S. M. Janiba, S. Liub, R. Parkb, M. K. Pastuszkaa, P. Shia, A. S. Mosesa, M. M. Oroscoc, Y.-A. Lind, H. Cuid, P. S. Contib, Z. Li*b and J. A. MacKay*a
aDepartment of Pharmacology and Pharmaceutical Sciences, University of Southern California, Los Angeles, CA 90033-9121, USA. E-mail: jamackay@usc.edu; Tel: +1 323-442-4118
bMolecular Imaging Center, Department of Radiology, Keck School of Medicine, University of Southern California, Los Angeles, 90033, USA. E-mail: ziboli@usc.edu; Tel: +1 323-442-3252
cAlfred E. Mann Institute at the University of Southern California, 1042 Downey Way, DRB Building, Suite 101, Los Angeles, CA 90089-1112, USA
dDepartment of Chemical and Biomolecular Engineering, Johns Hopkins University, Baltimore, MD 21218, USA
First published on 11th October 2012
Protein polymers are repetitive amino acid sequences that can assemble monodisperse nanoparticles with potential applications as cancer nanomedicines. Of the currently available molecular imaging methods, positron emission tomography (PET) is the most sensitive and quantitative; therefore, this work explores microPET imaging to track protein polymer nanoparticles over several days. To achieve reliable imaging, the polypeptides were modified by site-specific conjugation using a heterobifunctional sarcophagine chelator, AmBaSar, which was subsequently complexed with 64Cu. AmBaSar/64Cu was selected because it can label particles in vivo over periods of days, which is consistent with the timescales required to follow long-circulating nanotherapeutics. Using an orthotopic model of breast cancer, we observed four elastin-like polypeptides (ELPs)-based protein polymers of varying molecular weight, amino acid sequence, and nanostructure. To analyze this data, we developed a six-compartment image-driven pharmacokinetic model capable of describing their distribution within individual subjects. Surprisingly, the assembly of an ELP block copolymer (78 kD) into nanoparticles (Rh = 37.5 nm) minimally influences pharmacokinetics or tumor accumulation compared to a free ELP of similar length (74 kD). Instead, ELP molecular weight is the most important factor controlling the fate of these polymers, whereby long ELPs (74 kD) have a heart activity half-life of 8.7 hours and short ELPs (37 kD) have a half-life of 2.1 hours. These results suggest that ELP-based protein polymers may be a viable platform for the development of multifunctional therapeutic nanoparticles that can be imaged using clinical PET scanners.
Insight, innovation, integrationThe ability to interrogate living systems without perturbing it makes molecular imaging a powerful tool for the development of personalized medicine. By employing smart polymers coupled with imaging techniques we are better able to visualize how they distribute and behave in vivo. Being able to track them in this manner, could allow the visualization of cancer cells and the site of drug delivery by nanotherapeutics. Thus, individualize treatment monitoring and dose optimization becomes increasingly more feasible. |
Various imaging modalities have been used to explore nanoparticulate-based contrast agents, including ultrasound, magnetic resonance imaging, and PET.11 Here we selected microPET because it has high sensitivity, good resolution, no limitation caused by depth of penetration, and can be calibrated for quantification. While PET radioisotopes such as 18F, 11C, 13N, and 15O14,15 have been the mainstay of clinical and molecular imaging, continuing development of large biomolecules such as proteins, peptides, antibodies, and nanoparticles necessitates the development of non-traditional PET radioisotopes.16 In their application to protein nanoparticles, the aforementioned non-metallic radioisotopes possess critical limitations. Chief among them are their short radiological half-lives, which prohibit the investigation of biological processes over several days. To overcome this limitation metallic radioisotopes of Zr, Y, In, Ga, and Cu have been investigated as they provide a wide range of decay half-lives, which are compatible with long biological pharmacokinetic half-lives.17 In addition, metallic radioisotopes are amenable to non-covalent chelation, which makes them simple to attach to biological molecules immediately prior to administration. Of these radioisotopes, 64Cu is advantageous due to its low positron energy, high specific activity, availability, and reasonably long half-life (12.7 hours).18 These properties allow investigation of biological processes that take place over days.19–21
The standard approach to tagging a biomolecule with a metallic radionuclide such as 64Cu is to first conjugate a suitable chelating agent to the protein or nanoparticle and then to complex the metal to the chelated biomolecule. Chelates that can hold radiometals with high-stability under physiological conditions are essential in achieving high uptake of the copper radionuclide in the tissue or organ of interest while minimizing their non-selective binding or incorporation into non-target organs or tissues.22 Unfortunately, the cuprous ion does not chelate as effectively with the macrocyclic 1,4,7,10-Tetraazacyclododecane-1,4,7,10-tetraacetic acid (DOTA) as do other metals.17 In view of this, and based on the comparative stability of the sarcophagine-based chelator,23–25 the chelating agent AmBaSar (Fig. 1a) was selected for this study over a traditional macrocyclic chelator.
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Fig. 1 Conjugation scheme of the bifunctional chelating agent AmBaSar and ELP. (a) AmBaSar is chemically conjugated to the N-terminus of either linear ELPs or a block copolymer. AmBaSar then chelates 64Cu endowing the construct with radioactive properties. (b) The purified polymers were evaluated for identity and purity using SDS-PAGE and stained with copper chloride. Lane 1: Ladder; Lane 2: A96; Lane 3: A192; Lane 4: S192; Lane 5: A96I96. |
Many nanoparticle platforms are under investigation for packaging, transport, and delivery of imaging and therapeutic agents.11 One example is a class of protein polymers derived from human tropoelastin, called elastin-like polypeptides (ELPs).3,26,27 ELPs are composed of a five amino acid repeat (Val-Pro-Gly-Xaa-Gly)n. ELPs undergo an inverse phase transition above a transition temperature (Tt), which is primarily a function of the guest residue Xaa, n, and concentration.26,28 In solution, ELPs are structurally disordered. When the temperature is raised above their Tt, they undergo a sharp (2–3 °C range) phase transition, leading to biopolymer coacervation.26 This process is fully reversible when the temperature is lowered below Tt. Phase separation can be triggered by other external stimuli such as changes in ionic strength, pH, solvent, and magnetic fields.26,29,30 Here we report the characterization of ELPs with various lengths and nanoparticle structure using microPET imaging to track their pharmacokinetic and biodistribution properties.
Label | Amino acid sequencea | MW (Da)b | Tt at 25 μM (°C) | Construct |
---|---|---|---|---|
a Gene sequence confirmed by N and C terminal DNA sequences and diagnostic restriction digestion.b Estimated from open reading frame excluding methionine start codon and confirmed using SDS-PAGE.c Critical micelle temperature (CMT). | ||||
A96 | G(VPGAG)96Y | 36987.00 | 84.3 | ![]() |
A192 | G(VPGAG)192Y | 73604.56 | 61.9 | ![]() |
S192 | G(VPGSG)192Y | 76619.32 | 57.4 | ![]() |
A96I96 | G(VPGAG)96(VPGIG)96Y | 77655.30 | 20.6c | ![]() |
The images were processed and analyzed using ImageJ (NIH, USA).
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Fig. 2 ELP diblock copolymers assemble nanoparticles at physiological temperatures. Dynamic light scattering (DLS) was used to characterize the hydrodynamic radius of the protein polymers in phosphate buffered saline. (a) Hydrodynamic radius (Rh) of A96, A192, S192 and A96I96 at 37 °C before and after modification with AmBaSar (Sar). The ELP block copolymer A96I96 assembles nanoparticles. Bars represent mean ± SD. (b) Above 15–18 °C, A96I96 forms nanoparticles of stable hydrodynamic radii at 25 μM. (c) Distribution of hydrodynamic radii for linear ELPs at 37 °C. (d) Distribution of hydrodynamic radii for ELP block copolymer A96I96 at 10 and 37 °C. (e) Transmission electron microscopy (TEM) of negatively stained A96I96 nanoparticles (white round objects) with an average particle diameter of 33.3 ± 11.5 nm stained with uranyl acetate (black clusters). Scale bar 50 nm. (f) Histogram of A96I96 nanoparticles (n = 141) was obtained using image analysis across 9 TEM images. |
As independent confirmation of nanoparticle assembly, negative-stained transmission electron microscopy (TEM) was used to observe contrast-excluding (light) nanoparticles of A96I96 (Fig. 2e). These particles appeared as round, monodisperse particles with a diameter of 33.3 ± 11.5 nm (Fig. 2f). In this case, the radius of the particles by TEM is approximately half of that observed by DLS, which may result from several possible causes: (i) the hydrophilic block includes a significant fraction of water in solution, which increases the hydrodynamic radii compared to a sample dried for TEM; and (ii) the hydrophobic core of A96I96 nanoparticles excludes uranyl acetate contrast, while the hydrophilic corona does not. Currently, we are unable to distinguish between these possibilities; however, both DLS and TEM indicate that the diblock copolymer A96I96 assembles homogenous particles at physiological temperature.
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Fig. 3 64Cu-ELP constructs are stable in serum for 24 hours. Stability of radiolabeled ELPs over 48 h in serum and PBS was measured using retention in a dialysis cassette. (a) A96, (b) A192, (c) S192, and (d) A96I96. A two-way ANOVA at the 48 hour time point showed that all ELPs lose retention in serum compared to PBS (p = 3 × 10−6). Loss of retention depended significantly on the ELP identity (p = 1 × 10−8), with S192 and A96I96 losing significantly more than A192. Bars represent mean ± SD (n = 3/group). |
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Fig. 4 Serial microPET imaging of protein polymer nanoparticles in an orthotopic model of human breast cancer. 64Cu-labelled ELPs were administered systemically to mice carrying MDA-MB-231 tumors. Serial imaging was performed, and coronal images centered on the tumor for A96, A192, S192 and A96I96 are depicted at 0.08, 0.75, 1.33, 2.5, 4, 24 and 48 h post injection. A representative mouse is shown from each group (n = 3/group). Within each 5 min panel, two major pools of blood are present in the heart (top) and liver (middle). At later time points, the gastro-intestinal track (lower) and the bladder (bottom) enhance in contrast. The tumor locations are indicated by arrows. |
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Fig. 5 Non-compartmental pharmacokinetics of 64Cu-ELPs in the heart. (a) The time activity curve of blood concentration can be estimated using the intensity in the heart as a surrogate measure, whereby 64Cu-ELPs (n = 3/group) are expressed as %ID g−1. Values indicate the mean ± 95%CI. (b) By fitting the initial rate of log-linear decay (0–4 hours for A96; 0–24 hours for A192, S192 and A96I96), the half-life of activity in the heart was indicated as the mean ± 95%CI. |
In addition to the heart half-life, serial microPET imaging was used to estimate the kinetics and magnitude of accumulation in several other easily identifiable tissues (Fig. 6). Especially for the earlier time points, the tissues in the chest and abdominal cavity seem to overlap, making quantification difficult. However, since these tissues are easily discernible at later time points, we used the locations determined here, to guide the positioning of the ROI that optimally captures the different tissues with minimal overlap. Time-activity curves corresponding to the kidneys, liver and muscle (Fig. 6a–c) are presented. From these results, no obvious differences were observed between the constructs in terms of muscle accumulation (Fig. 6a). A96, the shortest and lowest molecular weight construct, exhibited the highest kidney uptake (Fig. 6c). Renal clearance of A96 occurs rapidly until it plateaus at 4 h post injection. Conversely the extent of renal clearance of A192, S192 and A96I96 was relatively low over time (Fig. 6b).
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Fig. 6 Biodistribution of 64Cu-ELP in athymic nude mice implanted with MDA-MB-231 cell line (n = 3) within (a) muscle, (b) kidneys, (c) liver and (d) tumor expressed as %ID g−1 calculated from ROI image analysis. A96 accumulates over time in the kidneys, while A192 and S192 do not. A96I96 accumulates over time in the liver. Values indicate the mean ± 95%CI. |
All 64Cu-ELP-Sar constructs exhibited hepatic clearance in varying degrees (Fig. 6c). Considering that A192, S192 and A96I96 have a hydrodynamic radius that is larger than the cutoff point for renal filtration; these constructs appear to be cleared primarily via immobilization in the liver. Notably, the liver accumulation for diblock copolymer A96I96 is more prominent compared to the monoblock ELPs, which is consistent with expectations of a nanoparticle. Hepatic concentrations for S192 reaches a maximum at 24 h where they remained constant until 48 h post injection. In contrast there was a decrease in liver accumulation over time for A192 and A96. A96 exhibited the lowest accumulation in the liver, perhaps due to its ability to be cleared by the kidney.
Tumor uptake profiles (Fig. 6d) are slightly different for each construct during the first hour post injection; A96 exhibits the earliest detectable tumor signal, which subsequently decreases, perhaps due to its lower molecular weight and higher vascular permeability. For both S192 and A192, the tumor signal can be easily detected at 45 and 80 min respectively and remained constant for the duration of the study. In contrast, a steady increase in tumor uptake can be observed with the nanoparticle-forming A96I96. Despite differences in the kinetics of uptake, all four constructs achieved a similar tumor concentration in the range of 3–4%ID g−1 body weight.
One of the challenges to the field of nanomedicine is the identification of patient-specific and tissue-specific biodistribution patterns and the development of a platform for interpreting this information.38 Typical preclinical tumor models average data across multiple animals to characterize biodistribution and pharmacokinetics, which are not translational approaches.39,40 In contrast, the molecular imaging approach used here can deliver quantitative spatio-temporal data within an individual. Armed with this information, clinicians and engineers can develop personalized pharmacokinetic models that directly describe the fate of nanomedicines within their patients. More importantly, this information may directly answer the question of whether or not a given nanoparticle preferentially interacts with its target in a patient.
To address this challenge, we developed a modeling approach related to one recently described by Qin and co-workers19 to decouple the tissue and blood pharmacokinetics (Fig. 7). This model provides pharmacokinetic rate constants that describe the distribution of protein polymers (Table 2); however, this approach may be useful to track any nanoparticles via quantified molecular imaging. Only tissues that could be clearly identified from microPET imaging were incorporated into the model fit; furthermore, the muscle compartment thus represents both muscle and unaccounted-for tissues, which may include bone, adipose, lung, etc.). After exploring a number of plausible models, we constructed a robust 6-compartment model (Fig. 7a) based on the following assumptions: (i) a bolus of the drug (ex1) enters the blood (q1) at time zero and distributes instantly into an apparent volume of distribution, V1; (ii) the activity in any tissue (s1-heart; s2-liver; s3-tumor; s4-kidney; and s5-muscle) is the weighted fractions, ftissue, of activity in the blood compartment (q1) divided by V1 and extravascular tissue compartment (q2-liver; q3-tumor; q4-kidney; q5-muscle; and q6-heart) divided by the mass of that tissue; (iii) the rate constant for influx from the blood into a tissue (k(2,1)-liver; k(3,1)-tumor; k(4,1)-kidney; k(5,1)-muscle; k(6,1)-heart) is much larger than the rate of efflux, which allows the rate of efflux to be neglected over short duration studies; and (iv) the blood clearance via each route is given by Cltissue = ktissueV1. These assumptions combined with estimates for the mass of the tumor, muscle, heart, liver, and kidneys (Table 2), make it possible to fit the pharmacokinetic profile for individual mice using nonlinear multiple regression with a compartmental modeling software package (SAAM II).
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Fig. 7 Pharmacokinetic modeling in individuals based on microPET imaging. (a) A multi-compartment model was developed to perform a simultaneous fit to the observed tissue concentrations in the heart, liver, tumor, kidney, and muscle (s1, s2, s3, s4, and s5 respectively) within each individual after an i.v. bolus of A96, A192, S192 and A96I96 (n = 3/group) (Table 2). MicroPET concentrations were modeled to contain a fraction of signal from both an intravascular (q1) and extravascular (q2, q3, q4, q5, q6) tissue component. This model was fit to every individual, and a representative individual is presented following administration of (b) A96, (c) A192, (c) S192, and (e) A96I96. (f) A comparison is presented of the five kinetic parameters exiting the central blood compartment, q1. (g) The renal and hepatic clearance are compared to the total clearance from the central blood compartment, q1. (h) Fitting observable tissue concentrations, enabled the non-invasive estimation of the blood half-life. (f–g) Values depict the Mean ± 95% CI (n = 3). |
Parameter | 64Cu-A96 (n = 3) | 64Cu-A192 (n = 3) | 64Cu-S192 (n = 3) | 64Cu-A96I96 (n = 3) | Overall (n = 12) |
---|---|---|---|---|---|
Mean ± SD | Mean ± SD | Mean ± SD | Mean ± SD | Mean ± SD | |
a Parameter fixed based on 0.63, 1.2, and 4.6% of body weight (g) for heart, kidneys, and liver respectively, all non-invasive estimates.b Parameter fixed as mass given by tumor length × width2/2, a non-invasive estimate.c Parameter fixed based on mouse body weight minus the mass of other tissues, a non-invasive estimate. | |||||
mlivera (g) | 0.97 ± 0.01 | 0.96 ± 0.07 | 0.95 ± 0.06 | 0.98 ± 0.10 | 0.96 ± 0.06 |
mtumorb (g) | 0.057 ± 0.015 | 0.068 ± 0.073 | 0.046 ± 0.030 | 0.060 ± 0.058 | 0.058 ± 0.043 |
mkidneya (g) | 0.247 ± 0.002 | 0.244 ± 0.018 | 0.243 ± 0.014 | 0.250 ± 0.024 | 0.246 ± 0.015 |
mmusclec (g) | 19.5 ± 0.2 | 19.2 ± 1.5 | 19.1 ± 1.1 | 19.7 ± 1.9 | 19.4 ± 1.2 |
mhearta (g) | 0.132 ± 0.001 | 0.130 ± 0.010 | 0.129 ± 0.008 | 0.133 ± 0.013 | 0.131 ± 0.008 |
V1 (ml) | 5.6 ± 0.3 | 5.4 ± 0.4 | 5.2 ± 0.3 | 4.8 ± 0.3 | 5.2 ± 0.4 |
fliver | 0.67 ± 0.04 | 0.65 ± 0.05 | 0.62 ± 0.07 | 0.70 ± 0.06 | 0.66 ± 0.06 |
ftumor | 0.14 ± 0.01 | 0.09 ± 0.02 | 0.14 ± 0.01 | 0.08 ± 0.05 | 0.11 ± 0.04 |
fkidney | 0.70 ± 0.09 | 0.63 ± 0.07 | 0.56 ± 0.04 | 0.54 ± 0.11 | 0.61 ± 0.09 |
fmuscle | 0.083 ± 0.002 | 0.066 ± 0.019 | 0.071 ± 0.010 | 0.029 ± 0.021 | 0.062 ± 0.025 |
fheart | 0.993 ± 0.002 | 0.993 ± 0.002 | 0.994 ± 0.001 | 0.941 ± 0.052 | 0.980 ± 0.032 |
k(2,1)liver (h−1) | 0.075 ± 0.030 | 0.038 ± 0.004 | 0.045 ± 0.006 | 0.105 ± 0.010 | 0.066 ± 0.031 |
k(3,1)tumor (h−1) | 0.0009 ± 0.0003 | 0.0004 ± 0.0005 | 0.0002 ± 0.0001 | 0.0003 ± 0.0003 | 0.0005 ± 0.0004 |
k(4,1)kidney (h−1) | 0.097 ± 0.005 | 0.013 ± 0.001 | 0.010 ± 0.001 | 0.012 ± 0.007 | 0.033 ± 0.039 |
k(5,1)muscle (h−1) | 0.086 ± 0.034 | 0.019 ± 0.010 | 0.027 ± 0.007 | 0.015 ± 0.011 | 0.037 ± 0.034 |
k(6,1)heart (h−1) | 0.14 ± 0.04 | 0.04 ± 0.02 | 0.05 ± 0.02 | 0.02 ± 0.03 | 0.06 ± 0.05 |
k(0,2)liver (h−1) | 0.001 ± 0.003 | 0.009 ± 0.006 | 0.001 ± 0.001 | 0.001 ± 0.002 | 0.003 ± 0.005 |
k(0,4)kidney (h−1) | 0.001 ± 0.002 | 0.011 ± 0.004 | 0.001 ± 0.002 | 0.002 ± 0.001 | 0.004 ± 0.005 |
thalf,blood (h) | 1.8 ± 0.3 | 6.3 ± 0.3 | 5.3 ± 0.6 | 4.6 ± 0.5 | 4.5 ± 1.8 |
Clhepatic (ml h−1) | 0.41 ± 0.15 | 0.20 ± 0.03 | 0.24 ± 0.04 | 0.51 ± 0.08 | 0.34 ± 0.15 |
Clrenal (ml h−1) | 0.54 ± 0.04 | 0.07 ± 0.01 | 0.05 ± 0.01 | 0.06 ± 0.03 | 0.18 ± 0.22 |
Cltotal (ml h−1) | 2.2 ± 0.3 | 0.6 ± 0.1 | 0.7 ± 0.1 | 0.7 ± 0.1 | 1.1 ± 0.7 |
The results of fitting this model (Fig. 7a) within representative mice administered with A96, A192, S192, and A96I96 are presented (Fig. 7b–e). A summary of the best-fit parameters for each protein polymer is provided (Table 2); furthermore, the kinetic rate constants for materials exiting the central blood compartment are plotted (Fig. 7f). The most notable observation is that the rate constant for influx into the tumor, k(3,1), is lower than for the other tissues. To a reasonable approximation the rate constants are proportional to the apparent permeability from the blood across the vasculature multiplied by the surface area of the vasculature.19 Naturally, large and highly vascularized tissues such as the liver, kidney, and heart are the major sinks for nanoparticles in circulation. When quantified using molecular imaging, the ‘muscle’ compartment represents simply a background compartment for uptake into the remaining mass of the animal that is unaccounted for by the other quantified tissues. Therefore, even though the apparent permeability into the ‘muscle’ compartment q5 may be low, the large surface area represented by all of the vasculature throughout the body makes q5 a substantial sink for the protein polymers (Fig. 7f). In contrast, the tumor may have a relatively low surface area due to their small mass 50–100 mg and limited vascularity.
Regardless of differences in magnitude between the kinetic rate constants exiting the central blood compartment, these rate constants (Fig. 7f) are powerful parameters for comparing tissue influx between different protein polymers. For example, the protein polymer nanoparticle (A96I96) have a large hydrodynamic radius compared to monomeric protein polymers of a similar molecular weight (A192, S192). Thus, they may reasonably be expected to increase their influx, k(2,1), into the liver. In fact, this approach detected a significant increase in the hepatic efflux of A96I96 k(2,1) = 0.11 ± 0.01 h−1 (SD, n = 3) vs. that for A192 with k(2,1) = 0.038 ± 0.004 h−1 (SD, n = 3, p = 0.005 vs. A96I96) and S192 with k(2,1) = 0.045 ± 0.006 h−1 (SD, n = 3, p = 0.009 vs. A96I96). In contrast, the data (Fig. 6b) also suggested that the renal influx for the lower molecular weight ELP, A96, would be relatively high. Likewise, this model revealed a significant increase in the influx rate into the kidney for A96 of k(4,1) = 0.097 ± 0.005 h−1 (SD, n = 3) vs. that for A192 with k(4,1) = 0.013 ± 0.001 h−1 (SD, n = 3, p = 5 × 10−8vs. A96), S192 with k(4,1) = 0.010 ± 0.001 h−1 (SD, n = 3, p = 4 × 10−8vs. A96), and A96S96 with k(4,1) = 0.012 ± 0.007 h−1 (SD, n = 3, p = 5 × 10−8vs. A96). Without analyzing waste byproducts (feces, urine) this modeling approach can also estimate the renal and hepatic blood clearance (Fig. 7g). Prolonged blood circulation is achieved by avoiding rapid renal clearance and opsonization by the mononuclear phagocytic system (MPS).41 Renal clearance can be slowed for nanocarriers with a hydrodynamic diameters greater than 5.5 nm.42 This was supported by the data obtained for A96 and A192 (Fig. 2a). Being the ELP with the shortest pentameric repeat with the smallest hydrodynamic radius, it is unsurprising that A96 was cleared more by the kidneys and has the shortest heart-activity half-life. Conversely an improved half-life was observed with the larger constructs. These exhibited similar circulation half-life regardless of the identity of the guest residue (A192 vs. S192) or nanostructure adopted (monomeric vs. nanoparticle).
In addition to quantifying the rate of tissue efflux, this model allows us to decouple the tissue activities from the concentration of the drug remaining in the blood, enabling the non-invasive estimation of the half-life in the blood (Fig. 7h). These values confirm that the half-life of activity in the heart (Fig. 4b) is a reasonable proxy measure for the half-life in the blood. However, the model (Fig. 7a) removes the potentially confounding accumulation of radioisotopes in the heart tissue, which dominate the signal at long time points (>24 hours). A192 has the longest blood half-life thalf,blood = 6.3 ± 0.3 h (SD, n = 3) vs. that for A96 with thalf,blood = 1.8 ± 0.3 h (SD, n = 3, p = 8 × 10−6vs. A192) and A96I96 with thalf,blood = 4.6 ± 0.5 h (SD, n = 3, p = 0.006 vs. A192). The blood half-lives for A192 and S192 did not differ significantly. Lastly, the rate constant for tumor influx trended towards faster transfer for A96 than for the other polymers; however, this effect was not significant. In future studies, we will use this model to explore whether k(3,1) can be influenced by ligands for tumor vasculature-specific transport processes.
In this study, no active targeting moiety was appended to the carriers and tumor sequestration was achieved only through passive or non-specific uptake mechanisms such as the enhance permeability retention (EPR) effect.43 While all of the constructs do exhibit tumor accumulation, no significant difference in extent of the accumulation was observed (Fig. 6d, 7f). Other than passive accumulation, no effort was made to characterize target-mediated delivery to the tumor; however, this model may have potential applications in future studies of targeted therapeutics. Active targeted delivery of therapeutic agents to tumor tissue remains a promising approach to improve cancer treatment, as it may deliver higher doses to tumor sites while minimizing exposure to normal tissues. This work shows that it is possible to tailor make specific protein polymer nanocarriers that shift their clearance from renal to hepatic routes of elimination; furthermore, both monomeric (A192) and nanoparticulate (A96I96) carriers appear to remain viable platforms for delivery. Partnered with molecular imaging to quickly select drug carriers with the most selective tumor accumulation, these protein polymer nanoparticles are an emerging solution to nanotherapeutics engineering.
Footnote |
† Electronic supplementary information (ESI) available. See DOI: 10.1039/c2ib20169k |
This journal is © The Royal Society of Chemistry 2013 |