Power in Numbers: Harnessing Combinatorial and Integrated Screens to Advance Nanomedicine

Multivariate Analysis of Cellular Uptake Characteristics for a (Co)polymer Particle Library

Controlling cellular responses to nanoparticles so far is predominantly empirical, typically requiring multiple rounds of optimization of particulate carriers. In this study, a systematic model-assisted approach should lead to the identification of key parameters that account for particle properties and their cellular recognition. A copolymer particle library was synthesized by a combinatorial approach in soap free emulsion copolymerization of styrene and methyl methacrylate, leading to a broad compositional as well as constitutional spectrum. The proposed structure-property relationships could be elucidated by multivariate analysis of the obtained experimental data, including physicochemical characteristics such as molar composition, molecular weight, particle diameter, and particle charge as well as the cellular uptake pattern of nanoparticles. It was found that the main contributors for particle size were the polymers' molecular weight and the zeta potential, while particle uptake is mainly directed by the particles' composition. This knowledge and the reported model-assisted procedure to identify relevant parameters affecting particle engulfment of particulate carriers by nonphagocytic and phagocytic cells can be of high relevance for the rational design of pharmaceutical nanocarriers and assessment of biodistribution and nanotoxicity, respectively.

A machine learning approach to predict cellular uptake of pBAE polyplexes

The delivery of genetic material (DNA and RNA) to cells can cure a wide range of diseases but is limited by the delivery efficiency of the carrier system. Poly β-amino esters (pBAEs) are promising polymer-based vectors that form polyplexes with negatively charged oligonucleotides, enabling cell membrane uptake and gene delivery. pBAE backbone polymer chemistry, as well as terminal oligopeptide modifications, define cellular uptake and transfection efficiency in a given cell line, along with nanoparticle size and polydispersity. Moreover, uptake and transfection efficiency of a given polyplex formulation also vary from cell type to cell type. Therefore, finding the optimal formulation leading to high uptake in a new cell line is dictated by trial and error, and requires time and resources. Machine learning (ML) is an ideal in silico screening tool to learn the non-linearities of complex data sets, like the one presented herein, with the aim of predicting cellular internalisation of pBAE polyplexes. A library of pBAE nanoparticles was fabricated and the uptake studied in 4 different cell lines, on which various ML models were successfully trained. The best performing models were found to be gradient-boosted trees and neural networks. The gradient-boosted trees model was then analysed using SHapley Additive exPlanations, to interpret the model and gain an understanding into the important features and their impact on the predicted outcome.

Surface Presentation of Hyaluronic Acid Modulates Nanoparticle-Cell Association

Nanoparticle (NP) drug carriers have revolutionized medicine and increased patient quality of life. Clinically approved formulations typically succeed because of reduced off-target toxicity of the cargo. However, increasing carrier accumulation at disease sites through precise targeting remains one of the biggest challenges in the field. Novel multivalent ligand presentations and self-assembled constructs can enhance cell association, but an inability to draw direct comparisons across formulations has hindered progress. Furthermore, how nanoparticle structure influences function often is unclear. In this report, we leverage the well-characterized hyaluronic acid (HA)-CD44 binding pair to investigate how the surface architecture of modified NPs impacts their association with ovarian cancer cells that overexpress CD44. We functionalized anionic liposomes with 5 kDa HA by either covalent conjugation via surface coupling or electrostatic self-assembly using the layer-by-layer (LbL) adsorption method. Comparing these two methods, we observed a consistent enhancement of NP-cell association with the self-assembly LbL technique, particularly with higher molecular weight (≥10 kDa) HA. To further optimize association, we increased the surface-available HA. We synthesized a bottlebrush glycopolymer composed of a polynorbornene backbone and pendant 5 kDa HA and layered this macromolecule onto NPs. Flow cytometry revealed that the LbL HA bottlebrush NP outperformed the LbL linear display of HA. Cellular visualization by deconvolution optical microscopy corroborated results from all three constructs. Using exogenous HA to block NP-CD44 interactions, we found the LbL HA bottlebrush NP had a 4-fold higher binding avidity than the best-performing LbL linear HA NP. We further observed that decreasing the density of HA bottlebrush side chains to 75% had minimal impact on LbL NP stability or cell association, though we did see a reduction in binding avidity with this side-chain-modified NP. Our studies indicate that LbL surfaces are highly effective for multivalent displays, and the mode in which they present a targeting ligand can be optimized for NP cell targeting.

Massively parallel pooled screening reveals genomic determinants of nanoparticle delivery

To accelerate the translation of cancer nanomedicine, we used an integrated genomic approach to improve our understanding of the cellular processes that govern nanoparticle trafficking. We developed a massively parallel screen that leverages barcoded, pooled cancer cell lines annotated with multiomic data to investigate cell association patterns across a nanoparticle library spanning a range of formulations with clinical potential. We identified both materials properties and cell-intrinsic features that mediate nanoparticle-cell association. Using machine learning algorithms, we constructed genomic nanoparticle trafficking networks and identified nanoparticle-specific biomarkers. We validated one such biomarker: gene expression of SLC46A3, which inversely predicts lipid-based nanoparticle uptake in vitro and in vivo. Our work establishes the power of integrated screens for nanoparticle delivery and enables the identification and utilization of biomarkers to rationally design nanoformulations.

Materiomically Designed Polymeric Vehicles for Nucleic Acids: Quo Vadis?

Despite rapid advances in molecular biology, particularly in site-specific genome editing technologies, such as CRISPR/Cas9 and base editing, financial and logistical challenges hinder a broad population from accessing and benefiting from gene therapy. To improve the affordability and scalability of gene therapy, we need to deploy chemically defined, economical, and scalable materials, such as synthetic polymers. For polymers to deliver nucleic acids efficaciously to targeted cells, they must optimally combine design attributes, such as architecture, length, composition, spatial distribution of monomers, basicity, hydrophilic-hydrophobic phase balance, or protonation degree. Designing polymeric vectors for specific nucleic acid payloads is a multivariate optimization problem wherein even minuscule deviations from the optimum are poorly tolerated. To explore the multivariate polymer design space rapidly, efficiently, and fruitfully, we must integrate parallelized polymer synthesis, high-throughput biological screening, and statistical modeling. Although materiomics approaches promise to streamline polymeric vector development, several methodological ambiguities must be resolved. For instance, establishing a flexible polymer ontology that accommodates recent synthetic advances, enforcing uniform polymer characterization and data reporting standards, and implementing multiplexed in vitro and in vivo screening studies require considerable planning, coordination, and effort. This contribution will acquaint readers with the challenges associated with materiomics approaches to polymeric gene delivery and offers guidelines for overcoming these challenges. Here, we summarize recent developments in combinatorial polymer synthesis, high-throughput screening of polymeric vectors, omics-based approaches to polymer design, barcoding schemes for pooled in vitro and in vivo screening, and identify materiomics-inspired research directions that will realize the long-unfulfilled clinical potential of polymeric carriers in gene therapy.

Prediction of Pulmonary Function Parameters Based on a Combination Algorithm

Objective: Pulmonary function parameters play a pivotal role in the assessment of respiratory diseases. However, the accuracy of the existing methods for the prediction of pulmonary function parameters is low. This study proposes a combination algorithm to improve the accuracy of pulmonary function parameter prediction. Methods: We first established a system to collect volumetric capnography and then processed the data with a combination algorithm to predict pulmonary function parameters. The algorithm consists of three main parts: a medical feature regression structure consisting of support vector machines (SVM) and extreme gradient boosting (XGBoost) algorithms, a sequence feature regression structure consisting of one-dimensional convolutional neural network (1D-CNN), and an error correction structure using improved K-nearest neighbor (KNN) algorithm. Results: The root mean square error (RMSE) of the pulmonary function parameters predicted by the combination algorithm was less than 0.39L and the R² was found to be greater than 0.85 through a ten-fold cross-validation experiment. Conclusion: Compared with the existing methods for predicting pulmonary function parameters, the present algorithm can achieve a higher accuracy rate. At the same time, this algorithm uses specific processing structures for different features, and the interpretability of the algorithm is ensured while mining the feature depth information.

Nanomaterials in diagnostics, imaging and delivery: Applications from COVID-19 to cancer

ABSTRACT

In the past two decades, the emergence of nanomaterials for biomedical applications has shown tremendous promise for changing the paradigm of all aspects of disease management. Nanomaterials are particularly attractive for being a modularly tunable system; with the ability to add functionality for early diagnostics, drug delivery, therapy, treatment and monitoring of patient response. In this review, a survey of the landscape of different classes of nanomaterials being developed for applications in diagnostics and imaging, as well as for the delivery of prophylactic vaccines and therapeutics such as small molecules and biologic drugs is undertaken; with a particular focus on COVID-19 diagnostics and vaccination. Work involving bio-templated nanomaterials for high-resolution imaging applications for early cancer detection, as well as for optimal cancer treatment efficacy, is discussed. The main challenges which need to be overcome from the standpoint of effective delivery and mitigating toxicity concerns are investigated. Subsequently, a section is included with resources for researchers and practitioners in nanomedicine, to help tailor their designs and formulations from a clinical perspective. Finally, three key areas for researchers to focus on are highlighted; to accelerate the development and clinical translation of these nanomaterials, thereby unleashing the true potential of nanomedicine in healthcare.