Capturing and Quantifying Particle Transcytosis with Microphysiological Intestine-on-Chip Models.

Understanding the intestinal transport of particles is critical in several fields ranging from optimizing drug delivery systems to capturing health risks from the increased presence of nano- and micro-sized particles in human environment. While Caco-2 cell monolayers grown on permeable supports are the traditional in vitro model used to probe intestinal absorption of dissolved molecules, they fail to recapitulate the transcytotic activity of polarized enterocytes. Here, an intestine-on-chip model is combined with in silico modeling to demonstrate that the rate of particle transcytosis is ≈350× higher across Caco-2 cell monolayers exposed to fluid shear stress compared to Caco-2 cells in standard "static" configuration. This relates to profound phenotypical alterations and highly polarized state of cells grown under mechanical stimulation and it is shown that transcytosis in the microphysiological model is energy-dependent and involves both clathrin and macropinocytosis mediated endocytic pathways. Finally, it is demonstrated that the increased rate of transcytosis through cells exposed to flow is explained by a higher rate of internal particle transport (i.e., vesicular cellular trafficking and basolateral exocytosis), rather than a change in apical uptake (i.e., binding and endocytosis). Taken together, the findings have important implications for addressing research questions concerning intestinal transport of engineered and environmental particles.

On predicting heterogeneity in nanoparticle dosage.

Nanoparticles are increasingly employed as a vehicle for the targeted delivery of therapeutics to specific cell types. However, much remains to be discovered about the fundamental biology that dictates the interactions between nanoparticles and cells. Accordingly, few nanoparticle-based targeted therapeutics have succeeded in clinical trials. One element that hinders our understanding of nanoparticle-cell interactions is the presence of heterogeneity in nanoparticle dosage data obtained from standard experiments. It is difficult to distinguish between heterogeneity that arises from stochasticity in nanoparticle-cell interactions, and that which arises from heterogeneity in the cell population. Mathematical investigations have revealed that both sources of heterogeneity contribute meaningfully to the heterogeneity in nanoparticle dosage. However, these investigations have relied on simplified models of nanoparticle internalisation. Here we present a stochastic mathematical model of nanoparticle internalisation that incorporates a suite of relevant biological phenomena such as multistage internalisation, cell division, asymmetric nanoparticle inheritance and nanoparticle saturation. Critically, our model provides information about nanoparticle dosage at an individual cell level. We perform model simulations to examine the influence of specific biological phenomena on the heterogeneity in nanoparticle dosage in the absence of heterogeneity in the cell population. Under certain modelling assumptions, we derive analytic approximations of the nanoparticle dosage distribution. We demonstrate that the analytic approximations are accurate, and show that nanoparticle dosage can be described by a Poisson mixture distribution with rate parameters that are a function of Beta-distributed random variables. We discuss the implications of the analytic results with respect to parameter estimation and model identifiability from standard experimental data. Finally, we highlight extensions and directions for future research.

Experimental Quantification of Interactions Between Drug Delivery Systems and Cells In Vitro: A Guide for Preclinical Nanomedicine Evaluation.

A major component of designing drug delivery systems concerns how to amplify or attenuate interactions with specific cell types. For instance, a chemotherapeutic might be functionalized with an antibody to enhance binding to cancer cells ("targeting") or functionalized with polyethylene glycol to help evade immune cell recognition ("stealth"). Even at a cellular level, optimizing the binding and uptake of a drug carrier is a complex biological design problem. Thus, it is valuable to separate how strongly a new carrier interacts with a cell from the functional efficacy of a carrier's cargo once delivered to that cell. To continue the chemotherapeutic example, "how well it binds to a cancer cell" is a separate problem from "how well it kills a cancer cell". Quantitative in vitro assays for the latter are well established and usually rely on measuring viability. However, most published research on cell-carrier interactions is qualitative or semiquantitative. Generally, these measurements rely on fluorescent labeling of the carrier and, consequently, report interactions with cells in relative or arbitrary units. However, this work can be standardized and be made absolutely quantitative with a small number of characterization experiments. Such absolute quantification is valuable, as it facilitates rational, inter- and intra-class comparisons of various drug delivery systems-nanoparticles, microparticles, viruses, antibody-drug conjugates, engineered therapeutic cells, or extracellular vesicles. Furthermore, quantification is a prerequisite for subsequent meta-analyses or in silico modeling approaches. In this article, video guides, as well as a decision tree for how to achieve in vitro quantification for carrier drug delivery systems, are presented, which take into account differences in carrier size and labeling modality. Additionally, further considerations for the quantitative assessment of advanced drug delivery systems are discussed. This is intended to serve as a valuable resource to improve rational evaluation and design for the next generation of medicine.

Equation learning to identify nano-engineered particle-cell interactions: an interpretable machine learning approach.

Designing nano-engineered particles capable of the delivery of therapeutic and diagnostic agents to a specific target remains a significant challenge. Understanding how interactions between particles and cells are impacted by the physicochemical properties of the particle will help inform rational design choices. Mathematical and computational techniques allow for details regarding particle-cell interactions to be isolated from the interwoven set of biological, chemical, and physical phenomena involved in the particle delivery process. Here we present a machine learning framework capable of elucidating particle-cell interactions from experimental data. This framework employs a data-driven modelling approach, augmented by established biological knowledge. Crucially, the model of particle-cell interactions learned by the framework can be interpreted and analysed, in contrast to the 'black box' models inherent to other machine learning approaches. We apply the framework to association data for thirty different particle-cell pairs. This library of data contains both adherent and suspension cell lines, as well as a diverse collection of particles. We consider hyperbranched polymer and poly(methacrylic acid) particles, from 6 nm to 1032 nm in diameter, with small molecule, monoclonal antibody, and peptide surface functionalisations. Despite the diverse nature of the experiments, the learned models of particle-cell interactions for each particle-cell pair are remarkably consistent: out of 2048 potential models, only four unique models are learned. The models reveal that nonlinear saturation effects are a key feature governing particle-cell interactions. Further, the framework provides robust estimates of particle performance, which facilitates quantitative evaluation of particle design choices.

Protein precoating modulates biomolecular coronas and nanocapsule-immune cell interactions in human blood.

The biomolecular corona that forms on particles upon contact with blood plays a key role in the fate and utility of nanomedicines. Recent studies have shown that precoating nanoparticles with serum proteins can improve the biocompatibility and stealth properties of nanoparticles. However, it is not fully clear how precoating influences biomolecular corona formation and downstream biological responses. Herein, we systematically examine three precoating strategies by coating bovine serum albumin (single protein), fetal bovine serum (FBS, mixed proteins without immunoglobulins), or bovine serum (mixed proteins) on three nanoparticle systems, namely supramolecular template nanoparticles, metal-phenolic network (MPN)-coated template (core-shell) nanoparticles, and MPN nanocapsules (obtained after template removal). The effect of protein precoating on biomolecular corona compositions and particle-immune cell interactions in human blood was characterized. In the absence of a pre-coating, the MPN nanocapsules displayed lower leukocyte association, which correlated to the lower amount (by 2-3 fold) of adsorbed proteins and substantially fewer immunoglobulins (more than 100 times) in the biomolecular corona relative to the template and core-shell nanoparticles. Among the three coating strategies, FBS precoating demonstrated the most significant reduction in leukocyte association (up to 97% of all three nanoparticles). A correlation analysis highlights that immunoglobulins and apolipoproteins may regulate leukocyte recognition. This study demonstrates the impact of different precoating strategies on nanoparticle-immune cell association and the role of immunoglobulins in bio-nano interactions.

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.

Isolating the sources of heterogeneity in nano-engineered particle-cell interactions.

Nano-engineered particles have the potential to enhance therapeutic success and reduce toxicity-based treatment side effects via the targeted delivery of drugs to cells. This delivery relies on complex interactions between numerous biological, chemical and physical processes. The intertwined nature of these processes has thus far hindered attempts to understand their individual impact. Variation in experimental data, such as the number of particles inside each cell, further inhibits understanding. Here, we present a mathematical framework that is capable of examining the impact of individual processes during particle delivery. We demonstrate that variation in experimental particle uptake data can be explained by three factors: random particle motion; variation in particle-cell interactions; and variation in the maximum particle uptake per cell. Without all three factors, the experimental data cannot be explained. This work provides insight into biological mechanisms that cause heterogeneous responses to treatment, and enables precise identification of treatment-resistant cell subpopulations.

Surface Modification of Spider Silk Particles to Direct Biomolecular Corona Formation.

In recent years, spider silk-based materials have attracted attention because of their biocompatibility, processability, and biodegradability. For their potential use in biomaterial applications, i.e., as drug delivery systems and implant coatings for tissue regeneration, it is vital to understand the interactions between the silk biomaterial surface and the biological environment. Like most polymeric carrier systems, spider silk material surfaces can adsorb proteins when in contact with blood, resulting in the formation of a biomolecular corona. Here, we assessed the effect of surface net charge of materials made of recombinant spider silk on the biomolecular corona composition. In-depth proteomic analysis of the biomolecular corona revealed that positively charged spider silk materials surfaces interacted predominantly with fibrinogen-based proteins. This fibrinogen enrichment correlated with blood clotting observed for both positively charged spider silk films and particles. In contrast, negative surface charges prevented blood clotting. Genetic engineering allows the fine-tuning of surface properties of the spider silk particles providing a whole set of recombinant spider silk proteins with different charges or peptide tags to be used for, for example, drug delivery or cell docking, and several of these were analyzed concerning the composition of their biomolecular corona. Taken together this study demonstrates how the surface net charge of recombinant spider silk surfaces affects the composition of the biomolecular corona, which in turn affects macroscopic effects such as fibrin formation and blood clotting.

Cellular Targeting of Bispecific Antibody-Functionalized Poly(ethylene glycol) Capsules: Do Shape and Size Matter?

In the present study, a capsule system that consists of a stealth carrier based on poly(ethylene glycol) (PEG) and functionalized with bispecific antibodies (BsAbs) is introduced to examine the influence of the capsule shape and size on cellular targeting. Hollow spherical and rod-shaped PEG capsules with tunable aspect ratios (ARs) of 1, 7, and 18 were synthesized and subsequently functionalized with BsAbs that exhibit dual specificities to PEG and epidermal growth factor receptor (EGFR). Dosimetry (variation between the concentrations of capsules present and capsules that reach the cell surface) was controlled through "dynamic" incubation (i.e., continuously mixing the incubation medium). The results obtained were compared with those obtained from the "static" incubation experiments. Regardless of the incubation method and the capsule shape and size studied, BsAb-functionalized PEG capsules showed >90% specific cellular association to EGFR-positive human breast cancer cells MDA-MB-468 and negligible association with both control cell lines (EGFR negative Chinese hamster ovary cells CHO-K1 and murine macrophages RAW 264.7) after incubation for 5 h. When dosimetry was controlled and the dose concentration was normalized to the capsule surface area, the size or shape had a minimal influence on the cell association behavior of the capsules. However, different cellular internalization behaviors were observed, and the capsules with ARs 7 and 18 were, respectively, the least and most optimal shape for achieving high cell internalization under both dynamic and static conditions. Dynamic incubation showed a greater impact on the internalization of rod-shaped capsules (∼58-67% change) than on the spherical capsules (∼24-29% change). The BsAb-functionalized PEG capsules reported provide a versatile particle platform for the evaluation and comparison of cellular targeting performance of capsules with different sizes and shapes in vitro.

Revisiting cell-particle association in vitro: A quantitative method to compare particle performance.

Nanoengineering has the potential to revolutionize medicine by designing drug delivery systems that are both efficacious and highly selective. Determination of the affinity between cell lines and nanoparticles is thus of central importance, both to enable comparison of particles and to facilitate prediction of in vivo response. Attempts to compare particle performance can be dominated by experimental artifacts (including settling effects) or variability in experimental protocol. Instead, qualitative methods are generally used, limiting the reusability of many studies. Herein, we introduce a mathematical model-based approach to quantify the affinity between a cell-particle pairing, independent of the aforementioned confounding artifacts. The analysis presented can serve as a quantitative metric of the stealth, fouling, and targeting performance of nanoengineered particles in vitro. We validate this approach using a newly created in vitro dataset, consisting of seven different disulfide-stabilized poly(methacrylic acid) particles ranging from ~100 to 1000 nm in diameter that were incubated with three different cell lines (HeLa, THP-1, and RAW 264.7). We further expanded this dataset through the inclusion of previously published data and use it to determine which of five mathematical models best describe cell-particle association. We subsequently use this model to perform a quantitative comparison of cell-particle association for cell-particle pairings in our dataset. This analysis reveals a more complex cell-particle association relationship than a simplistic interpretation of the data, which erroneously assigns high affinity for all cell lines examined to large particles. Finally, we provide an online tool (http://bionano.xyz/estimator), which allows other researchers to easily apply this modeling approach to their experimental results.

Link between Low-Fouling and Stealth: A Whole Blood Biomolecular Corona and Cellular Association Analysis on Nanoengineered Particles.

Upon exposure to human blood, nanoengineered particles interact with a multitude of plasma components, resulting in the formation of a biomolecular corona. This corona modulates downstream biological responses, including recognition by and association with human immune cells. Considerable research effort has been directed toward the design of materials that can demonstrate a low affinity for various proteins (low-fouling materials) and materials that can exhibit low association with human immune cells (stealth materials). An implicit assumption common to bio-nano research is that nanoengineered particles that are low-fouling will also exhibit stealth. Herein, we investigated the link between the low-fouling properties of a particle and its propensity for stealth in whole human blood. High-fouling mesoporous silica (MS) particles and low-fouling zwitterionic poly(2-methacryloyloxyethyl phosphorylcholine) (PMPC) particles were synthesized, and their interaction with blood components was assessed before and after precoating with serum albumin, immunoglobulin G, or complement protein C1q. We performed an in-depth proteomics characterization of the biomolecular corona that both identifies specific proteins and measures their relative abundance. This was compared with observations from a whole blood association assay that identified with which cell type each particle system associates. PMPC-based particles displayed reduced association both with cells and with serum proteins compared with MS-based particles. Furthermore, the enrichment of specific proteins within the biomolecular corona was found to correlate with association with specific cell types. This study demonstrates how the low-fouling properties of a material are indicative of its stealth with respect to immune cell association.

Minimum information reporting in bio-nano experimental literature.

Studying the interactions between nanoengineered materials and biological systems plays a vital role in the development of biological applications of nanotechnology and the improvement of our fundamental understanding of the bio-nano interface. A significant barrier to progress in this multidisciplinary area is the variability of published literature with regards to characterizations performed and experimental details reported. Here, we suggest a 'minimum information standard' for experimental literature investigating bio-nano interactions. This standard consists of specific components to be reported, divided into three categories: material characterization, biological characterization and details of experimental protocols. Our intention is for these proposed standards to improve reproducibility, increase quantitative comparisons of bio-nano materials, and facilitate meta analyses and in silico modelling.

An analytical approach for quantifying the influence of nanoparticle polydispersity on cellular delivered dose.

Nanoparticles provide a promising approach for the targeted delivery of therapeutic, diagnostic and imaging agents in the body. However, it is not yet fully understood how the physico-chemical properties of the nanoparticles influence cellular association and uptake. Cellular association experiments are routinely performed in an effort to determine how nanoparticle properties impact the rate of nanoparticle-cell association. To compare experiments in a meaningful manner, the association data must be normalized by the amount of nanoparticles that arrive at the cells, a measure referred to as the delivered dose. The delivered dose is calculated from a model of nanoparticle transport through fluid. A standard assumption is that all nanoparticles within the population are monodisperse, namely the nanoparticles have the same physico-chemical properties. We present a semi-analytic solution to a modified model of nanoparticle transport that allows for the nanoparticle population to be polydisperse. This solution allows us to efficiently analyse the influence of polydispersity on the delivered dose. Combining characterization data obtained from a range of commonly used nanoparticles and our model, we find that the delivered dose changes by more than a factor of 2 if realistic amounts of polydispersity are considered.

Rust-Mediated Continuous Assembly of Metal-Phenolic Networks.

The use of natural compounds for preparing hybrid molecular films-such as surface coatings made from metal-phenolic networks (MPNs)-is of interest in areas ranging from catalysis and separations to biomedicine. However, to date, the film growth of MPNs has been observed to proceed in discrete steps (≈10 nm per step) where the coordination-driven interfacial assembly ceases beyond a finite time (≈1 min). Here, it is demonstrated that the assembly process for MPNs can be modulated from discrete to continuous by utilizing solid-state reactants (i.e., rusted iron objects). Gallic acid etches iron from rust and produces chelate complexes in solution that continuously assemble at the interface of solid substrates dispersed in the system. The result is stable, continuous growth of MPN films. The presented double dynamic process-that is, etching and self-assembly-provides new insights into the chemistry of MPN assembly while enabling control over the MPN film thickness by simply varying the reaction time.

Metal-Phenolic Supramolecular Gelation.

Materials assembled by coordination interactions between naturally abundant polyphenols and metals are of interest for a wide range of applications, including crystallization, catalysis, and drug delivery. Such an interest has led to the development of thin films with tunable, dynamic properties, however, creating bulk materials remains a challenge. Reported here is a class of metallogels formed by direct gelation between inexpensive, naturally abundant tannic acid and group(IV) metal ions. The metallogels exhibit diverse properties, including self-healing and transparency, and can be doped with various materials by in situ co-gelation. The robustness and flexibility, combined with the ease, low cost, and scalability of the coordination-driven assembly process make these metallogels potential candidates for chemical, biomedical, and environmental applications.

Dynamic Flow Impacts Cell-Particle Interactions: Sedimentation and Particle Shape Effects.

The interaction of engineered particles with biological systems determines their performance in biomedical applications. Although standard static cell cultures remain the norm for in vitro studies, modern models mimicking aspects of the dynamic in vivo environment have been developed. Herein, we investigate fundamental cell-particle interactions under dynamic flow conditions using a simple and self-contained device together with standard multiwell cell culture plates. We engineer two particle systems and evaluate their cell interactions under dynamic flow, and we compare the results to standard static cell cultures. We find substantial differences between static and dynamic flow conditions and attribute these to particle shape and sedimentation effects. These results demonstrate how standard static assays can be complemented by dynamic flow assays for a more comprehensive understanding of fundamental cell-particle interactions.

Increasing the Impact of Materials in and beyond Bio-Nano Science.

This is an exciting time for the field of bio-nano science: enormous progress has been made in recent years, especially in academic research, and materials developed and studied in this area are poised to make a substantial impact in real-world applications. Herein, we discuss ways to leverage the strengths of the field, current limitations, and valuable lessons learned from neighboring fields that can be adopted to accelerate scientific discovery and translational research in bio-nano science. We identify and discuss five interconnected topics: (i) the advantages of cumulative research; (ii) the necessity of aligning projects with research priorities; (iii) the value of transparent science; (iv) the opportunities presented by "dark data"; and (v) the importance of establishing bio-nano standards.

A Framework to Account for Sedimentation and Diffusion in Particle-Cell Interactions.

In vitro experiments provide a solid basis for understanding the interactions between particles and biological systems. An important confounding variable for these studies is the difference between the amount of particles administered and that which reaches the surface of cells. Here, we engineer a hydrogel-based nanoparticle system and combine in situ characterization techniques, 3D-printed cell cultures, and computational modeling to evaluate and study particle-cell interactions of advanced particle systems. The framework presented demonstrates how sedimentation and diffusion can explain differences in particle-cell association, and provides a means to account for these effects. Finally, using in silico modeling, we predict the proportion of particles that reaches the cell surface using common experimental conditions for a wide range of inorganic and organic micro- and nanoparticles. This work can assist in the understanding and control of sedimentation and diffusion when investigating cellular interactions of engineered particles.

Nanoengineered Templated Polymer Particles: Navigating the Biological Realm.

Nanoengineered materials offer tremendous promise for developing the next generation of therapeutics. We are transitioning from simple research questions, such as "can this particle eradicate cancer cells?" to more sophisticated ones like "can we design a particle to preferentially deliver cargo to a specific cancer cell type?" These developments are poised to usher in a new era of nanoengineered drug delivery systems. We primarily work with templating methods for engineering polymer particles and investigate their biological interactions. Templates are scaffolds that facilitate the formation of particles with well-controlled size, shape, structure, stiffness, stability, and surface chemistry. In the past decade, breakthroughs in engineering new templates, combined with advances in coating techniques, including layer-by-layer (LbL) assembly, surface polymerization, and metal-phenolic network (MPN) coordination chemistry, have enabled particles with specific physicochemical properties to be engineered. While materials science offers an ever-growing number of new synthesis techniques, a central challenge of therapeutic delivery has become understanding how nanoengineered materials interact with biological systems. Increased collaboration between chemists, biologists, and clinicians has resulted in a vast research output on bio-nano interactions. Our understanding of cell-particle interactions has grown considerably, but conventional in vitro experimentation provides limited information, and understanding how to bridge the in vitro/in vivo gap is a continuing challenge. As has been demonstrated in other fields, there is now a growing interest in applying computational approaches to advance this area. A considerable knowledge base is now emerging, and with it comes new and exciting opportunities that are already being capitalized on through the translation of materials into the clinic. In this Account, we outline our perspectives gained from a decade of work at the interface between polymer particle engineering and bio-nano interactions. We divide our research into three areas: (i) biotrafficking, including cellular association, intracellular transport, and biodistribution; (ii) biodegradation and how to achieve controlled, responsive release of therapeutics; and (iii) applications, including drug delivery, controlling immunostimulatory responses, biosensing, and microreactors. There are common challenges in these areas for groups developing nanoengineered therapeutics. A key "lesson-learned" has been the considerable challenge of staying informed about the developments relevant to this field. There are a number of reasons for this, most notably the interdisciplinary nature of the work, the large numbers of researchers and research outputs, and the limited standardization in technique nomenclature. Additionally, a large body of work is being generated with limited central archiving, other than vast general databases. To help address these points, we have created a web-based tool to organize our past, present, and future work [Bio-nano research knowledgebase, http://bionano.eng.unimelb.edu.au/knowledge_base/ (accessed May 2, 2016)]. This tool is intended to serve as a first step toward organizing results in this large, complex area. We hope that this will inspire researchers, both in generating new ideas and also in collecting, collating, and sharing their experiences to guide future research.