have previously been shown to accumulate selectively in
tumour tissue via enhanced permeability and retention (EPR) that results from hyperpermeable
tumour vasculature and insufficient lymphatic drainage. Antibody-modified nanoparticles have
further demonstrated receptor-specific binding of cancer cells, inducing endocytosis and
providing a mechanism for selective drug uptake.
An amphiphilic graft copolymer, poly(TMCC-co-LA)-g-PEG, was previously designed to take
advantage of these features. Poly(TMCC-co-LA)-g-PEG nanoparticles form through self-
assembly into ordered nanostructures having a hydrophobic core for drug loading, and a
hydrophilic shell for extended blood circulation and solubility in aqueous media. Furan
functional groups on the free PEG termini allow covalent attachment of maleimide-modified
antibodies under mild reaction conditions via Diels-Alder chemistry, promoting binding activity
of the final construct. The successful application of poly(TMCC-co-LA)-g-PEG nanoparticles to
cancer targeting on a tissue and cellular level would establish their utility in biological
applications.
2
1.1 Hypothesis and objectives
To establish the utility of poly(TMCC-co-LA)-g-PEG nanoparticles in cancer targeting
applications, we hypothesized that:
Poly(TMCC-co-LA)-g-PEG nanoparticle micelles will provide specific anti-cancer drug delivery
at both tissue and cellular levels
To prove this hypothesis, this work was divided into three primary objectives:
1. To investigate the influence of cell injection site on nanocarrier targeting to tumour
xenografts developed in mice.
In Chapter 3, we characterized the pathophysiology of human tumour xenografts
developed in mice after introducing a breast cancer cell line orthotopically (mammary fat
pad) or ectopically (subcutaneous), both of which are common pre-clinical models.
Based on the improved tumour uptake of a model nanocarrier and the underlying vascular
and lymphovascular properties, the orthotopic model was selected for further in vivo
studies.
(2) To demonstrate that poly(TMCC-co-LA)-g-PEG nanoparticles improve pharmacokinetics
and biodistribution over a surfactant-based drug formulation.
In Chapter 4, we loaded a taxol drug, docetaxel, into poly(TMCC-co-LA)-g-PEG
nanoparticles and measured the resulting plasma and tissue levels in orthotopic tumour-
bearing mice. We established that the nanoparticle formulation results in greater blood
circulation and tumour retention over the conventional ethanolic polysorbate 80
formulation. These results suggest potential for improved anti-tumour efficacy based on
extended exposure of cancer cells to a high drug concentration.
(3) To verify that antibody-modified poly(TMCC-co-LA)-g-PEG nanoparticles bind
selectively to cells overexpressing a target surface antigen.
In Chapter 2, we used live breast cancer cells overexpressing human epidermal growth
factor receptor 2 (HER2) to verify receptor-specific binding of nanoparticles modified
with an anti-HER2 antibody (trastuzumab, trade name Herceptin). In Chapter 5, this
3
system was extended to quantify binding strength as a function of antibody conjugation
density. Empirical binding behaviour was consistent with a theoretical model of
monovalent binding (one antibody-antigen interaction per particle), demonstrating that
binding strength can be predicted and controlled.
4
2 Introduction Portions of this chapter are derived from the following manuscript:
Ho KS, Shoichet MS (2013). Design Considerations of Polymeric Nanoparticle Micelles for Targeted Chemotherapeutic Delivery. Current Opinion in Chemical Engineering, 2(1): 53-59.
Reprinted with permission from Elsevier.
Nanoparticle drug delivery systems present exciting opportunities for safer and more effective
anti-cancer drug therapy. By engineering intelligent biomaterials for these applications, it is
possible to develop platform technologies that can be used to target and destroy more cancer
cells, and with greater specificity. Toxic chemotherapeutics given in their free form distribute
broadly throughout the body, but by redirecting more of the drug dose towards tumour sites,
reduced systemic side effects are expected, making anti-cancer treatment safer than conventional
approaches.
2.1 Barriers to conventional anti-cancer drug delivery strategies
Conventional chemotherapy is used to treat cancer using systemic application of toxic
compounds at their maximum tolerated dose, resulting in unacceptable side effects due to their
broad distribution and activity [1-3]. This approach suffers from both the inherent difficulties in
formulating anti-cancer drugs for their free administration [4, 5], and from physical and chemical
barriers to targeting active compounds to tumour tissue [6].
Simple intravenous administration of free chemotherapeutic drugs results in broad
biodistribution because this conventional approach lacks a mechanism for tumour specific
accumulation. Moreover, these toxic compounds commonly exploit rapid cell division or other
poorly selective criteria to obtain selectivity for cancer cells, but their non-specific activity kills
many other healthy cell types (Figure 2.1) [7, 8]. Consequently, patients can experience severe
side effects resulting from toxicity against healthy tissues, restricting maximum drug doses and
treatment efficacy [9].
5
Figure 2.1 Broad drug distribution and activity can cause severe toxicity in healthy tissues. Resulting
side effects are commonly seen in (A) blood (anemia, immune suppression), (B) the digestive tract
(nausea, vomiting), and (C) hair follicles (hair loss).
Additionally, anticancer drugs are often bulky hydrophobic molecules, and to overcome their
poor aqueous solubility they are administered in surfactant-containing formulations.
Unfortunately, these surfactants (e.g., polysorbate 80 and cremophor EL) are each associated
with their own side effects, further limiting the therapeutic window [4, 5]. Their use also triggers
special handling in the clinic, and patient pre-treatment to improve surfactant tolerance during
drug therapy [5].
Degradation remains an important challenge in systemic drug delivery, as free drugs are not
protected from enzymes present in the bloodstream [10]. As a result, drugs may lose activity
before they are delivered to diseased cells. Certain byproducts of these reactions have
themselves been shown to cause non-specific toxicity [11]. Low stability of chemotherapeutic
drugs in blood circulation limits their utility in cancer treatment.
Physical barriers to chemical treatment also apply to solid tumour systems. Due to the increased
metabolic rate associated with cancer cells, they are often in acidic and hypoxic environments;
both efficient chemical killing and sensitization of cells to radiation killing demand high rates of
cell division which cannot be supported by low oxygen levels [6]. Tumour vasculature is also
A B C
6
poorly organized and heterogeneously arranged, leading to areas of poor perfusion [12]. As a
result, mass transport can also be an issue, as drug molecules themselves must cross large
distances to penetrate tumour tissues completely [13]. Increased interstitial fluid pressure is
another factor, further limiting transcapillary fluid flow and convective transport, which can be
particularly restrictive in the case of large molecules like antibodies and protein drugs that rely
on convection more than on diffusion [6]. Furthermore, the majority of anti-cancer drugs require
cellular uptake to reach their sites of action [14].
While problematic, it is important to note that systemic circulation can be utilized to deliver anti-
cancer drugs to sites that have not been defined in advance, such as to metastases or cancer cells
that invaded beyond the margin of surgical resection. However, compound degradation in the
bloodstream and non-specific uptake represent important challenges.
2.2 Targeting the solid tumour microenvironment
Rapid cell growth is not the only distinguishing feature of cancer; other cancer features can offer
completely different approaches to cancer targeting. Abnormal vascular structure and changes in
gene expression are two features of tumour pathophysiology that can be exploited in drug
targeting.
2.2.1 Hyperpermeable tumour vasculature and tissue level accumulation
In healthy tissue, pro-angiogenic and anti-angiogenic factors balance one another to maintain an
organized vascular system [6, 15]. Initially, these balancing stimuli cause tumours to be
restricted in size by their available blood supply. To surpass a critical size, tumours must first
induce an imbalance in vascular regulation and develop new blood vessels to provide nutrients to
rapidly dividing cancer cells [16, 17]. In addition to neovascularization, they can also remodel
existing blood vessels [15, 18]. However, the resulting vasculature associated with tumours is
disorganized, structurally flawed, and immature [6].
Disorganized tumour architecture is characterized by several pathological features that ultimately
lead to large fenestrations in the blood vessel walls [19, 20]. In normal tissue, endothelial cells
are arranged in a monolayer with tight junctions between cells, creating a continuous and
selective barrier that regulates transport of macromolecules [19]. By contrast, tumour
vasculature is comprised of endothelial cells that are poorly aligned, multi-layered, and
7
discontinuous [17]. This structure forms the basis for pathological fenestrations. Consequently,
large molecules that would ordinarily be retained by healthy blood vessels (> 2 nm) have been
shown to cross these gaps (> 100 nm) [21, 22]. Additionally, the pericytes that normally
stabilize the endothelium are often detached or absent, leading to poor control over vessel
permeability and blood flow [18]. The basement membrane may also be abnormally thick or
absent, resulting in changes in mass transport across the extracellular matrix [20].
Further compounding the enhanced transport of fluid and material into tumour tissue, functional
lymphatic drainage is often impaired [16]. As a result, tumours experience inadequate removal
of plasma components and cellular waste material, leading to slow convective material transport,
increased interstitial fluid pressure, and lymphedema [15].
To exploit these features, it is possible to reformulate drugs with macromolecules or nanoscale
drug delivery systems to channel a greater portion of the drug dose through hyperpermeable
vasculature into tumour tissue [17]. While free drugs are small enough to pass through healthy
blood vessels, nanocarriers have improved selectivity for leaky tumour vasculature. This size-
based approach, also called passive targeting, provides a basis for targeting nanoscale materials
to cancer on a tissue level (Figure 2.2). Poor lymphatic drainage in tumours further promotes
nanocarrier retention in the extracellular space [16]. Together these phenomena are called
enhanced permeability and retention (EPR) [17, 23, 24].
To observe these effects in vivo, this underlying pathophysiology will need to be replicated. In
the case of tumour xenograft models, this may be contingent on the host organ environment
selected [25, 26].
8
Figure 2.2 Solid tumour pathophysiology is permissive to active and passive targeting of nanoscale
carriers. Features include disorganized blood vessel architecture, leading to gaps in the blood vessel wall
that allow unregulated transport of macromolecules and other nanoscale materials into tumour tissue.
Nanoparticles modified with antibodies are depicted here selectively extravasating across these gaps.
Certain oncogenes may also be overexpressed as cell surface markers, providing a target for antibody
binding and a means for cellular uptake.
2.2.2 Overexpressed cancer genes and receptor mediated cell uptake
Tumours are comprised of a heterogeneous cell population that includes cancer cells, stromal
cells, and other supporting cell types. Even within the tumour cell population several phenotypes
are present [27]. Tumour cells have subtle differences compared to healthy cells, as certain
genes are turned down (tumour suppressor genes) and others are turned up (oncogenes) [28, 29].
Selected oncogenes result in increased expression of markers on the cancer cell surface. Binding
these markers is called active targeting, and it allows us to target cancer, or potentially
subpopulations within cancer, on a cellular level (Figure 2.2) [30]. Notably, by using a
nanoscale drug delivery vehicle, it becomes possible to access intracellular targets because
nanoparticles are 2-3 orders of magnitude smaller than cells [14]. It also becomes possible to
target specific subpopulations within solid tumours based on phenotypes that are associated with
9
higher instances of relapse [31]. Through these combined approaches, there is potential to
provide better cancer specificity than traditional chemotherapy, which may translate into
improved tumour remission rates and lower toxic side effects for patients.
Overexpression of the human epidermal growth factor receptor 2 (HER2) oncogene characterizes
20-30% of breast cancers [32, 33]. This cell surface marker can be present in levels exceeding
100-fold increases over normal cells [34], and this altered phenotype has important implications
on cell behavior. HER2 is one of a family of receptor tyrosine kinases that can form
homodimers or heterodimers, resulting in cross-phosphorylation and sending a proliferative
signal to the cell [33, 35, 36]. Notably, HER2 does not require activation by a ligand, and is
therefore constitutively active, leading to a continuous proliferative signal [35]. Consequently
this oncogene is associated with chemoresistance, higher metastatic potential, and a poorer
prognosis for patients [31]. By unique association with aggressive disease and metastasis, HER2
cell surface overexpression is an ideal candidate for active targeting strategies [37].
Unlike other potential target receptors that have specificity for a natural ligand, such as folate
and folate receptor, HER2 has no known ligand. To take advantage of this promising target, a
humanized monoclonal antibody, Herceptin (trastuzumab), has been developed to bind the HER2
extracellular domain. Herceptin has been approved for clinical use and derives its activity
through two main mechanisms: inhibition of cell growth and concomitant apoptosis, and
stimulation of natural killer cells through antibody-dependent cellular cytotoxicity (ADCC) [38,
39]. However, Herceptin has only shown limited success as a standalone treatment [32, 39].
Fortunately, when administered with chemotherapeutic agent, the success rate of the
combination therapy increases [32, 40, 41]. This presents an exciting opportunity to combine
nanoscale systems to reformulate anti-cancer drugs for tissue level targeting with the Herceptin
antibody for cellular level targeting.
2.3 Polymeric nanoparticles and design elements in nanomedicine
Polymeric nanoparticles represent one approach to nanomedicine that takes advantage of
hyperpermeable tumour vasculature to improve drug distribution via the EPR effect.
Engineering the composition of amphiphilic copolymers gives control over many aspects of the
resulting micelles that form in aqueous systems [42]. Greater selectivity and concomitant
10
reduction in systemic toxicity create opportunities to broaden the therapeutic window and
improve the clinical outcomes of cancer treatment [3, 8, 43]. By utilizing the bloodstream for
distribution, there is also potential to reach both primary and secondary tumours. The polymer
can also provide protection against non-specific drug uptake and enzyme mediated drug
degradation in the bloodstream [10]. Targeting ligands are also commonly attached to add cell-
specific targeting and receptor-mediated uptake [14, 44, 45]. Polymeric systems promise
flexible chemical modification strategies, simple and tunable self-assembly into ordered
structures, and control over physical properties, all through rational design of their composition
[42].
Nanoparticles have been produced using a variety of biodegradable polymers as the core forming
segment to give a versatile suite of materials for drug delivery. Polyesters (eg. poly(lactic acid)
(PLA)) [46], poly(amino acids) (eg. poly(aspartic acid)) [47], and poly(oxypropylene) (eg.
poloxamers or Pluronics) [48] are amongst the most well studied materials for cancer drug
delivery.
2.3.1 Controlling self-assembly and particle size
Nanoparticle micelles can form spontaneously when amphiphilic block or graft co-polymers are
introduced into aqueous environments [49]. The hydrophobic polymer segments form the
micelle core and have the ability to physically load hydrophobic chemotherapeutic drugs [42].
Their size and shape influence their pharmacokinetics and biodistribution properties.
Nanoparticles under 10 nm can be quickly cleared in capillary beds and lymph nodes, while
those above 200 nm are rapidly removed from circulation via splenic filtration [50]. Size and
shape also impact particle transport, immune recognition, and cell uptake. Indeed, particle
curvature and aspect ratio determine their transport behaviour in the bloodstream [51], and
influence cellular internalization processes [52]. Additionally, discs and rod-shaped nanocarriers
have shown improved blood circulation properties over spherical particles [51, 53, 54], leading
to increased interest in developing drug carriers that circulate a particular geometry and break
into smaller nanocarriers for improved tumour accumulation, penetration, and cell uptake [54,
55].
Size also impacts passive targeting because accumulation in tumour tissue via EPR depends on
extravasation through gaps in hyperpermeable tumour vasculature, putting restrictions on
11
nanoparticle size. While the ideal size range is a topic of debate, a generally accepted range is
50-150 nm. Nonetheless, several ongoing studies argue the utility of nanocarriers outside this
range. Generally, larger nanoparticles can carry greater drug loads because of the larger
available volume for encapsulation [42]. However, unless large nanoparticles are flexible and
easily compressed, they may encounter difficulty crossing tumour vasculature [56]. Another
balancing consideration is tumour penetration, because intratumoural distribution of large
macromolecular assemblies is driven primarily by convection, leaving larger nanoparticles
trapped close to blood vessels [2]. Ultrasmall (<10 nm) gold nanoparticles show more uniform
distribution because they are able to diffuse through tissue [57].
Self-assembly is influenced by several factors, including the respective lengths of the core and
shell forming blocks, which influence the critical micelle concentration, an important measure of
nanoparticle stability [58]. The nanoparticle preparation method also determines the size and
shape of the micelles that form, although the resulting drug loading and micelle structure may be
kinetically unstable [42].
2.3.2 Extending circulation time and drug activity
A long circulation half life is a pre-requisite to tumour accumulation via passive targeting;
multiple passes through hyperpermeable tumour vasculature are required to observe EPR [49, 51,
59], and this means that drug-loaded nanoparticles must be designed to evade rapid drug
degradation and non-specific uptake (Figure 2.3). To accomplish this, the most common strategy
has been to incorporate poly(ethylene glycol) (PEG) as the hydrophilic block in the copolymers
used to prepare nanoparticles. The PEGylation strategy has many benefits, including stabilizing
nanoparticles against aggregation, providing a neutral surface charge, and limiting adsorption of
proteins and opsonins that would invoke clearance by the immune system [60]. Ideally, the
length and density of PEG in each micelle would be adequate to create a brush layer, shielding
the core from interactions with blood proteins [61, 62]. Polymer nanoparticles are well suited to
dense PEGylation because stable incorporation of PEG can be achieved simply by adjusting the
hydrophobic segment length in parallel; PEG incorporation is limited in liposomal systems,
where high PEG-lipid content tends to form small curved micelles instead of stable membrane
structures [62].
12
Figure 2.3 Long plasma circulation times promote passive targeting of nanocarriers via EPR by
increasing the number of passes through hyperpermeable tumour vasculature. PEGylated nanoparticles
are depicted here passing through gaps between disorganized endothelial cells in a tumour blood vessel.
While PEGylation extends the circulation of the nanocarrier, encapsulated drug activity must
also be protected. Metabolic processes are a major concern in conventional drug delivery
strategies where the free drug is in direct contact with blood. When drugs are loaded in the
nanoparticle core, degradation is inhibited by physically preventing enzymes from accessing the
encapsulated material, improving the pharmacokinetic profile. The inverse effect is that the drug
is also physically prevented from accessing cells while circulating, limiting non-specific toxicity.
All of these benefits have potential to increase accumulation and specificity of active drug
compounds at tumour sites.
2.3.3 Increasing drug loading and micelle stability
An important advantage of incorporating drugs into nanoparticles is that the polymer provides a
hydrophobic space to solubilize drug compounds. Typically, poor drug solubility in aqueous
media necessitates their formulation in surfactants and organic solvents, which cause side effects
of their own. Biocompatible polymers that form stable and drug-loaded nanoparticles are
therefore an attractive alternative from a formulations perspective [49, 63].
Drug loading in polymeric nanoparticles has been achieved in many ways, usually falling into
the broad categories of covalent attachment to the polymer, or physical entrapment via
hydrophobic interactions in the nanoparticle core. Considering that nanoparticles may represent
13
less than 1% of the total volume in a colloidal suspension, even less of which corresponds to the
hydrophobic core, high and stable drug loading is important [42]. Polymeric nanoparticles have
potential to attain higher drug loading than liposomes, where lipophilic drugs partition primarily
into the lipid membrane, further restricting the available space [64]. To enhance drug loading,
pH gradients (citrate) have been used to actively load drug compounds through precipitation
[65], and alternative core materials have been co-encapsulated into liposomes [66]. However,
these approaches are restricted to compounds that are relatively hydrophilic. Many promising
drug candidates derive their potency from strong interactions with biological lipid membranes
(cells and cell nuclei), which often are often accompanied by elevated hydrophobicity [49].
One way to ensure stable drug loading is to use covalent attachment to the polymer. This
polymer-drug conjugate method is useful when using a drug that contains easily modified
functional groups, such as free amines, carboxylic acids, or hydroxyl groups [67]. The
corresponding functionality required for attachment can either then be designed into the polymer,
or the drug can first be modified to fit the required chemical linkage already designed into a
particular polymer. This approach has found interesting applications in triggered release, where
the polymer-drug conjugate is a prodrug that is cleaved through a labile linker when exposed to
specific conditions, such as low pH or enzymatic degradation [67]. Drugs or polymers that are
relatively hydrophilic may also be combined using this strategy, where hydrophobic interactions
alone would not effectively keep the drug from partitioning out of the core.
Highly hydrophobic drugs do not require chemical modification to be stably incorporated into a
hydrophobic nanoparticle core. In these cases, the drug may simply be loaded during
nanoparticle preparation and will preferentially partition into the core. This effect may be further
increased by integrating components that encourage greater loading, such as covalently attached
drug molecules to promote stacking during encapsulation. With physical drug encapsulation, it
is critical that the nanoparticles are stable; because their disassembly would trigger immediate
drug release, intact nanoparticles are vital to targeting strategies. Ideally, the polymer being used
would have a low critical micelle concentration (CMC), which confers thermodynamic stability
even under the considerable dilution that occurs immediately on injection into the blood
compartment, which only intensifies with time as the polymer distributes [68]. However, many
polymer systems have high kinetic stability, especially those with high glass transition
temperatures, which means they exhibit slow rates of disassembly even when diluted below their
14
CMC [42]. High stability and drug loading are both important features of polymeric systems that
lead to their utility in targeting applications.
2.3.4 Achieving bioactive surface modification
In addition to chemotherapeutic agents, nanoparticles can be modified with targeting ligands,
such as the native ligand to a receptor [69, 70], receptor antagonists [71], peptides [72],
aptamers [73, 74], and antibodies [75] or their fragments [76]. Targeting ligands may exert their
own therapeutic effects, contributing to treatment efficacy beyond their role in targeting and
specificity. In selecting an appropriate coupling chemistry, the goal is to achieve high coupling
efficiency without sacrificing binding activity and specificity of the ligand [30, 77]. Especially
where chemical modifications are made on assembled nanoparticles, reactions and processing
conditions can disrupt micelle structure or negatively impact drug activity. The required
reagents, potential byproducts, temperature, solvent, and necessary purification steps must all be
given careful consideration. Ideally, the reaction should proceed under mild conditions, in an
aqueous environment, and require minimal post-processing. With desired chemical functional
groups in mind, polymers can be chosen or synthesized to provide platforms for simple surface
modification protocols. By preserving binding activity, selective nanoparticle uptake by a target
cell population is enabled through receptor-mediated endocytosis (Figure 2.4).
Figure 2.4 Active recognition and binding of cancer cells can be achieved using targeting ligands against
an overexpressed oncogene on the cell surface to provide a means of selective cellular uptake. As an
15
example, an antibody-modified nanoparticle is depicted here binding a cell through an antigen-specific
interaction.
2.4 Poly(TMCC-co-LA)-g-PEG graft copolymer for nanoparticle drug delivery
With these broad criteria in mind, poly(TMCC-co-LA)-g-PEG, an amphiphilic graft copolymer,
was previously developed to take advantage of tumour targeting on both a cellular and tissue
level. This novel material has a block-like structure and carboxylic acid groups on the
hydrophobic poly(TMCC-co-LA) backbone and furan groups on the free PEG termini present
versatile options for site-specific modification of the nanoparticle core and surface, respectively.
As a result, targeting ligands can be displayed fully on the nanoparticle surface, maximizing their
presentation to target receptors, while compounds that negatively impact the surface properties
of the nanoparticle can be confined to the core.
2.4.1 Material properties
In the studies that follow, we used a 20 kDa polymer backbone modified with either a 3.4 kDa or
10 kDa PEG graft (Figure 2.5) [78]. The molecular weight and composition of this material
make it biodegradable and bioeliminable, making it an attractive candidate for biological
applications [79]. We have also previously shown that this polymer self-assembles into spherical
nanoparticle micelles of tunable size through a simple dialysis process [78, 80], during which
small molecule hydrophobic agents can be encapsulated in the core. The properties of the
micelles used in this thesis are summarized in Table 2.1.
Table 2.1 Summary of polymer and micelle properties.
Chapter 4 Chapters 2 and 5 Hydrodynamic micelle diameter 83 87 nm PDI 0.43 0.28 Backbone molecular weight 20 20 kDa Backbone TMCC content 13 13 mol % PEG molecular weight 10 3.4 kDa Injected polymer 34 -‐ mg/kg Injected docetaxel 1.5 -‐ mg/kg Docetaxel loading 4.2 -‐ wt %
16
The kinetic stability of nanoparticles of poly(TMCC-co-LA)-g-PEG nanoparticles loaded with
dyes has been shown in biologically relevant media [68]. Furthermore, we can easily control the
respective lengths of the hydrophobic and hydrophilic segments, which have been shown to
affect nanoparticle size, stability, and PEG coverage [42, 58]. We have also demonstrated
control over the composition of the hydrophobic backbone (ratio of TMCC:LA) by controlling
monomer feed ratios [58]. Beyond influencing nanoparticle size and stability, the backbone
composition also determines the hydrophobicity of the nanoparticle core. TMCC is relatively
hydrophilic compared to LA, and so controlling the relative amount of TMCC in the backbone
influences its overall hydrophobicity. Another option is to control the extent of benzyl
deprotection of the TMCC units after polymerization. Manipulating these parameters may
enable the nanoparticle core properties to be adjusted to optimize drug loading according to the
hydrophobicity of the drug compound. While these features are attractive from a design
standpoint, graft copolymers for anti-cancer drug delivery are not well characterized at biological
interfaces. The aims of this thesis were built around the biological assessment of this material as
a targeted drug delivery vehicle to model tumours and cancer cells.
Figure 2.5 Synthesis of poly(TMCC-co-LA)-g-PEG through ring opening polymerization. Highlighted:
hydrophobic backbone (green); PEG graft (grey); furan functional group (red). (Reprinted with permission
from John Wiley and Sons [78])
17
2.4.2 Chemical modification
The application of polymers to targeted drug delivery systems is motivated largely by our ability
to tune their composition easily on the bench so we can engineer and tune their properties. With
poly(TMCC-co-LA)-g-PEG, it is possible to carry out covalent modification easily and
specifically, either on the carboxylic acids present on the hydrophobic backbone, or at the furans
present on the free ends of the hydrophilic graft. This presents opportunities to deliver multiple
agents in a common vehicle, such as combining drug therapies, or consolidating therapy with a
diagnostic contrast agent.
Notably, poly(TMCC-co-LA)-g-PEG was designed with antibody modification in mind.
Antibodies represent a powerful tool for active targeting, as they are highly specific and can bind
their targets strongly and avidly, and can be produced to target a wide variety of epitopes to suit
different applications. However, antibodies are also large biological molecules that are sensitive
to harsh reaction conditions that can lead to denaturation and loss of function. Therefore,
antibody attachment is ideally carried out on prepared nanoparticles to reduce the number of
handling steps, and the reaction must proceed under mild conditions to prevent damage to the
antibody, the carrier and the encapsulated compounds [81]. To accommodate these
requirements, the furan functional group was included in our polymer composition to attach
antibodies to the surface of prepared nanoparticles via Diels-Alder chemistry (Figure 2.6).
Diels-Alder reactions proceed at mild temperatures and pH and in aqueous media without
requiring a catalyst or producing byproducts. The resulting bond is stable under physiological
conditions. An interesting feature of the linker between the PEG chain and the furan group is
that it contains a serum-stable amide bond which can rapidly hydrolyze under acidic conditions
in the presence of hydrolases in lysosomes [82]. As a result, cellular trafficking of the
nanoparticle can be independent of the targeting antibody or other molecules added to the
nanoparticle surface.
18
Figure 2.6 Covalent antibody attachment to prepared nanoparticles via Diels-Alder chemistry. Diels-
Alder coupling was selected to preserve antibody bioactivity because it proceeds under mild reaction
conditions and in aqueous media. (Reprinted with permission from John Wiley and Sons [78])
2.5 Antibody-modified nanoparticles demonstrate antigen-specific activity
Poly(TMCC-co-LA)-g-PEG nanoparticles were designed to conserve antibody activity after
covalent attachment. To verify that binding and specificity were retained in the final
immunonanoparticles, we tested them using live cell assays.
2.5.1 Herceptin immunonanoparticle live cell equilibrium binding
This section contains data from the following manuscript:
Shi M, Wosnick JH, Ho K, Keating A, and Shoichet MS (2007). Immuno-polymeric nanoparticles by Diels-Alder chemistry. Angewandte Chemie International Edition, 46(32): 6126-6131.
Reprinted with permission from John Wiley and Sons.
To demonstrate the utility of Herceptin-modified nanoparticles, we performed a selectivity test
using a series of human cells lines with various levels of HER2 expression: SKBR-3 (HER2
19
overexpression); MCF-7 (normal HER2 expression); and MDA-MB-468 (HER2 negative). By
incubation with fluorescently labeled nanoparticles, equilibrium binding was assessed on live
cells using flow cytometry. High levels of Herceptin immunonanoparticle binding was observed
in the SKBR-3 cell line, with a four-fold increase in fluorescence over the isotype control (Figure
2.7). The same formulation gave a much lower fluorescence signal on incubation with MCF-7
cells, which confirms that binding levels are a function of antigen expression levels. Likewise,
no binding was observed with MDA-MB-468 cells where HER2 was absent.
To verify that these observations resulted from specific antibody-antigen binding events, and not
due to non-specific adsorption from other differences in expression profiles, several controls
were run in parallel. These controls demonstrated little binding in absence of Herceptin
(unmodified nanoparticles, and IgG1κ isotype control) or when HER2 receptors were blocked by
pre-incubation with free Herceptin (Figure 2.7). Based on these results, Herceptin
immunonanoparticles conserved binding activity and selectivity of Herceptin for the HER2
antigen, demonstrating their utility in active targeting applications.
20
Figure 2.7 Flow cytometry analysis of Herceptin immunonanoparticle binding to cell lines at equilibrium.
SKBR-3 (HER2 overexpressing), MCF-7 (normal HER2 expression), and MDA-MB-468 (HER2 negative)
cell lines were incubated with fluorescently labeled nanoparticles to confirm selectivity of Herceptin
immunonanoparticles for HER2 overexpressing cells, and specific antibody-antigen interactions versus a
series of controls for non-specific adsorption.
2.5.2 Herceptin immunonanoparticle cell cytotoxicity
This section contains data from the following manuscript:
Shi M, Ho K, Keating A, and Shoichet MS (2009). Doxorubicin-conjugated immuno-nanoparticles for intracellular anticancer drug delivery. Advanced Functional Materials, 19(11): 1689-1696.
Reprinted with permission from John Wiley and Sons.
To validate the use of Herceptin immunonanoparticles as drug delivery vehicles, we sequentially
modified assembled nanoparticles via Diels-Alder chemistry: first with Herceptin as a targeting
ligand, then with doxorubicin (DOX) as a cytotoxic drug compound. These Herceptin-DOX-
nanoparticles (NP-aHER2-DOX) were then used to treat HER2 overexpressing SKBR-3 cells.
21
Using confocal microscopy, their uptake was observed versus unmodified nanoparticles, and
nanoparticles modified with only Herceptin (NP-aHER2) or only DOX (NP-DOX). DOX-
containing particles were visualized via red DOX autofluorescence, and the remaining
formulations were modified with green Alexa-fluor 488. Unmodified nanoparticles had no
mechanism for active cellular uptake, and as expected, no membrane binding or intracellular
accumulation was observed (Figure 2.8A). The remaining formulations were surface-modified
with DOX and/or Herceptin, giving rise to substantial intracellular accumulation within 6 h of
incubation with SKBR-3 cells. These results verified that surface modification with the
Herceptin antibody conferred not only specific binding activity to our nanoparticles, but also
selectivity in uptake by inducing receptor mediated endocytosis (Figure 2.8B). Interestingly, the
placement of DOX on the nanoparticle surface also conferred non-specific uptake to DOX
nanoparticles (Figure 2.8C), likely via hydrophobic interactions with the cell membrane that the
free compound also known to exert. Fluorescence was also observed when both Herceptin and
DOX were present on the nanoparticle surface (Figure 2.8D). Moreover, the average
fluorescence intensity associated with NP-aHER2-DOX was greater than NP-DOX, suggesting
that Herceptin provides a means to direct greater amounts of DOX into HER2 positive cells via
targeting (Figure 2.9).
Figure 2.8 Confocal images showing cellular uptake of various nanoparticle formulations in SKBR-3 cells
after 6 h of incubation at 37 °C. Blue indicates cell nuclei (A-D), green indicates fluorescently labeled
polymer (A-B), and red indicates DOX autofluorescence (C-D). (A) shows unmodified nanoparticles,
where no uptake or binding is observed. (B) depicts Herceptin nanoparticles, where binding and
A Unmodified NP B NP-aHER2 C NP-DOX D NP-aHER2-DOX
22
receptor-mediated uptake are both observed. (C) shows that DOX nanoparticles are also internalized
and accumulate intracellularly via hydrophobic interactions between DOX and the cell membrane. (D)
illustrates that even greater uptake is observed with Herceptin-DOX-nanoparticles, as both uptake
mechanisms are present.
Figure 2.9 Average fluorescence measurements from confocal images of DOX-conjugated nanoparticles
inside SKBR-3 cells after 6 h of incubation at 37 °C. These data show that while both formulations
undergo rapid internalization, Herceptin enhances cell uptake.
To test whether the increased uptake of NP-aHER2-DOX translated into greater reductions in
cell number, we compared them to NP-DOX and free DOX using a colourimetric assay of live
cell metabolism of a tetrazolium salt (MTS assay) after a 72 h treatment period. To investigate
cell selectivity, we tested these formulations using both target cells (SKBR-3) and healthy
endothelial cells (HMEC-1). NP-aHER2-DOX induced greater reductions in viable cell number
in the target cell population than in the healthy cell population (Figure 2.10); this measure of
effective cytotoxicity may reflect a combination of cytotoxic and cytostatic effects. This
formulation similarly outperformed NP-DOX, indicating that Herceptin modification enhances
the effective cytotoxicity. The most remarkable difference in formulations is that although free
DOX produced the greatest cell number reduction, only the nanoparticle formulations
demonstrated selectivity for the target cell population after 72 h.
23
Figure 2.10 Cell growth relative to controls for DOX treated SKBR-3 breast cancer cells versus HMEC-1
healthy endothelial cells after 72 h of treatment at 5.0 µg/mL DOX or DOX equivalent (IC50 of free DOX
against SKBR-3 cells). This measure of effective cytotoxicity reflects a combination of cytotoxic and
cytostatic effects.
To elucidate the mechanism of viable cell reduction with our nanoparticle formulations, we used
flow cytometry to assess treated cells stained for propidium iodide (PI) to label dead cells, and
Annexin V to mark apoptotic cells. In this way we categorized cell number reductions via the
following types of observations: (1) treatments where cells stalled in the cell cycle remain
distributed similarly to controls (mainly PI and Annexin V negative); (2) treatments that
triggered early apoptosis (PI negative and Annexin V positive); (3) cells that are in late apoptosis
(PI and Annexin V positive); or cells undergoing necrosis (PI positive and Annexin V negative).
After 24 h of incubation with each formulation, the percentage of the cell population represented
by each category is listed in Table 2.2. Both Herceptin and DOX retained their original
mechanism of action when conjugated to nanoparticles alone: Herceptin reduces viable cell
counts through a cytostatic mechanism after binding HER2, whereas DOX causes apoptosis after
cell uptake and nuclear transport. However, when both are conjugated to a common particle,
NP-aHER-DOX demonstrated an even greater percentage of cells undergoing apoptosis than NP-
DOX. This observation suggests that although the improved effective cytotoxicity of NP-
24
aHER2-DOX may have been enhanced by a cytostatic mechanism of action, increased DOX
uptake via HER2 mediated endocytosis also led to synergistic levels of apoptotic cell death.
Table 2.2 Flow cytometry data showing apoptotic mechanism of cell death for SKBR-3 cells treated with
1.75 µg/mL DOX or DOX equivalent after 24 hours of incubation.
Percentage of cell population Treatment Live Early apoptotic Late apoptotic Necrotic
Untreated cells 84.5 8.9 3.6 3.0 Free aHER2 84.2 4.6 8.1 3.1 Free DOX 60.9 12.7 19.5 6.9 NP-‐aHER2 85.6 4.2 7.2 3.0 NP-‐DOX 77.5 7.2 8.8 6.5 NP-‐aHER2-‐DOX 68.8 9.5 15.4 6.3
2.6 Conclusions
Broad activity and distribution of chemotherapeutic drugs administered in their conventional free
forms has unacceptable side effects on healthy tissues. Using nanomedicine to reformulate anti-
cancer agents in targeted delivery platforms has previously shown utility in drug delivery
applications. Polymers offer tunable composition and properties, and nanoparticles prepared
from poly(TMCC-co-LA)-g-PEG have shown promise as kinetically stable structures that have
demonstrated receptor-specific binding, uptake, and cytotoxicity when modified with an
antibody for active targeting. This versatile platform is expected to be useful in biological
applications while providing simple methods for site-specific covalent modifications.
To advance our understanding of our material, we tested poly(TMCC-co-LA)-g-PEG
nanoparticles at a variety of biological interfaces that are relevant to their utility in drug targeting
applications. In the studies that follow, we challenged our nanoparticles to deliver a small
molecule drug in a relevant mouse model of breast cancer and proved that our material improved
the retention of a drug compound in tumour tissue through passive targeting. Beyond simple
confirmation of selective cell targeting, we also demonstrated that it is possible to predict and
tune binding strength using a mathematical model that we validated empirically. These studies
justify further pre-clinical evaluation of our system as a candidate for targeted drug formulations.
25
2.7 Thesis scope
To assess the ability of poly(TMCC-co-LA)-g-PEG nanoparticles to target cancer on both tissue
and cellular levels, this thesis was divided into three sections:
1. Animal models of disease are essential to the pre-clinical evaluation of treatment
strategies, but to be effective they must capture the underlying pathophysiology of human
diseases. Human tumour xenografts represent the most widely used model of solid
cancers because they are simple to replicate. To be useful in evaluating drug distribution
in the context of nanomedicine, these tumour models must demonstrate the
hyperpermeable vasculature and insufficient lymphatic drainage that enable EPR. In
Chapter 3, accumulation of a model nanocarrier and immunostaining of vascular and
lymphovascular structures were used to compare tumours that developed after cell
injection into ectopic (subcutaneous) and orthotopic (mammary fat pad) sites. This study
was used to select orthotopic tumour xenografts as the more relevant model for
subsequent in vivo testing.
2. The pharmacokinetics and biodistribution of drug formulations illustrate the transport and
metabolism of drug compounds after administration. This information can then be used
to determine dosage, scheduling, areas for improvement, and potential for systemic
toxicity. In Chapter 4, we encapsulated a taxol drug, docetaxel, in the core of
poly(TMCC-co-LA)-g-PEG nanoparticles to evaluate pharmacokinetic parameters and
measure tissue uptake in orthotopic tumour-bearing mice. After intravenous injection,
our nanoparticles were shown to prolong the plasma circulation time of docetaxel and to
extend docetaxel retention at the tumour site when compared to the conventional
ethanolic polysorbate 80 formulation. As a result, our nanoparticle formulation is likely
to enhance efficacy at matched dosages, as cytotoxicity depends both on concentration
and exposure time. These data validated the utility of our polymeric nanoparticles in
passive tumour targeting applications.
3. In introductory work for this thesis, we verified that poly(TMCC-co-LA)-g-PEG
nanoparticles could be successfully modified with Herceptin to introduce selective
binding and uptake in live HER2 overexpressing cells. In Chapter 5, we were interested
in tuning the strength of binding between these immunonanoparticles and cells because
26
this behaviour may influence cell uptake and tumour penetration. We confirmed that
nanoparticle binding strength is a function of antibody conjugation density by preparing a
series of nanoparticles having different average numbers of Herceptin antibodies per
particle. By modeling the resulting binding isotherms and calculating the equilibrium
binding constants, Keq, for each formulation, we found that Keq increased in a manner
consistent with monovalent binding (one antibody-antigen interaction per particle). This
theoretical model, which is broadly applicable to targeted nanoparticle systems, enables
binding strength of our Herceptin-modified nanoparticles to be adjusted in a predictive
manner.
In this work, we validated poly(TMCC-co-LA)-g-PEG nanoparticles as a platform capable of
targeted delivery to cancer on both a tissue and cellular level. This forms a compelling
justification for further pre-clinical evaluation of our system for safety and efficacy in vivo.
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3 Pathophysiological assessment of human tumour xenografts as models of EPR in breast cancer
This chapter is derived from the following manuscript:
Ho KS, Poon PC, Owen SC, and Shoichet MS (2013) Blood vessel hyperpermeability and pathophysiology in human tumour xenograft models of breast cancer: a comparison of ectopic and orthotopic tumours. BMC Cancer, 12: 579.
Reprinted with permission from BioMed Central.
3.1 Abstract
Background Human tumour xenografts in immune compromised mice are widely used as cancer
models because they are easy to reproduce and simple to use in a variety of pre-clinical
assessments. Developments in nanomedicine have led to the use of tumour xenografts in testing
nanoscale delivery devices, such as nanoparticles and polymer-drug conjugates, for targeting and
efficacy via the enhanced permeability and retention (EPR) effect. For these results to be
meaningful, the hyperpermeable vasculature and reduced lymphatic drainage associated with
tumour pathophysiology must be replicated in the model. In pre-clinical breast cancer xenograft
models, cells are commonly introduced via injection either orthotopically (mammary fat pad,
MFP) or ectopically (subcutaneous, SC), and the organ environment experienced by the tumour
cells has been shown to influence their behaviour. Methods To evaluate xenograft models of
breast cancer in the context of EPR, both orthotopic MFP and ectopic SC injections of MDA-
MB-231-H2N cells were given to NOD scid gamma (NSG) mice. Animals with matched
tumours in two size categories were tested by injection of a high molecular weight dextran as a
model nanocarrier. Tumours were collected and sectioned to assess dextran accumulation
compared to liver tissue as a positive control. To understand the cellular basis of these
observations, tumour sections were also immunostained for endothelial cells, basement
membranes, pericytes, and lymphatic vessels. Results SC tumours required longer development
times to become size matched to MFP tumours, and also presented wide size variability and
ulcerated skin lesions 6 weeks after cell injection. The 3 week MFP tumour model demonstrated
greater dextran accumulation than the size matched 5 week SC tumour model (for P < 0.10).
Immunostaining revealed greater vascular density and thinner basement membranes in the MFP
tumour model 3 weeks after cell injection. Both the MFP and SC tumours showed evidence of
34
insufficient lymphatic drainage, as many fluid-filled and collagen IV-lined spaces were
observed, which likely contain excess interstitial fluid. Conclusions Dextran accumulation and
immunostaining results suggest that small MFP tumours best replicate the vascular permeability
required to observe the EPR effect in vivo. A more predictable growth profile and the absence of
ulcerated skin lesions further point to the MFP model as a strong choice for long term treatment
studies that initiate after a target tumour size has been reached.
Keywords
Tumour xenograft models, orthotopic transplantation, ectopic transplantation, enhanced
permeability and retention, breast cancer, blood vessel hyperpermeability, nanomedicine,
targeting
3.2 Background
Pre-clinical development of anti-cancer therapeutics relies on availability of relevant and
reproducible in vivo tumour models. Human tumour xenograft models in immunodeficient mice
are widely used to assess pharmacokinetics, biodistribution, and treatment efficacy because they
are inexpensive and easy to replicate [1]. However, their utility in evaluating potential treatment
strategies depends on their capacity to recapitulate human disease conditions.
Progress in nanomedicine seeks to shift distribution of therapeutic compounds to tumour tissue
by targeting hyperpermeable tumour vasculature [2, 3]. Tumours are restricted in size until they
can trigger greater blood vessel density through angiogenesis and blood vessel remodeling [4, 5].
Compared to normal tissue, tumour tissue has been demonstrated to be more permissive to
extravasation of macromolecules as a result of abnormal blood vessel structure [3]. Moreover,
tumour tissue is subject to poor lymphatic drainage, leading to greater retention of material in the
extravascular space. These combined phenomena are called enhanced permeability and retention
(EPR) and form the basis for improved selectivity of nanoscale drug delivery for solid tumour
targeting [2, 4, 6].
Several pathological features of tumour vasculature lead to its utility in targeting applications.
Pathological tumour vessels are dynamic, and can result both from angiogenesis and remodeling
of existing vessels [5, 7]. Endothelial cells that comprise tumour blood vessels have poor
35
organization, leading to gaps between cells, multiple endothelial cell layers, and unusual
tortuosity and branching [8, 9]. These openings allow unregulated movement of macromolecules
and nanoscale carriers across tumour vessel walls and into the surrounding tissue [10]. In
response, the associated basement membrane is also often thickened or absent [9, 11]. This
apparent dichotomy stems from a dynamic interaction between increased and multilayered
collagen deposition in the basement membrane [10, 12-14] and increased expression of matrix
metalloproteinases (MMPs) that can result in collagen degradation [15]. Further enhancing the
aberrant permeability of tumour blood vessels, the pericytes that normally cover and stabilize the
outer vessel wall can also be missing or detached, leading to a more immature vessel structure
[5]. The absence of these contractile support cells may lead to further increased vessel
permeability and weakened control over blood flow [7]. Lymphatic vessels are closely
associated with and derived from the blood vessel network. They are responsible for
transporting waste out of tissues, but tumours are often deficient in lymphatic drainage, leading
to increased accumulation of macromolecular material in tumour tissue [4]. Each of these
features contributes to the pathophysiology that enables the EPR effect.
To validate the use of human tumour xenografts in mouse models of breast cancer to investigate
tumour targeting via EPR, we studied MDA-MB-231-H2N cells transplanted in NOD scid
gamma (NSG) mice and compared two common cell injection sites in the context of EPR
permissive pathology. Owing to its simplicity, tumour cells are often introduced ectopically as
subcutaneous (SC) injections, regardless of their native tissue type [16-18]. Cells injected
orthotopically (eg. breast cancer cells into mammary tissue) are subject to biological cues present
in the relevant organ environment [17]. Allowing tumour cells to grow in their orthotopic
environment influences growth rate, blood and lymphatic vessel development, metastatic
potential, interstitial pressure, and response to therapy [5, 17-20]. We hypothesized that the
orthotopic environment may also influence the permeability of the resulting tumour vasculature.
Groups of animals were compared as cohorts of matching tumour size because size, and not
elapsed time, is a standard prognostic measure used to assess breast cancer stage [21].
Currently, the benefit of using either SC or MFP in xenograft models of breast cancer in
assessing targeting through the EPR effect is not well characterized. To investigate vessel
permeability, orthotopic and ectopic tumour-bearing NSG mice were given intravenous
injections of a fluorescently labeled high molecular weight dextran (FITC-Dextran, 2 MDa, ~80
36
nm [8]) as a model nanocarrier. After allowing the dextran to circulate, animals were sacrificed,
their tumours removed, measured using calipers, and fixed with paraformaldehyde. Tumours
were cryosectioned and examined for dextran accumulation. Tissue sections were also
immunostained for markers of vascular endothelial cells (CD31), basement membrane (collagen
IV), pericytes (alpha smooth muscle actin (αSMA)), and lymphatic vessels (lymphatic vessel
endothelial hyaluronan receptor (LYVE-1)).
3.3 Methods
3.3.1 Materials
All cell culture materials were purchased from Gibco-Invitrogen (Burlington, ON, Canada).
MDA-MB-231-H2N cells and NOD scid gamma (NSG) mice were generous gifts from Dr.
Robert Kerbel (Sunnybrook Research Institute, Toronto, ON, Canada), which were then
maintained or bred in-house. Lysine-fixable dextran-FITC (MW 2 MDa) was purchased from
Invitrogen (Burlington, ON, Canada). Slides and cover slips were purchased from Fisher
Scientific (Ottawa, ON, Canada). Primary antibodies were purchased from Abcam (Cambridge,
MA, USA) for CD31 (ab28364), LYVE-1 (ab14917), collagen IV (ab19808), and αSMA
(ab5694). Immunostaining reagents (rabbit IgG Elite ABC kit, avidin/biotin kit, enzyme
substrates, Vectashield mounting medium) were purchased from Vector Labs (Burlington, ON,
Canada). Entellan hard mounting medium was purchased from EMD Millipore (Billerica, MA,
USA). All other materials were purchased from Sigma-Aldrich (Mississauga, ON, Canada) and
used as received unless otherwise noted.
3.3.2 Cell maintenance and preparation
MDA-MB-231-H2N cells were maintained in RPMI 1640 culture medium, supplemented with
10% heat-inactivated fetal bovine serum (FBS), 50 units/mL penicillin and 50 mg/mL
streptomycin under a humidified 5% CO2 environment. To prepare cell suspensions for injection,
adherent cells were first rinsed with phosphate buffered saline, pH 7.4 (PBS), and then incubated
briefly with trypsin-ethylenediamine tetraacetic acid (trypsin-EDTA, 0.25%/0.038%). Once the
cells were suspended, enzymatic digestion was inhibited with FBS, and the cells were pelleted
and washed 3 times in PBS before resuspension at the desired concentration. Cells were kept on
ice prior to injection.
37
3.3.3 Tumour xenograft models
The protocols used in these in vivo studies were approved by the University Health Network
Animal Care Committee and performed in accordance with current institutional and national
regulations. Animals were housed in a 12 h light and 12 h dark cycle with free access to food and
water. NSG mice were bred in-house, and 7-9 week old female mice were selected for tumour
xenotransplantation.
Mice in all experimental groups were inoculated with 106 MDA-MB-231-H2N cells suspended
in 50 µL of sterile PBS. Prior to injection, mice were anaesthetized with isoflurane-oxygen. To
form ectopic SC tumours, anaesthetized mice were injected with cells under the skin in the right
dorsal flank. To form orthotopic mammary fat pad (MFP) tumours, the surgical area was
depilated and swabbed with 70% ethanol and betadine before making an incision in the skin of
the lower abdomen to the right of the midline, uncovering the mammary fat pad in the right
inguinal region where cells were injected into the fat pad. The incision was then sutured closed
and lactated Ringer’s solution and buprenorphine were given post-operatively for recovery and
pain management. Solid tumours were allowed to form over a period of 3-5 weeks. Cohorts of
tumour-bearing animals were divided into two groups to proceed onwards for testing; the first
group was tested once their tumours reached an average diameter along the major axis of 7 mm
as measured through the skin using calipers, and the second group tested the following week.
3.3.4 Dye injections and tissue collection
Once tumours reached their target size, mice were injected with 0.5 mg of FITC-dextran in 200
µL of PBS via intravenous (IV) tail vein injection [8]. After 1 h, animals were sacrificed by CO2
asphyxiation and tissue samples (tumour and liver) were collected by dissection; tumour samples
were directly measured for diameter along both the major and minor axes (L and W) and
thickness (H) using calipers (ellipsoid volume calculated as 𝜋 6×𝐿×𝑊×𝐻 [22]), and each
sample was placed separately in cassettes and submerged in 4% paraformaldehyde for 24 h at 4
°C. Tissue samples were then cryoprotected in 30% sucrose in PBS and stored at 4 °C. Tissue
samples were cryosectioned in 10 µm sections, and pairs of slices 50 µm apart were mounted
onto slides, and stored at -80 °C. For fluorescence analysis, slides were rehydrated in PBS and
coverslipped using Vectashield mounting medium.
38
3.3.5 Immunostaining
Three slides (six tissue sections) from each tumour were selected for each set of stains such that
each slide contained sections a minimum of 300 µm away from the previous slide. Thawed
slides were hydrated and washed in PBS and incubated with 0.3% H2O2 in methanol for 20 min
before being washed in PBS again and blocked in 1.5% normal goat serum (NGS) in PBS (see
Table 3.1 for details). Avidin and biotin blocking reagents were applied sequentially for 15 min
each before incubating with the primary antibody at 4 °C overnight (dilutions noted in Table
3.1). The following day, slides were washed in PBS and incubated with a biotinylated secondary
goat anti-rabbit IgG (1:200 dilution as instructed in kit) , followed by incubation with avidin-
biotinylated enzyme complex (ABC reagent) (times noted in Table 3.1). Rinsed sections were
then developed using 3,3’-diaminobenzidine (DAB) enzyme substrate for 1-10 min (brown
product). If applicable, slides were then co-stained by repeating the above procedure beginning
at the NGS blocking step, and developed in VIP enzyme substrate for 5-7 min (violet product).
All slides were counter stained by applying 0.5% methyl green for 10 min (blue-green nuclear
stain), washed in distilled water, dried in 1-butanol, and transferred to xylene before being
coverslipped using Entellan hard mounting medium.
Table 3.1 Immunostaining protocol details listed by antigen
CD31 Collagen IV αSMA LYVE-‐1
NGS blocking incubation time 1 h 1 h 1 h 20 min
Primary antibody dilution 1:200 1:1000 1:1000 1:1000
Secondary antibody incubation time 1 h 30 min 30 min 30 min
ABC reagent incubation time 1 h 30 min 30 min 30 min
3.3.6 Image acquisition and analysis
All fluorescence images were acquired with a fixed exposure time for each channel using an
Olympus BX50 with a UPlanSApo 10×/0.40 objective, Photometrics CoolSNAP HQ2
monochrome camera, and motorized stage (Olympus Canada Inc., Richmond Hill, ON, Canada).
Images were tiled together using Metamorph, and analyzed using ImageJ. Brightfield images
39
were acquired using an Aperio ScanScope XT (Aperio, Vista, CA, USA) for whole slide
scanning at 20× magnification and analyzed using ImageScope Microvessel Analysis. Statistical
significance between groups was first tested with Bartlett’s test for equality of variance (P <
0.05). Where variances were equivalent, one-way ANOVA was applied, followed by a corrected
unpaired t-test; differences are denoted by square bracket symbols connecting the differing
groups (P < 0.05, unless otherwise noted).
3.4 Results and discussion
3.4.1 Orthotopic cell transplantation influences tumour growth rate and size variation
Tumour size of human tumour xenograft models grown in mice both orthotopically (MFP) and
ectopically (SC) was monitored weekly through the skin in live animals using calipers.
Following cell injection, MFP tumours reached a target size of 7 mm in diameter across the
major axis by 3 weeks post-injection whereas SC tumours took an additional 2 weeks to reach
this size. Differences in growth rate were expected, as each injection site provides a different
microenvironment. Cohorts of animals were selected based on tumour size matching instead of
development time because size is one of three standard measurements that determines breast
cancer prognosis [21]. After resection, tumours were measured directly using calipers and the
volumes were calculated based on measurements of the major and minor axes and thickness
(Figure 3.1). The difference in time needed to achieve size matched populations for MFP and
SC tumour models suggests that the organ environment influences the growth rate of xenografted
cells.
To investigate effects associated with tumour size, 4 animals from each tumour type were
randomly selected for dextran injection and tumour resection once the 7 mm major axis diameter
was reached, with the remaining animals evaluated the following week. The 7 mm target size
was selected to allow adequate vascular pathology to develop, as neovascularization of the
tumour is most pronounced over serveral days immediately after a palpable mass (20 mm3) has
formed [23]. Notably, the week after this target size was reached, tumour size variability
increased in both tumour sites (P < 0.05 by Bartlett’s test of equality of variances).
Unexpectedly, several tumours in the SC group at 6 weeks post-cell injection were smaller than
those observed the previous week (Figure 3.1). Additionally, several replicates were of similar
40
size or larger size, resulting in a broad size distribution of the resulting tumours. In this group, 4
out of 6 animals developed hard fibrotic tissue leading to an ulcerated skin lesion by this time (an
indication for humane sacrifice). These lesions, which made these animals unsuitable for further
study beyond this time, were not observed in any other group. MFP tumours were grown from
cells injected directly into the centre of the MFP, surrounding transplanted cells with endogenous
support cells and forming a biological barrier against contact with the skin, which may have
prevented ulceration. These injected cells also had access to the pre-existing vascular network
and biological signaling molecules present in the MFP. Overall, the MFP tumours were more
consistent in size than the SC tumours. Given the large variability in the 6 week SC tumour
group, these samples were not further analyzed. Instead, 5 week SC tumours were compared
with 3 and 4 week MFP tumours, which were similar in size.
Figure 3.1 MFP and SC tumour sizes. Tumour volumes were calculated based on caliper measurements
post-dissection of the major and minor axes and thickness (n = 4-6). SC tumours required longer
development times to become size matched to MFP tumours. Greater variability was also observed at
longer times, particularly in SC tumours, where several animals had smaller tumours than the cohort
examined the week before.
0
50
100
150
200
250
300
MFP 3 wks MFP 4 wks SC 5 wks SC 6 wks
Tumou
r volum
e (m
m3 )
41
3.4.2 MFP tumours exceed SC tumours in model nanocarrier accumulation
Prior to sacrifice, a high molecular weight FITC-dextran, used as a model nanocarrier, was
injected to assess blood vessel permeability. Data were normalized to liver tissue collected as a
positive control: liver endothelial cells have natural fenestrations (123 ± 24 nm diameter) [24]
for transfer of substrates from the blood to hepatocytes, making the liver an ideal organ for
observing nanocarrier uptake. In mice, blood flow through the liver is also estimated at 23% of
cardiac output, making it one of the best perfused organs on a per gram basis [25].
Based on fluorescence images of tissue sections, relatively poor dextran uptake was observed in
tumour tissue compared to liver tissue across all groups. A threshold was defined to exclude
background signal detected in blank tumour and liver tissue and the remaining areas,
representing levels above this threshold, were quantified. Less than 1% of the positive signal
area observed in the liver control was observed in tumour slices (Figure 3.2). This can partially
be explained by relatively low blood flow through tumour tissue, which has previously been
reported to be up to 5-fold lower than in liver [26]. The remaining discrepancy between the
dextran accumulation between tumour and liver samples suggests that the model tumour
vasculature was less permissive to dextran uptake than the fenestrated liver endothelium, and/or
that the lymphatic drainage in the model tumour prevented stable dextran accumulation.
Interestingly, dextran uptake in 3 week old MFP tumours was higher than size matched 5 week
old SC tumours at 90% confidence (P = 0.08 by one-way ANOVA), suggesting that the
orthotopic MFP environment encouraged EPR permissive vasculature and/or lymphovasculature.
42
Figure 3.2 FITC-Dextran accumulation in tumour tissue normalized to liver tissue control. High molecular
weight dextran (2 MDa, ~80 nm) was injected IV into tumour animals as a model nanocarrier and allowed
to distribute prior to sacrifice. 3 week old MFP tumours showed higher accumulation of the nanocarrier
than 5 week old SC tumours at a 90% confidence interval. All data are shown as the mean of n = 4
animals ± SD. Lines connecting bars denote statistical significance, P < 0.10.
3.4.3 Elements of tumour vascular pathophysiology observed in tumour models
To better understand the underlying vascular pathophysiology present in both tumour models,
tumour slices were immunostained to provide information on the blood and lymphatic vessels
present. Tissue was stained for CD31, an endothelial cell marker, to locate and characterize
blood vessels. In normal blood vessels, an intact monolayer of endothelials cells is expected,
whereas hyperpermeable tumour blood vessels are characterized by multiple layers of
discontinuous endothelial cells that may sprout outwards or project into the vessel lumen [10,
13]. The CD31 staining revealed greater vessel wall thickness across all groups when compared
to liver tissue (represented by a dashed line) which was used as a healthy tissue control (Figure
3.3A). This observation suggests that blood vessels present in all models, whether they are
0
0.2
0.4
0.6
0.8
1
1.2
MFP 3 wks MFP 4 wks SC 5 wks
Percen
t pixels a
bove th
reshold
(normalize
d to liver)
FITC-‐Dextran P < 0.10
43
existing vessels that have been remodeled or newly formed vessels, have the abnormal multi-
layered endothelial cell structure associated with solid tumours. The vessel thickness was
highest in the 3 week old MFP tumours, indicating a greater level of endothelial cell
disorganization in this group. It is possible that this led to the increased permeability observed in
the 3 week MFP tumours using a relatively large model nanocarrier (~80 nm), an effect that is
more pronounced in other studies utilizing models such as albumin (~7 nm) [27, 28].
Separate sections were also co-stained for collagen IV to visualize the thickness of the associated
basement membrane. The basement membrane forms a physical barrier that inhibits transport of
high molecular weight materials across blood vessel walls [15, 29]. In tumour pathophysiology,
opposing phenomena have been observed: the basement membrane can thicken, thin, or even be
absent. In the MFP and SC tumour models, the basement membrane was thickened compared to
healthy liver blood vessels (Figure 3.3B). This observation is consistent with the xenografted
MDA-MB-231-H2N cell line being poorly invasive like its parental line, MDA-MB-231 [30].
Conversely, a more metastatic cell line is often capable of using MMPs to degrade the basement
membrane to enable cell migration through neighbouring blood vessels [30]. The 5 week old SC
tumours were observed to have the highest basement membrane thickness, indicating the greatest
mass transport barrier against nanocarrier delivery.
44
Figure 3.3 CD31 and collagen IV immunostaining. Mean blood vessel wall thickness visualized through
(A) CD31 (endothelial cells) and (B) collagen IV (basement membrane). Both are abnormally thick as
compared to healthy liver control tissue, which is denoted by the dashed line. (C) shows that mean blood
vessel density assayed using CD31 staining is greatest in 3 week old MFP tumours. (D) indicates mean
vascular area as a measure of blood vessel size and capacity. Their small size categorizes them as
microvasculature. All data are shown as the mean of n = 4 animals ± SD. Starred lines connecting bars
denote statistical significance, P < 0.05.
0
0.5
1
1.5
2
2.5
MFP 3 wks MFP 4 wks SC 5 wks
Mean vessel wall thickne
ss (µ
m) CD31
* *
0
0.5
1
1.5
2
2.5
MFP 3 wks MFP 4 wks SC 5 wks
Mean vessel wall thickne
ss (µ
m) Collagen IV
* *
0
50
100
150
200
MFP 3 wks MFP 4 wks SC 5 wks
Mean vessel den
sity (#/m
m2 ) CD31
* *
0
50
100
150
200
MFP 3 wks MFP 4 wks SC 5 wks
Mean vascular area (µm
2 ) CD31
*
A B
C D
45
CD31 staining also revealed differences in vascular density, with the 3 week old MFP tumours
having a significantly greater vessel density than the other groups (Figure 3.3C). The decrease in
vascular density from 3 weeks to 4 weeks in the MFP model suggests that the tumour cell growth
may be too rapid for the corresponding new blood vessels to form. The thick basement
membranes observed in the tumour tissue may also contribute to this deficiency as the basement
membrane must be degraded before vascular branching can occur [15]. Although the 3 week old
MFP and 5 week old SC tumours were size matched, the MFP model had greater blood vessel
density, which may be attributed to greater vascular density in the MFP. Together these
observations suggest that remodeling blood vessels already present in the transplantation site are
important in establishing relevant tumour vasculature. The relatively poor vascular density in SC
tumours may also explain the poor engraftment after 6 weeks, as a lack of blood flow may inhibit
further growth and lead to necrosis.
The mean vascular area was also quantified, giving an indication of the size, and therefore the
capacity of the blood vessels present in each tumour type. The vascular area in 3 week old MFP
tumours was significantly higher than the 4 week old MFP tumours (Figure 3.3D), indicating that
in addition to decreasing vessel density with increasing tumour size, there is on average a lower
capacity for blood in the vessels present. Having a greater density and capacity for blood
perfusion enhances the likelihood for delivery of materials to the 3 week old MFP tumours
through systemic circulation.
At the same time, all of the evaluated models are likely underperfused as their small size
categorizes them as microvasculature [31]. This low overall capacity for blood flow impacts
their utility in assessing nanocarrier accumulation via EPR, and likely results in regions of
hypoxia and heterogeneous drug distribution.
CD31 was also co-stained with αSMA to visualize differences in pericyte association with blood
vessels. Pericytes are important blood vessel support cells that help to regulate blood flow and
vessel permeability, but are often detached in tumour pathophysiology. This was indeed
observed across all tumour models (Figure 3.4A-C), where pericytes (violet) were distributed
throughout tumour tissue instead of associating exclusively with blood vessels (brown) and
forming uniform layers around the endothelial cell layer, as observed in healthy liver tissue
(Figure 3.4D).
46
Figure 3.4 CD31 and αSMA co-staining. Representative images of pericytes (αSMA, violet) that are
detached from blood vessels (CD31, brown) in: (A) 3 week MFP, (B) 4 week MFP, and (C) 5 week SC
tumours. Several blood vessels are highlighted with black arrows; blue staining represents cell nuclei.
(D) shows that pericytes are exclusively associated with blood vessels in healthy liver control tissue.
Scale bars represent 200 µm.
LYVE-1 staining was used to detect lymphatic vessels in tumour tissue. Lymphatic vessels
provide a network to drain protein rich interstitial fluid back into circulation. By the nature of
their function, these vessels are porous to allow macromolecules to be transported [32], and
therefore nanocarrier accumulation in tumour tissue may increase when their expression is
impaired. Mouse models of lymphatic impairment can be generated by surgically ablating
lymphatic vessels in the tail, resulting in lymphedema. In these models, the surrounding tissue
attempts to restore homeostasis by generating new lymphatic vessels and dilating the remaining
lymphatic vessels, suggesting that both density and diameter impact drainage capacity [33].
A B
C D
47
LYVE-1 stained sections were used to quantify lymphatic vessel size and density (Figure 3.5A-
B). Both of these measures gave different variances between groups (P < 0.05 by Bartlett’s test
of equality of variances) meaning that the groups tested were not equivalent. While the mean
lymphatic vessel density was highest in the 3 week old MFP tumours, the 5 week old SC
tumours demonstrated the highest mean lymphatic vessel area. These factors counterbalance one
another, as density and capacity each contribute to overall drainage.
There is evidence that both the MFP and SC tumour models yielded poor lymphatic drainage
compared to healthy tissue. Accumulation of interstitial fluid in cases of lymphedema has been
shown to lead to the deposition of collagen [34]. Visual examination of the tumour slices
revealed a high density of collagen IV-lined spaces that were CD31 negative, which likely
represent fluid-filled cavities in the tumour tissue (Figure 3.5C-D). These likely contain excess
interstitial fluid resulting from a combination of increased vascular permeability and deficient
lymphatic drainage.
48
Figure 3.5 LYVE-1 immunostaining. (A) shows mean lymphatic vessel density, and (B) shows mean
vessel area, both of which are indicators of lymphovascular capacity. Both measures were found to have
unequal variance between groups, and therefore although the groups were not equivalent, ANOVA could
not be used to verify their differences. While 3 week old MFP tumours had the highest mean lymphatic
vessel density, 5 week old SC tumours had greater mean vessel size, both of which contribute to overall
lymphatic drainage capacity. All data are shown as the mean of n = 4 animals ± SD. Representative
images of collagen (violet) positive but CD31 (brown) negative fluid filled spaces are shown in: (C) 3 week
MFP and (D) 5 week SC tumours. Several of these spaces are highlighted with black arrows; blue
staining represents cell nuclei. Scale bars represent 200 µm.
0
5
10
15
20
25
MFP 3 wks MFP 4 wks SC 5 wks
Mean vessel den
sity (#
/mm
2 ) LYVE-‐1
0
50
100
150
200
250
MFP 3 wks MFP 4 wks SC 5 wks
Mean vessel area (µm
2 )
LYVE-‐1 A B
C D
49
Taken together, the data gathered through CD31 and collagen IV immunostaining suggest that,
of the models tested, the 3 week MFP tumour best replicates the vascular permeability required
to observe the EPR effect in vivo. However, the blood vessels visualized are sparse and small,
contributing to low accumulation of the model nanocarrier used in this study. Both MFP and SC
tumours showed evidence of excess interstitial fluid accumulation, suggesting poor lymphatic
drainage in both models. While MFP tumours demonstrated greater lymphatic vessel density,
SC tumours had greater lymphatic vessel size, both of which contribute to drainage, making it
difficult to easily differentiate the two models in terms of drainage capacity. MFP tumours also
demonstrated greater utility for long-term treatment studies, as their growth is more consistent at
large tumour sizes, and no skin ulcerations were observed.
3.5 Conclusions
This study provides insight into the vascular properties of human tumour xenograft models of
breast cancer in both MFP (orthotopic) and SC (ectopic) environments, two common pre-clinical
models. When both animal models were challenged with a high molecular weight dextran as a
model nanocarrier, there was higher accumulation in MFP tumours 3 weeks after cell injection.
Further adding to the evidence that MFP tumour vasculature has greater permeability to
macromolecules – a pathological feature relevant to nanocarrier accumulation via EPR – CD31
and collagen IV immunostaining revealed greater vascular density and size, as well as thinner
basement membranes, in MFP tumours collected 3 weeks after cell injection. Both models
demonstrated poor dextran accumulation compared to the liver as a positive control, suggesting
that although several pathological features were observed, low vascular density and small blood
vessel size led to relatively poor tumour perfusion. Both the MFP and SC tumour models
showed evidence of poor lymphatic drainage, as several CD31 negative and collagen IV positive
fluid filled cavities were observed. The MFP environment offered several practical benefits,
including shorter development times to reach a target tumour size, more consistent growth
profiles, and the absence of ulcerated skin lesions observed in SC tumour animals.
3.6 List of abbreviations used
α-SMA Alpha smooth muscle actin
DAB 3,3’-diaminobenzidine
EPR Enhanced permeability and retention
50
FBS Fetal bovine serum
LYVE-1 Lymphatic vessel endothelial hyaluronan receptor
MFP Mammary fat pad
MMP Matrix metalloproteinase
NGS Normal goat serum
NSG mice NOD scid gamma mice
PBS Phosphate buffered saline, pH 7.4
SC Subcutaneous
3.7 Authors’ contributions
KSH designed the study and protocols, performed animal experiments, immunostained tissue,
collected images, maintained and prepared cells for transplantation, executed the data analysis,
and prepared the manuscript. PP was responsible for the breeding the mouse colony, performing
cell injections, monitoring tumour growth, and assisted in designing protocols, performing the
animal experiments, immunostaining tissue, and collecting images. SCO participated in
designing the study and protocols, and assisted in performing SC cell injections. MSS
participated in study design and was involved in writing the manuscript. All authors read and
approved the final manuscript.
3.8 Acknowledgements
We thank: Drs. Robert Kerbel (Sunnybrook Health Science Centre), Armand Keating and Yoko
Kosaka (Princess Margaret Hospital) for their help and advice in establishing the mouse tumour
model. We are grateful to the Canadian Institutes of Health Research (CIHR to MSS) for funding
of this research.
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[19] Wilmanns C, Fan D, Obrian CA, Bucana CD, Fidler IJ. Orthotopic and Ectopic Organ Environments Differentially Influence the Sensitivity of Murine Colon-Carcinoma Cells to Doxorubicin and 5-Fluorouracil. International Journal of Cancer 1992;52:98-104.
[20] Francia G, Cruz-Munoz W, Man S, Xu P, Kerbel RS. Mouse models of advanced spontaneous metastasis for experimental therapeutics. Nat Rev Cancer 2011;11:135-141.
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[24] Cogger VC, McNerney GP, Nyunt T, DeLeve LD, McCourt P, Smedsrod B, et al. Three-dimensional structured illumination microscopy of liver sinusoidal endothelial cell fenestrations. J Struct Biol 2010;171:382-388.
[25] Davies B, Morris T. Physiological Parameters in Laboratory-Animals and Humans. Pharmaceut Res 1993;10:1093-1095.
[26] Hori K, Saito S, Takahashi H, Sato H, Maeda H, Sato Y. Tumor-selective blood flow decrease induced by an angiotensin converting enzyme inhibitor, temocapril hydrochloride. Jpn J Cancer Res 2000;91:261-269.
[27] Chen B, Pogue BW, Zhou XD, O'Hara JA, Solban N, Demidenko E, et al. Effect of tumor host microenvironment on photodynamic therapy in a rat prostate tumor model. Clin Cancer Res 2005;11:720-727.
[28] Tong RT, Boucher Y, Kozin SV, Winkler F, Hicklin DJ, Jain RK. Vascular normalization by vascular endothelial growth factor receptor 2 blockade induces a pressure gradient across the vasculature and improves drug penetration in tumors. Cancer Res 2004;64:3731-3736.
[29] Kong G, Braun RD, Dewhirst MW. Hyperthermia enables tumor-specific nanoparticle delivery: Effect of particle size. Cancer Res 2000;60:4440-4445.
[30] Abdelkarim M, Vintonenko N, Starzec A, Robles A, Aubert J, Martin M-L, et al. Invading Basement Membrane Matrix Is Sufficient for MDA-MB-231 Breast Cancer Cells to Develop a Stable In Vivo Metastatic Phenotype. PLoS ONE 2011;6:e23334.
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53
[32] Rockson SG. Diagnosis and management of lymphatic vascular disease. J Am Coll Cardiol 2008;52:799-806.
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54
4 Drug-loaded nanoparticles for targeted delivery to a mouse model of breast cancer
This chapter is derived from the following manuscript:
Ho KS, Aman AM, Al-awar RS, and Shoichet MS (2012) Amphiphilic micelles of poly(2-methyl-2-carboxytrimethyle carbonate-co-D,L-lactide)-graft-poly(ethylene glycol) deliver anti-cancer drugs to solid tumours. Biomaterials, 33 (7), 2223–2229.
Reprinted with permission from Elsevier.
4.1 Abstract
Drug delivery to solid tumours remains a challenge because both tumour physiology and drug
solubility are unfavourable. Engineered materials can provide the basis for drug reformulation,
incorporating active compounds and modulating their pharmacokinetic and biodistribution
behaviour. To this end, we encapsulated docetaxel, a poorly soluble taxane drug, in a self-
assembled polymeric nanoparticle micelle of poly(2-methyl-2-carboxytrimethylene carbonate-
co-D,L-lactide)-graft-poly(ethylene glycol) (poly(TMCC-co-LA)-g-PEG). This formulation was
compared with its conventional ethanolic polysorbate 80 formulation in terms of plasma
circulation and biodistribution in an orthotopic mouse model of breast cancer. Notably, the
polymeric nanoparticle formulation achieved greater tumour retention, resulting in prolonged
exposure of cancer cells to the active drug. This behaviour was unique to the tumour tissue. The
active drug was eliminated at equal or greater rates in all other tissues assayed when delivered in
the polymeric nanoparticles vs. the free drug formulation. Thus, these polymeric nanoparticles
are promising vehicles for solid tumour drug delivery applications, offering greater tumour
exposure while eliminating the need for toxic solvents and surfactants in the dosing formulation.
4.2 Introduction
Solid tumours, such as breast cancer, present several physical barriers against effective drug
delivery, as therapeutic agents must cross into, and remain, at the tumour site despite high
interstitial pressures and low vascular densities [1-3]. Additionally, many anti-cancer drugs have
non-specific modes of action, so when coupled with a broad systemic distribution, the resulting
impact on healthy cells leads to dose-limiting toxicity [4]. Nanoparticle targeting exploits a
unique physiological feature of solid tumours resulting from rapid malignant growth:
hyperpermeable vasculature and poor lymphatic drainage lead to enhanced permeability and
55
retention (EPR) of large molecules and small particles on the nanometer scale, providing a
means for selective tissue accumulation [5,6]. Well-designed nanoscale drug delivery systems
have the potential to increase the therapeutic index of small molecule drugs by extending drug
circulation while boosting solid tumour specificity and accumulation through the EPR effect. To
take advantage of EPR, several technologies have been developed, including liposomes [7],
dendrimers [8], and polymeric nanoparticles [9].
Polymeric nanoparticles, comprised of a hydrophobic core and hydrophilic corona, are
particularly compelling for the encapsulation and delivery of hydrophobic and poorly water
soluble chemotherapeutic drugs. Many of these polymers are block copolymers of hydrophobic
poly(aspartic acid) or poly(lactide-co-glycolide) and hydrophilic poly(ethylene glycol) [10,11].
Several parameters have been investigated in terms of circulation half-life, including the length
and density of the PEG block [12] and size and shape of the nanoparticles [13,14]. Interestingly
few polymeric nanoparticles have been designed with functional groups.
The copolymer poly(2-methyl-2-carboxytrimethylene carbonate-co-D,L-lactide)-graft-
poly(ethylene glycol) (poly(TMCC-co-LA)-g-PEG) was designed to have either a PEG-furan or
a PEG-azide for facile click modification of the nanoparticle surface by either Diels-Alder [15]
or Huisgen 1,3-dipolar cycloaddition [16], respectively. By combining the hydrophobic
backbone of poly(TMCC-co-LA) with hydrophilic PEG, the resulting amphiphilic copolymer
spontaneously self-assembles into nanoscale core-shell micelles on contact with water through a
simple dialysis process [15]. Interestingly, there is consistently only one PEG per poly(TMCC-
co-LA) backbone, giving this polymer a block-like structure. Moreover, the polymeric
nanoparticles have demonstrated stability in blood serum proteins in vitro [17]. We hypothesized
that these poly(TMCC-co-LA)-g-PEG nanoparticle micelles would be beneficial in vivo, where
the hydrophobic inner core would load a hydrophobic chemotherapeutic drug for delivery, and
the PEG corona would reduce protein binding and thereby allow longer circulation and greater
tumour accumulation before elimination by the reticuloendothelial system (RES) [9].
Biologically active anti-cancer drugs are often hydrophobic, bulky, and polycyclic, leading to
poor aqueous solubility and limited utility [18]. Consequently, such compounds are often
formulated with organic co-solvents and surfactants, each with their own systemic toxicities.
Docetaxel (DTX) is a small molecule taxane drug that falls into this category: it demonstrates
56
excellent clinical activity against breast cancer but requires a high concentration of polysorbate
80 (PS80 or tween 80) to solubilize relevant concentrations for dosing. Unfortunately, dosing
these levels of PS80 causes hypersensitivity reactions, necessitating pre-treatment with
corticosteroids and further reducing the mean tolerable dose [19-21].
To take advantage of the potency of DTX without being limited by current formulations, we
endeavoured to encapsulate it in the poly(TMCC-co-LA)-g-PEG nanoparticles, taking advantage
of its solubility in the hydrophobic core of our polymeric nanoparticles (Figure 4.1). Importantly,
this methodology required neither chemical modification nor the use of toxic co-solvents in the
final formulation. Success here allowed us to test, for the first time, these polymeric
nanoparticles in terms of the in vivo circulation and biodistribution of DTX vs. standard
formulations.
Figure 4.1 Poly(TMCC-co-LA)-g-PEG, shown here with a furan group at the PEG terminus, is an
amphiphilic co-polymer that self assembles into polymeric nanoparticle micelles with a core-shell
structure on dialysis against water. DTX and the polymer are first co-dissolved in organic solvent before
dialysis. During dialysis, DTX partitions into the hydrophobic core, thereby encapsulating it. The
polymeric nanoparticles have functional groups available for further modification: carboxylic acid groups
on the poly(TMCC-co-LA) backbone and furan moieties on the PEG corona.
To understand the pharmacokinetic behaviour and biodistribution of DTX-loaded nanoparticles
(DTX-NP), we compared their performance vs. free DTX in the conventional ethanolic PS80
formulation after dose matched IV injection in tumour-bearing mice. Solid orthotopic tumours
were established by transplanting human breast cancer cells into the mammary fat pads of female
mice. Ultra performance liquid chromatography-coupled with mass spectrometry (UPLC-MS)
detects the unaltered therapeutic compound without labelling, making it possible to distinguish
Self-assembly
via dialysis
DTX
57
the active compound from inactive fragments, metabolites, or uncoupled tags. This analytical
technique is quantitative, sensitive to nM levels, and compatible with small sample volumes [22].
While radiolabeling is commonly used to quantitatively track a drug in tissues and plasma after
dosing [23], it relies on a tag as a reporter, and degradation products from the tagged compound
can result in misleading data. The UPLC-MS method used here allowed us to directly measure
the concentrations of unmetabolized DTX in plasma and tissues after intravenous (IV) injection.
Blood samples were drawn via tail vein or cardiac puncture. Using UPLC-MS, we quantified
DTX concentrations in the plasma fraction over an 8 h time course and calculated
pharmacokinetic (PK) parameters for each formulation.
4.3 Experimental
4.3.1 Materials
All cell culture materials were purchased from Gibco-Invitrogen (Burlington, ON, Canada).
MDA-MB-231-H2N cells and NOD scid gamma (NSG) mice were generous gifts from Dr.
Robert Kerbel (Sunnybrook Research Institute, Toronto, ON, Canada), which were then
maintained or bred in-house. Dialysis membranes were acquired from Spectrum Laboratories
(Rancho Dominguez, CA, USA). Docetaxel was obtained through LC Laboratories (Woburn,
MA, United States). Poly(TMCC-co-LA) was synthesised as previously described [15,24].
Furan-PEG-NH2 was prepared from 10 kDa Boc-NH-PEG-NHS obtained from Rapp Polymere
(Tuebingen, Germany), and grafted to the polymer backbone as previously described [15,24].
The resulting grafted copolymer is shown in Figure 4.1. Heparinized capillary tubes were
purchased through Sarstedt (Montreal, QC, Canada). All other materials were purchased from
Sigma-Aldrich (Mississauga, ON, Canada) and used as received unless otherwise noted.
4.3.2 DTX concentration measurement
Chromatographic separations were carried out on an ACQUITY UPLC BEH C18 (2.1 × 50 mm,
1.7 µm) column using ACQUITY UPLC system. The mobile phase was 0.1% formic acid in
water (solvent A) and 0.1% formic acid in acetonitrile (solvent B). The column was equilibrated
for 1 min in 95% solvent A as the starting point for the gradient, dropping to 5% over 4.5 min,
holding for 0.5 min, and moving back to 95% in 0.5 min. A Waters Xevo QTof MS equipped
with an atmospheric pressure ionization source was used for MS analysis. For quantification,
58
stock standard solutions of the active DTX compound were added to the final appropriate matrix
for comparison: 2:1 acetonitrile:water, v/v for nanoparticle suspensions, or precipitated plasma
or pooled tissue homogenates as appropriate. Under these conditions the polymer nanoparticles
are dissolved, resulting in a combined measurement of both encapsulated and released DTX
present in the original sample. The instrument was sensitive to DTX concentrations as low as 5
ng/mL. All values shown are the average of 5 samples with error bars representing their standard
deviation. Group means were compared by one-way ANOVA followed by a corrected unpaired
t-test; differences are denoted by starred symbols (p < 0.05). MassLynx 4.1 was used for peak
area analysis and WinNonLin was used to obtain pharmacokinetic parameters in a non-
compartmental model.
4.3.3 Free DTX and DTX-NP formulation
An aqueous suspension of DTX-NP was prepared by self-assembly via a simple dialysis process.
First, 15 mg of poly(TMCC-co-LA)-g-PEG and 6 mg of DTX were dissolved together in 1.425
mL of dimethylformamide (DMF). The solution pH was then adjusted with 75 µL of 500 mM
borate buffer, pH 9.0. This mixture was then transferred to a 12-14 kDa molecular weight cut off
membrane and dialysed a minimum of four times against distilled water over 24 h at room
temperature. This process yielded polymeric nanoparticles loaded with 4.2 wt% DTXwith a z-
average diameter of 80nm as measured by dynamic light scattering (Malvern, Zetasizer). Just
prior to injection, suspensions of DTX-NP were adjusted for physiological salt content by
addition of 10× phosphate buffered saline, pH 7.4 (PBS). Free DTX was prepared by first
dissolving DTX in a mixture of ethanol and PS80 before final concentration adjustment with
PBS (10% ethanol, 7.5% PS80, 82.5% PBS) directly prior to injection.
4.3.4 Cell maintenance and preparation
MDA-MB-231-H2N cells were maintained in RPMI 1640 culture medium, supplemented with
10% heat-inactivated fetal bovine serum (FBS), 50 units/mL penicillin and 50 mg/mL
streptomycin under a humidified 5% CO2 environment. To prepare cell suspensions for
injection, adherent cells were first rinsed with PBS, and then incubated briefly with trypsin-
ethylenediamine tetraacetic acid (trypsin-EDTA, 0.25%/0.038%). Once the cells were
suspended, enzymatic digestion was inhibited with FBS, and the cells were pelleted and washed
59
3 times in PBS before resuspension at the desired concentration. Cells were kept on ice prior to
injection.
4.3.5 Tumour xenograft model
The protocols used in these in vivo studies were approved by the University Health Network
Animal Care Committee and performed in accordance with current institutional and national
regulations. Animals were housed in a 12 h light and 12 h dark cycle with free access to food and
water. NSG mice were bred in-house, and 7-9 week old female mice were selected for tumour
xenotransplantation. To form orthotopic mammary fat pad tumours, mice were inoculated with
106 MDA-MB-231-H2N cells suspended in 50 µL of sterile PBS via the following surgical
procedures. Prior to surgery, mice were anaesthetized with isoflurane-oxygen. The surgical area
was depilated and swabbed with betadine before making an incision in the skin of the lower
abdomen to the right of the midline, uncovering the mammary fat pad in the right inguinal region
where cells were injected into the fat pad. The incision was then sutured closed and lactated
Ringer’s solution and buprenorphine were given post-operatively for recovery and pain
management. Solid tumours were allowed to form over a period of 3-4 weeks. Cohorts of
tumour-bearing animals proceeded onwards for testing once their tumours reached an average
diameter of 7 mm as measured through the skin using calipers.
4.3.6 Pharmacokinetics and biodistribution
DTX-NP and free DTX were compared by giving IV doses of 1.5 mg/kg DTX or DTX
equivalent as 200 µL tail vein injections into tumour-bearing mice. Groups of 15 mice were
randomly assigned to each formulation. These groups were subdivided into 3 groups of 5 mice
with terminal end points at 2, 4, and 8 h. Each of these subgroups was placed on a staggered
blood sampling schedule such that each mouse was sampled for blood via the tail vein no more
than twice; blood samples were collected using heparinized capillary tubes and immediately
centrifuged to collect the plasma fraction. At each terminal time point, animals were sacrificed
by CO2 asphyxiation, and blood was collected via cardiac puncture using heparinized needles
and the plasma fraction was immediately isolated by centrifugation. Tissue samples (heart, lung,
liver, kidney, spleen, tumour) were also collected by dissection and placed separately in vials.
All plasma and tissue samples were snap frozen immediately after collection and kept on dry ice
before transfer to -80 ºC for long term storage.
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4.3.7 Plasma preparation
To prepare samples for UPLC-MS, plasma samples were thawed and immediately combined
with twice their volume in acetonitrile to induce protein precipitation. The supernatant was
transferred to an MS vial and stored at 4 ºC until analysis. The plasma concentration of DTX was
calculated by comparison against blank plasma samples that were spiked with a known
concentration of DTX (125-2000 ng/mL as a two-fold dilution series).
4.3.8 Tissue preparation
Tissue samples were first thawed, accurately weighed, and transferred to vials containing beads
for homogenization (zirconia beads for spleen samples, stainless steel beads for all remaining
tissue samples). Based on the weight, a multiple of that amount was recorded and added in
distilled water (2× for each spleen vial, or 2-4× to a minimum 600 mg total weight in each of the
remaining vials) to facilitate homogenization. Samples were then vigorously agitated 3 times in
30 s on/30 s off intervals using a bead beater instrument to mechanically disrupt the tissues.
Aliquots of tissue homogenate were transferred into tubes containing double the volume in
acetonitrile for protein precipitation. The supernatant was transferred to an MS vial and stored at
4 ºC until analysis. The tissue concentration of DTX was calculated by comparison against blank
tissue homogenate samples that were spiked with a known concentration of DTX (125-2000
ng/mL as two-fold dilutions) and adjusted for the applied dilution factor.
4.4 Results
4.4.1 Pharmacokinetics
Following IV injection, drug compounds distribute through the body and are in turn metabolized
and eliminated, and these processes can be modelled with pharmacokinetic parameters using the
plasma profile. Both polymeric and conventional formulations exhibited a sharp initial drop in
plasma concentration (Figure 4.2), with nearly 90% of the detectable DTX dose leaving
circulation within 10 min. The steep initial decrease in plasma DTX concentration observed for
both polymeric nanoparticle and standard formulations is characteristic of bolus dosing followed
by rapid distribution to surrounding tissues [25]. Metabolic processes likely also contributed
because only the intact compound was measured. Remarkably, the plasma profiles diverged
significantly at 2 h post injection, with the DTX-NP formulation stabilizing at its terminal
61
elimination phase by 1 h, while the free DTX formulation continued its initial rapid distribution
phase until 2 h. By 2 h post injection, the performance of the DTX-NP formulation exceeded that
of the free DTX formulation by producing a greater than 8-fold plasma concentration difference
(3.62% vs. 0.43% initial dose remaining), widening to a 14-fold difference by 8 h (1.71% vs.
0.12% initial dose remaining).
Figure 4.2 Pharmacokinetic profiles of free DTX (o) and DTX-NP (�) in tumour-bearing mice. The
plasma profiles differ significantly by 2 h post injection. The DTX-NP formulation reached its terminal
elimination phase earlier, and coupled with a slower terminal elimination rate, the enhanced plasma
retention continued to amplify over time. Points shown are the mean of n=5 animals, with error bars
representing their standard deviation. Starred points represent statistically different group means (p <
0.05).
The improved circulation properties of the polymeric nanoparticle formulation were also reflected
in the formulation’s pharmacokinetic parameters (
Table 4.1). Even at early times, the modeled initial plasma concentration, Co, which accounts for
the instantaneous dilution due to distribution, was maintained at higher levels for the DTX-NP
62
formulation vs. free DTX, despite the doses being matched at 1.5 mg/kg. For each formulation,
the volume of distribution, Vd, was calculated to reflect the theoretical volume over which the
DTX is evenly distributed after injection. The calculated Vd for the DTX-NP formulation was
half of that for free DTX, further indicating greater retention of active DTX in plasma circulation
when delivered by polymeric nanoparticles.
Table 4.1 Pharmacokinetic parameters calculated for DTX formulations after bolus IV administration of
1.5 mg/kg DTX to tumour-bearing mice
Formulation Pharmacokinetic parameter Units Free DTX DTX np Co Initial plasma concentration ng mL-1 1.47 × 103 1.87 × 103 Vd Volume of distribution mL kg-1 4.59 × 103 2.17 × 103 t1/2, λ Lambda half life h 3.32 5.33 AUCall Area under the curve (to t = 8 h) h ng mL-1 1.49 × 103 3.52 × 103 AUC∞ Area under the curve (to t = ∞) h ng mL-1 1.57 × 103 5.31 × 103
AUMC∞ Area under the first moment curve (to t = ∞) h2 ng mL-1 2.55 × 103 3.82 × 104
Cl Clearance mL h-1 kg-1 958 282
The terminal portions of each plasma profile further distinguished the two groups. Owing to the
1.6-fold longer lambda half life, t1/2,λ, for the DTX-NP group, the profiles continued to diverge as
more time elapsed. The increasing concentration differences at later times profoundly impacted
the pharmacokinetic measures of drug exposure: AUC (area under the curve) and AUMC (area
under the first moment curve). Indeed, the AUC for concentration vs. time for the DTX-NP
group showed a greater than 2-fold increase over the 8 h observation period, and a greater than 3-
fold increase when the duration was extended to infinite time relative to free DTX. Plasma
concentrations at later times had an amplified impact on the AUMC for concentration × time vs.
time and this value increased by an order of magnitude for the DTX-NP formulation vs. the
conventional free DTX formulation. The clearance, Cl, is a measure of the blood volume that is
processed and completely cleared of the injected compound over time. Cl decreased by more
than 3-fold when DTX was formulated in polymeric nanoparticles vs. conventional PS80. This
dramatic decrease suggests that encapsulated DTX is more slowly metabolised and excreted by
63
the body. The fold changes in AUC, AUMC, Vd, and Cl values reported here are all consistent
with the ranges published elsewhere for polymeric and liposomal DTX delivery systems [26-31].
In addition to demonstrating similar biodistribution, our polymeric nanoparticles have the
advantage of having functional groups available for facile water-based chemistry, allowing
further modification [15]. Overall, our pharmacokinetic analysis indicates that with the same
initial DTX dose, greater drug exposure was achieved when the drug was formulated in
polymeric nanoparticles vs. conventional surfactants, which have the added disadvantage of
being cytotoxic and dose-limiting. As a result, the enhanced drug circulation time increased the
number of passes through the hyperpermeable tumour vasculature and likely promoted tumour
accumulation.
4.4.2 Biodistribution
To evaluate how encapsulation in our poly(TMCC-co-LA)-g-PEG nanoparticles affects tissue
distribution of DTX, a panel of organs from the same experimental groups were harvested at the
sacrificial time points. These samples were later homogenized and assayed for DTX content by
UPLC-MS. Nanoparticle formulations often accumulate in the organs rich in RES cells, such as
the liver and spleen. Remarkably, there was no significant enhancement of DTX levels in the
RES organs resulting from nanoparticle encapsulation (Figure 4.3A and B). While accumulation
in RES organs was expected, the observation that uptake was not increased in the liver or spleen,
relative to free DTX formulations, suggests that the PEG layer on the nanoparticle surface was
successful in moderating the RES response [27].
64
Figure 4.3 Biodistribution profiles of free DTX (o) and DTX-NP (�) in (A) liver, (B) spleen, (C) lung, (D)
kidney, (E) heart, and (F) tumour tissue. Points shown are the mean of n=5 animals, with error bars
representing their standard deviation. Starred points represent statistically different group means (p <
0.05).
Organs that are active in filtration, such as the lungs and kidneys, are also common sites for
nanoparticle accumulation. Filtration through the lungs resulted in high initial entrapment of
DTX for both the conventional and nanoparticle formulations, followed by rapid elimination,
with no significant differences between group means (Figure 4.3C). The lungs often act as a
filter for particles as the first capillary bed encountered after tail vein injection [32].
Consequently, the lungs did show the highest DTX concentration of all the organs tested at 2 h
post injection, but there were no significant differences between group means, and the
concentrations rapidly declined at similar rates. This suggests that a portion of each formulation
became transiently entrapped in the lung tissue, possibly due to larger nanoparticles or
aggregates, but these particles (of free drug or drug-loaded nanoparticles) subsequently cleared.
The kidneys also acted as filters for the injected DTX, which was observed in particular with the
D E F
C B A
65
DTX-NP formulation, with significantly elevated DTX accumulation in the kidneys throughout
the 8 h period of observation (Figure 4.3D). Although increased concentrations of DTX in the
kidneys were detected for the DTX-NP formulation, the elimination rate constant was higher
(1.3-fold increase), resulting in a projected convergence to a level equal to that of the free drug
by 24 h.
The next tissue analysed was the heart, where little accumulation was expected. The nanoparticle
formulation trended towards a reduced heart accumulation at the early 2 h time point (Figure
4.3E); although the difference was not significant, a large variance was observed for the free
DTX formulation at the 2 h post injection measurement, and a general upward trend for heart
accumulation at this early time.
Significantly greater tumour retention was observed when DTX was encapsulated in
nanoparticles starting at 4 h, and maintained at 8 h post injection (Figure 4.3F). Indeed, when the
tumour accumulation data were fitted with a first order decay, DTX-NP had a greater than five-
fold lower elimination rate constant than free DTX, demonstrating significantly greater
accumulation of DTX as a result of its delivery in the nanoparticles. This divergence of tissue
accumulation was uniquely observed in the tumour and demonstrates the benefit of DTX
delivery in nanoparticles.
4.5 Discussion
We designed the amphiphilic poly(TMCC-co-LA)-g-PEG to self-assemble into nanoparticle
micelles, where the hydrophobic biodegradable core of poly(TMCC-co-LA) allows for
hydrophobic drug encapsulation and the hydrophilic corona of PEG permits longer circulation
time by reducing protein adsorption and cellular recognition. PEG has been shown to be critical
design parameter for longer circulation: early particle formulations without PEG demonstrated
that particulate drug delivery systems were completely eliminated from circulation within
seconds to minutes [33]. The goal of longer circulation is to achieve greater and selective tumour
accumulation. In fact, additional passes through the hyperpermeable vasculature associated with
solid tumours generally enhances tumour accumulation [34,35]. Enhanced circulation of our
DTX-NP was verified using standard PK parameters, demonstrating that greater exposure was
achieved with this formulation, even in this model where there was a single injection of a fixed
dose. Importantly, enhanced circulation may also increase systemic exposure and general
66
toxicity because drug activity is not limited to cancer cells. As a result, there is a compromise
between these opposing factors that requires a balance between high tumour accumulation and
low systemic distribution [36]. Importantly, we observed both longer circulation and greater
tumour accumulation of these polymeric nanoparticles.
The overall distribution profile of the DTX-NP formulation is encouraging, as the poly(TMCC-
co-LA)-g-PEG nanoparticles established a strong contrast between accumulation in diseased
tumour tissues and clearance from healthy tissues, likely due to their engineered material
properties. For example, the low critical micelle concentration, measured at 3 µg/mL [15],
exceeds the injected concentration by three orders of magnitude, which likely allowed a
significant portion of polymeric nanoparticles to circulate intact, instead of rapidly disassembling
after dilution in blood. The serum stability of poly(TMCC-co-LA)-g-PEG nanoparticles has also
been confirmed in vitro using biologically relevant media, further suggesting high stability of the
nanoparticles and their encapsulated load in circulation [17]. PEG itself has several important
properties. Firstly, the 10 kDa molar mass exceeds the typical 1-5 kDa range that is commonly
used, lowering the PEG density required to reach the more effective brush regime for enhanced
circulation [37,38]. Liposomal systems are limited to lower PEG molecular weights because
longer PEG chains compromise liposomal stability. Polymeric systems, such as our poly(TMCC-
co-LA)-g-PEG nanoparticles, can stably incorporate higher molar mass PEG by manipulating the
molar mass of the hydrophobic region [12]. Secondly, each poly(TMCC-co-LA) chain is
modified with an average of one PEG chain [15], leading to excellent coverage of the
nanoparticle core, which is further reflected by the nearly neutral surface charge of the
assembled nanoparticles [16]. Thirdly, the amide bond between PEG and poly(TMCC-co-LA) is
one of the more serum-stable bonds [39], ensuring lasting nanoparticle coverage after injection.
While all particle systems are ultimately cleared, ideally adequate tumour accumulation is
reached prior to clearance. This process is normally triggered by degradation or erosion of the
polymer comprising the nanoparticle [32]. The poly(TMCC-co-LA)-g-PEG nanoparticles are
subject to eventual erosion because the polymer chains are not cross-linked. The polymer chains
are of sufficiently low molar mass (<30 kDa) to be cleared by the kidneys [40] and are
themselves biodegradable. These design elements each provide qualities that favour solid tumour
drug delivery while minimizing systemic accumulation.
67
Thorough biodistribution analysis allowed us to quantify the final concentration of DTX in
different organs. Notably, DTX did not accumulate in RES organs (liver or spleen) over free
DTX controls, suggesting that the inclusion of PEG successfully reduced the expected RES
response to foreign particles. The DTX-NP formulation did increase the kidney accumulation
over free DTX, pointing to a partial shift to renal clearance, where free DTX is mainly
metabolized and excreted by biliary clearance [41,42].
Analysis of the heart demonstrated lower variability of DTX accumulation when administered
via polymeric nanoparticles vs. as free DTX. In clinical use, taxanes such as DTX are commonly
paired with doxorubicin to treat metastatic breast cancer, but cardiotoxicity is a primary side
effect of this drug combination. When used in combination therapy, their administration is
staggered, thereby lowering concurrent levels of both drugs to reduce this interaction and
reducing cardiotoxicity [43]. Consistently lower initial accumulation in the heart (as was
observed with DTX-NP) may allow increased flexibility in the dosing schedule without the risk
of introducing severe cardiotoxicity.
The tumour specificity of the DTX-NP formulation is of particular interest. The greater retention
of DTX in the tumour when delivered in our polymeric nanoparticles is consistent with the EPR
effect observed with other particulate systems. Because this behaviour was uniquely observed in
the tumour tissue, there is potential for specific cytotoxic impact on cancer cells while the drug
compound is eliminated from the rest of the body. Our system also compared favourably with
radiolabeled liposomal DTX formulations: the latter delivered DTX and/or its metabolites to
subcutaneous tumours at levels between 2 and 8% initial dose/g, 6 h post injection, depending in
the extent of PEGylation [27], whereas our DTX-NP delivered 5% initial dose/g at 6 h post
injection based on fitted values from a first order decay for DTX-NP. Similarly, folate targeted
PEGylated liposomes achieved 7% initial dose/g of non-degraded DTX 4 h post injection to
intradermal tumours measured by LC-MS [28], vs. our 6% initial dose/g for DTX-NP at 4 h.
Indeed, our polymeric nanoparticle formulation delivered an active anti-cancer drug to solid
tumours with improved retention over the free drug alone, and this has important implications for
anti-tumour efficacy. By extending the drug exposure of diseased cells, the required cumulative
dose will likely decrease, as DTX cytotoxicity depends on both concentration and contact time
[44]. Moreover, the dose used in this study (1.5 mg/kg) is consistent with DTX levels
68
administered in metronomic dosing schedules in ovarian cancer models, where several small
doses were given at high frequency [45]. This strategy is in contrast with mean tolerated dose
approaches, where the highest tolerated dose is given at low frequency to allow healthy tissues to
recover between treatments. Metronomic dosing was shown to reduce both systemic toxicity and
cumulative dose while improving anti-tumour efficacy, under the premise that the cancer cells
have less recovery time between dosing. In combination with more sustained drug levels at each
dose, the polymeric nanoparticle formulation offers great potential to demonstrate improved
efficacy as a result of greater targeting. Moreover, the nanoparticle DTX delivery system
obviates the use of conventional surfactant-based delivery systems which themselves are
cytotoxic and cause systemic toxicity.
4.6 Conclusions
Nanoparticle formulations for anti-cancer drugs are designed to couple specific tumour tissue
accumulation with quick elimination from healthy organs. Our DTX-loaded poly(TMCC-co-
LA)-g-PEG nanoparticles achieved this with increased DTX accumulation in the tumour and
simultaneous DTX clearance from other organs to which it distributed over an 8 h period of
observation. The pharmacokinetic profile of the polymeric nanoparticle formulation vs. free
DTX demonstrated improved circulation properties at later times, which likely contributed to the
favourable tumour accumulation of DTX when delivered via our engineered formulation. The
specific retention of DTX in tumour tissue suggests that this polymeric nanoparticle delivery
strategy will be efficacious against solid tumours.
4.7 Acknowledgements
We thank: Drs. Robert Kerbel (Sunnybrook Health Science Centre), Armand Keating and Yoko
Kosaka (Princess Margaret Hospital) for their help and advice in establishing the mouse tumour
model; and Mr. Peter Poon (University of Toronto) for help in growing the tumours and
obtaining serum and tissue samples. We are grateful to the Canadian Institutes of Health
Research (CIHR to MSS, RSA) the Ontario Centres of Excellence (OCE to MSS), the Ontario
Institute for Cancer Research (OICR to RSA), the Natural Sciences and Engineering Research
Council (NSERC to KSH), and the Government of Ontario/DuPont Canada Scholarship in
Science and Technology (OGSST to KSH) for funding of this research.
69
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5 Antibody-modified nanoparticles for active and tunable binding to cancer cells
This chapter is derived from the following manuscript:
Ho K, Lapitsky Y, Shi M, and Shoichet MS (2009). Tunable immunonanoparticle binding to cancer cells: thermodynamic analysis of targeted drug delivery vehicles. Soft Matter, 5(5): 1074-80.
Reprinted with permission from The Royal Society of Chemistry.
5.1 Abstract
Tumour cells are often associated with altered surface receptor profiles, and these changes can
provide a basis for targeted delivery of anti-cancer agents. Functionalizing a colloidal drug
delivery vehicle, such as a polymeric nanoparticle, with several targeting ligands has
qualitatively been shown to increase the effective affinity of the nanoparticle for its target
receptor over the affinity of the free ligand. However, whether this increase results from multiple
simultaneous interactions per particle (multivalent binding) or increased configurations for single
binding events per particle (monovalent binding) is unclear. A quantitative approach was
required to distinguish between these possible mechanisms. In this study, human epidermal
growth factor receptor 2 (HER2) overexpressing cancer cells (SKBR-3) were used as the target
for anti-HER2 (trastuzumab, HerceptinTM) immunonanoparticles. We varied the antibody
conjugation density on the immunonanoparticles and measured their cellular binding by a flow
cytometric immunoassay. Using this method, we were able to directly assay the targeted cells
and quantify immunonanoparticle binding strength, allowing us to better understand whether
immunonanoparticles were bound by monovalent or multivalent interactions. The binding data
for each formulation were fitted to Langmuir isotherms, and based on the theory presented
herein, it was concluded that the system studied behaved in a manner consistent with monovalent
binding. Understanding this property of immunonanoparticle binding is useful in drug delivery
applications, where manipulating the strength of such interactions is essential to controlling their
targeting capacity on both tissue and cellular levels. The models developed here can be used to
quantitatively predict binding strength for rational immunonanoparticle design.
74
5.2 Introduction
The development of targeted drug carriers is driven by limitations identified with the free
administration of anti-cancer agents, including short plasma half lives, systemic toxicity, and
mass transport barriers restricting accumulation at tumour sites [1–4]. The altered phenotype of
cancer cells often includes changes to their surface receptor profiles, providing a basis for active
targeting using monoclonal antibodies that recognize and bind specific receptors with elevated
expression levels [1,5,6]. Covalent attachment of such antibodies to polymeric drug carriers
allows their guided transport to the surfaces of targeted cells, while the polymer is designed to
protect drug bioactivity, increase circulation time, and shield healthy cells from cytotoxic agents
[2–4,7,8]. Moreover, binding can enhance retention at tumour sites and can introduce a means
for rapid receptor-mediated internalization of drug-loaded carriers into the intracellular
compartment, a common site of action for cytotoxic drugs [2,4,9,10].
In the case of colloidal drug-loaded polymer aggregates, including self-assembled polymeric
nanoparticles, every polymer chain can participate in drug delivery, but the direct modification
of each polymer chain with a targeting antibody becomes unnecessary; unmodified polymer
chains can be targeted as members of a modified aggregate, and fewer targeting antibodies are
then required overall [11]. The number of antibodies per aggregate can be controlled by varying
the reaction conditions during their attachment (e.g., reaction time, feed ratio of antibody to
polymer) [6]. Enhanced binding strength of immunonanoparticles over free antibody can occur
through two possible mechanisms: the introduction of multiple simultaneous interactions per
particle (multivalent binding, Figure 5.1A) or the increase in possible configurations for single
binding events per particle (monovalent binding, Figure 5.1B).
75
Figure 5.1 Functionalizing immunonanoparticles with greater numbers of targeting antibodies enhances
their ability to associate with target cells. This effect can result from (A) increases in binding events per
particle (multivalent binding) or (B) increases in possible binding configurations with a single interaction
(monovalent binding). Illustrated here are immunonanoparticles with Ω = 3 attached antibodies. In (A),
the number of antibodies bound to cell receptors, α, is shown as α = 3 (left) and α = 2 (right). In (B) the
number of antibodies bound to cell receptors, α, is shown as α =1 for all nanoparticles. The mechanism
is an important consideration in immunonanoparticle design, as it dictates how binding strength will
increase as the antibody conjugation density increases.
Multivalent binding events would greatly enhance binding through avidity, where the
presentation of multiple tethers to the cell surface maintains association and cell-particle
proximity after a single dissociation event, thereby promoting re-attachment [12]. A more
moderate increase in binding strength is associated with monovalent binding [13]. The dramatic
increase in binding strength associated with avidity is a phenomenon that would be most
beneficial for antibodies that have poor affinity with their targets [8,14,15]. Conversely, in cases
where the binding affinity is very high, there is decreased utility in treating solid tumours, where
complete tumour penetration can be limited by strong association with cells directly adjacent to
tumour vasculature [7,16,17]. Heterogeneity of targeting within the tumour mass leads to
incomplete eradication of tumour cells, as certain cells will receive either no drug or levels of
drug below the therapeutic index [16]. Furthermore, binding strength influences the cell-
associated fraction at a given particle concentration, and this information helps assess whether
the particle dosage administered delivers drug levels within the therapeutic index [18].
Whether multivalent interactions can occur is predicated on both the density of the targeted
receptor on the cell surface and the density of binding sites on the drug carrier. Quantitative
A B
76
avidity measurements have been performed using receptors immobilized onto a hard synthetic
substrate [19–21]. However, interactions with cells can be dramatically different, in part because
lateral movement of receptors within the cell membrane can result in transient increases in local
receptor density; this phenomenon has previously been shown with solutions of free antibody
[22]. Having a flexible polymeric spacer, such as poly(ethylene glycol) (PEG), between the
nanoparticle core and the targeting molecule, provides the latter with greater free volume (and
thus greater likelihood) to interact with cellular receptors [23].
A simple, quantitative method to investigate the binding interactions between cells and
immunonanoparticles by directly assaying the targeted cells is lacking. We developed a flow
cytometric immunoassay to assess equilibrium cell binding of an anti-human epidermal growth
factor receptor 2 (anti-HER2) immunonanoparticle system. HER2 is a cell surface receptor that
becomes overexpressed in 20–30% of breast cancer cases. It is used as an indication for
treatment with Herceptin (trastuzumab) [24–27], which is the anti-HER2 monoclonal antibody
conjugated to the polymeric nanoparticles in this study. SKBR-3 cells were chosen as an in vitro
model of HER2 overexpression, and express 1 × 106 receptors/cell [28]; using this model, we
have shown previously that the binding of Herceptin-immunonanoparticles is receptor specific,
with little non-specific adsorption [6]. Here we varied the number of conjugated antibodies per
nanoparticle, measured dose responsive binding, and, by fitting binding isotherms to each,
quantified how the cell-particle binding varies with the density of conjugated antibodies.
Thermodynamic analysis of these variations elucidates the nature of the binding events between
our anti-HER2 immunonanoparticles and HER2 overexpressing SKBR-3 cells in culture. This
reveals a linear scaling between the immunonanoparticle binding strength and the number of
conjugated antibodies, thereby providing quantitative guidelines for tuning cell-particle
interactions in the design of colloidal vehicles for targeted drug delivery.
5.3 Experimental
5.3.1 Materials
All cell culture materials were purchased from Gibco-Invitrogen (Burlington, ON, Canada).
SKBR-3 cells were obtained through ATCC (Manassas, VA, USA). Dialysis membranes were
acquired from Spectrum Laboratories (Rancho Dominguez, CA, USA). The Herceptin antibody
was purchased through Hoffmann-La Roche Limited (Mississauga, ON, Canada). The polymeric
77
nanoparticles were synthesized as previously described [6,29]. All other materials were
purchased from Sigma-Aldrich (Mississauga, ON, Canada) and used as received unless
otherwise noted.
5.3.2 Nanoparticle synthesis
An aqueous suspension of poly(2-methyl-2-carboxytrimethylene carbonate-co-D,L-lactide)-
graft-poly(ethylene glycol)-furan (poly(TMCC-co-LA)-g-PEG-furan) nanoparticles was
prepared by dialysis, as reported previously [6,29]. Briefly, the polymer was first dissolved in a
mixture of 95 vol% dimethylformamide (DMF) and 5 vol% 500 mM borate buffer, pH 9.0 at a
final concentration of 10 mg/mL; the solution was then dialysed a minimum of four times against
distilled water at room temperature over 24 h using a 12–14 kDa molecular weight cut off
membrane. This procedure yielded nanoparticles with a mean hydrodynamic diameter of 80 nm
as measured by dynamic light scattering (Brookhaven 90Plus Particle Sizer, Brookhaven
Instruments, Holtsville, NY, USA) with the hydrophobic poly(TMCC-co-LA) backbone
comprising the nanoparticle core, and the hydrophilic, flexible, furan-terminated PEG grafts
comprising the nanoparticle shell. Site-specific chemical modification of carbohydrates on the Fc
region of the Herceptin antibody provided a maleimide functional group, allowing covalent
attachment of Herceptin to the PEG-furan termini through Diels–Alder chemistry [6,29].
Specifically, a furan-functionalized nanoparticle solution (4 mg in 1 mL of distilled water) was
mixed with maleimide modified Herceptin (100 mg in 120 µL of 100 mM β-
morpholinoethanesulfonic acid (MES) buffer, pH 5.5) and incubated at 37 ºC. By adjusting the
reaction time (20 min, 1, 2, and 4 h), the antibody conjugation density was varied to have an
average of 1.9 ± 0.3, 3.2 ± 0.5, 5.9 ± 0.2, and 9.4 ± 0.9 antibodies/nanoparticle, based on a 95%
confidence interval. The average values were estimated as previously reported, based on the
hydrodynamic particle diameter, by comparing the fluorescence intensity of the Alexa Fluor®
430-labelled Herceptin and immunonanoparticles made by reaction with this fluorescent
Herceptin [6]. The resulting immunonanoparticles were then purified using a Sephacryl S-
300HR column equilibrated in phosphate buffered saline, pH 7.4 (PBS).
5.3.3 Cell lines and maintenance
SKBR-3 cells were maintained in McCoy’s 5A culture medium, supplemented with 10% heat-
inactivated fetal bovine serum (FBS), 50 units/mL penicillin and 50 mg/mL streptomycin under a
78
humidified 5% CO2 environment. To prepare cell suspensions, adherent cells were first rinsed
with PBS, then incubated briefly with trypsin-ethylenediamine tetraacetic acid (trypsin-EDTA,
0.25%/0.038%). Once the cells were suspended, enzymatic digestion was inhibited with FBS,
and the cells were pelleted and resuspended at the desired concentration.
5.3.4 Flow cytometric analysis
To quantify immunonanoparticle binding, a fluorescently labeled secondary antibody was used
for detection. Subsequent intensity measurements could then be carried out on a cell by cell basis
by fluorescence activated cell sorting (FACS), where the intensity values are proportional to the
number of bound immunonanoparticles. To do this, SKBR-3 cells were first suspended, as
described above, in PBS at a final concentration of 1 × 106 cells/mL and distributed into 200 µL
aliquots in 1.7 mL centrifuge tubes. The cells were then incubated for 30 min at 4 ºC to inhibit
endocytosis (cellular uptake), and resuspended in 200 µL of immunonanoparticle solution at
varying concentrations in triplicate. The cells were again incubated for 30 min at 4 ºC to reach
equilibrium binding [22,30,31], and washed with 1 mL of cold PBS, pelleted, and resuspended in
50 µL of FACS buffer (PBS supplemented with 1% FBS and 2 mM EDTA). Rabbit antihuman
immunoglobulin G-fluorescein isothiocyanate (IgG-FITC) secondary antibody was diluted 1/200
in PBS, and 10 µL was added to each of the cell suspensions. After 30 min incubation at 4 ºC,
the cells were washed with 1 mL cold FACS buffer, resuspended in 550 µL of fresh FACS
buffer with 0.6 mg/mL propidium iodide (PI), and transferred to 5 mL FACS tube for analysis.
Data acquisition was performed on a FACS Calibur (Becton Dickinson, Mississauga, ON,
Canada) and analysis was performed using CellQuest software (Becton Dickinson). The first
10,000 events were recorded, and the live cell population was gated for analysis of FITC
fluorescence intensity. All values shown are the average of triplicate samples with error bars
representing their standard deviation.
5.4 Theory
As a first order approximation, the binding of polymeric immunonanoparticles (diameter ~ 100
nm) to much larger cancer cells (diameter ~ 10 mm) can be quantified using the Langmuir
binding isotherm, where the binding strength is quantified using a single equilibrium binding
constant, Keq:
79
NPeq
NPeq
CKCK
+=1
θ (1)
Here, θ represents the fraction of the occupied binding surface on the cell and NPC is the solution
nanoparticle concentration. The number of spaces available for nanoparticle binding is
dependent on the density of the targeted receptor, and in the case of very high receptor
expression levels, is sterically limited by the volumes occupied by bound nanoparticles [32].
The available potential binding space can be further limited in cases where receptor clustering
occurs. Indeed, cancer cells with varying levels of receptor overexpression have previously been
shown to have similar saturation concentrations, likely because further binding is sterically
hindered [18]. The isotherm accounts for these variations by defining θ as a fraction of
saturation, the upper limits of which are influenced by receptor density and distribution, as well
as particle size.
The binding constant, Keq (which is often also reported as its reciprocal, the dissociation
constant, Kd), is related to the molar Gibbs free energy of binding ( GΔ ) via:
⎟⎟⎠
⎞⎜⎜⎝
⎛ Δ−=RTGKeq exp (2)
The variations in eqK and GΔ with the number of antibodies on the nanoparticle surface is
influenced by two primary effects: (1) multivalent cell-nanoparticle interactions (avidity) and (2)
an increase in the number of possible monovalent binding configurations for a single cell-
nanoparticle pair. The thermodynamic analysis of these two mechanisms is outlined below.
5.4.1 Multivalent Binding
Multivalent binding enhances binding stability by establishing multiple tethers to the binding
surface; when a single interaction is disrupted, the remaining interactions maintain cell-particle
proximity, thereby promoting subsequent re-attachment. In the case where the number of
antibodies on the nanoparticle surface, Ω, affects the valency of the cell-particle interaction
(Figure 5.1A), the molar Gibbs free energy of nanoparticle binding is roughly proportional to the
average number of antigen-antibody interactions, α [33]:
80
)1(~)( GG ΔΩΔ α (3)
Where )(ΩΔG is the molar Gibbs free energy of the α-valent antibody-antigen binding and
)1(GΔ is the molar Gibbs free energy of binding for a single antigen-antibody pair. This
linearity reflects the additivity of the antigen-antibody interactions. However, deviations from
this relationship can exist due to cooperative and anti-cooperative interactions between the
coupled antigen-antibody pairs [33]. Combining this result with Equation 2 indicates an
exponential dependence between eqK and the number of interacting nanoparticles:
α)1(~)( eqeq KK Ω (4)
Thus, multivalent interactions can result in an increase of several orders of magnitude in the
immunonanoparticle binding strength as the number of conjugated antibodies is increased, with α
being a function of Ω. In the event of infrequent multivalent interactions (1 < α < 2), this
increase will be less dramatic, but the value for eqK should increase exponentially.
5.4.2 Monovalent Binding
In contrast to multivalent binding, for monovalent interaction, the amplified binding affinity may
reflect an increase in the number of unique configurations for a single nanoparticle with Ω
conjugated antibodies to bind to the cell surface (Figure 5.1B and Figure 5.5, Supplementary
Information). In this case, the binding strength can be approximated theoretically by defining a
canonical partition function, Q(M,N,T), for N nanoparticles binding to M binding sites [34]:
( ) ⎟⎟
⎠
⎞⎜⎜⎝
⎛ −×
−×Ω=
TkN
NMNMTMNQ
B
N εexp!!
!),,( (5)
where the first term accounts for the internal degrees of freedom of N nanoparticles bound to the
cells, the second term accounts for the number of lattice configurations in which these particles
81
bind to M sites, and the third term accounts is the Boltzmann factor for N nanoparticles binding
to the cells with the molecular energy, ε (Figure 5.5, Supplementary Information).
The chemical potential of the cell-bound nanoparticles ( Aµ ) can be calculated using the
following relationship [34]:
TM
BA NTMNQTk
,
),,(ln⎟⎠
⎞⎜⎝
⎛∂
∂−=µ (6)
This yields the expression:
⎟⎠
⎞⎜⎝
⎛−
+Ω−=θ
θεµ
1lnln TkTk BBA (7)
where θ is equal to N/M. Because at equilibrium this chemical potential is equal to the chemical
potential of the nanoparticles in solution (i.e., NPBSS CTk ln0, += µµ ), θ can be solved as a
function of the free nanoparticles in solution, NPC :
NPeq
NPeq
CKCK)(1)(Ω+
Ω=θ (8a)
where
⎟⎟⎠
⎞⎜⎜⎝
⎛ −⋅Ω=Ω
TkK
B
deq
εµ 0,exp)( (8b)
From these expressions for variations in eqK and GΔ with Ω are obtained as:
)1()( eqeq KK Ω=Ω (9)
Ω−Δ=ΩΔ ln)1()( RTGG (10)
82
Thus, in the absence of multivalent interactions (and the presence of monovalent interactions),
eqK is predicted to increase linearly with Ω, and GΔ to vary logarithmically through the
proportionality constant, RT.
5.5 Results and Discussion
Over the range of Herceptin and nanoparticle concentrations studied, Herceptin
immunonanoparticles (bearing between 1.9 and 9.4 antibodies per particle) bound to the SKBR-3
cells in a dose dependent manner. This binding was detected on FACS using a FITC-conjugated
secondary antibody against Herceptin. Secondary antibody detection of primary binding events
is a common technique used for FACS analysis and has been shown to have greater sensitivity
than directly labeling the primary antibody [35].
The binding assay was performed at 4 °C to inhibit cellular internalization of Herceptin
immunonanoparticles [36]. By excluding cellular uptake, cellular interactions included only
binding and dissociation events, thereby allowing the measurement of the equilibrium binding
isotherm. Importantly, surface bound immunonanoparticles were accessible to the secondary
antibody used in the FACS analysis and thus did not require permeabilization of the cell
membrane for detection. Because the fluorescence intensity is proportional to the number of
immunonanoparticles bound to cells, the binding constant eqK and the saturation fluorescence
intensity MAXI can be fitted using the Langmuir model via:
NPeq
NPeqMAX
NP CKCKI
CI+
=1
)( (11)
where )( NPCI is the concentration-dependent measured fluorescence intensity. The fractional
coverage, θ, can then be calculated by dividing the measured fluorescence intensity by the
saturation fluorescence intensity. The equilibrium binding of single Herceptin antibodies and
Herceptin immunonanoparticles to SKBR-3 cells closely follow the Langmuir isotherms
indicated by the solid lines (Figure 5.2). The R2 value for each fitted line exceeds 0.95.
83
Figure 5.2 Fractional coverage of Herceptin immunonanoparticles bound to HER2 overexpressing SKBR-
3 cells as a function of immunonanoparticle concentration. The arrow indicates ascending order of
antibody conjugation density: Herceptin immunonanoparticles bearing 1.9 (�), 3.2 (r), 5.9 (�), and 9.4
(n) antibodies; inset shows fractional coverage for free Herceptin (p).
The fitted eqK values increase linearly with the antibody conjugation density from 0.11 nM-1 for
single Herceptin antibodies (which are likely similar to those that would be obtained from
immunonanoparticles bearing 1.0 antibody per particle) to 1.03 nM-1 for the
immunonanoparticles bearing 9.4 antibodies per particle (Figure 5.3A). This 10-fold increase in
eqK corresponds to a nearly 10-fold increase in the number of antibodies available per
immunonanoparticle. These variations agree well with the model for monovalent binding
behaviour, described by Equation 9, where the cell-particle affinity increases due to an amplified
number of unique binding states. Likewise, the variations in GΔ (Figure 5.3B) are in remarkable
agreement with logarithmic scaling (Equation 10), where a fitted proportionality constant
(1.01RT) is within 1% of the theoretical value (RT) obtained, along with a )1(GΔ of – 51.5
84
kJ/mol. No threshold antibody density for binding was observed, which is consistent with
previous reports on targeted particles having flexible spacers between the targeting molecule and
the core [32, 37, 38]. This immunonanoparticle approach yields the flexibility to use a particular
IgG antibody for a targeting application with eqK up to an order of magnitude greater than its
original value in a tunable manner.
Figure 5.3 (A) eqK and (B) GΔ increase in absolute value as the number of Herceptin antibodies per
nanoparticle increases, thereby indicating greater binding affinity. The open symbols (¯) represent the
values calculated for free Herceptin, which denotes a monovalent case, and the closed symbols (u)
represent Herceptin immunonanoparticles. The trends in (A) and (B) follow the theoretical behaviour of
monovalent immunonanoparticle binding.
85
The variations in eqK and GΔ do not support the occurrence of multivalent binding, which is
predicted to give rise to a much more dramatic, near-exponential increase in eqK and a near-
linear increase in GΔ in the case where all antibodies on a bound immunonanoparticle
participate in binding (α = Ω, Equations 3 and 4). Even in the case where only a fraction of
antibodies are bound (α < Ω), if they occur with great enough frequency to influence the average
system behaviour, the increased binding valency should strengthen binding over the predicted
values for increased binding configurations given by the monovalent binding model. Instead, the
monovalent binding model accurately described the magnitude of the increases in eqK without
the need to account for contributions due to multivalent interactions. These findings support the
increase in the number of possible binding configurations associated with monovalent binding as
the main driver of the enhanced binding strength.
These observations are consistent with the small fraction of the nanoparticle surface that comes
in contact with the cell upon binding. Herceptin is an IgG class antibody, an isotype which
occupies an area with a 30 nm diameter [39]. The 80 nm immunonanoparticles tested are highly
curved compared to the cell surfaces and likely give rise to a small cell-particle contact area; the
small contact area compared to the area occupied by each antibody makes it improbable for
multiple antibodies to be localized at the cell-particle interface, even with the antibody mobility
provided by the flexible PEG spacer as an attachment point to the particle core. Hence, the
amplified binding of nanoparticles targeted using large targeting molecules (e.g., whole
antibodies or antibody fragments) [40,41] is likely caused by an increase in the number of
monovalent binding states, and not the multivalent interactions to which it has formerly been
attributed. Nanoparticles that are densely covered with smaller targeting ligands (e.g., hundreds
or thousands of low molecular weight molecules per particle) [9, 19, 38, 42] may still exhibit
multivalent cell-particle interactions, which, unlike the system described here, can lead to a non-
linear eqK versus Ω dependence.
The agreement between the data in Figure 5.3 and monovalent binding model suggests that
immunonanoparticle binding strength can be predictably tuned by adjusting the number of
conjugated Herceptin antibodies according to Equation 9. This agreement between the predicted
fractional coverage (calculated from the fitted )1(GΔ -value) and that obtained experimentally
86
using either free Herceptin or the Herceptin immunonanoparticles is further illustrated in Figure
5.4. The experimental θ values are closely correlated to the theoretical predictions (R2 = 0.99),
supporting Equation 9 as a useful quantitative guideline for designing immunonanoparticles for
targeted drug delivery to tumour sites.
Figure 5.4 Comparison of the experimental and theoretical fractional coverages (θ) of SKBR-3 cells by
free Herceptin (p) and Herceptin immunonanoparticles bearing 1.9 (�), 3.2 (r), 5.9 (�), and 9.4 (n)
antibodies exhibiting monovalent binding. The experimentally derived θ values closely match the
theoretically predicted θ values, with R2 = 0.99.
Looking forward, quantification of the binding isotherm also guides in vivo dosage requirements
by expressing the intratumoural particle concentration required to reach a desired fraction of
saturation binding. Approaching saturating particle levels maximizes receptor binding as a
gateway to receptor mediated cellular uptake, making this an important parameter for many
targeted drug delivery strategies [18].
87
5.6 Conclusions
The binding isotherms of Herceptin immunonanoparticles bound to HER2 overexpressing
SKBR-3 cells were measured at varying levels of antibody conjugation using a flow cytometric
immunoassay, thereby quantifying binding strength using a direct live cell assay. Based on these
measurements, a thermodynamic analysis of the binding valency was completed and the
resulting valency of antibody-receptor binding interactions of immunonanoparticles bearing
multiple targeting antibodies was investigated. Binding affinity increases with increasing
antibody conjugation density in a manner consistent with the theory for monovalent binding,
suggesting that multivalent interactions are not the primary cause of the amplified binding
strength. The Herceptin immunonanoparticle formulations tested can be selected for values of
Keq up to an order of magnitude greater than the value for free Herceptin. This method can also
be applied to other particle formulations having multiple targeting ligands to better understand
how the number of ligands affects the binding valency of a particular system, and how this
property can then be manipulated to control effective binding affinity. The resulting
understanding of the mechanism governing the increase in binding strength can be used in a
predictive manner to guide nanoparticle design.
5.7 Acknowledgements
We thank Simone Helke for her advice and technical support in developing our methods for
analysis of antibody binding using FACS. We are grateful to the Natural Sciences and
Engineering Research Council and the Canadian Institutes for Health Research for funding
through the Collaborative Health Research Program.
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5.9 Supplementary information
Figure 5.5 In the case of monovalent binding, increasing the number of antibodies per particle, Ω, results
in an increase in the number of possible binding configurations where only one interaction occurs. (A)
The number of possible combinations of monovalent binding events increases with the number of
conjugated antibodies due to an amplified number of possible rotational binding orientations for N bound
nanoparticles (first term of Equation 5). (B) Also, given a lattice of M potential binding sites, the number
of distinct lattice configurations in which the particles can bind is given by a binomial coefficient (second
term of Equation 5).
For example, when Ω=2:
N=1 N=2 N=3 …
Possible combinations of bound
antibodies = ΩN
B
A
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6 Discussion Cancer is a disease that is remarkably difficult to eradicate fully. Malignant cells are derived
from a patient’s own healthy cells and evolve slowly, developing a phenotype that can evade
immune recognition [1, 2], and invading or metastasizing into healthy tissue [3]. However, they
conserve many similarities to normal cells, and biochemical pathways are often interrelated; it is
difficult to find effective anti-cancer compounds that are specific to pathological biochemistry to
avoid killing healthy cells in the process. Unfortunately, when broad drug activity is coupled
with broad distribution, it leads to unacceptable systemic toxicity, limiting the dose and utility of
drug therapy. Localized administration to the primary tumour site is one approach to limiting
distribution [4], but by definition, distant metastases would be overlooked.
To combine the broad reach of systemic administration with the specific toxicity of localized
delivery, nanoparticle systems target tumour pathophysiology. Here, we examined a polymer
approach to drug targeting. We first selected an orthotopic tumour xenograft model of breast
cancer for its ability to replicate the pathophysiology required to observe EPR. Next, we
formulated docetaxel in poly(TMCC-co-LA)-g-PEG nanoparticles, and demonstrated that our
nanoparticles improved plasma circulation and tumour retention over the conventional
formulation. Finally, we demonstrated that after covalent modification with the Herceptin
antibody, we retained selective binding to live HER2 overexpressing cells, and that we could
predict and tune their binding strength using a mechanistic model. In the following discussion
we will explore the contributions of these findings on the field of anti-cancer drug targeting.
6.1 Validated pre-clinical tumour models
Pre-clinical evaluation of drug formulations relies on animal models to replicate relevant features
of clinical pathology. Nanoparticle accumulation in tumour tissue depends on hyperpermeable
tumour vasculature and reduced lymphatic drainage to lead to the EPR effect. Testing these
formulations in tumours that lack these features would lead to poor tumour accumulation
regardless of the true performance of the delivery system.
Several pharmacokinetic studies continue to be executed in healthy animals, yet there are global
changes to the biochemistry and protein expression profiles of tumour animals that extend
beyond the tumour site itself. Using tumour models enables tumour accumulation to be
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measured in the same group of animals, saving time and resources. There is a resulting need for
simple but relevant tumour models for preliminary in vivo work.
Tumour xenograft models are attractive because they are simple to replicate and cohorts of mice
having well matched tumours are easily generated. In Chapter 3 we examined the EPR related
pathophysiology of two common preclinical models of breast cancer: orthotopic MFP and
ectopic SC human tumour xenografts in immune deficient mice. SC injection is simpler to
perform, while MFP and other orthotopic injections require surgical procedures and greater
technical skill. However, growing evidence suggests that the organ environment critically
influences cell engraftment and behaviour. Orthotopic models are increasingly being used to
study metastasis and treatment strategies for resulting secondary tumours because their strong
metastatic potential has been proven. If EPR has a similar dependence on the orthotopic cell
environment, then pharmacokinetic and biodistribution studies should also utilize orthotopic
tumour models.
To validate these models in terms of EPR permissive pathophysiology, we used a high molecular
weight dextran (2 MDa) as a model nanocarrier. Studies of tumour vascular permeability often
use smaller model particles, such as labeled albumin (7 nm). However, the typical size range of
drug-loaded nanoparticles is 50-150 nm, which is large enough to evade rapid renal clearance
and small enough to cross gaps in tumour blood vessel walls. Consequently, we chose a larger
model material (80 nm) and demonstrated that EPR could still be observed. This assessment also
demonstrated that MFP tumours were more permissive to nanocarrier accumulation than size
matched SC tumours.
To form a more compelling case, we also investigated the underlying tumour pathophysiology
leading to enhanced tissue accumulation using immunostaining. Both the MFP and SC tumours
showed evidence of excess interstitial fluid, suggesting poor lymphatic drainage in both models.
We also observed detached pericytes in both models, suggesting vessel immaturity and lack of
control over vessel permeability. However, the MFP tumours showed measurable changes in
vascular structure that led to greater permeability: greater endothelium thickness, vascular
density, and thinner basement membranes were all observed in comparison to the SC model.
Several practical considerations also favoured MFP tumours for pre-clinical studies. The
orthotopic environment yielded faster tumour development time, and also avoided the skin
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ulceration that was observed in SC tumour animals at later development times. These results
provide a rationale for selecting an orthotopic tumour model when studying nanoparticle
distribution.
6.2 Quantitative pharmacokinetics and biodistribution
Many pharmacokinetic and biodistribution studies use radiolabels or fluorescent tags to track
compounds after administration. While these are useful approaches that can provide a more
complete mass balance, these reporters can be cleaved and may not accurately reflect the final
location of the original intact molecule. Furthermore, large and hydrophobic fluorescent
molecules may alter the system behaviour. Instead of applying a tag directly to poly(TMCC-co-
LA)-g-PEG, we encapsulated docetaxel into the core of our nanoparticles, allowing us to track
the location of the intact drug compound tag-free using UPLC-MS.
These techniques introduced several tools for investigating the effectiveness of poly(TMCC-co-
LA)-g-PEG nanoparticles. This was the first time we successfully encapsulated an adequate
drug load in the core of our nanoparticles for a delivery study. We were able to match dosages
relevant to metronomic dosing (1.5 mg/kg), a low dose strategy that favours more frequent doses
over high doses with long latency times between treatments. In Chapter 2.5.2 we investigated
the behaviour of DOX-conjugated nanoparticles in vitro, where the drug compound was
covalently attached to the surface of our nanoparticles instead of being loaded in the core. This
produced several interesting and unexpected results in terms of toxicity and cellular trafficking.
However, the altered surface chemistry would likely negatively impact nanoparticle circulation,
as the high density of DOX molecules would counteract the PEG chains designed to extend
circulation time. This methodology also removed the obstacles associated with identifying
reactive groups on the drug compound to add maleimide functional groups for Diels-Alder
bonding to the nanoparticles, as well as regulatory concerns that arise from chemically altering
an approved drug molecule.
Furthermore, by monitoring the intact drug molecule directly using UPLC-MS, we could
distinguish docetaxel from metabolites. This is significant because drug fragments likely suffer
lost activity. Additionally, known metabolites that may cause non-specific toxicity can
simultaneously be investigated in the UPLC-MS spectra based on a distinct product peak.
Moreover, this technique is quantitative and sensitive down to nM levels, and only a small
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sample volume (~10 uL) is required. This allowed us to monitor the plasma concentration at
many time points without sacrificing animals each time. By staggering the blood collection
schedule over several cohorts of animals we were able to collect a comprehensive set of samples
for pharmacokinetic analysis. These protocols can be broadly applied to a variety of drug
candidates given similar hydrophobicity (encapsulation) and size (UPLC-MS detection).
6.3 Long circulating polymer nanoparticles
Using the MFP tumour model and the UPLC-MS detection method, the pharmacokinetic
properties of docetaxel encapsulated in poly(TMCC-co-LA)-g-PEG nanoparticles were
quantified. As a benchmark, we compared this performance to docetaxel in the conventional
ethanolic polysorbate 80 formulation. This work validated poly(TMCC-co-LA)-g-PEG
nanoparticles as a long circulating drug delivery system, a property that we anticipated based on
high PEG density, low critical micelle concentration, and strong nanoparticle kinetic stability.
Nanoparticle encapsulation was also expected to inhibit docetaxel degradation by preventing
direct enzyme contact. Higher docetaxel concentration in plasma at longer times suggests that
this was achieved, because only the active compound was measured.
The pharmacokinetic parameters calculated showed a 1.6-fold increase in lambda half life (t1/2), a
3-fold increase in area under the curve (AUC∞), and an order of magnitude increase in the area
under the first moment curve (AUMC∞) for DTX formulated in nanoparticles. These parameters
all indicate greater drug exposure given an equal initial dose. Extended circulation encourages
greater tumour accumulation by allowing more passes through hyperpermeable tumour
vasculature.
The improved circulation properties of docetaxel reformulated in our nanoparticles implies
utility for delivery of other drug molecules as well. By formulating docetaxel in the nanoparticle
core, the surface properties of poly(TMCC-co-LA)-g-PEG nanoparticles was unaltered. The
size, geometry, and surface properties of the nanoparticles are the primary determinants of their
elimination rate. Other compounds having similar encapsulation stability should demonstrate
similar pharmacokinetic profiles.
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6.4 Successful passive tumour targeting
In the same animals, biodistribution was investigated by collecting a panel of organs and
measuring their docetaxel content. Enhanced retention of docetaxel after administration in
unmodified poly(TMCC-co-LA)-g-PEG nanoparticles was exclusively observed in tumour
tissue, with a 5-fold decrease in the first-order elimination rate constant versus the free docetaxel
formulation. This has encouraging implications on the safety of docextaxel administered in our
nanoparticle formulation: in healthy tissues, increased accumulation was not observed over the
clinically prescribed formulation, except in transient cases. Potential for increased efficacy is
also implied because the tumour is exposed to a high docetaxel concentration for an extended
period of time. As a result, greater levels of toxicity are expected.
Of the healthy organ tissues observed, only the kidney saw greater accumulation of docetaxel
when formulated in nanoparticles, suggesting a partial shift to renal clearance. However, kidney
retention was not observed: the first order elimination rate constant was 1.3-fold higher for the
docetaxel formulated in nanoparticles, leading to an overall convergence of the two profiles.
Lack of accumulation in the RES organs (liver and spleen) suggest that the PEG coverage on
poly(TMCC-co-LA)-g-PEG nanoparticles successfully modulated the RES response.
However, introducing targeting ligands to the nanoparticle surface may impact circulation time
and biodistribution. Previous studies have shown that targeting ligands can decrease circulation
time, and these effects are likely more pronounced in immune competent animals [5, 6]. We
studied Herceptin-modified nanoparticles in immune compromised mice (Chapter 8), and
although we did not observe decreased circulation time, we did observe decreased tissue uptake
across our organ panel. Although the antibody modification did not result in increased particle
size, it likely reduced the ability of our nanoparticles to deform by steric hindrance, thereby
decreasing tissue accumulation [7, 8]. Using a smaller alternative targeting ligand, such as a Fab
antibody fragment, may better preserve the original distribution properties of unmodified
poly(TMCC-co-LA)-g-PEG nanoparticles.
6.5 Quantitative live cell binding
In addition to tumour targeting, we were interested in evaluating poly(TMCC-co-LA)-g-PEG
nanoparticles after antibody modification to validate their selective binding to live cells. Using
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flow cytometry, we were able to confirm live cell binding of Herceptin-nanoparticles to HER2
overexpressing cells. In addition, we were able to abrogate binding by blocking the HER2
receptor with pre-incubation with free Herceptin, or by replacing Herceptin with a non-specific
IgG1κ isotype control, confirming an antibody-antigen specific interaction. Also, in the absence
of HER2 expression, no binding was observed.
Notably, this means that the original binding activity of Herceptin was conserved. Poly(TMCC-
co-LA)-g-PEG was designed with this goal in mind. By designing the polymer composition to
include furan functional groups on the free ends of the PEG chains, site-specific modification of
the polymer with a maleimide-modified antibody was achieved using Diels-Alder chemistry.
This verified two important objectives that were set forth when designing the polymer: using the
mild reaction conditions for Diels-Alder chemistry preserved antibody binding activity during
the conjugation process; and antibody placement on the PEG chain termini positioned them
appropriately to bind their target antigen.
Taking these results and this technique one step further, we devised a method to quantify binding
strength. Investigating binding as a dose response, we were able to empirically fit the resulting
binding isotherms and calculate the equilibrium binding strength. This strategy allowed us to use
a semi-quantitative fluorescent method to extract quantitative information. Remarkably, we
were also able to apply this approach to live cells instead of antigens immobilized onto hard
synthetic substrates, taking full advantage of the natural membrane fluidity and receptor spacing
to produce the most accurate in vitro response.
6.6 Mechanistic predictions of binding behaviour
By developing a theoretical model of monovalent (one antibody-antigen interaction per particle)
and multivalent (multiple antibody-antigen interactions per particle) binding, we were able to
evaluate the empirical behaviour of Herceptin-nanoparticles. In our system, the equilibrium
binding constant (Keq) was directly proportional to the average number of antibodies per particle.
This is consistent with monovalent binding, where Keq increases due to increased available
configurations for binding. Multiple connections increase Keq exponentially through avidity.
Monovalent interaction suggests that although PEG has great flexibility, the overall nanoparticle
structure is rigid enough that antibody-modified chains do not migrate to the cell surface.
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A mechanistic understanding of binding behaviour enables predictive nanoparticle design.
While strong binding demonstrates positive outcomes in vitro due to enhanced uptake, it may be
detrimental in vivo where tumour penetration becomes an important factor. High targeting
ligand density may also diminish the long-circulating properties of PEGylated nanocarriers:
while PEG extends circulation by providing a neutral charge and reducing opsonization,
targeting ligands must be presented beyond the PEG border to be available for binding, and this
alters the surface properties of the nanocarrier. It has previously been shown that molecular
targeting via natural ligands to a receptor [6] or antibodies [5] results in immune recognition and
decreased circulation times in immune competent animals. Being able to predict binding
strength gives the flexibility to tune particle binding as new information becomes available using
a parameter that is easily controlled on the bench via reaction time or feed ratios.
6.7 Conclusions
This work established for the first time the utility of poly(TMCC-co-LA)-g-PEG nanoparticles in
biological applications. We first established orthotopic MFP tumour xenografts as a valid pre-
clinical model of breast cancer for testing nanoparticle targeting based on the capacity to
demonstrate EPR. To better understand the vascular and lymphovascular properties the capture
this disease condition, we used immunostaining to confirm poor lymphatic drainage and blood
vessel immaturity, in addition to greater endothelium thickness, vascular density, and thinner
basement membranes than size-matched SC tumours. Using this model, we tested the
pharmacokinetics and biodistribution of docetaxel encapsulated in poly(TMCC-co-LA)-g-PEG
nanoparticles, demonstrating improved blood circulation over the conventional surfactant-based
formulation. We also examined a panel of tissues and found that in the tumour alone, there was
greater retention of docetaxel when reformulated in nanoparticles. Based on this observation,
enhanced efficacy is expected because extended exposure to high drug concentrations should
lead to greater toxicity. Next, we developed a live cell binding assay and verified that Herceptin-
nanoparticles selectively bind HER2 overexpressing cells based on a specific antibody-antigen
interaction. Based on binding isotherms, we quantified the binding strength of a series of
Herceptin-nanoparticles having varying antibody conjugation density. We found that Keq is
directly proportional to the average number of antibodies per particle, demonstrating empirically
that Herceptin-nanoparticles follow a theoretical model of monovalent binding. We also
developed a corresponding model for multivalent binding. Once the behaviour of a particular
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system has been identified, these models can be applied widely to guide nanoparticle design.
These properties validate poly(TMCC-co-LA)-g-PEG as an alternative to surfactant-based
formulations with improved physical targeting to cancer on tissue and cellular levels.
6.8 Achievement of objectives
This research was motivated by the following hypothesis:
Poly(TMCC-co-LA)-g-PEG nanoparticle micelles will provide specific anti-cancer drug delivery
at both tissue and cellular levels
Herein, we described in vitro and in vivo tests that confirmed that poly(TMCC-co-LA)-g-PEG
nanoparticles provided tissue and cell specific targeting. Achievement of the objectives
originally laid out in Chapter 1 is summarized below.
(1) To confirm that the EPR effect can be observed in mice using in a tumour xenograft
model.
To observe nanoparticle targeting in a solid tumour model, we challenged MFP and SC
tumour xenografts using a model nanocarrier and monitored its accumulation in tumour
tissue sections. Based on these results, the MFP tumours demonstrated higher tumour
accumulation than size-matched SC tumours. This result was supported by the vascular
and lymphovascular properties revealed using immunostaining. Together these
observations validated MFP tumours as appropriate models for observing nanoparticle
targeting via EPR, and this model was carried forward to test objective 2.
(2) To demonstrate that poly(TMCC-co-LA)-g-PEG nanoparticles improve pharmacokinetics
and biodistribution over a surfactant-based drug formulation.
After loading docetaxel into poly(TMCC-co-LA)-g-PEG nanoparticles, we were able to
quantitatively monitor its distribution in MFP tumour-bearing mice using UPLC-MS.
Pharmacokinetic parameters established that the nanoparticle formulation improved blood
circulation over the conventional ethanolic polysorbate 80 formulation, improving the
odds of passive targeting by allowing multiple passes through the hyperpermeable tumour
vasculature. We also demonstrated enhanced retention of docetaxel reformulated in
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nanoparticles only in tumour tissue. These results suggest potential for improved anti-
tumour efficacy based on extended exposure of cancer cells to a high drug concentration.
(3) To verify that antibody-modified poly(TMCC-co-LA)-g-PEG nanoparticles bind
selectively to cells overexpressing a target surface antigen.
Using live cells, we demonstrated selective binding of Herceptin-nanoparticles to HER2
overexpressing cells, and that this binding was antibody-antigen specific. Building on
this observation, we used binding isotherms to quantify the strength of these interactions.
Based on this assessment, we concluded that empirical binding behaviour of Herceptin-
nanoparticles was consistent with a theoretical model of monovalent binding (one
antibody-antigen interaction per particle), demonstrating that binding strength can be
predicted and controlled.
6.9 References
[1] Strand S, Hofmann WJ, Hug H, Muller M, Otto G, Strand D, et al. Lymphocyte apoptosis induced by CD95 (APO-1/Fas) ligand-expressing tumor cells--a mechanism of immune evasion? Nat Med 1996;2:1361-1366.
[2] Huang B, Zhao J, Li HX, He KL, Chen YB, Mayer L, et al. Toll-like receptors on tumor cells facilitate evasion of immune surveillance. Cancer Res 2005;65:5009-5014.
[3] Seton-Rogers SE, Lu Y, Hines LM, Koundinya M, LaBaer J, Muthuswamy SK, et al. Cooperation of the ErbB2 receptor and transforming growth factor beta in induction of migration and invasion in mammary epithelial cells. P Natl Acad Sci USA 2004;101:1257-1262.
[4] Arias JL. Drug Targeting Strategies in Cancer Treatment: An Overview. Mini-Rev Med Chem 2011;11:1-17.
[5] Ferrari M. Nanogeometry: beyond drug delivery. Nat Nanotechnol 2008;3:131-132.
[6] McNeeley KM, Annapragada A, Bellamkonda RV. Decreased circulation time offsets increased efficacy of PEGylated nanocarriers targeting folate receptors of glioma. Nanotechnology 2007;18.
[7] Hwang HY, Kim IS, Kwon IC, Kim YH. Tumor targetability and antitumor effect of docetaxel-loaded hydrophobically modified glycol chitosan nanoparticles. J Control Release 2008;128:23-31.
[8] Moghimi SM, Hunter AC, Murray JC. Long-circulating and target-specific nanoparticles: Theory to practice. Pharmacol Rev 2001;53:283-318.
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7 Limitations and recommendations for future work
7.1 Mixed cell populations in tumor xenograft models
In Chapter 3, we investigated human tumour xenografts in immune deficient mice and were able
to observe EPR and EPR permissive pathophysiology, especially in orthotopic MFP tumours.
However, there was a general lack of vascular capacity, which can be partially attributed to the
non-invasive nature of the cell line used [1]; a more metastatic variant may exert greater
basement membrane degradation which would enable more rapid vascular branching, leading to
improved blood perfusion [1, 2]. However, in our experience with a metastatic variant of MDA-
MB-231-H2N (LM2-4 [3]), we observed accelerated tumour development rates that made size
matching difficult and provided only short windows in which to perform studies because
unacceptable tumour burdens were quickly reached. This also raised debate over whether
adequate time for neovascularization and blood vessel remodeling had elapsed.
A possible solution is to replace a single clonal cell line with mixed populations of cells to
provide a blend of properties in the resulting tumour. A mixed model may also provide a more
clinically relevant response, as tumours are comprised of many cell types, and even within the
cancer cell population there are many subpopulations having varying phenotypes [4]. A mixed
cell population approach would also approach animal models utilizing primary tumour biopsy
cells [5] while remaining widely available to researchers without access to primary samples.
Moreover, many current studies will investigate cellular responses to nanocarriers with targeting
ligands by introducing two distinct tumours on either side of a single animal, one that is antigen
overexpressing and the other that is antigen negative [6]. However, this is less relevant than
targeting the antigen-overexpressing cells as a subpopulation within a tumour having mixed
cells, which is likely to be the case in a clinical setting. Selecting a target cell line with a
reporter, like GFP, would allow the separate populations to be distinguished so that the resulting
toxicities could be compared by microscopy or flow cytometry.
A mixed cell approach may also provide insight into targeting as it pertains to cancer stem cells.
There is increasing evidence that only a small fraction of cells in a tumour give rise to new cells
[7, 8]. In this case, if the cancer stem cells are left untreated, then remission will only be
102
transient. On the other hand, if only the cancer stem cells are targeted, then the initial response
may be negligible, as they represent such a small proportion of the overall tumour mass.
However, eventually the tumour will regress fully, having no source for new cancer cells to
propagate [9]. By creating a mixed cell population where the target cells represent only a small
fraction of the tumour as in the case of cancer stem cells, the delivery strategy would need to
overcome similar transport barriers as it would clinically.
7.2 Cellular uptake as a function of antibody density
In Chapter 5, we discussed how to control nanoparticle binding by adjusting the antibody
conjugation density. To demonstrate this, we looked at nanoparticle-cell interactions at
equilibrium while inhibiting endocytosis. With this information in hand, we can design a study
that instead looks at the kinetics of cellular uptake as a function of binding strength.
Adapting our previous secondary antibody detection methodology, it is possible to monitor
nanoparticles over time in live cells. To avoid kinetic effects of particle binding, cells can be
incubated with nanoparticles first at 4 °C to reach equilibrium binding, and then transferred to
37 °C to initiate internalization. Moreover, to ensure that receptor recycling does not contribute
to these measurements, geldanamycin can be used to inhibit HER2 recycling [10]. To capture
snapshots of particle localization, it is probable that cells will need to be fixed, but simply
transferring them to ice may stall further cellular trafficking events. There are two possible
methods to enable distinction between membrane-bound and internalized nanoparticles by
secondary antibody detection: (1) the signal from cells that have been permeabilized and that
have not been permeabilized represent the total signal and membrane-bound signal respectively
[11], and the internalized signal can be calculated by subtraction; (2) membrane-bound
nanoparticles can be removed via acid stripping, and the internalized and membrane-bound
signals measured separately [12].
It has previously been shown that endocytic rate constant (ke) is equal to the slope of the ratio of
internalized:surface-bound targeting ligand as a function of time [13]. Flow cytometry will
enable measurement of a large cell sample size, and has been successfully applied to simple
receptor-ligand internalization studies [12]. These measurements will allow us to calculate ke as
a function of binding strength at a given nanoparticle concentration, or as a function of
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nanoparticle concentration at a set antibody conjugation density, which will provide estimates for
the antibody modification or dosage requirements to achieve a target uptake rate.
7.3 Tumour penetration as a function of antibody density
Heterogeneous drug distribution in a tumour mass leads to poor treatment uniformity and drug
resistance [14]. Poor vascular architecture already contributes to regions of hypoxia, which
correspond to the regions that are most distant from functioning blood flow [15]. Active
targeting strategies can further intensify the difficulty in distributing drugs to all areas of a
tumour, in part due to the slow diffusion of large nanocarriers, but also due to active binding and
depletion by the cells closest to an active blood vessel [16-18]. Even the free Herceptin antibody
can take up to 24 hours to distribute uniformly in a HER2 overexpressing tumour xenografts
[19]. Consequently, understanding how binding strength influences tumour penetration is an
important consideration in nanoparticle design.
While tumour penetration distance can be computationally difficult to measure in animal models,
a simplified approach would be to investigate concentration profiles in spherical cell aggregates
in vitro [15]. This also removes changing concentrations in blood circulation and transport
barriers across tumour blood vessels. These simplifications would enable direct measurement of
penetration as a function of binding strength by controlling antibody density.
To measure the nanoparticle concentration profile in cell aggregates, a flow cytometry approach
is still relevant. Submerging the aggregates in a non-specific stain, such as Hoechst 33342,
establishes a gradient of staining intensity that can be used to categorize cells according to depth
within the aggregate [20]. After dissociating the aggregates and using the secondary antibody
approach to co-stain for the nanoparticles, nanoparticle association with cells at different depths
can be determined by correlation with the Hoechst signal using flow cytometry [21].
This study would provide guidelines for the maximum antibody conjugation density that would
be permissive to uniform tumour distribution. Measurements of tumour vascular density
(Chapter 3) and in vivo tumour accumulation data (Chapter 4) also inform these estimates by
providing the approximate distance between blood vessels and the total nanoparticle load that
initially enters the tumour and is available for eventual distribution.
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7.4 Evaluating alternative targeting ligands
Herceptin-nanoparticles demonstrated utility in vitro via receptor-specific binding and uptake,
but failed to accumulate in tumour tissue in vivo (Appendices). We were initially interested in
using a full antibody for three reasons: (1) by virtue of having two binding sites, antibodies can
enhance binding through avidity, and therefore generally have greater affinity for their targets
[22]; and (2) the full IgG structure of Herceptin and other therapeutically relevant antibodies is
required to induce antibody dependent cellular cytotoxicity (ADCC), an important mechanism
for their therapeutic activity [23, 24]; (3) site-specific modification of the saccharide chains on
the constant (non-binding) region of IgG provided a maleimide modification site that did not
interfere with binding activity [25]. However, using a fragment antigen binding (Fab fragment)
or single chain variable fragment (scFv) will retain the specificity of the parent IgG, and may
still allow inhibition of DNA repair, and therefore synergistic effects of combining HER2
targeting with chemotherapeutic delivery [26]. Moreover, if the amino acids in the binding
region are known, chemistry schemes can be tailored to avoid altering binding activity.
Replacing an antibody with an antibody fragment as the targeting ligand has the potential to
maintain receptor-specific targeting and uptake while reducing many of the negative changes we
observed in vivo. A full human IgG has a ~150 kDa molecular weight, whereas Fab fragments
are ~55 kDa, and scFv are ~30 kDa. These considerable reductions in molecular size may have a
significant impact on the ability of the modified nanoparticles to extravasate through
hyperpermeable tumour vasculature. These properties may also enhance tumour penetration by
accommodating greater nanoparticle flexibility [27]. A reduced influence on the nanoparticle
surface properties would also be expected. For these reasons, substituting for an alternative
targeting ligand may increase tumour uptake to levels similar to unmodified nanoparticles, while
still encouraging receptor-specific uptake and toxicity.
7.5 Safety and efficacy
In Chapter 4, we observed that poly(TMCC-co-LA)-g-PEG nanoparticles improved circulation
properties and tumour retention of docetaxel over the conventional surfactant-based formulation.
Simply by providing a suitable alternative to ethanolic polysorbate 80 while maintaining
adequate docetaxel concentrations for dosing, there is significant potential for reduced systemic
toxicity. The enhanced tumour retention has further potential influence on treatment efficacy, as
105
greater tumour toxicity is expected based on extended exposure to high docetaxel concentration.
Combining docetaxel encapsulation and a targeting ligand for selective uptake presents even
more exciting opportunities for selective toxicity, especially if greater tumour accumulation can
be achieved using an alternative ligand.
A metronomic dosing approach would be readily achieved using our existing protocols. While
1.5 mg/kg represents only a fraction of the 15-20 mg/kg mean tolerated dose [28], frequent (3
times per week) low doses of docetaxel have previously shown excellent tolerance and tumour
regression in models of ovarian cancer [29]. However, in our experience, higher doses (~6
mg/kg) could be achieved by particle concentration using a tangential flow filter.
A suggested dosing schedule is to administer dose equivalents of docetaxel at 1.5 mg/kg by bolus
IV injection three times a week for two weeks. To take advantage of EPR, treatment should
begin a minimum of 2-3 days after MFP tumours become palpable [30]. Animals should be
monitored daily for mortality, symptoms of humane endpoints, and weight loss. Plotting tumour
size as a function of time would be a useful measure of disease progression. Survival data
should also be recorded, although deaths are not anticipated under this treatment regimen. After
sacrifice, blood and tissue samples should be collected for analysis. A common toxicological
measure is toxicity in vital organs such as the liver, kidney, and spleen, which can be assessed
visually using hematoxylin and eosin staining [31].
This study would confirm whether drug targeting using poly(TMCC-co-LA)-g-PEG
nanoparticles translates into enhanced tumour regression and suppressed systemic toxicity, the
two primary end goals of tumour targeting. Together with the data presented here, these results
would provide a complete pre-clinical picture of our nanoparticle system.
7.6 References
[1] Abdelkarim M, Vintonenko N, Starzec A, Robles A, Aubert J, Martin M-L, et al. Invading Basement Membrane Matrix Is Sufficient for MDA-MB-231 Breast Cancer Cells to Develop a Stable <italic>In Vivo</italic> Metastatic Phenotype. PLoS ONE 2011;6:e23334.
[2] Lu P, Weaver VM, Werb Z. The extracellular matrix: a dynamic niche in cancer progression. J Cell Biol 2012;196:395-406.
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[3] Munoz R, Man S, Shaked Y, Lee CR, Wong J, Francia G, et al. Highly efficacious nontoxic preclinical treatment for advanced metastatic breast cancer using combination oral UFT-cyclophosphamide metronomic chemotherapy. Cancer Res 2006;66:3386-3391.
[4] Evan GI, Vousden KH. Proliferation, cell cycle and apoptosis in cancer. Nature 2001;411:342-348.
[5] Marsden CG, Wright MJ, Carrier L, Moroz K, Pochampally R, Rowan BG. A novel in vivo model for the study of human breast cancer metastasis using primary breast tumor-initiating cells from patient biopsies. Bmc Cancer 2012;12.
[6] Gong HB, Kovar J, Little G, Chen HX, Olive DM. In Vivo Imaging of Xenograft Tumors Using an Epidermal Growth Factor Receptor-Specific Affibody Molecule Labeled with a Near-infrared Fluorophore. Neoplasia 2010;12:139-U159.
[7] Visvader JE, Lindeman GJ. Cancer stem cells in solid tumours: accumulating evidence and unresolved questions. Nat Rev Cancer 2008;8:755-768.
[8] Dick JE. Breast cancer stem cells revealed. P Natl Acad Sci USA 2003;100:3547-3549.
[9] Wicha MS, Liu SL, Dontu G. Cancer stem cells: An old idea - A paradigm shift. Cancer Research 2006;66:1883-1890.
[10] Austin CD, De Maziere AM, Pisacane PI, van Dijk SM, Eigenbrot C, Sliwkowski MX, et al. Endocytosis and sorting of ErbB2 and the site of action of cancer therapeutics trastuzumab and geldanamycin. Mol Biol Cell 2004;15:5268-5282.
[11] Hallden G, Andersson U, Hed J, Johansson SGO. A New Membrane Permeabilization Method for the Detection of Intracellular Antigens by Flow-Cytometry. J Immunol Methods 1989;124:103-109.
[12] Schmidt-Glenewinkel H, Reinz E, Eils R, Brady NR. Systems Biological Analysis of Epidermal Growth Factor Receptor Internalization Dynamics for Altered Receptor Levels. J Biol Chem 2009;284:17243-17252.
[13] Wiley HS, Cunningham DD. The Endocytotic Rate-Constant - a Cellular-Parameter for Quantitating Receptor-Mediated Endocytosis. J Biol Chem 1982;257:4222-4229.
[14] Tredan O, Galmarini CM, Patel K, Tannock IF. Drug resistance and the solid tumor microenvironment. J Natl Cancer I 2007;99:1441-1454.
[15] Minchinton AI, Tannock IF. Drug penetration in solid tumours. Nat Rev Cancer 2006;6:583-592.
[16] Baker JHE, Lindquist KE, Huxham L, Kyle AH, Sy JT, Minchinton AI. Direct visualization of heterogeneous extravascular distribution of trastuzumab in human epidermal growth factor receptor type 2 overexpressing xenografts. Clin Cancer Res 2008;14:2171-2179.
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[17] Dreher MR, Liu WG, Michelich CR, Dewhirst MW, Yuan F, Chilkoti A. Tumor vascular permeability, accumulation, and penetration of macromolecular drug carriers. J Natl Cancer I 2006;98:335-344.
[18] Juweid M, Neumann R, Paik C, Perezbacete MJ, Sato J, Vanosdol W, et al. Micropharmacology of Monoclonal-Antibodies in Solid Tumors - Direct Experimental-Evidence for a Binding-Site Barrier. Cancer Res 1992;52:5144-5153.
[19] Lee CM, Tannock IF. The distribution of the therapeutic monoclonal antibodies cetuximab and trastuzumab within solid tumors. Bmc Cancer 2010;10.
[20] Durand RE. Use of Hoechst-33342 for Cell Selection from Multicell Systems. J Histochem Cytochem 1982;30:117-122.
[21] Olive PL, Chaplin DJ, Durand RE. Pharmacokinetics, Binding and Distribution of Hoechst 33342 in Spheroids and Murine Tumors. Brit J Cancer 1985;52:739-746.
[22] Hudson PJ. Recombinant antibody constructs in cancer therapy. Curr Opin Immunol 1999;11:548-557.
[23] Iannello A, Ahmad A. Role of antibody-dependent cell-mediated cytotoxicity in the efficacy of therapeutic anti-cancer monoclonal antibodies. Cancer Metast Rev 2005;24:487-499.
[24] Arnould L, Gelly M, Penault-Llorca F, Benoit L, Bonnetain F, Migeon C, et al. Trastuzumab-based treatment of HER2-positive breast cancer: an antibody-dependent cellular cytotoxicity mechanism? Br J Cancer 2006;94:259-267.
[25] Shi M, Wosnick JH, Ho K, Keating A, Shoichet MS. Immuno-polymeric nanoparticles by Diels-Alder chemistry. Angew Chem Int Edit 2007;46:6126-6131.
[26] Nahta R, Esteva FJ. Herceptin: mechanisms of action and resistance. Cancer Lett 2006;232:123-138.
[27] Hwang HY, Kim IS, Kwon IC, Kim YH. Tumor targetability and antitumor effect of docetaxel-loaded hydrophobically modified glycol chitosan nanoparticles. J Control Release 2008;128:23-31.
[28] Dykes DJ, Bissery MC, Harrison SD, Waud WR. Response of Human Tumor Xenografts in Athymic Nude-Mice to Docetaxel (Rp-56976, Taxotere(R)). Invest New Drug 1995;13:1-11.
[29] Kamat AA, Kim TJ, Landen CN, Lu CH, Han LY, Lin YG, et al. Metronomic chemotherapy enhances the efficacy of antivascular therapy in ovarian cancer. Cancer Res 2007;67:281-288.
[30] Schiffelers RM, Metselaar JM, Fens MHAM, Janssen APCA, Molema G, Storm G. Liposome-encapsulated prednisolone phosphate inhibits growth of established tumors in mice. Neoplasia 2005;7:118-127.
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[31] Yang K, Wan JM, Zhang SA, Zhang YJ, Lee ST, Liu ZA. In Vivo Pharmacokinetics, Long-Term Biodistribution, and Toxicology of PEGylated Graphene in Mice. Acs Nano 2011;5:516-522.
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Copyright Acknowledgements
Portions of Chapter 2 are reprinted with permission from Elsevier from the following publication:
Ho KS, Shoichet MS (2013). Design Considerations of Polymeric Nanoparticle Micelles for Targeted Chemotherapeutic Delivery. Current Opinion in Chemical Engineering, 2(1): 53-59.
Available online at: http://dx.doi.org/10.1016/j.coche.2013.01.003
Chapter 3 is reprinted with permission from Biomed Central from the following publication:
Ho KS, Poon PC, Owen SC, and Shoichet MS (2013) Blood vessel hyperpermeability and pathophysiology in human tumour xenograft models of breast cancer: a comparison of ectopic and orthotopic tumours. BMC Cancer, 12: 579.
Available online at: http://www.biomedcentral.com/1471-2407/12/579
Figures 2.5, 2.6, and 2.7 are reprinted with permission from John Wiley and Sons from the following publication:
Shi M, Wosnick JH, Ho K, Keating A, and Shoichet MS (2007). Immuno-polymeric nanoparticles by Diels-Alder chemistry. Angewandte Chemie International Edition, 46(32): 6126-6131.
Available online at: http://onlinelibrary.wiley.com/doi/10.1002/anie.200701032/abstract
Figures 2.8, 2.9, and 2.10, and Table 2.1 are reprinted with permission from John Wiley and Sons from the following publication:
Shi M, Ho K, Keating A, and Shoichet MS (2009). Doxorubicin-conjugated immuno-nanoparticles for intracellular anticancer drug delivery. Advanced Functional Materials, 19(11): 1689-1696.
Available online at: http://onlinelibrary.wiley.com/doi/10.1002/adfm.200801271/abstract
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Chapter 4 is reprinted with permission from Elsevier from the following publication:
Ho KS, Aman AM, Al-awar RS, and Shoichet MS (2012) Amphiphilic micelles of poly(2-methyl-2-carboxytrimethyle carbonate-co-D,L-lactide)-graft-poly(ethylene glycol) deliver anti-cancer drugs to solid tumours. Biomaterials, 33 (7), 2223–2229.
Available online at: http://www.sciencedirect.com/science/article/pii/S0142961211014268
Chapter 5 is reprinted with permission from the Royal Society of Chemistry from the following publication:
Ho K, Lapitsky Y, Shi M, and Shoichet MS (2009). Tunable immunonanoparticle binding to cancer cells: thermodynamic analysis of targeted drug delivery vehicles. Soft Matter, 5(5): 1074-80.
Available online at: http://pubs.rsc.org/en/Content/ArticleLanding/2009/SM/b814204a
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8 Appendices
8.1 List of abbreviations α-SMA Alpha smooth muscle actin
ADCC Antibody-dependent cellular cytotoxicity
aHER2-DTX-NP Herceptin-modified nanoparticles containing encapsulated docetaxel
CMC Critical micelle concentration
DAB 3,3’-diaminobenzidine
DMF Dimethylformamide
DOX Doxorubicin
DTX Docetaxel
DTX-NP Nanoparticles containing encapsulated docetaxel
EPR Enhanced permeability and retention
FBS Fetal bovine serum
FITC Fluorescein isothiocyanate
HER2 Human epidermal growth factor receptor 2
HMEC-1 Healthy endothelial cell line
IgG Immunoglobulin G
IV Intravenous
LYVE-1 Lymphatic vessel endothelial hyaluronan receptor
MCF-7 HER2 normal breast cancer cell line
MDA-MB-231-H2N Tumourigenic and HER2 overexpressing cell line
MDA-MB-468 HER2 negative breast cancer cell line
MES Morpholinoethanesulfonic acid
MFP Mammary fat pad
MMP Matrix metalloproteinase
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MTS assay Colourimetric metabolic assay using tetrazolium salt
NGS Normal goat serum
NP-aHER2 Herceptin-modified nanoparticles
NP-aHER2-DOX Herceptin- and doxorubicin-modified nanoparticles
NP-DOX Doxorubicin-modified nanoparticles
NSG mice NOD scid gamma mice
PBS Phosphate buffered saline, pH 7.4
PEG Poly(ethylene glycol)
PI Propidium iodide
PK Pharmacokinetics
PLA Poly(lactic acid)
Poly(TMCC-co-LA)-g-PEG Poly(2-methyl-2-carboxytrimethylene carbonate-co-D,L-lactide)-graft-poly(ethylene glycol)-furan
PS80 Polysorbate (Tween) 80
RES Reticuloendothelial system
SC Subcutaneous
SKBR-3 HER2 overexpressing breast cancer cell line
Trypsin-EDTA Trypsin-ethylenediamine tetraacetic acid
UPLC-MS Ultra performance liquid chromatography-coupled with mass spectrometry
8.2 List of parameters and mathematical notation
α Average number of antigen-antibody interactions
ε Molecular energy
Aµ Chemical potential of cell-bound nanoparticles
Sµ Chemical potential of nanoparticles in solution
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Ω Average number of antibodies per particle
θ Fractional coverage, N/M
AUCall Area under the curve (to t = 8 h)
AUC∞ Area under the curve (to t = ∞)
AUMC∞ Area under the first moment curve (to t = ∞)
Co Initial plasma concentration
Cl Clearance
GΔ Molar Gibbs free energy of binding
MAXI Saturation fluorescence intensity
)( NPCI Concentration-dependent (measured) fluorescence intensity
kB Boltzmann constant
Kd Dissociation constant
eqK Equilibrium binding constant
Q(M,N,T) Canonical partition function for N nanoparticles binding to M binding sites at temperature T
M Available binding sites
N Number of bound nanoparticles
R Universal gas constant
T Temperature
t1/2, λ Lambda half life
Vd Volume of distribution
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8.3 Additional data
Pharmacokinetics and biodistribution of docetaxel loaded in Herceptin-modified
nanoparticles
We investigated the PK and biodistribution of Herceptin-modified docetaxel-nanoparticles (NP-
aHER2-DTX-NP) using the same methods as in Chapter 4. There was no statistical difference
between the nanoparticle formulations in the plasma profile of docetaxel (Figure 8.1). Based on
this observation, we also anticipated similar tumour accumulation.
Figure 8.1 Plasma concentration profile for docetaxel in tumour-bearing mice. Orange symbols show
Herceptin-docetaxel-nanoparticles, black symbols show docetaxel nanoparticles, white symbols show
free docetaxel.
However, when the biodistribution panel was investigated, little accumulation was observed
across the panel, including in the tumour. The tissue profiles did not parallel those observed in
unmodified DTX-NP or free DTX (Figure 8.2).
115
Figure 8.2 Tissue concentration profile for docetaxel in tumour-bearing mice. Orange symbols show
Herceptin-docetaxel-nanoparticles, black symbols show docetaxel nanoparticles, white symbols show
free docetaxel.
This unexpected behaviour must result from physical changes to the nanoparticle system in the
presence of the Herceptin modification. It is unlikely that reduced kinetic stability led to DTX
release because the new behaviour did not resemble the profiles of free DTX. A more likely
explanation is that the altered nanoparticle surface properties presented new barriers to targeted
delivery. Particle composition and surface properties are key factors in determining
biodistribution and elimination rate [1]. As surface properties change (such as by addition of
IgG) the bound plasma protein profile changes [2, 3]; however, protein association is a balance
between what is most abundant and what has greatest affinity for the particle surface, meaning
that the profile will evolve over time [4]. The initial profile for both formulations is likely very
similar because proteins with general high concentration and high association rates will
dominate. Low concentration proteins will slowly displace some of these via higher affinity or
slower dissociation rates.
116
However, while extended circulation is a pre-requisite for EPR, passive targeting is not
guaranteed based on this property alone. Herceptin likely restricts the mobility of the
surrounding PEG chains through steric hindrance, limiting the flexibility of the construct. This
lack of deformation may have a direct impact on the ability of aHER2-DTX-NP to cross the gaps
presented by leaky tumour vasculature or other normal fenestrations in healthy organ tissue [5].
Additionally, particle geometry and flexibility impact the dynamics of motion in blood. As a
result the Herceptin modification may negatively impact the nanoparticles’ ability to enter and
drift to the edges of capillaries [6].
These results suggest that this formulation of poly(TMCC-co-LA)-g-PEG nanoparticles for
active targeting does not maximize the system’s potential for localized delivery. Although gains
in toxicity may still be observed via cellular uptake of the material that does enter the tumour [7],
a greater response would be expected with greater tumour accumulation. To return to particle
properties more similar to the unmodified nanoparticle formulation, it may be possible to use an
alternative targeting ligand to achieve active targeting. Using a smaller targeting ligand, such as
Fab antibody fragments, may provide improved tumour targeting by reducing steric resistance to
deformation or by making a lesser contribution to the surface properties of the modified
nanoparticles.
8.4 References
[1] Nel AE, Madler L, Velegol D, Xia T, Hoek EMV, Somasundaran P, et al. Understanding biophysicochemical interactions at the nano-bio interface. Nat Mater 2009;8:543-557.
[2] Schmidt S, Gonzalez D, Derendorf H. Significance of Protein Binding in Pharmacokinetics and Pharmacodynamics. J Pharm Sci-Us 2010;99:1107-1122.
[3] Aggarwal P, Hall JB, McLeland CB, Dobrovolskaia MA, McNeil SE. Nanoparticle interaction with plasma proteins as it relates to particle biodistribution, biocompatibility and therapeutic efficacy. Adv Drug Deliver Rev 2009;61:428-437.
[4] Rolan PE. Plasma protein binding displacement interactions--why are they still regarded as clinically important? Br J Clin Pharmacol 1994;37:125-128.
[5] Hwang HY, Kim IS, Kwon IC, Kim YH. Tumor targetability and antitumor effect of docetaxel-loaded hydrophobically modified glycol chitosan nanoparticles. J Control Release 2008;128:23-31.
[6] Caldorera-Moore M, Guimard N, Shi L, Roy K. Designer nanoparticles: incorporating size, shape and triggered release into nanoscale drug carriers. Expert Opin Drug Del 2010;7:479-495.
117
[7] Kirpotin DB, Drummond DC, Shao Y, Shalaby MR, Hong KL, Nielsen UB, et al. Antibody targeting of long-circulating lipidic nanoparticles does not increase tumor localization but does increase internalization in animal models. Cancer Res 2006;66:6732-6740.
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