A computational framework for identifying design guidelines to increase the penetration of targeted nanoparticles into tumors

Drug-loaded nanoparticles for cancer therapy: a high-throughput multicellular agent-based modeling study

Interactions between biological systems and engineered nanomaterials have become an important area of study due to the application of nanomaterials in medicine. In particular, the application of nanomaterials for cancer diagnosis or treatment presents a challenging opportunity due to the complex biology of this disease spanning multiple time and spatial scales. A system-level analysis would benefit from mathematical modeling and computational simulation to explore the interactions between anticancer drug-loaded nanoparticles (NPs), cells, and tissues, and the associated parameters driving this system and a patient's overall response. Although a number of models have explored these interactions in the past, few have focused on simulating individual cell-NP interactions. This study develops a multicellular agent-based model of cancer nanotherapy that simulates NP internalization, drug release within the cell cytoplasm, "inheritance" of NPs by daughter cells at cell division, cell pharmacodynamic response to the intracellular drug, and overall drug effect on tumor dynamics. A large-scale parallel computational framework is used to investigate the impact of pharmacokinetic design parameters (NP internalization rate, NP decay rate, anticancer drug release rate) and therapeutic strategies (NP doses and injection frequency) on the tumor dynamics. In particular, through the exploration of NP "inheritance" at cell division, the results indicate that cancer treatment may be improved when NPs are inherited at cell division for cytotoxic chemotherapy. Moreover, smaller dosage of cytostatic chemotherapy may also improve inhibition of tumor growth when cell division is not completely inhibited. This work suggests that slow delivery by "heritable" NPs can drive new dimensions of nanotherapy design for more sustained therapeutic response.

Nanoscale Surface Topography of Polyethylene Glycol-Coated Nanoparticles Composed of Bottlebrush Block Copolymers Prolongs Systemic Circulation and Enhances Tumor Uptake

Improving the performance of nanocarriers remains a major challenge in the clinical translation of nanomedicine. Efforts to optimize nanoparticle formulations typically rely on tuning the surface density and thickness of stealthy polymer coatings, such as poly(ethylene glycol) (PEG). Here, we show that modulating the surface topography of PEGylated nanoparticles using bottlebrush block copolymers (BBCPs) significantly enhances circulation and tumor accumulation, providing an alternative strategy to improve nanoparticle coatings. Specifically, nanoparticles with rough surface topography achieve high tumor cell uptake in vivo due to superior tumor extravasation and distribution compared to conventional smooth-surfaced nanoparticles based on linear block copolymers. Furthermore, surface topography profoundly impacts the interaction with serum proteins, resulting in the adsorption of fundamentally different proteins onto the surface of rough-surfaced nanoparticles formed from BBCPs. We envision that controlling the nanoparticle surface topography of PEGylated nanoparticles will enable the design of improved nanocarriers in various biomedical applications.

Global parameter optimisation and sensitivity analysis of antivenom pharmacokinetics and pharmacodynamics

In recent years it has become possible to design snakebite antivenoms with diverse pharmacokinetic properties. Owing to the pharmacokinetic variability of venoms, the choice of antivenom scaffold may influence a treatment's neutralisation coverage. Computation offers a useful medium through which to assess the pharmacokinetics and pharmacodynamics of envenomation-treatment systems, as antivenoms with identical neutralising capacities can be simulated. In this study, we simulate envenomation and treatment with a variety of antivenoms, to define the properties of effective antivenoms. Systemic envenomation and treatment were described using a two-compartment pharmacokinetic model. Treatment of Naja sumatrana and Cryptelytrops purpureomaculatus envenomation was simulated with a set of 200,000 theoretical antivenoms across 10 treatment time delays. These two venoms are well-characterised and have differing pharmacokinetic properties. The theoretical antivenom set varied across molecular weight, dose, kon, koff, and valency. The best and worst treatments were identified using an area under the curve metric, and a global sensitivity analysis was performed to quantify the influence of the input parameters on treatment outcome. The simulations show that scaffolds of diverse molecular formats can be effective. Molecular weight and valency have a negligible direct impact on treatment outcome, however low molecular weight scaffolds offer more flexibility across the other design parameters, particularly when treatment is delayed. The simulations show kon to primarily mediate treatment efficacy, with rates above 105 M-1s-1 required for the most effective treatments. koff has the greatest impact on the performance of less effective scaffolds. While the same scaffold preferences for improved treatment are seen for both model snakes, the parameter bounds for C. purpureomaculatus envenomation are more constrained. This paper establishes a computational framework for the optimisation of antivenom design.

Chemical tunability of advanced materials used in the fabrication of micro/nanobots

Micro and nanobots (MNBs) are unprecedented in their ability to be chemically tuned for autonomous tasks with enhanced targeting and functionality while maintaining their mobility. A myriad of chemical modifications involving a large variety of advanced materials have been demonstrated to be effective in the design of MNBs. Furthermore, they can be controlled for their autonomous motion, and their ability to carry chemical or biological payloads. In addition, MNBs can be modified to achieve targetability with specificity for biological implications. MNBs by virtue of their chemical compositions may be limited by their biocompatibility, tissue accumulation, poor biodegradability and toxicity. This review presents a note on artificial intelligence materials (AIMs), their importance, and the dimensional scales at which intrinsic autonomy can be achieved for diverse utility. We briefly discuss the evolution of such systems with a focus on their advancements in nanomedicine. We highlight MNBs covering their contemporary traits and the emergence of a few start-ups in specific areas. Furthermore, we showcase various examples, demonstrating that chemical tunability is an attractive primary approach for designing MNBs with immense capabilities both in biology and chemistry. Finally, we cover biosafety and ethical considerations in designing MNBs in the era of artificial intelligence for varied applications.

Computational model of silica nanoparticle penetration into tumor spheroids: Effects of methoxy and carboxy PEG surface functionalization and hyperthermia

Drug delivery to tumors suffers from poor solubility, specificity, diffusion through the tumor micro-environment and nonoptimal interactions with components of the extracellular matrix and cell surface receptors. Nanoparticles and drug-polymer complexes address many of these problems. However, large size exasperates the problem of slow diffusion through the tumor. Three-dimensional tumor spheroids are good models to evaluate approaches to mitigate these difficulties and aid in design strategies to improve the delivery of drugs to treat cancer effectively. Diffusion of drug carriers is highly dependent on cell uptake rate parameters (association/dissociation) and temperature. Hyperthermia increases molecular transport and is known to act synergistically with chemotherapy to improve treatment. This study presents a new inverse estimation approach based on Bayesian probability for estimating nanoparticle cell uptake rates from experiments. The parameters were combined with a finite element computational model of nanoparticle transport under hyperthermia conditions to explore its effect on tumor porosity, diffusion and particle binding (association and dissociation) at cell surfaces. Carboxy-PEG-silane (cPEGSi) nanoparticles showed higher cell uptake compared to methoxy-PEG-silane (mPEGSi) nanoparticles. Simulations were consistent with experimental results from Skov-3 ovarian cancer spheroids. Amorphous silica (cPEGSi) nanoparticles (58 nm) concentrated at the periphery of the tumor spheroids at 37°C but mild hyperthermia (43°C) increased nanoparticle penetration. Thus, hyperthermia may enhance cancer treatment by improving blood delivery to tumors, enhancing extravasation and penetration into tumors, trigger release of drug from the carrier at the tumor site and possibly lead to synergistic anti-cancer activity with the drug.

Understanding nano-engineered particle-cell interactions: biological insights from mathematical models

Understanding the interactions between nano-engineered particles and cells is necessary for the rational design of particles for therapeutic, diagnostic and imaging purposes. In particular, the informed design of particles relies on the quantification of the relationship between the physicochemical properties of the particles and the rate at which cells interact with, and subsequently internalise, particles. Quantitative models, both mathematical and computational, provide a powerful tool for elucidating this relationship, as well as for understanding the mechanisms governing the intertwined processes of interaction and internalisation. Here we review the different types of mathematical and computational models that have been used to examine particle-cell interactions and particle internalisation. We detail the mathematical methodology for each type of model, the benefits and limitations associated with the different types of models, and highlight the advances in understanding gleaned from the application of these models to experimental observations of particle internalisation. We discuss the recent proposal and ongoing community adoption of standardised experimental reporting, and how this adoption is an important step toward unlocking the full potential of modelling approaches. Finally, we consider future directions in quantitative models of particle-cell interactions and highlight the need for hybrid experimental and theoretical investigations to address hitherto unanswered questions.

Measuring Nanoparticle Penetration Through Bio-Mimetic Gels

Background

In cancer nanomedicine, drugs are transported by nanocarriers through a biological system to produce a therapeutic effect. The efficacy of the treatment is affected by the ability of the nanocarriers to overcome biological transport barriers to reach their target. In this work, we focus on the process of nanocarrier penetration through tumour tissue after extravasation. Visualising the dynamics of nanocarriers in tissue is difficult in vivo, and in vitro assays often do not capture the spatial and physical constraints relevant to model tissue penetration.

Methods

We propose a new simple, low-cost method to observe the transport dynamics of nanoparticles through a tissue-mimetic microfluidic chip. After loading a chip with triplicate conditions of gel type and loading with microparticles, microscopic analysis allows for tracking of fluorescent nanoparticles as they move through hydrogels (Matrigel and Collagen I) with and without cell-sized microparticles. A bespoke image-processing codebase written in MATLAB allows for statistical analysis of this tracking, and time-dependent dynamics can be determined.

Results

To demonstrate the method, we show size-dependence of transport mechanics can be observed, with diffusion of fluorescein dye throughout the channel in 8 h, while 20 nm carboxylate FluoSphere diffusion was hindered through both Collagen I and Matrigel™. Statistical measurements of the results are generated through the software package and show the significance of both size and presence of microparticles on penetration depth.

Conclusion

This provides an easy-to-understand output for the end user to measure nanoparticle tissue penetration, enabling the first steps towards future automated experimentation of transport dynamics for rational nanocarrier design.

Harnessing adaptive novelty for automated generation of cancer treatments

Nanoparticles have the potential to modulate both the pharmacokinetic and pharmacodynamic profiles of drugs, thereby enhancing their therapeutic effect. The versatility of nanoparticles allows for a wide range of customization possibilities. However, it also leads to a rich design space which is difficult to investigate and optimize. An additional problem emerges when they are applied to cancer treatment. A heterogeneous and highly adaptable tumour can quickly become resistant to primary therapy, making it inefficient. To automate the design of potential therapies for such complex cases, we propose a computational model for fast, novelty-based machine learning exploration of the nanoparticle design space. In this paper, we present an evolvable, open-ended agent-based model, where the exploration of an initially small portion of the given state space can be expanded by an ongoing generation of adaptive novelties, whenever the simulated tumour makes an adaptive leap. We demonstrate that the nano-agents can continuously reshape themselves and create a heterogeneous population of specialized groups of individuals optimized for tracking and killing different phenotypes of cancer cells. In the conclusion, we outline further development steps so this model could be used in real-world research and clinical practice.

Toward Engineering Biosystems With Emergent Collective Functions

Many complex behaviors in biological systems emerge from large populations of interacting molecules or cells, generating functions that go beyond the capabilities of the individual parts. Such collective phenomena are of great interest to bioengineers due to their robustness and scalability. However, engineering emergent collective functions is difficult because they arise as a consequence of complex multi-level feedback, which often spans many length-scales. Here, we present a perspective on how some of these challenges could be overcome by using multi-agent modeling as a design framework within synthetic biology. Using case studies covering the construction of synthetic ecologies to biological computation and synthetic cellularity, we show how multi-agent modeling can capture the core features of complex multi-scale systems and provide novel insights into the underlying mechanisms which guide emergent functionalities across scales. The ability to unravel design rules underpinning these behaviors offers a means to take synthetic biology beyond single molecules or cells and toward the creation of systems with functions that can only emerge from collectives at multiple scales.

Meta-Analysis of Nanoparticle Delivery to Tumors Using a Physiologically Based Pharmacokinetic Modeling and Simulation Approach

Numerous studies have engineered nanoparticles with different physicochemical properties to enhance the delivery efficiency to solid tumors, yet the mean and median delivery efficiencies are only 1.48% and 0.70% of the injected dose (%ID), respectively, according to a study using a nonphysiologically based modeling approach based on published data from 2005 to 2015. In this study, we used physiologically based pharmacokinetic (PBPK) models to analyze 376 data sets covering a wide range of nanomedicines published from 2005 to 2018 and found mean and median delivery efficiencies at the last sampling time point of 2.23% and 0.76%ID, respectively. Also, the mean and median delivery efficiencies were 2.24% and 0.76%ID at 24 h and were decreased to 1.23% and 0.35%ID at 168 h, respectively, after intravenous administration. While these delivery efficiencies appear to be higher than previous findings, they are still quite low and represent a critical barrier in the clinical translation of nanomedicines. We explored the potential causes of this poor delivery efficiency using the more mechanistic PBPK perspective applied to a subset of gold nanoparticles and found that low delivery efficiency was associated with low distribution and permeability coefficients at the tumor site (P < 0.01). We also demonstrate how PBPK modeling and simulation can be used as an effective tool to investigate tumor delivery efficiency of nanomedicines.

Improving nanotherapy delivery and action through image-guided systems pharmacology

Despite recent advances in the translation of therapeutic nanoparticles (TNPs) into the clinic, the field continues to face challenges in predictably and selectively delivering nanomaterials for the treatment of solid cancers. The concept of enhanced permeability and retention (EPR) has been coined as a convenient but simplistic descriptor of high TNP accumulation in some tumors. However, in practice EPR represents a number of physiological variables rather than a single one (including dysfunctional vasculature, compromised lymphatics and recruited host cells, among other aspects of the tumor microenvironment) — each of which can be highly heterogenous within a given tumor, patient and across patients. Therefore, a clear need exists to dissect the specific biophysical factors underlying the EPR effect, to formulate better TNP designs, and to identify patients with high-EPR tumors who are likely to respond to TNP. The overall pharmacology of TNP is governed by an interconnected set of spatially defined and dynamic processes that benefit from a systems-level quantitative approach, and insights into the physiology have profited from the marriage between in vivo imaging and quantitative systems pharmacology (QSP) methodologies. In this article, we review recent developments pertinent to image-guided systems pharmacology of nanomedicines in oncology. We first discuss recent developments of quantitative imaging technologies that enable analysis of nanomaterial pharmacology at multiple spatiotemporal scales, and then examine reports that have adopted these imaging technologies to guide QSP approaches. In particular, we focus on studies that have integrated multi-scale imaging with computational modeling to derive insights about the EPR effect, as well as studies that have used modeling to guide the manipulation of the EPR effect and other aspects of the tumor microenvironment for improving TNP action. We anticipate that the synergistic combination of imaging with systems-level computational methods for effective clinical translation of TNPs will only grow in relevance as technologies increase in resolution, multiplexing capability, and in the ability to examine heterogeneous behaviors at the single-cell level.

Mathematical modeling in cancer nanomedicine: a review

Cancer continues to be among the leading healthcare problems worldwide, and efforts continue not just to find better drugs, but also better drug delivery methods. The need for delivering cytotoxic agents selectively to cancerous cells, for improved safety and efficacy, has triggered the application of nanotechnology in medicine. This effort has provided drug delivery systems that can potentially revolutionize cancer treatment. Nanocarriers, due to their capacity for targeted drug delivery, can shift the balance of cytotoxicity from healthy to cancerous cells. The field of cancer nanomedicine has made significant progress, but challenges remain that impede its clinical translation. Several biophysical barriers to the transport of nanocarriers to the tumor exist, and a much deeper understanding of nano-bio interactions is necessary to change the status quo. Mathematical modeling has been instrumental in improving our understanding of the physicochemical and physiological underpinnings of nanomaterial behavior in biological systems. Here, we present a comprehensive review of literature on mathematical modeling works that have been and are being employed towards a better understanding of nano-bio interactions for improved tumor delivery efficacy.

Synthetic and living micropropellers for convection-enhanced nanoparticle transport

Nanoparticles (NPs) have emerged as an advantageous drug delivery platform for the treatment of various ailments including cancer and cardiovascular and inflammatory diseases. However, their efficacy in shuttling materials to diseased tissue is hampered by a number of physiological barriers. One hurdle is transport out of the blood vessels, compounded by difficulties in subsequent penetration into the target tissue. Here, we report the use of two distinct micropropellers powered by rotating magnetic fields to increase diffusion-limited NP transport by enhancing local fluid convection. In the first approach, we used a single synthetic magnetic microrobot called an artificial bacterial flagellum (ABF), and in the second approach, we used swarms of magnetotactic bacteria (MTB) to create a directable "living ferrofluid" by exploiting ferrohydrodynamics. Both approaches enhance NP transport in a microfluidic model of blood extravasation and tissue penetration that consists of microchannels bordered by a collagen matrix.

Advances in targeted nanotherapeutics: From bioconjugation to biomimicry

Since the emergence of cancer nanomedicine, researchers have had intense interest in developing nanoparticles (NPs) that can specifically target diseased sites while avoiding healthy tissue to mitigate the off-target effects seen with conventional treatments like chemotherapy. Initial endeavors focused on the bioconjugation of targeting agents to NPs, and more recently, researchers have begun to develop biomimetic NP platforms that can avoid immune recognition to maximally accumulate in tumors. In this review, we describe the advantages and limitations of each of these targeting strategies. First, we review developments in bioconjugation strategies, where NPs are coated with biomolecules such as antibodies, aptamers, peptides, and small molecules to enable cell-specific binding. While bioconjugated NPs offer many exciting features and have improved pharmacokinetics and biodistribution relative to unmodified NPs, they are still recognized by the body as "foreign", resulting in their clearance by the mononuclear phagocytic system (MPS). To overcome this limitation, researchers have recently begun to investigate biomimetic approaches that can hide NPs from immune recognition and reduce clearance by the MPS. These biomimetic NPs fall into two distinct categories: synthetic NPs that present naturally occurring structures, and NPs that are completely disguised by natural structures. Overall, bioconjugated and biomimetic NPs have substantial potential to improve upon conventional treatments by reducing off-target effects through site-specific delivery, and they show great promise for future standards of care. Here, we provide a summary of each strategy, discuss considerations for their design moving forward, and highlight their potential clinical impact on cancer therapy.

An experimental and computational framework for engineering multifunctional nanoparticles: designing selective anticancer therapies

A key challenge in the treatment of cancer with nanomedicine is to engineer and select nanoparticle formulations that lead to the desired selectivity between tumorigenic and non-tumorigenic cells. To this aim, novel designed nanomaterials, deep biochemical understanding of the mechanisms of interaction between nanomaterials and cells, and computational models are emerging as very useful tools to guide the design of efficient and selective nanotherapies. This works shows, using a combination of detailed experimental approaches and simulations, that the specific targeting of cancer cells in comparison to non-tumorigenic cells can be achieved through the custom design of multivalent nanoparticles. A theoretical model that provides simple yet quantitative predictions to tune the nanoparticles targeting and cytotoxic properties by their degree of functionalization is developed. As a case study, a system that included a targeting agent and a drug and is amenable to controlled experimental manipulation and theoretical analysis is used. This study shows how at defined functionalization levels multivalent nanoparticles can selectively kill tumor cells, while barely affecting non-tumorigenic cells. This work opens a way to the rational design of multifunctionalized nanoparticles with defined targeting and cytotoxic properties for practical applications.

Strategies for improving drug delivery: nanocarriers and microenvironmental priming

INTRODUCTION

The ultimate goal in the field of drug delivery is to exclusively direct therapeutic agents to pathological tissues in order to increase therapeutic efficacy and eliminate side effects. This goal is challenging due to multiple transport obstacles in the body. Strategies that improve drug transport exploit differences in the characteristics of normal and pathological tissues. Within the field of oncology, these concepts have laid the groundwork for a new discipline termed transport oncophysics. Areas covered: Efforts to improve drug biodistribution have mainly focused on nanocarriers that enable preferential accumulation of drugs in diseased tissues. A less common approach to enhance drug transport involves priming strategies that modulate the biological environment in ways that favor localized drug delivery. This review discusses a variety of priming and nanoparticle design strategies that have been used for drug delivery. Expert opinion: Combinations of priming agents and nanocarriers are likely to yield optimal drug distribution profiles. Although priming strategies have yet to be widely implemented, they represent promising solutions for overcoming biological transport barriers. In fact, such strategies are not restricted to priming the tumor microenvironment but can also be directed toward healthy tissue in order to reduce nanoparticle uptake.

Drug delivery in a tumour cord model: a computational simulation

The tumour vasculature and microenvironment is complex and heterogeneous, contributing to reduced delivery of cancer drugs to the tumour. We have developed an in silico model of drug transport in a tumour cord to explore the effect of different drug regimes over a 72 h period and how changes in pharmacokinetic parameters affect tumour exposure to the cytotoxic drug doxorubicin. We used the model to describe the radial and axial distribution of drug in the tumour cord as a function of changes in the transport rate across the cell membrane, blood vessel and intercellular permeability, flow rate, and the binding and unbinding ratio of drug within the cancer cells. We explored how changes in these parameters may affect cellular exposure to drug. The model demonstrates the extent to which distance from the supplying vessel influences drug levels and the effect of dosing schedule in relation to saturation of drug-binding sites. It also shows the likely impact on drug distribution of the aberrant vasculature seen within tumours. The model can be adapted for other drugs and extended to include other parameters. The analysis confirms that computational models can play a role in understanding novel cancer therapies to optimize drug administration and delivery.

Assessing the Benefits of Drug Delivery by Nanocarriers: A Partico/Pharmacokinetic Framework

OBJECTIVE

An in vivo kinetic framework is introduced to analyze and predict the quantitative advantage of using nanocarriers to deliver drugs, especially anticancer agents, compared to administering the same drugs in their free form.

METHODS

This framework recognizes three levels of kinetics. First is the particokinetics associated with deposition of nanocarriers into tissues associated with drug effect and toxicity, their residence inside those tissues, and elimination of the nanocarriers from the body. Second is the release pattern in time of free drug from the nanocarriers. Third is the pharmacokinetics of free drug, as it relates to deposition and elimination processes in the target and toxicity associated tissues, and total body clearance. A figure of merit, the drug targeting index (DTI), is used to quantitate the benefit of nanocarrier-based drug delivery by considering the effects of preferential deposition of nanoparticles into target tissues and relative avoidance of tissues associated with drug toxicity, compared to drug that is administered in its free form.

RESULTS

General methods are derived for calculating DTI when appropriate particokinetic, pharmacokinetic, and drug release rate information is available, and it is shown that relatively simple algebraic forms result when some common assumptions are made.

CONCLUSION

This approach may find use in developing and selecting nanocarrier formulations, either for populations or for individuals.

Novel targets for paclitaxel nano formulations: Hopes and hypes in triple negative breast cancer

Triple negative breast cancer is defined as one of the utmost prevailing breast cancers worldwide, possessing an inadequate prognosis and treatment option limited to chemotherapy and radiotherapy, creating a challenge for researchers as far as developing a specific targeted therapy is concerned. The past research era has shown several promising outcomes for TNBC such as nano-formulations of the chemotherapeutic agents already used for the management of the malignant tumor. Taking a glance at paclitaxel nano formulations, it has been proven beneficial in several researches in the past decade; nevertheless its solubility is often a challenge to scientists in achieving success. We have henceforth discussed the basic heterogeneity of triple negative breast cancer along with the current management options as well as a brief outlook on pros and cons of paclitaxel, known as the most widely used chemotherapeutic agent for the treatment of the disease. We further analyzed the need of nanotechnology pertaining to the problems encountered with the current paclitaxel formulations available discussing the strategic progress in various nano-formulations till date taking into account the basic research strategies required in terms of solubility, permeability, physicochemical properties, active and passive targeting. A thorough review in recent advances in active targeting for TNBC was carried out whereby the various ligands which are at present finding its way into TNBC research such as hyaluronic acid, folic acid, transferrin, etc. were discussed. These ligands have specific receptor affinity to TNBC tumor cells hence can be beneficial for novel drug targeting approaches. Conversely, there are currently several novel strategies in the research pipeline whose targeting ligands have not yet been studied. Therefore, we reviewed upon the numerous novel receptor targets along with the respective nano-formulation aspects which have not yet been fully researched upon and could be exemplified as outstanding target strategies for TNBC which is currently an urgent requirement.

Mechanistic and quantitative insight into cell surface targeted molecular imaging agent design

Molecular imaging agent design involves simultaneously optimizing multiple probe properties. While several desired characteristics are straightforward, including high affinity and low non-specific background signal, in practice there are quantitative trade-offs between these properties. These include plasma clearance, where fast clearance lowers background signal but can reduce target uptake, and binding, where high affinity compounds sometimes suffer from lower stability or increased non-specific interactions. Further complicating probe development, many of the optimal parameters vary depending on both target tissue and imaging agent properties, making empirical approaches or previous experience difficult to translate. Here, we focus on low molecular weight compounds targeting extracellular receptors, which have some of the highest contrast values for imaging agents. We use a mechanistic approach to provide a quantitative framework for weighing trade-offs between molecules. Our results show that specific target uptake is well-described by quantitative simulations for a variety of targeting agents, whereas non-specific background signal is more difficult to predict. Two in vitro experimental methods for estimating background signal in vivo are compared – non-specific cellular uptake and plasma protein binding. Together, these data provide a quantitative method to guide probe design and focus animal work for more cost-effective and time-efficient development of molecular imaging agents.

Antibody-targeted biodegradable nanoparticles for cancer therapy

The use of nanotechnology has great potentials to revolutionize the future cancer diagnosis and therapy. In this context, various nanoparticles (NPs) have been developed for targeted delivery of diagnostic/therapeutic agents to the tumor sites, which thus result in greater efficacy and much less side effects. The targeting property of NPs is often achieved by functionalizing their surface with tumor-specific ligands, such as antibodies, peptides, small molecules and oligonucleotides. In this review, we will discuss recent progress in the multifunctional design of antibody-targeted NPs with a special focus on liposomal, polymeric and protein-based delivery systems.

Diblock- and triblock-copolymer based mixed micelles with high tumor penetration in vitro and in vivo

A series of self-assembled mixed micelles composed of TPGS and Pluronics were fabricated and their cellular uptake and exocytosis behaviors were studied in 2D cell and 3D tumor spheroid models.

Nanoparticle Mediated Tumor Vascular Disruption: A Novel Strategy in Radiation Therapy

More than 50% of all cancer patients receive radiation therapy. The clinical delivery of curative radiation dose is strictly restricted by the proximal healthy tissues. We propose a dual-targeting strategy using vessel-targeted-radiosensitizing gold nanoparticles and conformal-image guided radiation therapy to specifically amplify damage in the tumor neoendothelium. The resulting tumor vascular disruption substantially improved the therapeutic outcome and subsidized the radiation/nanoparticle toxicity, extending its utility to intransigent or nonresectable tumors that barely respond to standard therapies.

Mathematical framework for activity-based cancer biomarkers

Advances in nanomedicine are providing sophisticated functions to precisely control the behavior of nanoscale drugs and diagnostics. Strategies that coopt protease activity as molecular triggers are increasingly important in nanoparticle design, yet the pharmacokinetics of these systems are challenging to understand without a quantitative framework to reveal nonintuitive associations. We describe a multicompartment mathematical model to predict strategies for ultrasensitive detection of cancer using synthetic biomarkers, a class of activity-based probes that amplify cancer-derived signals into urine as a noninvasive diagnostic. Using a model formulation made of a PEG core conjugated with protease-cleavable peptides, we explore a vast design space and identify guidelines for increasing sensitivity that depend on critical parameters such as enzyme kinetics, dosage, and probe stability. According to this model, synthetic biomarkers that circulate in stealth but then activate at sites of disease have the theoretical capacity to discriminate tumors as small as 5 mm in diameter-a threshold sensitivity that is otherwise challenging for medical imaging and blood biomarkers to achieve. This model may be adapted to describe the behavior of additional activity-based approaches to allow cross-platform comparisons, and to predict allometric scaling across species.

Fluorescent carbon nanoparticles derived from natural materials of mango fruit for bio-imaging probes

Water soluble fluorescent carbon nanoparticles (FCP) obtained from a single natural source, mango fruit, were developed as unique materials for non-toxic bio-imaging with different colors and particle sizes. The prepared FCPs showed blue (FCP-B), green (FCP-G) and yellow (FCP-Y) fluorescence, derived by the controlled carbonization method. The FCPs demonstrated hydrodynamic diameters of 5-15 nm, holding great promise for clinical applications. The biocompatible FCPs demonstrated great potential in biological fields through the results of in vitro imaging and in vivo biodistribution. Using intravenously administered FCPs with different colored particles, we precisely defined the clearance and biodistribution, showing rapid and efficient urinary excretion for safe elimination from the body. These findings therefore suggest the promising possibility of using natural sources for producing fluorescent materials.

Mechanisms of cooperation in cancer nanomedicine: towards systems nanotechnology

Nanoparticles are designed to deliver therapeutics and diagnostics selectively to tumors. Their size, shape, charge, material, coating, and cargo determine their individual functionalities. A systems approach could help predict the behavior of trillions of nanoparticles interacting in complex tumor environments. Engineering these nanosystems may lead to biomimetic strategies where interactions between nanoparticles and their environment give rise to cooperative behaviors typically seen in natural self-organized systems. Examples include nanoparticles that communicate the location of a tumor to amplify tumor homing or self-assemble and disassemble to optimize nanoparticle transport. The challenge is to discover which nanoparticle designs lead to a desired system behavior. To this end, novel nanomaterials, deep understanding of biology, and computational tools are emerging as the next frontier.

Multichannel imaging to quantify four classes of pharmacokinetic distribution in tumors

Low and heterogeneous delivery of drugs and imaging agents to tumors results in decreased efficacy and poor imaging results. Systemic delivery involves a complex interplay of drug properties and physiological factors, and heterogeneity in the tumor microenvironment makes predicting and overcoming these limitations exceptionally difficult. Theoretical models have indicated that there are four different classes of pharmacokinetic behavior in tissue, depending on the fundamental steps in distribution. In order to study these limiting behaviors, we used multichannel fluorescence microscopy and stitching of high-resolution images to examine the distribution of four agents in the same tumor microenvironment. A validated generic partial differential equation model with a graphical user interface was used to select fluorescent agents exhibiting these four classes of behavior, and the imaging results agreed with predictions. BODIPY-FL exhibited higher concentrations in tissue with high blood flow, cetuximab gave perivascular distribution limited by permeability, high plasma protein and target binding resulted in diffusion-limited distribution for Hoechst 33342, and Integrisense 680 was limited by the number of binding sites in the tissue. Together, the probes and simulations can be used to investigate distribution in other tumor models, predict tumor drug distribution profiles, and design and interpret in vivo experiments.