Spatial and temporal mapping of heterogeneity in liposome uptake and microvascular distribution in an orthotopic tumor xenograft model

    Spatial and temporal mapping of heterogeneity in liposome uptake and microvascular distribution in an orthotopic tumor xenograft model Sandra N. Ekdawi, James M.P. Stewart, Michael Dunne, Shawn Stapleton, Nicholas Mitsakakis, Yannan N. Dou, David A. Jaffray, Christine Allen PII: DOI: Reference:

S0168-3659(15)00224-2 doi: 10.1016/j.jconrel.2015.04.006 COREL 7627

To appear in:

Journal of Controlled Release

Received date: Revised date: Accepted date:

26 December 2014 21 March 2015 4 April 2015

Please cite this article as: Sandra N. Ekdawi, James M.P. Stewart, Michael Dunne, Shawn Stapleton, Nicholas Mitsakakis, Yannan N. Dou, David A. Jaffray, Christine Allen, Spatial and temporal mapping of heterogeneity in liposome uptake and microvascular distribution in an orthotopic tumor xenograft model, Journal of Controlled Release (2015), doi: 10.1016/j.jconrel.2015.04.006

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ACCEPTED MANUSCRIPT Spatial and temporal mapping of heterogeneity in liposome uptake and microvascular distribution in an orthotopic tumor xenograft model

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Sandra N. Ekdawi1, James M. P. Stewart2, Michael Dunne1, Shawn Stapleton3,5, Nicholas Mitsakakis1, Yannan N. Dou1, David A. Jaffray2-7 and Christine Allen1* 1

Leslie Dan Faculty of Pharmacy, University of Toronto, Toronto, Ontario M5S 3M2, Canada Institute of Biomaterials and Biomedical Engineering, University of Toronto, Toronto, Ontario M5S 3G9, Canada 3 Department of Medical Biophysics, University of Toronto, Toronto, Ontario M5G 1L7, Canada 4 Department of Radiation Oncology, University of Toronto, Toronto, Ontario M5S 3E2, Canada 5 Radiation Medicine Program, Princess Margaret Cancer Centre, University Health Network, Toronto, Ontario M5G 2M9, Canada 6 Ontario Cancer Institute, Toronto, Ontario M5G 2M9, Canada 7 Techna Institute, University Health Network, Toronto, Ontario M5G 1P5, Canada

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*Corresponding author: Christine Allen, PhD Leslie Dan Faculty of Pharmacy, University of Toronto 144 College Street, Toronto, Ontario, M5S 3M2, Canada Tel.: +1 416 946 8594 Fax: +1 416 978 8511 e-mail: [email protected]

Existing paradigms in nano-based drug delivery are currently being challenged. Assessment of bulk tumor accumulation has been routinely considered an indicative measure of nanomedicine potency. However, it is now recognized that the intratumoral distribution of nanomedicines also impacts their therapeutic effect. At this time, our understanding of the relationship between the bulk (i.e., macro-) tumor accumulation of nanocarriers and their intratumoral (i.e., micro-) distribution remains limited. Liposome-based drug formulations, in particular, suffer from diminished efficacy in vivo as a result of transport-limiting properties, combined with the heterogeneous nature of the tumor microenvironment. In this report, we perform a quantitative image-based assessment of macro- and microdistribution of liposomes. Multi-scalar assessment of liposome distribution was enabled by a stable formulation which co-encapsulates an iodinated contrast agent and a near-infrared fluorescence probe, for computed tomography (CT) and optical microscopy, respectively. Spatio-temporal quantification of tumor uptake in orthotopic xenografts was performed using CT at the bulk tissue level, and within defined sub-volumes of the tumor (i.e., rim, periphery and core). Tumor penetration and relative distribution of liposomes were assessed by fluorescence microscopy of whole tumor sections. Microdistribution analysis of whole tumor images exposed a heterogeneous distribution of both liposomes and tumor vasculature. Highest levels of liposome uptake were achieved and maintained in the wellvascularized tumor rim over the study period, corresponding to a positive correlation between liposome and microvascular density. Tumor penetration of liposomes was found to be timedependent in all regions of the tumor however independent of location in the tumor. Importantly, a multi-scalar comparison of liposome distribution reveals that macro-accumulation

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ACCEPTED MANUSCRIPT in tissues (e.g., blood, whole tumor) may not reflect micro-accumulation levels present within specific regions of the tumor as a function of time. Keywords

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Nanomedicine; liposome; intratumoral distribution; tumor accumulation; computed tomography; optical microscopy

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1. Introduction

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Tumor resistance to liposome-based therapy has been linked to heterogeneous tissue distribution and limited penetration of both nanocarrier and drug. Increasing reports of the intratumoral distribution of small-molecule [1-5], macromolecular [6], and nanoparticle-based agents [7-10] have yielded insight into the impact of the physico-chemical properties of the drug delivery system as well as that of the tumor microenvironment [11] on anti-tumor efficacy. Specifically, the fate of nano-based agents at the tumor site has been examined in relation to select pathophysiological properties of tumors deemed critical to the success of nanomedicines such as the distribution of the tumor vascular network [11], vascular density [12, 13] and permeability [14, 15], as well as the composition and density of scaffold proteins of the extracellular matrix [16, 17]. Such studies have significantly contributed to our understanding of the underlying barriers hindering the homogeneous distribution of nanomedicines within tumors. In turn, strategic exploitation of tumor-specific properties has been achieved through physical and pharmacological modulators, enabling enhanced delivery of drug, and/or superior anti-tumor efficacy [7, 18-21].

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Despite an increased focus on the microscopic distribution of nanoparticles and/or their cargo in tumors, the relationship between macro- (i.e., bulk) and microdistribution of advanced drug delivery systems remains to be elucidated. This is particularly important given the chronic overreliance on the evaluation of bulk tumor accumulation of nanosystems as indicative of their in vivo performance. The ability of nanosystems to accumulate preferentially at the tumor site is attributed to the hyperpermeability of the tumor vasculature and impaired lymphatic clearance system; a phenomenon defined as the enhanced permeability and retention (EPR) effect [22, 23]. Recognized over the past three decades as a universal trait of solid tumors, the EPR effect has recently become known as somewhat of a “moving target” [24]. Indeed, inherent pathophysiological variability, as well as the impact of therapy and/or modulators, influence the status of the EPR effect, both spatially and temporally, in a given tumor and for a given therapeutic [25, 26]. The ensuing effect on both macro- and microdistribution of nanomedicines, and in turn on their anti-tumor efficacy, remains poorly characterized Similarly, further investigation into the relationship between the microdistribution of nanoparticles and tumor microenvironmental parameters, such as microvessel density (MVD), is pertinent. The tumor microenvironment (TME) has indeed been implicated in the resistance of lesions to both conventional and nanoparticle-based therapy. In particular, aberrant tumor vascular structure and function, solid stress, and interstitial hypertension [27] exacerbate the heterogeneous tumor distribution of delivered therapeutics, resulting in their limited penetration and/or anti-tumor activity [28-30]. Variability in tumor properties has been reported across tumor types, among tumors of the same type as well as within the same tumor [31-33]. As such, the heterogeneity itself in the status of such properties calls for their spatio-temporal characterization and subsequent relation to the delivery, and ultimately the efficacy, of a specific nanomedicine.

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We investigate the relationship between the tumor macro- and microdistribution of liposomes, as well as that between their microdistribution and properties of the TME. Such characterization is expected to yield a methodological platform which may further enable a greater understanding of macro- and microscopic parameters as potential determinants of the efficacy of nanomedicines. Hence, an imageable and stable liposome formulation is required which can be detected at both levels of resolution over the course of the experiment. We have therefore built upon our previous studies which have employed computed tomography (CT) as a quantitative imaging modality to assess the macrodistribution of liposomes [34, 35], and optical microscopy as a means to assess the tumor penetration of block copolymer micelles [8, 9]. As such, tissue deposition, distribution and penetration can be measured using the same liposome formulation via complementary contrast agents and corresponding imaging modalities. CT permits quantification and sub-mm resolution of liposomes while fluorescence microscopy enables visualization of liposome distribution at the sub-μm level relative to select factors of the TME. Overall, this study presents a framework to analyze the macro- and microdistribution of nanosystems in vivo. Specifically, spatio-temporal characterization of the intratumoral distribution of liposomes and tumor properties is performed quantitatively. Beyond bulk tumor characterization, microdistribution measurements provide site-specific information, revealing differences in inter-region liposome accumulation and microvascular density. Such differences may reveal trends. This is shown in the relationship found between liposome concentration and MVD, highlighting the key role that the tumor vasculature plays in defining the spatio-temporal tumor distribution of nanoparticles. Tumor penetration of liposomes is also characterized as a function of tumor region and time, revealing the contribution of both variables in determining liposome transport. Importantly, we show that systemic (i.e., plasma) and bulk tumor accumulation levels of liposomes are not necessarily predictive of the levels present within specific regions of the tumor. Such findings are expected to guide the evaluation and successful implementation of nanomedicines. 2. Materials and methods 2.1. Materials

1,2-dipalmitoyl-sn-glycero-3-phosphocholine (DPPC) and N-(carbonyl-methoxypolyethylene glycol-2000)-1,2-distearoyl-sn-glycero-3-phosphoethanolamine (mPEG2000-DSPE) were purchased from Genzyme Pharmaceuticals (Cambridge, MA). Cholesterol was obtained from Northern Lipids (Burnaby, BC, Canada). The small-molecule iodinated CT agent, iohexol (IOX), was obtained as a 350 mg I / mL solution of Omnipaque® from GE Healthcare (Mississauga, Canada). The near-infrared (NIR) fluorescent dye, Genhance™ 680 (GH680), was purchased from PerkinElmer (Woodbridge, Canada). 2.2. Liposome preparation The liposomes used in this work were developed from previously published protocols employing IOX as a contrast agent [35, 36], to co-encapsulate the NIR fluorescent dye GH680. Briefly, lipids were dissolved in anhydrous ethanol at 72°C in a molar ratio of 55:40:5 DPPC:CH:PEG2000-DSPE. Following evaporation of the ethanol, an aqueous solution of IOX (350 mg I /mL) and GH680 (1.11 mg/mL) was added to yield a lipid concentration of 100 mM. The solution was kept at 72°C for 4 hours with frequent vortexing, followed by stirring at room temperature overnight. Unilamellar liposomes were obtained by 5 extrusion cycles through two

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stacked 200 nm pore size Track-Etch polycarbonate membranes (Whatman Inc., Clifton, NJ) at a pressure of 250 psi, followed by 10 cycles through two stacked 80 nm membranes at 400 psi using a 10 mL Lipex Extruder (Northern Lipids Inc., Burnaby, Canada). Between extrusion through the 200 nm and 80 nm membranes, the liposomes were allowed to stir overnight at room temperature. Unencapsulated IOX and GH680 were removed by dialysis (MWCO 50 kDa, Spectrum Labs, Rancho Dominguez, CA) against a minimum 250-fold volume excess of 0.02 mM HEPES-buffered saline solution (HBS, pH 7.4) over a period of 4 days. The liposome solution was then concentrated using a tangential flow column (MidGee ultrafiltration cartridge, 750,000 NMWC pore size, GE Healthcare, Mississauga, ON, Canada) and peristaltic pump (Watson Marlow Inc., Wilmington, MA) to a final lipid concentration of approximately 200 mM, and an iodine concentration of approximately 70 mg/mL.

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2.3. Liposome characterization

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The hydrodynamic diameter, zeta potential, and contrast agent loading of the liposomes were measured. Briefly, the liposomes were diluted 250x in deionized water and their mean intensitybased and number-based diameters were recorded, as well as zeta potential, using a Malvern zetasizer instrument (Malvern, UK). The concentrations of encapsulated IOX and GH680 were determined by HPLC (Agilent Technologies 1200 Series). Specifically, a 10-fold volume excess of methanol, followed by thorough vortexing, was used to rupture the liposomes and a further 100-fold dilution in 90% methanol was performed in order to simultaneously detect both imaging agents within their respective linear ranges. The samples were injected onto an XTerra® MS C18 reverse-phase column (5 μm particle size, 4.6 mm x 250 mm dimensions; Waters Ltd., Mississauga, Canada) and eluted using a mobile phase consisting of a gradient of triethylammomium acetate (TEAA) buffer (pH 5.2) and methanol at a flow rate of 0.8 mL/min. IOX was detected at 245 nm (Waters 2487 Dual λ Absorbance Detector), while GH680 was detected using a Series 200a Fluorescence Detector (PerkinElmer, Woodbridge, Canada). The concentrations of encapsulated IOX and GH680 were determined from standard curves for each agent and are reported as the mean ± standard deviation of three independent liposome preparations. All measurements for each liposome batch were performed in triplicate. 2.4. Liposome pharmacokinetics and in vivo stability Severe combined immunodeficient (SCID) female mice (8-9 weeks old) were injected intravenously (iv) via the tail vein with the liposomes (1.22 mg lipid/ g mouse; 0.7 mg I per g mouse; 2 μg GH680 per g mouse). At 0.17, 2, 4, 8, 18, 24, 48, 72, 96 and 120 h post-injection (hpi), blood samples were collected in a heparinized tube following puncture of the saphenous vein. Similarly, a control group of mice received the same dose of free contrast agents in solution (i.e., not liposome-encapsulated). Immediately following blood collection, plasma was isolated via centrifugation at 2320 g for 5 minutes at 4°C and stored at -20°C until subsequent HPLC analysis. Extraction of IOX and GH680 from plasma was performed using a 10-fold volume excess of ice-cold methanol, followed by centrifugation at 20000 g for 30 minutes. The resulting supernatant was aliquoted for direct HPLC analysis under the same conditions described for liposome loading analysis. Plasma concentration versus time curves of the contrast agents were fit using a two-compartment model weighted by 1/SD2 in GraphPad Prism v 5.03. For each contrast agent, the distribution half-life (t1/2ɑ ), elimination half-life (t1/2β), volume of distribution (Vd), plasma area under the curve (AUC0-120h) and clearance (CL) were calculated and presented 4

ACCEPTED MANUSCRIPT as mean ± standard deviation of n=8 (liposome-) injected mice. A Pearson product-moment correlation was performed as a measure of retention of both contrast agents within the liposomes.

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2.5. Animal imaging and CT analysis

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All animal procedures were approved by the University Health Network Animal Care Committee. ME180 cervical tumors were grown in SCID mice by orthotopic implantation as described previously [37, 38]. Once tumors reached approximately 400-600 mm3 in volume, as determined by MRI, mice were iv administered liposomes at the same dose as the pharmacokinetics study (i.e., 1.22 mg lipid/ g mouse). The animals were subsequently scanned by CT (GE Locus Ultra MicroCT, GE Healthcare, Waunakee, WI) at 0.17, 2, 4, 8, 18, 24, 48, 72, 96, and 120 hpi, with pre-injection scans recorded for all mice as baseline. As described previously [35], CT scans of whole mouse were acquired at 80 kVp and 50 mA within a defined field of view. Scans were then reconstructed with an isotropic voxel size of ~153 μm and subsequently, three-dimensional (3D) volumes of interest (VOI) were generated for tumor and normal tissues (i.e., kidneys, liver and spleen) at each time point. The VOIs were created using a semi-automatic approach that combined manual contouring and threshold refinement (MicroView, GE Healthcare, Waunakee, WI). Mean CT number (expressed in Hounsfield units, HU) resulting from liposome-mediated contrast enhancement was determined for the whole tumor, kidneys, liver and spleen at all time points defined. These data were converted to %ID/cm3 tissue following a linear relationship between iodine concentration and HU as reported previously [35, 39]. Intratumoral analysis of liposome accumulation employed a custom erosion algorithm implemented in MATLAB (Mathworks, Natick, MA) in order to divide each tumor VOI into 3 concentric sub-volumes that encompassed the tumor rim, periphery and core. The width of the rim was defined as 10% of the radius of the VOI (where the radius was determined from a sphere whose volume is equal to the tumor VOI). The tumor concentration of the liposomes was determined in each sub-volume following the same method outlined above for determining bulk accumulation as %ID/cm3. The plasma concentration of the liposomes was determined in the descending aorta and adjusted for the arterial hematocrit ( ). The average plasma volume fraction of each tumor was estimated using early time point imaging of the liposomes (i.e., at 10 min post-injection). At an early time point, the liposomes are assumed to be predominantly intravascular. The plasma volume fraction was determined by taking the ratio of average liposome iodine concentration measured in each tumor sub-volume to that in plasma 10 min post-injection. The plasma volume fraction was used to subtract the contribution of the vascular liposomes from the measured tumor concentration of liposomes, providing an estimate of the extravascular concentration of liposomes in each tumor sub-volume. 2.6. Histology and fluorescence microscopy Following iv liposome administration, ME180 tumors were excised at 2, 18, 48, and 120 hpi for analysis of liposome microdistribution. Following animal sacrifice, tumors were excised and placed in cryomolds (Sakura Finetek, Torrance, CA) using OCT compound (Tissue-Tek, Sakura Finetek, Torrance, CA) as embedding medium and immediately frozen in liquid nitrogen. Tumor blocks were subsequently stored at -80°C until histological processing. Tumor sections 5 μmthick were prepared using a cryostat and mounted on glass slides. Sections were then imaged

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using an Olympus BX50 upright fluorescence microscope (Olympus, PA) at 10x magnification (Olympus UPlanSApo 10x/0.40) using a Semrock Quad Sedat filter set. Liposome signal was first captured from unstained tumor sections in the near-infrared range (650/684 nm). Subsequently, the tumor sections were stained and re-imaged to visualize blood vessels (antiCD31; 560/607 nm). The microscope was equipped with an EXFO fluorescence illumination source for which illumination power was monitored over the course of the studies. Images were acquired using a Photometrics CoolSnap HQ2 CCD camera and a motorized stage. Fixed exposure and contrast settings were applied in all tumor images.

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2.7. Fluorescence image pre-processing

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Images acquired from the microscope were comprised of individual tiles of whole tumor sections that were stitched together to generate a whole tumor image (MetaMorph®, Molecular Devices, Sunnyvale, CA) for the unstained section (i.e., liposomes) and stained section that was subsequently imaged (i.e., CD31-positive blood vessels) (Figure 1A). All images obtained for a given tumor were cropped to the same dimensions and aligned (i.e., registered without morphological changes) using ImagePro PLUS software (Media Cybernetics, Rockville, MD). Individual tumor masks were then created to exclude non-tumor tissue and artifact resulting from sectioning and staining (e.g., tissue folds, tears) using FIJI software [40] and applied to all images of a given tumor section. Resulting masked images were preserved as 16-bit grayscale and employed as such in subsequent image analysis (Figure 1B).

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Figure 1. Representation of fluorescence image analysis process. (A) Liposome (red) and blood vessel (BV, green) signals are acquired separately, requiring (B) pre-processing to align both images. (C) Images are then processed via a customized MATLAB algorithm which permits segmentation of liposome and BV signals. BV signal (binary) produces a distance map illustrated in grayscale whereby black represents pixels most proximal to the nearest BV and white represents those most distal, according to a gradient. Liposome signal intensity (SI, continuous) is segmented according to a global threshold and represented as mean SI as a function of distance to nearest BV, measured in bins of 1 μm, within the tumor rim (r), periphery (p) and core (c). Scale bars in whole tumor images represent 1 mm while those in the magnifications represent 200 μm.

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2.8. Fluorescence image analysis

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A computational methodology for analyzing the distance of liposomes from blood vessels within whole tumor sections has been developed based on previous work with block copolymer micelles [8, 9]. In our study, we generated a distance map based on the image of CD31-stained and thresholded blood vessels within the whole tumor section, and subsequently identified liposome signal within three distinct, concentric regions of the corresponding tumor section (i.e., rim, periphery and core). The mean liposome signal intensity versus distance to nearest vessel was then extracted and plotted for each of the three regions (Figure 1C). The motivation for such spatial partitioning in our analysis emerged from the observation of a characteristic “rim effect” found in several different tumor models, revealing the region-specific accumulation of liposomes similar in composition and size to those in the present study [27, 34], as well as reports of a radial pressure gradient typically observed in solid tumors [41, 42]. The width of the rim was defined as 10% of the maximum radius of the tumor, while the periphery and core were divided into areas of equal width. The tissue background levels in each liposome image were measured and subtracted in order to normalize background levels across the data set. All liposome images were analyzed using a global threshold value in order to systematically segment liposome-positive signal over time. Liposome concentration was approximated using the mean fluorescent signal intensity (i.e., total fluorescent signal per unit area) and was measured as a function of distance to nearest blood vessel. Vascular images underwent automated segmentation using Otsu’s method to differentiate between CD31-positive and negative regions, and were subsequently masked to generate a distance map. A characteristic penetration length (CPL) was defined as the distance from the tumor vascular endothelium within which lies 50% of the cumulative mean liposome signal intensity. CD31-positive objects less than 10 μm in size were excluded from the analysis based on an estimated minimum capillary diameter [43]. Microvessel density (MVD) within each region, as well as in the tumor as a whole, was determined as CD31positive area over total tumor area. Images displayed in this paper were adjusted for brightness and contrast for presentation purposes only. 2.9. Statistical analysis Comparisons between two groups were conducted using Student’s t-test; namely, for in vivo stability of the liposome construct (i.e., between co-encapsulated contrast agents). Pearson correlation was employed for further analysis of in vivo liposome stability, as well as to assess

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the relationship between tumor MVD and liposome accumulation. Linear regression analysis was used to evaluate liposome penetration over time. Measures of tumor accumulation, distribution, and penetration were assessed over time and/or within regions by one-way ANOVA followed by Tukey’s HSD test. Multiple testing corrections were not applied, and statistical significance was attained when p<0.05. All analyses were carried out using IBM® SPSS® Statistics v20.

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3. Results

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3.1. Liposome properties in vitro

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Characterization of the physico-chemical properties of the dual-modality imaging construct (i.e., dual-modality liposomes for CT imaging and optical microscopy) demonstrated an iohexol (IOX) loading of 147.9 ± 16.9 mg IOX/mL which corresponds to 68.6 ± 7.9 mg iodine (I)/mL and a Genhance™ 680 (GH680) loading of 0.22 ± 0.02 mg/mL (Figure 2). These corresponded to encapsulation efficiencies of 20.9 ± 2.9 %, and 19.3 ± 1.4 %, respectively (Supplementary data, Table S1). The intensity-based hydrodynamic diameter was found to be 96.4 ± 5.4 nm, exhibiting a polydispersity index (PDI) of 0.048 ± 0.015.

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3.2. Liposome properties in vivo HPLC-based assessment of the pharmacokinetics (PK) of IOX and GH680 following liposome administration yielded necessary insight into the in vivo stability of this formulation (Figure 3A). Based on a two-compartment model (Supplementary data), the elimination half-lives, t1/2β, of each contrast agent were found to be 60.44 h (95% CI = 47.74 - 82.43) and 51.72 h (95% CI = 33.80 – 110.1) for the encapsulated IOX and GH680, respectively. A significant difference was found in the estimated distribution rate constants between contrast agents (p<0.001). However, there was no statistically significant difference found when comparing the estimated elimination rate constants between each contrast agent (kIOX=0.01147, 95% CI = 0.008409 – 0.01453; and kGH680=0.01340, 95% CI = 0.006298 – 0.02051 p=0.523) as well as within the replicate samples tested for each contrast agent (n=8, p=0.063). Furthermore, there was no difference observed in the volume of distribution, Vd, for each contrast agent (p=0.260). Significant differences were observed in the plasma AUC0-120h owing to differences in the loading levels of IOX and GH680

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(p<0.0001). The clearance, CL, was also found to differ between contrast agents (p=0.010) (Figure 3B). A strong, statistically significant positive correlation was observed between the plasma concentrations of both contrast agents (r=0.9887, n=10, R2=0.9774, p<0.0001) (Figure 3C). Finally, as a control, the same dose of contrast agents was administered in free form as a solution of sterile HBS buffer (pH 7.4). The free contrast agents were found to clear from the systemic circulation relatively quickly with approximately 10% or less of the initial levels of IOX and GH680 detected 30 min post-injection (Supplementary data, Figure S1).

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3.3. Macrodistribution

Normal tissue and tumor accumulation of the liposomes were assessed in mice non-invasively by CT and quantified over time (Figure 4). Contrast enhancement is evident in the aorta 2 h following systemic administration (Figure 4A). The observed tumor accumulation was found to be similar to previous reports using single-modality liposomes for CT imaging in the same tumor model [27]. Figure 4B shows an increase in uptake until approximately 72 hpi (4.0 ± 0.59 %ID/cm3 at 72 hpi, n=4), followed by a gradual decrease over time. Similar to previous reports, normal tissue distribution was predominant in the spleen and liver (e.g., 25.2 ± 3.14 and 23.4 ± 3.21 %ID/cm3, respectively, at 72 hpi, n=4), and lowest in the kidneys (e.g., 7-8 % ID/cm3 at 72 hpi, n=4; Figure 4C).

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Figure 4. Non-invasive assessment of liposome accumulation in tumor and normal tissues. (A) Panel of coronal images depicting mice bearing orthotopic cervical ME180 tumors (tumor site indicated by orange arrow) at select time points. Liver and spleen show considerable contrast enhancement by 18 hpi and a characteristic enhancement of the tumor rim is apparent at 120 hpi (red arrow). (B) Bulk tumor accumulation profile generated owing to the linearity in liposomal iodine concentration and CT number. Dotted line indicates temporal range 8-120 hpi at which there is a statistically significant difference in the tumor uptake levels relative to pre-injection, *p<0.05. (C) Normal tissue uptake of liposomes was similarly quantified over time in the spleen, liver and kidneys.

3.4. Microdistribution

Qualitatively, substantial differences were observed in both the intratumoral distribution (i.e., localization within the tumor tissue) and tumor penetration (i.e., localization away from tumor blood vessels) of the liposomes as a function of time. Liposome signal is visible around a relatively small number of vessels in the rim at 2 hpi (Figure 5, A and E) and overall in the tumor at 18 and 48 hpi (Figure 5, B and C, respectively). By 48 hpi, the accumulation and penetration distance of the liposomes continue to increase in the peripheral regions of the tumor (Figure 5, C and G). Finally, at 120 hpi, a significantly greater accumulation of liposomes is apparent within the rim and periphery of the tumor (Figure 5D), while continued dispersion away from blood vessels is also noted, populating a considerable degree of the inter-vessel space in these regions (Figure 5H). Conversely, there appears to be little-to-no new “extravasation events” as in earlier time points. In addition, liposome levels in the core region of the tumors appear relatively stable. These observations are represented semi-quantitatively in the corresponding penetration plots adjacent to each set of micrographs. 10

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Distance from nearest CD31-positive vessel (µm) Figure 5. Representative fluorescence microscopy images of whole ME180 tumor tissue sections. Liposome signal (red) is visible at (A, E) 2 hpi, (B, F) 18 hpi, (C, G) 48 hpi, and (D, H) 120 hpi. (A) As early as 2 hpi, liposomes are localized around tumor blood vessels (green), albeit restricted to only a few vessels within the entire tumor section. (B, C) At 18 hpi, a greater number of blood vessels exhibit surrounding liposome signal while at 48 hpi, an increase in mean signal intensity is maintained. (D, H) By 120 hpi, greater levels and dispersion of liposome signal are observed in the highly vascularized rim of the tumor. Overall, extravascular signal in the rim and periphery appears to increase over time while localization in the core remains relatively low and unchanged. Qualitative observations were translated semi-quantitatively as total fluorescence intensity (F.I.) per unit area in the corresponding penetration profiles in the right panel (mean ± SD, n=3 tumors per time point). Scale bars represent 1 mm in micrographs A-D and 200 μm in E-H.

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Figure 6. Intratumoral liposome accumulation. (A) Intratumoral liposome levels were quantified by CT. A transverse slice of a mouse bearing an orthotopic ME180 cervical xenograft is shown, segmenting the tumor into three concentric sub-volumes. (B) Analysis of microdistribution reveals predominant liposome uptake in the rim over time with no change in the core. ** p<0.05 relative to all time points for a given region. *p<0.05 between select time points for a given region. (C) Tumor vascular characterization was conducted histologically following tumor excision by quantifying the area of CD31-positive pixels over total pixels in each defined region of the tumor, as well as in the total tumor section as a whole. **p<0.05 relative to all groups.

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Quantitatively, the intratumoral accumulation of liposomes within the tumor volume was assessed using CT (Figure 6A) and showed time- and space-dependent uptake in the whole tumor (Figure 6B) that was consistent with the obtained bulk tumor accumulation profile (Figure 4B). Within the defined tumor sub-volumes however, an increase was most pronounced in the rim of the tumor with significant levels reached and increasing by 18 hpi (p<0.001). A temporal trend was also observed in the periphery (p<0.05) but not in the core (p>0.1 for all comparisons over time). Quantification of MVD using fluorescence image analysis of tumor tissue sections revealed a greater MVD in the rim than in all other regions of the tumor (i.e., 3.6 ± 0.9, p<0.001). The MVD in the total tumor section and periphery did not differ significantly (1.6 ± 0.4 and 1.3 ± 0.4, respectively, p=0.309); however, the MVD was lowest in the core (0.7 ± 0.3, p=0.003; Figure 6C). As shown in Figures 6, B and C, higher liposome accumulation was observed in regions of higher MVD.

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3.5. Microdistribution of liposomes and tumor vasculature Liposome concentration determined by volumetric analysis of CT data was positively correlated to tumor MVD determined via immunofluorescence of whole-tumor sections (Figure 7). An increasingly significant positive correlation was obtained over time; specifically, the correlation was statistically significant at 18 (n=12, p=0.009), 48 (n=18, p=0.002) and 120 (n=13, p<0.0001) hpi, but not at 2 (n=7, p=0.418) hpi.

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Figure 7. Correlation between tumor microvessel density (MVD) and accumulation of liposomes. Intratumoral liposome accumulation levels determined by CT imaging were plotted against tumor MVD estimated from whole-tumor sections by immunofluorescence. Pearson productmoment correlation between liposome concentration and tumor MVD was determined at each time point, revealing a statistically significant positive correlation over time. Points represent individual tumors. Spatial heterogeneity is also highlighted; values obtained from the rim, periphery and core are represented by triangles, squares, and circles, respectively.

3.6. Tumor penetration and relative distribution of liposomes Tumor penetration, represented by the CPL, was found to be time-dependent. Specifically, a linear regression analysis established that time could statistically significantly predict the penetration distance within the rim (R2 = 0.534, p<0.0001), periphery (R2 = 0.678, p<0.0001), and core (R2 = 0.287, p = 0.009) of the tumor (Figure 8A). On a regional basis, the CPL was greatest in the rim at 48 and 120 hpi relative to the 2 h time point (29 and 36 μm versus 22 μm, p<0.001 and p=0.027, respectively), while this effect was not evident between 2 and 18 hpi (22 versus 28 μm, p=0.093, respectively). In the periphery, all penetration distances were 13

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significantly greater beyond 2 hpi (p<0.05) although appeared overall slightly lower than those achieved in the rim of the tumor. Within the core of the tumor, the data again did not support a difference between 2 and 18 hpi (21 versus 23 μm, p=0.93), while the CPL at 48 hpi was greatest (31 μm, p=0.03), matching the CPL found at 120 hpi (Figure 8B). At a given time, the CPL did not vary between regions (p>0.09 for all comparisons; i.e., the CPL remained equivalent in all regions of the tumor for the same time point).

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The relative distribution of liposomes (i.e., proportion of the total mean liposome signal intensity) was further ascertained using fluorescence. The consistency in trends is observed among individual animals (Figure 8C), revealing mean liposome concentrations in the rim, periphery and core that were approximately 2.4-fold, 0.8-fold and 0.4-fold of the whole tumor concentration, respectively, independent of time. This result was confirmed using the CT-based analysis of liposomal iodine (data not shown).

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Figure 8. Spatio-temporal liposome transport and distribution. (A) The characteristic penetration length (CPL) was investigated as a function of time and (B) tumor region. (C) The proportion of total mean liposome signal intensity is represented per tumor region. The dotted line indicates the normalized total mean liposome signal intensity of the tumor (i.e., 100% of the mean liposome intensity). Plots A and C depict individual replicates of n=5-6 tumors/time point. Plot B represents the mean ± SD of n=5-6 tumors/time point.

4. Discussion Underwhelming therapeutic outcomes using nanomedicines have prompted renewed considerations of their design [18], as well as of their progression thus far as cancer therapeutics [33, 44, 45]. Elucidations into the structural and physiological properties of tumors, and their 14

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impact on the effectiveness of a given nanomedicine, may yield additional insight for more efficient utilization of this therapeutic modality [46]. While the EPR effect has been repeatedly described in preclinical studies [47-52], its expression as a function of tumor vascular density, permeability, and nature of the interstitium has been subject to significant inter- and intratumoral heterogeneity. Indeed, such heterogeneity in tumors has been implicated in resistance to therapies, while its characterization remains somewhat elusive. Poor tumor penetration and distribution of nanomedicines and/or drugs arise from tumor-specific transport barriers [28, 29, 53, 54] and have been implicated in such attenuated outcomes [7, 11, 55]. As a result, conventional measures of nanomedicine performance at the whole-tumor level may no longer be considered an adequate endpoint to sufficiently predict for their efficacy and translatability [46].

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In this work, we relate bulk tumor accumulation of liposomes to intratumoral measurements of distribution and penetration in an orthotopic xenograft model of human cervical cancer. CTbased measurement of biodistribution confirmed characteristic bulk accumulation profiles in this tumor model [27] and liposome system in normal tissues [35, 56, 57]. While such a method enables longitudinal, quantitative and non-invasive assessment of nanoparticle uptake, studies have revealed that there may be significant underlying differences in tumor distribution in spite of similar levels of accumulation. El Emir et al. evaluated the tumor accumulation and distribution of the anti-carcinoembryonic antigen (CEA) antibody, A5B7, in two structurally distinct xenograft models of human colorectal cancer, LS174T and SW1222. In their study, vastly different responses to radioimmunotherapy were observed upon iv administration of 131Ilabeled antibody, despite equivalent levels of tumor accumulation in both models. Restricted tumor penetration was indeed found to be greater in the poorly responsive LS174T tumor model, which exhibited a more heterogeneous blood vessel architecture and distribution relative to the SW1222 model. The authors also confirmed that there was no significant difference in the degree of vessel permeability and CEA antigen expression between the two tumor models. Further, the authors found that differences in interstitial pressure or intercellular gap junctions between the two models were unlikely to significantly impact antibody transport [11]. While these findings highlight the contribution of microenvironmental parameters on therapeutic efficacy of nanobased therapies, they do not negate the assessment of such parameters on a macroscopic level. For instance, whole-tumor characterization of vascular properties has been shown to determine accumulation and efficacy of nanomedicines in select preclinical studies. Karathanasis et al. effectively correlate tumor accumulation of iodinated liposomes to markers of blood vessel permeability (i.e., VEGF and VEGF receptor-2) [58], as well as therapeutic efficacy of doxorubicin-loaded liposomes in a separate report [15]. Other studies have demonstrated the impact of nanosystems, tumor properties, and modulating interventions on assessing the status and exploitation of the EPR effect at the whole-tumor level [14, 59-61]. Beyond the goal of achieving maximum tumor accumulation macroscopically – for which the direct impact on antitumor efficacy remains elusive - achievement of a favorable underlying micro-accumulation of nanomedicines may contribute significantly to an overall anti-tumor response. A longstanding measure in drug delivery, macrodistribution may be a necessary however insufficient one for a comprehensive assessment of nanomedicine efficacy in vivo. Microdistribution measurements in this work quantitatively exposed the degree of heterogeneity present in this tumor model. While limited distribution of liposomes has been observed at the intratumoral level [7, 10, 21, 62, 63], a systematic assessment of their distribution as a function of time and space enables an estimation of the contribution of select variables on their

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localization in the tumor. The preferential rim uptake of liposomes observed in 2D whole-tumor sections was confirmed as a 3D effect by quantification of liposome accumulation in the corresponding tumor sub-volume delineated by CT imaging. In particular, the tumor rim revealed the highest levels of liposome uptake over the 5-day study period. Spatial characterization of the intratumoral MVD revealed the highest density in the rim and a corresponding positive correlation with liposome density, which was maintained over several days. Despite the limitations of 2D image analysis, such representation of tumor vascularity is supported by similar studies whereby good correlation was found between 2D and 3D measurements [11, 64]. Immunohistochemical evaluation may indeed present a relatively accessible means to gauge tumor vascularity as a contributor to the EPR effect; nevertheless, such estimation would be strengthened by integration of additional parameters (e.g., perfusion status) and/or dimensions [30, 64]. In the periphery of the tumor, time- and MVD-dependent accumulation was also found, while no significant change is observed in the core of the tumor over 5 days. These data may suggest an isolated system in the core region of the tumor whereby no significant influx or efflux of liposomes exists and/or one that is equal in rate. This is postulated given the low MVD in the core relative to the other regions of the tumor. Thus, an MVD threshold for an increase in liposome accumulation may also be postulated; application of this methodology to further tumor models may elucidate this claim. In addition, increasing levels in the rim, despite decreasing systemic levels, may reflect an outward displacement of liposomes originating from the periphery of the tumor. This hypothesis is further supported by relatively low, apparently decreasing, liposome levels in the periphery at 120 hpi. In this study, MVD appears as a relatively strong contributor to liposome uptake in defined regions of the tumor. It is recognized that MVD alone may not constitute a sufficient cause for increased accumulation [65]; nevertheless, the greater number of vessels per area provides an increased probability that more of those vessels will be patent and able to deliver liposomes. Increased uptake of macromolecular and nano-based agents in areas of high tumor vascular density has indeed been reported [13, 66, 67]. Finally, a time-dependent penetration of liposomes was observed, as significantly greater distances were achieved over time in each region. As such, we hypothesize that greater movement of resident liposomes may occur over time in the tumor as a result of pressure- and/or concentration-mediated gradients. In effect, penetration of the liposomes was observed in the tumor rim relatively early following iv administration, likely due to the presence of functional lymphatic vessels in this region, and thus action of a transvascular pressure gradient [18, 27]. However, localization in the tumor did not appear to influence penetration distance in this model. Over time, the well-characterized spatial restriction of liposomes [7, 63] is compensated for by the achievement and maintenance of their significant concentration. Such time-dependent penetration, concordant with other nanosystems [9, 68] and coupled with the high drug loading capacity of liposomes, may potentially be exploited for depot-based or metronomic therapy. An important perivascular concentration may also generate an onion skinning effect following extravasation, as has been postulated for repeated dosing of smallmolecule drugs [69]. Critically, however, too few studies have correlated distribution of nano-based delivery systems in tumors, whether macro- or microscopic, to their corresponding anti-tumor efficacy. Theranostic and diagnostic formulation equivalents of select nanomedicines, that rely on imagebased assessment of distribution, have been proposed as a means to predict anti-tumor efficacy [15, 70]. However, these formulations require labeling with or encapsulation of the appropriate contrast agent or probe, and they must then undergo the required evaluation of safety and 16

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toxicity. Alternately, a more straightforward approach may be to employ image-based assessment of the TME as a means to inform the microdistribution of nanomedicines [30] and/or their anti-tumor effect. Zhang et al. employed a multimodality imaging approach to monitor the induction of apoptosis following administration of Doxil®. In particular, tumor cellular density was characterized by diffusion-weighted MRI (DW-MRI) – a quantitative and non-invasive technique which revealed changes in the extracellular water volume of the tumor as a result of apoptosis-mediated shrinkage of the cell cytoplasm [71]. This provides an example of imagebased assessment of biomarkers of drug effect that is clinically relevant and translatable.

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In the present study, 4D quantitation resulting from CT-based volumetric measurements over time enabled a clinically accessible and relevant means to assess liposome macro- and microdistribution. Multi-scalar measurements of liposome distribution were feasible as a result of the demonstrated stability of the dual-modality imaging liposome construct. Indeed, coencapsulation and stable retention of multiple agents within liposomes is non-trivial; in many studies, stability of the construct is not assessed; while in others, one or more of the encapsulated probes have been shown to be released in vivo [72]. As a result, concomitant assessment of the microdistribution of liposomes and vasculature may extend our understanding of the EPR effect, and perhaps restrict quasi-instinctive attribution of its role in determining the therapeutic efficacy of nanomedicines. Despite a relatively static assessment of liposome transport, we posit that this methodology may complement macroscopic measurements, and provide a systematic framework in the evaluation of nanomedicines. The spatio-temporal microdistribution of nanosystems is expected to complement the macroscopic estimation of the EPR effect. Specifically, microdistribution of such systems relative to key microenvironmental parameters may elucidate the role and/or response of select parameters as a function of the administered nanomedicine. Further, corroboration between 2D and 3D measurements reveals an ability to strengthen the representation of tumor heterogeneity, and ultimately, characterize it across varying models and/or interventions.

5. Conclusions

A spatio-temporal quantification of tumor macro- and microdistribution of liposomes was performed in an orthotopic xenograft model of human cervical cancer. Multi-scalar assessment of liposome distribution was possible by virtue of a stable construct co-encapsulating iodinated and near-infrared fluorescence contrast agents. CT imaging enabled bulk and intratumoral 4D measurement of liposome uptake, while optical microscopy elucidated the localization of the liposomes relative to components of the tumor microenvironment. Heterogeneity in liposome and microvascular distribution was characterized, revealing highest liposome uptake in the wellvascularized rim of the tumor. Tumor accumulation was found to be region-specific; conversely, tumor penetration increased with time, irrespective of tumor region. Overall, measurements of tumor macro- and microdistribution were deemed distinct. In particular, the limited therapeutic efficacy of nanomedicines attributed to their heterogeneous distribution calls for a robust framework for their evaluation. Beyond the EPR effect alone, measurements of its manifestation at both macro- and microscopic levels may prove capable of better predicting therapeutic outcomes.

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The authors thank James Jonkman and members of the Advanced Optical Microscopy Facility (AOMF) as well as Melanie Macasaet-Peralta and staff at the Pathology Research Program (PRP) at Toronto General Hospital for assistance with fluorescence image analysis and histological services, respectively. S. N. Ekdawi is grateful for the Pfizer Canada Graduate Fellowship in Pharmaceutical Sciences and a fellowship from the CIHR Strategic Training Program in Biological Therapeutics. J. M. P Stewart acknowledges NSERC and a MITACSAccelerate fellowship. M. Dunne acknowledges funding from the Leslie Dan Faculty of Pharmacy Dean’s Fund. S. Stapleton is grateful for funding from the NSERC Postgraduate Scholarship Program and the Terry Fox Foundation Strategic Initiative for Excellence in Radiation Research for the 21st Century (EIRR21) at CIHR. C. Allen acknowledges funding from a CIHR operating grant, and GlaxoSmithKline for an endowed chair in Pharmaceutics and Drug Delivery.

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