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

The Influence of Liquid Menisci on Nanoparticle Dosimetry in Submerged Cells

Understanding the interaction between cells and nanoparticles (NPs) is vital to understand the hazard associated with nanoparticles. This requires quantifying and interpreting dose-response relationships. Experiments with cells cultured in vitro and exposed to particle dispersions mainly rely on mathematical models that estimate the received nanoparticle dose. However, models need to consider that aqueous cell culture media wets the inner surface of hydrophilic open wells, which results in a curved liquid-air interface called the meniscus. Here the impact of the meniscus on nanoparticle dosimetry is addressed in detail. Experiments and build an advanced mathematical model, to demonstrate that the presence of the meniscus may bring about systematic errors that must be considered to advance reproducibility and harmonization is presented. The script of the model is co-published and can be adapted to any experimental setup. Finally, simple and practical solutions to this problem, such as covering the air-liquid interface with a permeable lid or soft rocking of the cell culture well plate is proposed.

Effect of nanoparticles on gouty arthritis: a systematic review and meta-analysis

OBJECTIVE

The purpose of this study was to explore the effects of nanoparticles on gouty arthritis, and to provide evidence for the preclinical application of nanoparticles in gouty arthritis and ideas for nanomedicine improvement for nanoparticle researchers.

METHODS

Five databases including the Cochrane Library, PubMed, Scopus, Web of Science, and Embase were searched for eligible studies until April 2022. The quality of the selected studies was assessed by SYRCLE's risk of bias (RoB) tool, and the random-effects model was used to calculate the overall effect sizes of weighted mean differences (WMD).

RESULTS

Ten studies met the inclusion criteria. Results showed that nanoparticles were effective in reducing uric acid levels (WMD: -4.91; 95% confidence interval (CI): - 5.41 to - 4.41; p < 0.001), but were not better than allopurinol (WMD: -0.20; 95% CI: - 0.42 to 0.02; p = 0.099). It was worth noting that the nanoparticles were safer than allopurinol. Subgroup analyses indicated that nanoparticle encapsulated substance, animal species, nanoparticle dosage, animal quantity, and animal gender were all sources of heterogeneity.

CONCLUSION

The nanoparticles are safe medications for gouty arthritis which can effectively reduce uric acid levels in rodents. Although the results are still uncertain, it is expected to have certain clinical application value. The nanoparticles may be the preclinical medications for gouty arthritis in the future.

Physiological relevance of in-vitro cell-nanoparticle interaction studies as a predictive tool in cancer nanomedicine research

Cell uptake study is a routine experiment used as a surrogate to predict in vivo response in cancer nanomedicine research. Cell culture conditions should be designed in such a way that it emulates 'real' physiological conditions and avoid artefacts. It is critical to dissect the steps involved in cellular uptake to understand the physical, chemical, and biological factors responsible for particle internalization. The two-dimensional model (2D) of cell culture is overly simplistic to mimic the complexity of cancer tissues that exist in vivo. It cannot simulate the critical tissue-specific properties like cell-cell interaction and cell-extracellular matrix (ECM) interaction and its influences on the temporal and spatial distribution of nanoparticles (NPs). The three dimensional model organization of heterogenous cancer and normal cells with the ECM acts as a formidable barrier to NP penetration and cellular uptake. The three dimensional cell culture (3D) technology is a breakthrough in this direction that can mimic the barrier properties of the tumor microenvironment (TME). Herein, we discuss the physiological factors that should be considered to bridge the translational gap between in and vitro cell culture studies and in-vivo studies in cancer nanomedicine.

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.

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.

A Focus on "Bio" in Bio-Nanoscience: The Impact of Biological Factors on Nanomaterial Interactions

Bio-nanoscience research encompasses studies on the interactions of nanomaterials with biological structures or what is commonly referred to as the biointerface. Fundamental studies on the influence of nanomaterial properties, including size, shape, composition, and charge, on the interaction with the biointerface have been central in bio-nanoscience to assess nanomaterial efficacy and safety for a range of biomedical applications. However, the state of the cells, tissues, or biological models can also influence the behavior of nanomaterials at the biointerface and their intracellular processing. Focusing on the "bio" in bio-nano, this review discusses the impact of biological properties at the cellular, tissue, and whole organism level that influences nanomaterial behavior, including cell type, cell cycle, tumor physiology, and disease states. Understanding how the biological factors can be addressed or exploited to enhance nanomaterial accumulation and uptake can guide the design of better and suitable models to improve the outcomes of materials in nanomedicine.

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.

A quantitative view on multivalent nanomedicine targeting

Although the concept of selective delivery has been postulated over 100 years ago, no targeted nanomedicine has been clinically approved so far. Nanoparticles modified with targeting ligands to promote the selective delivery of therapeutics towards a specific cell population have been extensively reported. However, the rational design of selective particles is still challenging. One of the main reasons for this is the lack of quantitative theoretical and experimental understanding of the interactions involved in cell targeting. In this review, we discuss new theoretical models and experimental methods that provide a quantitative view of targeting. We show the new advancements in multivalency theory enabling the rational design of super-selective nanoparticles. Furthermore, we present the innovative approaches to obtain key targeting parameters at the single-cell and single molecule level and their role in the design of targeting nanoparticles. We believe that the combination of new theoretical multivalent design and experimental methods to quantify receptors and ligands aids in the rational design and clinical translation of targeted nanomedicines.

Person-Specific Biomolecular Coronas Modulate Nanoparticle Interactions with Immune Cells in Human Blood

When nanoparticles interact with human blood, a multitude of plasma components adsorb onto the surface of the nanoparticles, forming a biomolecular corona. Corona composition is known to be influenced by the chemical composition of nanoparticles. In contrast, the possible effects of variations in the human blood proteome between healthy individuals on the formation of the corona and its subsequent interactions with immune cells in blood are unknown. Herein, we prepared and examined a matrix of 11 particles (including organic and inorganic particles of three sizes and five surface chemistries) and plasma samples from 23 healthy donors to form donor-specific biomolecular coronas (personalized coronas) and investigated the impact of the personalized coronas on particle interactions with immune cells in human blood. Among the particles examined, poly(ethylene glycol) (PEG)-coated mesoporous silica (MS) particles, irrespective of particle size (800, 450, or 100 nm in diameter), displayed the widest range (up to 60-fold difference) of donor-dependent variance in immune cell association. In contrast, PEG particles (after MS core removal) of 860, 518, or 133 nm in diameter displayed consistent stealth behavior (negligible cell association), irrespective of plasma donor. For comparison, clinically relevant PEGylated doxorubicin-encapsulated liposomes (Doxil) (74 nm in diameter) showed significant variance in association with monocytes and B cells across all plasma donors studied. An in-depth proteomic analysis of each biomolecular corona studied was performed, and the results were compared against the nanoparticle-blood cell association results, with individual variance in the proteome driving differential association with specific immune cell types. We identified key immunoglobulin and complement proteins that explicitly enriched or depleted within the corona and which strongly correlated with the cell association pattern observed across the 23 donors. This study demonstrates how plasma variance in healthy individuals significantly influences the blood immune cell interactions of nanoparticles.

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.