Automation of data analysis in molecular cancer imaging and its potential impact on future clinical practice.

Digitalization, especially the use of machine learning and computational intelligence, is considered to dramatically shape medical procedures in the near future. In the field of cancer diagnostics, radiomics, the extraction of multiple quantitative image features and their clustered analysis, is gaining increasing attention to obtain more detailed, reproducible, and meaningful information about the disease entity, its prognosis and the ideal therapeutic option. In this context, automation of diagnostic procedures can improve the entire pipeline, which comprises patient registration, planning and performing an imaging examination at the scanner, image reconstruction, image analysis, and feeding the diagnostic information from various sources into decision support systems. With a focus on cancer diagnostics, this review article reports and discusses how computer-assistance can be integrated into diagnostic procedures and which benefits and challenges arise from it. Besides a strong view on classical imaging modalities like x-ray, CT, MRI, ultrasound, PET, SPECT and hybrid imaging devices thereof, it is outlined how imaging data can be combined with data deriving from patient anamnesis, clinical chemistry, pathology, and different omics. In this context, the article also discusses IT infrastructures that are required to realize this integration in the clinical routine. Although there are still many challenges to comprehensively implement automated and integrated data analysis in molecular cancer imaging, the authors conclude that we are entering a new era of medical diagnostics and precision medicine.

Micro-CT imaging of tumor angiogenesis: quantitative measures describing micromorphology and vascularization.

Angiogenesis is a hallmark of cancer, and its noninvasive visualization and quantification are key factors for facilitating translational anticancer research. Using four tumor models characterized by different degrees of aggressiveness and angiogenesis, we show that the combination of functional in vivo and anatomical ex vivo X-ray micro-computed tomography (µCT) allows highly accurate quantification of relative blood volume (rBV) and highly detailed three-dimensional analysis of the vascular network in tumors. Depending on the tumor model, rBV values determined using in vivo µCT ranged from 2.6% to 6.0%, and corresponds well with the values assessed using IHC. Using ultra-high-resolution ex vivo µCT, blood vessels as small as 3.4 µm and vessel branches up to the seventh order could be visualized, enabling a highly detailed and quantitative analysis of the three-dimensional micromorphology of tumor vessels. Microvascular parameters such as vessel size and vessel branching correlated very well with tumor aggressiveness and angiogenesis. In rapidly growing and highly angiogenic A431 tumors, the majority of vessels were small and branched only once or twice, whereas in slowly growing A549 tumors, the vessels were much larger and branched four to seven times. Thus, we consider that combining highly accurate functional with highly detailed anatomical µCT is a useful tool for facilitating high-throughput, quantitative, and translational (anti-) angiogenesis and antiangiogenesis research.

Passive versus active tumor targeting using RGD- and NGR-modified polymeric nanomedicines.

Enhanced permeability and retention (EPR) and the (over-) expression of angiogenesis-related surface receptors are key features of tumor blood vessels. As a consequence, EPR-mediated passive and Arg-Gly-Asp (RGD) and Asn-Gly-Arg (NGR) based active tumor targeting have received considerable attention in the last couple of years. Using several different in vivo and ex vivo optical imaging techniques, we here visualized and quantified the benefit of RGD- and NGR-based vascular vs EPR-mediated passive tumor targeting. This was done using ∼ 10 nm sized polymeric nanocarriers, which were either labeled with DY-676 (peptide-modified polymers) or with DY-750 (peptide-free polymers). Upon coinjection into mice bearing both highly leaky CT26 and poorly leaky BxPC3 tumors, it was found that vascular targeting did work, resulting in rapid and efficient early binding to tumor blood vessels, but that over time, passive targeting was significantly more efficient, leading to higher overall levels and to more efficient retention within tumors. Although this situation might be different for larger carrier materials, these insights indicate that caution should be taken not to overestimate the potential of active over passive tumor targeting.

Engineering Mesoscopic 3D Tumor Models with a Self-Organizing Vascularized Matrix.

Advanced in vitro systems such as multicellular spheroids and lab-on-a-chip devices have been developed, but often fall short in reproducing the tissue scale and self-organization of human diseases. A bioprinted artificial tumor model is introduced with endothelial and stromal cells self-organizing into perfusable and functional vascular structures. This model uses 3D hydrogel matrices to embed multicellular tumor spheroids, allowing them to grow to mesoscopic scales and to interact with endothelial cells. It is shown that angiogenic multicellular tumor spheroids promote the growth of a vascular network, which in turn further enhances the growth of cocultivated tumor spheroids. The self-developed vascular structure infiltrates the tumor spheroids, forms functional connections with the bioprinted endothelium, and can be perfused by erythrocytes and polystyrene microspheres. Moreover, cancer cells migrate spontaneously from the tumor spheroid through the self-assembled vascular network into the fluid flow. Additionally, tumor type specific characteristics of desmoplasia, angiogenesis, and metastatic propensity are preserved between patient-derived samples and tumors derived from this same material growing in the bioreactors. Overall, this modular approach opens up new avenues for studying tumor pathophysiology and cellular interactions in vitro, providing a platform for advanced drug testing while reducing the need for in vivo experimentation.

Generation of Antibodies Selectively Recognizing Epitopes in a Formaldehyde-Fixed Cell-Surface Antigen Using Virus-like Particle Display and Hybridoma Technology.

Efficient induction of target-specific antibodies can be elicited upon immunization with highly immunogenic virus-like particles (VLPs) decorated with desired membrane-anchored target antigens (Ags). However, for example, for diagnostic purposes, monoclonal antibodies (mAbs) are required to enable the histological examination of formaldehyde-fixed paraffin-embedded (FFPE) biopsy tissue samples. Aiming at the generation of FFPE-antigen-specific mAbs and as a proof of concept (POC), we first established a simplified protocol using only formaldehyde and 90 °C heat fixation (FF90) of cells expressing the target Ag nerve growth factor receptor (NGFR). The FF90 procedure was validated using flow cytometric analysis and two mAbs recognizing either the native and FFPE-Ag or exclusively the native Ag. C-terminally truncated NGFR (trNGFR)-displaying native and FF90-treated VLPs derived from HIV-1 did not reveal distinctive changes in particle morphology using transmission electron microscopy (TEM) and dynamic light scattering (DLS) analysis. Mice were subsequently repetitively immunized with trNGFR-decorated FF90-VLPs and hybridoma technology was used to establish mAb-producing cell clones. In multiple screening rounds, nine cell clones were identified producing mAbs distinctively recognizing epitopes in FF90- and FFPE-NGFR. This POC of a new methodology should foster the future generation of mAbs selectively targeting FFPE-fixed cell-surface Ags.

Influence of the Computer-Aided Decision Support System Design on Ultrasound-Based Breast Cancer Classification.

Automation of medical data analysis is an important topic in modern cancer diagnostics, aiming at robust and reproducible workflows. Therefore, we used a dataset of breast US images (252 malignant and 253 benign cases) to realize and compare different strategies for CAD support in lesion detection and classification. Eight different datasets (including pre-processed and spatially augmented images) were prepared, and machine learning algorithms (i.e., Viola-Jones; YOLOv3) were trained for lesion detection. The radiomics signature (RS) was derived from detection boxes and compared with RS derived from manually obtained segments. Finally, the classification model was established and evaluated concerning accuracy, sensitivity, specificity, and area under the Receiver Operating Characteristic curve. After training on a dataset including logarithmic derivatives of US images, we found that YOLOv3 obtains better results in breast lesion detection (IoU: 0.544 ± 0.081; LE: 0.171 ± 0.009) than the Viola-Jones framework (IoU: 0.399 ± 0.054; LE: 0.096 ± 0.016). Interestingly, our findings show that the classification model trained with RS derived from detection boxes and the model based on the RS derived from a gold standard manual segmentation are comparable (p-value = 0.071). Thus, deriving radiomics signatures from the detection box is a promising technique for building a breast lesion classification model, and may reduce the need for the lesion segmentation step in the future design of CAD systems.

Monitoring EPR Effect Dynamics during Nanotaxane Treatment with Theranostic Polymeric Micelles.

Cancer nanomedicines rely on the enhanced permeability and retention (EPR) effect for efficient target site accumulation. The EPR effect, however, is highly heterogeneous among different tumor types and cancer patients and its extent is expected to dynamically change during the course of nanochemotherapy. Here the authors set out to longitudinally study the dynamics of the EPR effect upon single- and double-dose nanotherapy with fluorophore-labeled and paclitaxel-loaded polymeric micelles. Using computed tomography-fluorescence molecular tomography imaging, it is shown that the extent of nanomedicine tumor accumulation is predictive for therapy outcome. It is also shown that the interindividual heterogeneity in EPR-based tumor accumulation significantly increases during treatment, especially for more efficient double-dose nanotaxane therapy. Furthermore, for double-dose micelle therapy, tumor accumulation significantly increased over time, from 7% injected dose per gram (ID g-1 ) upon the first administration to 15% ID g-1 upon the fifth administration, contributing to more efficient inhibition of tumor growth. These findings shed light on the dynamics of the EPR effect during nanomedicine treatment and they exemplify the importance of using imaging in nanomedicine treatment prediction and clinical translation.

What scans we will read: imaging instrumentation trends in clinical oncology.

Oncological diseases account for a significant portion of the burden on public healthcare systems with associated costs driven primarily by complex and long-lasting therapies. Through the visualization of patient-specific morphology and functional-molecular pathways, cancerous tissue can be detected and characterized non-invasively, so as to provide referring oncologists with essential information to support therapy management decisions. Following the onset of stand-alone anatomical and functional imaging, we witness a push towards integrating molecular image information through various methods, including anato-metabolic imaging (e.g., PET/CT), advanced MRI, optical or ultrasound imaging.This perspective paper highlights a number of key technological and methodological advances in imaging instrumentation related to anatomical, functional, molecular medicine and hybrid imaging, that is understood as the hardware-based combination of complementary anatomical and molecular imaging. These include novel detector technologies for ionizing radiation used in CT and nuclear medicine imaging, and novel system developments in MRI and optical as well as opto-acoustic imaging. We will also highlight new data processing methods for improved non-invasive tissue characterization. Following a general introduction to the role of imaging in oncology patient management we introduce imaging methods with well-defined clinical applications and potential for clinical translation. For each modality, we report first on the status quo and, then point to perceived technological and methodological advances in a subsequent status go section. Considering the breadth and dynamics of these developments, this perspective ends with a critical reflection on where the authors, with the majority of them being imaging experts with a background in physics and engineering, believe imaging methods will be in a few years from now.Overall, methodological and technological medical imaging advances are geared towards increased image contrast, the derivation of reproducible quantitative parameters, an increase in volume sensitivity and a reduction in overall examination time. To ensure full translation to the clinic, this progress in technologies and instrumentation is complemented by advances in relevant acquisition and image-processing protocols and improved data analysis. To this end, we should accept diagnostic images as "data", and - through the wider adoption of advanced analysis, including machine learning approaches and a "big data" concept - move to the next stage of non-invasive tumour phenotyping. The scans we will be reading in 10 years from now will likely be composed of highly diverse multi-dimensional data from multiple sources, which mandate the use of advanced and interactive visualization and analysis platforms powered by Artificial Intelligence (AI) for real-time data handling by cross-specialty clinical experts with a domain knowledge that will need to go beyond that of plain imaging.

Multimodal and multiscale optical imaging of nanomedicine delivery across the blood-brain barrier upon sonopermeation.

Rationale: The blood-brain barrier (BBB) is a major obstacle for drug delivery to the brain. Sonopermeation, which relies on the combination of ultrasound and microbubbles, has emerged as a powerful tool to permeate the BBB, enabling the extravasation of drugs and drug delivery systems (DDS) to and into the central nervous system (CNS). When aiming to improve the treatment of high medical need brain disorders, it is important to systematically study nanomedicine translocation across the sonopermeated BBB. To this end, we here employed multimodal and multiscale optical imaging to investigate the impact of DDS size on brain accumulation, extravasation and penetration upon sonopermeation. Methods: Two prototypic DDS, i.e. 10 nm-sized pHPMA polymers and 100 nm-sized PEGylated liposomes, were labeled with fluorophores and intravenously injected in healthy CD-1 nude mice. Upon sonopermeation, computed tomography-fluorescence molecular tomography, fluorescence reflectance imaging, fluorescence microscopy, confocal microscopy and stimulated emission depletion nanoscopy were used to study the effect of DDS size on their translocation across the BBB. Results: Sonopermeation treatment enabled safe and efficient opening of the BBB, which was confirmed by staining extravasated endogenous IgG. No micro-hemorrhages, edema and necrosis were detected in H&E stainings. Multimodal and multiscale optical imaging showed that sonopermeation promoted the accumulation of nanocarriers in mouse brains, and that 10 nm-sized polymeric DDS accumulated more strongly and penetrated deeper into the brain than 100 nm-sized liposomes. Conclusions: BBB opening via sonopermeation enables safe and efficient delivery of nanomedicine formulations to and into the brain. When looking at accumulation and penetration (and when neglecting issues such as drug loading capacity and therapeutic efficacy) smaller-sized DDS are found to be more suitable for drug delivery across the BBB than larger-sized DDS. These findings are valuable for better understanding and further developing nanomedicine-based strategies for the treatment of CNS disorders.

Semi-Automated Segmentation of the Tumor Vasculature in Contrast-Enhanced Ultrasound Data.

The vascular architecture in tumors contains relevant information for tumor classification and evaluation of therapy responses. To develop a reliable and user-independent analysis tool, a foreground detection algorithm was combined with a maximum-intensity projection to obtain a high signal-to-noise image from contrast-enhanced B-mode data sets, enabling vessel segmentation by thresholding. Parameters describing the density of the vascular network, the number of vessels and the number of branches were extracted. The highly angiogenic A431 tumors had a relative blood volume of 49%, a mean pixel distance to the next vessel of 1.8 ± 0.3 px, 51 ± 29 individual vessels and 478 ± 184 branching points, whereas the more mature and heterogeneous vascularized human epithelial ovarian carcinoma (MLS) and A549 tumors had values of 30%, 3.7 ± 2.7 px, 65 ± 12 and 220 ± 159, and 13%, 7.4 ± 2 px, 31 ± 9 and 59 ± 40, respectively. Thus, our semi-automated analysis method enables the extraction of quantitative vascular features that may help to simplify and standardize tumor characterization.

Radiomic analysis of contrast-enhanced ultrasound data.

Radiomics describes the use radiological data in a quantitative manner to establish correlations in between imaging biomarkers and clinical outcomes to improve disease diagnosis, treatment monitoring and prediction of therapy responses. In this study, we evaluated whether a radiomic analysis on contrast-enhanced ultrasound (CEUS) data allows to automatically differentiate three xenograft mouse tumour models. Next to conventional imaging biomarker classes, i.e. intensity-based, textural, and wavelet-based features, we included biomarkers describing morphological and functional characteristics of the tumour vasculature. In total, 235 imaging biomarkers were extracted and evaluated. Dedicated feature selection allowed us to identify user-independent and stable imaging biomarkers for each imaging biomarker class. The selected radiomic signature, composed of median image intensity, energy of grey-level co-occurrence matrix, vessel network length, and run length nonuniformity of the grey-level run length matrix from the diagonal details, was used to train a linear support vector machine (SVM) to classify tumour phenotypes. The model was trained by using a four-fold cross-validation scheme and achieved 82.1% (95% CI [0.64 0.92]) correct classifications. In conclusion, our results show that a radiomic analysis can be successfully performed on CEUS data and may help to render ultrasound-based tumour imaging more accurate, reproducible and reliable.

Advanced Ultrasound Technologies for Diagnosis and Therapy.

Ultrasound is among the most rapidly advancing imaging techniques. Functional methods such as elastography have been clinically introduced, and tissue characterization is improved by contrast-enhanced scans. Here, novel superresolution techniques provide unique morphologic and functional insights into tissue vascularization. Functional analyses are complemented by molecular ultrasound imaging, to visualize markers of inflammation and angiogenesis. The full potential of diagnostic ultrasound may become apparent by integrating these multiple imaging features in radiomics approaches. Emerging interest in ultrasound also results from its therapeutic potential. Various applications of tumor ablation with high-intensity focused ultrasound are being clinically evaluated, and its performance strongly benefits from the integration into MRI. Additionally, oscillating microbubbles mediate sonoporation to open biologic barriers, thus improving the delivery of drugs or nucleic acids that are coadministered or coformulated with microbubbles. This article provides an overview of recent developments in diagnostic and therapeutic ultrasound, highlighting multiple innovation tracks and their translational potential.

Engineering biofunctional in vitro vessel models using a multilayer bioprinting technique.

Recent advances in the field of bioprinting have led to the development of perfusable complex structures. However, most of the existing printed vascular channels lack the composition or key structural and physiological features of natural blood vessels or they make use of more easily printable but less biocompatible hydrogels. Here, we use a drop-on-demand bioprinting technique to generate in vitro blood vessel models, consisting of a continuous endothelium imitating the tunica intima, an elastic smooth muscle cell layer mimicking the tunica media, and a surrounding fibrous and collagenous matrix of fibroblasts mimicking the tunica adventitia. These vessel models with a wall thickness of up to 425 µm and a diameter of about 1 mm were dynamically cultivated in fluidic bioreactors for up to three weeks under physiological flow conditions. High cell viability (>83%) after printing and the expression of VE-Cadherin, smooth muscle actin, and collagen IV were observed throughout the cultivation period. It can be concluded that the proposed novel technique is suitable to achieve perfusable vessel models with a biofunctional multilayer wall composition. Such structures hold potential for the creation of more physiologically relevant in vitro disease models suitable especially as platforms for the pre-screening of drugs.

Tumor targeting via EPR: Strategies to enhance patient responses.

The tumor accumulation of nanomedicines relies on the enhanced permeability and retention (EPR) effect. In the last 5-10 years, it has been increasingly recognized that there is a large inter- and intra-individual heterogeneity in EPR-mediated tumor targeting, explaining the heterogeneous outcomes of clinical trials in which nanomedicine formulations have been evaluated. To address this heterogeneity, as in other areas of oncology drug development, we have to move away from a one-size-fits-all tumor targeting approach, towards methods that can be employed to individualize and improve nanomedicine treatments. To this end, efforts have to be invested in better understanding the nature, the complexity and the heterogeneity of the EPR effect, and in establishing systems and strategies to enhance, combine, bypass and image EPR-based tumor targeting. In the present manuscript, we summarize key studies in which these strategies are explored, and we discuss how these approaches can be employed to enhance patient responses.

Motion model ultrasound localization microscopy for preclinical and clinical multiparametric tumor characterization.

Super-resolution imaging methods promote tissue characterization beyond the spatial resolution limits of the devices and bridge the gap between histopathological analysis and non-invasive imaging. Here, we introduce motion model ultrasound localization microscopy (mULM) as an easily applicable and robust new tool to morphologically and functionally characterize fine vascular networks in tumors at super-resolution. In tumor-bearing mice and for the first time in patients, we demonstrate that within less than 1 min scan time mULM can be realized using conventional preclinical and clinical ultrasound devices. In this context, next to highly detailed images of tumor microvascularization and the reliable quantification of relative blood volume and perfusion, mULM provides multiple new functional and morphological parameters that discriminate tumors with different vascular phenotypes. Furthermore, our initial patient data indicate that mULM can be applied in a clinical ultrasound setting opening avenues for the multiparametric characterization of tumors and the assessment of therapy response.

Histidine-rich glycoprotein-induced vascular normalization improves EPR-mediated drug targeting to and into tumors.

Tumors are characterized by leaky blood vessels, and by an abnormal and heterogeneous vascular network. These pathophysiological characteristics contribute to the enhanced permeability and retention (EPR) effect, which is one of the key rationales for developing tumor-targeted drug delivery systems. Vessel abnormality and heterogeneity, however, which typically result from excessive pro-angiogenic signaling, can also hinder efficient drug delivery to and into tumors. Using histidine-rich glycoprotein (HRG) knockout and wild type mice, and HRG-overexpressing and normal t241 fibrosarcoma cells, we evaluated the effect of genetically induced and macrophage-mediated vascular normalization on the tumor accumulation and penetration of 10-20 nm-sized polymeric drug carriers based on poly(N-(2-hydroxypropyl)methacrylamide). Multimodal and multiscale optical imaging was employed to show that normalizing the tumor vasculature improves the accumulation of fluorophore-labeled polymers in tumors, and promotes their penetration out of tumor blood vessels deep into the interstitium.

Automated Generation of Reliable Blood Velocity Parameter Maps from Contrast-Enhanced Ultrasound Data.

Objectives: The purpose of this study was the automated generation and validation of parametric blood flow velocity maps, based on contrast-enhanced ultrasound (CEUS) scans. Materials and Methods: Ethical approval for animal experiments was obtained. CEUS destruction-replenishment sequences were recorded in phantoms and three different tumor xenograft mouse models. Systematic pixel binning and intensity averaging was performed to generate parameter maps of blood flow velocities with different pixel resolution. The 95% confidence interval of the mean velocity, calculated on the basis of the whole tumor segmentation, served as ground truth for the different parameter maps. Results: In flow phantoms the measured mean velocity values were only weakly influenced by the pixel resolution and correlated with real velocities (r2 ≥ 0.94, p < 0.01). In tumor xenografts, however, calculated mean velocities varied significantly (p < 0.0001), depending on the parameter maps' resolution. Pixel binning was required for all in vivo measurements to obtain reliable parameter maps and its degree depended on the tumor model. Conclusion: Systematic pixel binning allows the automated identification of optimal pixel resolutions for parametric maps, supporting textural analysis of CEUS data. This approach is independent from the ultrasound setup and can be implemented in the software of other (clinical) ultrasound devices.

Molecular Ultrasound Imaging of αvß3-Integrin Expression in Carotid Arteries of Pigs After Vessel Injury.

OBJECTIVES: Interventions such as balloon angioplasty can cause vascular injury leading to platelet activation, thrombus formation, and inflammatory response. This induces vascular smooth muscle cell activation and subsequent re-endothelialization with expression of αvß3-integrin by endothelial cells and vascular smooth muscle cell. Thus, poly-N-butylcyanoacrylate microbubbles (MBs) targeted to αvß3-integrin were evaluated for monitoring vascular healing after vessel injury in pigs using molecular ultrasound imaging. MATERIALS AND METHODS: Approval for animal experiments was obtained. The binding specificity of αvß3-integrin-targeted MB to human umbilical vein endothelial cells was tested with fluorescence microscopy. In vivo imaging was performed using a clinical ultrasound system and an 8-MHz probe. Six mini pigs were examined after vessel injury in the left carotid artery. The right carotid served as control. Uncoated MB, cDRG-coated MB, and αvß3-integrin-specific cRGD-coated MB were injected sequentially. Bound MBs were assessed 8 minutes after injection using ultrasound replenishment analysis. Measurements were performed 2 hours, 1 and 5 weeks, and 3 and 6 months after injury. In vivo data were validated by immunohistochemistry. RESULTS: Significantly stronger binding of cRGD-MB than MB and cDRG-MB to human umbilical vein endothelial cells was found (P < 0.01). As vessel injury leads to upregulation of αvß3-integrin, cRGD-MBs bound significantly stronger (P < 0.05) in injured carotid arteries than at the counter side 1 week after vessel injury and significant differences could also be observed after 5 weeks. After 3 months, αvß3-integrin expression decreased to baseline and binding of cRGD-MB was comparable in both vessels. Values remained at baseline also after 6 months. CONCLUSIONS: Ultrasound imaging with RGD-MB is promising for monitoring vascular healing after vessel injury. This may open new perspectives to assess vascular damage after radiological interventions.

Sonoporation enhances liposome accumulation and penetration in tumors with low EPR.

The Enhanced Permeability and Retention (EPR) effect is a highly variable phenomenon. To enhance EPR-mediated passive drug targeting to tumors, several different pharmacological and physical strategies have been evaluated over the years, including e.g. TNFα-treatment, vascular normalization, hyperthermia and radiotherapy. Here, we systematically investigated the impact of sonoporation, i.e. the combination of ultrasound (US) and microbubbles (MB), on the tumor accumulation and penetration of liposomes. Two different MB formulations were employed, and their ability to enhance liposome accumulation and penetration was evaluated in two different tumor models, which are both characterized by relatively low levels of EPR (i.e. highly cellular A431 epidermoid xenografts and highly stromal BxPC-3 pancreatic carcinoma xenografts). The liposomes were labeled with two different fluorophores, enabling in vivo computed tomography/fluorescence molecular tomography (CT-FMT) and ex vivo two-photon laser scanning microscopy (TPLSM). In both models, in spite of relatively high inter- and intra-individual variability, a trend towards improved liposome accumulation and penetration was observed. In treated tumors, liposome concentrations were up to twice as high as in untreated tumors, and sonoporation enhanced the ability of liposomes to extravasate out of the blood vessels into the tumor interstitium. These findings indicate that sonoporation may be a useful strategy for improving drug targeting to tumors with low EPR.

Complete Regression of Xenograft Tumors upon Targeted Delivery of Paclitaxel via Π-Π Stacking Stabilized Polymeric Micelles.

Treatment of cancer patients with taxane-based chemotherapeutics, such as paclitaxel (PTX), is complicated by their narrow therapeutic index. Polymeric micelles are attractive nanocarriers for tumor-targeted delivery of PTX, as they can be tailored to encapsulate large amounts of hydrophobic drugs and achiv prolonged circulation kinetics. As a result, PTX deposition in tumors is increased, while drug exposure to healthy tissues is reduced. However, many PTX-loaded micelle formulations suffer from low stability and fast drug release in the circulation, limiting their suitability for systemic drug targeting. To overcome these limitations, we have developed PTX-loaded micelles which are stable without chemical cross-linking and covalent drug attachment. These micelles are characterized by excellent loading capacity and strong drug retention, attributed to π-π stacking interaction between PTX and the aromatic groups of the polymer chains in the micellar core. The micelles are based on methoxy poly(ethylene glycol)-b-(N-(2-benzoyloxypropyl)methacrylamide) (mPEG-b-p(HPMAm-Bz)) block copolymers, which improved the pharmacokinetics and the biodistribution of PTX, and substantially increased PTX tumor accumulation (by more than 2000%; as compared to Taxol or control micellar formulations). Improved biodistribution and tumor accumulation were confirmed by hybrid µCT-FMT imaging using near-infrared labeled micelles and payload. The PTX-loaded micelles were well tolerated at different doses, while they induced complete tumor regression in two different xenograft models (i.e., A431 and MDA-MB-468). Our findings consequently indicate that π-π stacking-stabilized polymeric micelles are promising carriers to improve the delivery of highly hydrophobic drugs to tumors and to increase their therapeutic index.