A combinatorial library of biodegradable polyesters enables non-viral gene delivery to post-mitotic human stem cell-derived polarized RPE monolayers

Beyond Lipids: Exploring Advances in Polymeric Gene Delivery in the Lipid Nanoparticles Era

The recent success of gene therapy during the COVID-19 pandemic has underscored the importance of effective and safe delivery systems. Complementing lipid-based delivery systems, polymers present a promising alternative for gene delivery. Significant advances have been made in the recent past, with multiple clinical trials progressing beyond phase I and several companies actively working on polymeric delivery systems which provides assurance that polymeric carriers can soon achieve clinical translation. The massive advantage of structural tunability and vast chemical space of polymers is being actively leveraged to mitigate shortcomings of traditional polycationic polymers and improve the translatability of delivery systems. Tailored polymeric approaches for diverse nucleic acids and for specific subcellular targets are now being designed to improve therapeutic efficacy. This review describes the recent advances in polymer design for improved gene delivery by polyplexes and covalent polymer-nucleic acid conjugates. The review also offers a brief note on novel computational techniques for improved polymer design. The review concludes with an overview of the current state of polymeric gene therapies in the clinic as well as future directions on their translation to the clinic.

Arp2/3 mediated dynamic lamellipodia of the hPSC colony edges promote liposome-based DNA delivery

Cationic liposome-mediated delivery of drugs, DNA, or RNA plays a pivotal role in small molecule therapy, gene editing, and immunization. However, our current knowledge regarding the cellular structures that facilitate this process remains limited. Here, we used human pluripotent stem cells (hPSCs), which form compact colonies consisting of dynamically active cells at the periphery and epithelial-like cells at the core. We discovered that cells at the colony edges selectively got transfected by cationic liposomes through actin-related protein 2/3 (Arp2/3) dependent dynamic lamellipodia, which is augmented by myosin II inhibition. Conversely, cells at the core establish tight junctions at their apical surfaces, impeding liposomal access to the basal lamellipodia and thereby inhibiting transfection. In contrast, liposomes incorporating mannosylated lipids are internalized throughout the entire colony via receptor-mediated endocytosis. These findings contribute a novel mechanistic insight into enhancing therapeutic delivery via liposomes, particularly in cell types characterized by dynamic lamellipodia, such as immune cells, or those comprising the epithelial layer.

Biodegradable Polyester Nanoparticle Vaccines Deliver Self-Amplifying mRNA in Mice at Low Doses

Delivery of self-amplifying mRNA (SAM) has high potential for infectious disease vaccination due its self-adjuvating and dose-sparing properties. Yet a challenge is the susceptibility of SAM to degradation and the need for SAM to reach the cytosol fully intact to enable self-amplification. Lipid nanoparticles have been successfully deployed at incredible speed for mRNA vaccination, but aspects such as cold storage, manufacturing, efficiency of delivery, and the therapeutic window would benefit from further improvement. To investigate alternatives to lipid nanoparticles, we developed a class of >200 biodegradable end-capped lipophilic poly(beta-amino ester)s (PBAEs) that enable efficient delivery of SAM in vitro and in vivo as assessed by measuring expression of SAM encoding reporter proteins. We evaluated the ability of these polymers to deliver SAM intramuscularly in mice, and identified a polymer-based formulation that yielded up to 37-fold higher intramuscular (IM) expression of SAM compared to injected naked SAM. Using the same nanoparticle formulation to deliver a SAM encoding rabies virus glycoprotein, the vaccine elicited superior immunogenicity compared to naked SAM delivery, leading to seroconversion in mice at low RNA injection doses. These biodegradable nanomaterials may be useful in the development of next-generation RNA vaccines for infectious diseases.

Machine learning guided structure function predictions enable in silico nanoparticle screening for polymeric gene delivery

Developing highly efficient non-viral gene delivery reagents is still difficult for many hard-to-transfect cell types and, to date, has mostly been conducted via brute force screening routines. High throughput in silico methods of evaluating biomaterials can enable accelerated optimization and development of devices or therapeutics by exploring large chemical design spaces quickly and at low cost. This work reports application of state-of-the-art machine learning algorithms to a dataset of synthetic biodegradable polymers, poly(beta-amino ester)s (PBAEs), which have shown exciting promise for therapeutic gene delivery in vitro and in vivo. The data set includes polymer properties as inputs as well as polymeric nanoparticle transfection performance and nanoparticle toxicity in a range of cells as outputs. This data was used to train and evaluate several state-of-the-art machine learning algorithms for their ability to predict transfection and understand structure-function relationships. By developing an encoding scheme for vectorizing the structure of a PBAE polymer in a machine-readable format, we demonstrate that a random forest model can satisfactorily predict DNA transfection in vitro based on the chemical structure of the constituent PBAE polymer in a cell line dependent manner. Based on the model, we synthesized PBAE polymers and used them to form polymeric gene delivery nanoparticles that were predicted in silico to be successful. We validated the computational predictions in two cell lines in vitro, RAW 264.7 macrophages and Hep3B liver cancer cells, and found that the Spearman's R correlation between predicted and experimental transfection was 0.57 and 0.66 respectively. Thus, a computational approach that encoded chemical descriptors of polymers was able to demonstrate that in silico computational screening of polymeric nanomedicine compositions had utility in predicting de novo biological experiments. STATEMENT OF SIGNIFICANCE: Developing highly efficient non-viral gene delivery reagents is difficult for many hard-to-transfect cell types and, to date, has mostly been explored via brute force screening routines. High throughput in silico methods of evaluating biomaterials can enable accelerated optimization and development for therapeutic or biomanufacturing purposes by exploring large chemical design spaces quickly and at low cost. This work reports application of state-of-the-art machine learning algorithms to a large compiled PBAE DNA gene delivery nanoparticle dataset across many cell types to develop predictive models for transfection and nanoparticle cytotoxicity. We develop a novel computational pipeline to encode PBAE nanoparticles with chemical descriptors and demonstrate utility in a de novo experimental context.

Poly(Beta-Amino Ester)s as High-Yield Transfection Reagents for Recombinant Protein Production

Purpose

Transient transfection is an essential tool for recombinant protein production, as it allows rapid screening for expression without stable integration of genetic material into a target cell genome. Poly(ethylenimine) (PEI) is the current gold standard for transient gene transfer, but transfection efficiency and the resulting protein yield are limited by the polymer’s toxicity. This study investigated the use of a class of cationic polymers, poly(beta-amino ester)s (PBAEs), as reagents for transient transfection in comparison to linear 25 kDa PEI, a commonly used transfection reagent.

Methods

Transfection efficiency and protein production were assessed in human embryonic kidney 293F (HEK) and Chinese hamster ovary-S (CHO) cell suspensions using PBAE-based nanoparticles in comparison to linear 25 kDa PEI. Production of both a cytosolic reporter and secreted antibodies was investigated.

Results

In both HEK and CHO cells, several PBAEs demonstrated superior transfection efficiency and enhanced production of a cytosolic reporter compared to linear 25 kDa PEI. This result extended to secreted proteins, as a model PBAE increased the production of 3 different secreted antibodies compared to linear 25 kDa PEI at culture scales ranging from 20 to 2000 mL. In particular, non-viral gene transfer using the lead PBAE/plasmid DNA nanoparticle formulation led to robust transfection of mammalian cells across different constructs, doses, volumes, and cell types.

Conclusion

These results show that PBAEs enhance transfection efficiency and increase protein yield compared to a widespread commercially available reagent, making them attractive candidates as reagents for use in recombinant protein production.

High-throughput and high-content bioassay enables tuning of polyester nanoparticles for cellular uptake, endosomal escape, and systemic in vivo delivery of mRNA

Nanoparticle-based mRNA therapeutics hold great promise, but cellular internalization and endosomal escape remain key barriers for cytosolic delivery. We developed a dual nanoparticle uptake and endosomal disruption assay using high-throughput and high-content image-based screening. Using a genetically encoded Galectin 8 fluorescent fusion protein sensor, endosomal disruption could be detected via sensor clustering on damaged endosomal membranes. Simultaneously, nucleic acid endocytosis was quantified using fluorescently tagged mRNA. We used an array of biodegradable poly(beta-amino ester)s as well as Lipofectamine and PEI to demonstrate that this assay has higher predictive capacity for mRNA delivery compared to conventional polymer and nanoparticle physiochemical characteristics. Top nanoparticle formulations enabled safe and efficacious mRNA expression in multiple tissues following intravenous injection, demonstrating that the in vitro screening method is also predictive of in vivo performance. Efficacious nonviral systemic delivery of mRNA with biodegradable particles opens up new avenues for genetic medicine and human health.

High-throughput screening of nanoparticles in drug delivery

The use of pharmacologically active compounds to manage and treat diseases is of utmost relevance in clinical practice. It is well recognized that spatial-temporal control over the delivery of these biomolecules will greatly impact their pharmacokinetic profile and ultimately their therapeutic effect. Nanoparticles (NPs) prepared from different materials have been tested successfully in the clinic for the delivery of several biomolecules including non-coding RNAs (siRNA and miRNA) and mRNAs. Indeed, the recent success of mRNA vaccines is in part due to progress in the delivery systems (NP based) that have been developed for many years. In most cases, the identification of the best formulation was done by testing a small number of novel formulations or by modification of pre-existing ones. Unfortunately, this is a low throughput and time-consuming process that hinders the identification of formulations with the highest potential. Alternatively, high-throughput combinatorial design of NP libraries may allow the rapid identification of formulations with the required release and cell/tissue targeting profile for a given application. Combinatorial approaches offer several advantages over conventional methods since they allow the incorporation of multiple components with varied chemical properties into materials, such as polymers or lipid-like materials, that will subsequently form NPs by self-assembly or chemical conjugation processes. The current review highlights the impact of high-throughput in the development of more efficient drug delivery systems with enhanced targeting and release kinetics. It also describes the current challenges in this research area as well as future directions.

Automation and data-driven design of polymer therapeutics

Polymers are uniquely suited for drug delivery and biomaterial applications due to tunable structural parameters such as length, composition, architecture, and valency. To facilitate designs, researchers may explore combinatorial libraries in a high throughput fashion to correlate structure to function. However, traditional polymerization reactions including controlled living radical polymerization (CLRP) and ring-opening polymerization (ROP) require inert reaction conditions and extensive expertise to implement. With the advent of air-tolerance and automation, several polymerization techniques are now compatible with well plates and can be carried out at the benchtop, making high throughput synthesis and high throughput screening (HTS) possible. To avoid HTS pitfalls often described as "fishing expeditions," it is crucial to employ intelligent and big data approaches to maximize experimental efficiency. This is where the disruptive technologies of machine learning (ML) and artificial intelligence (AI) will likely play a role. In fact, ML and AI are already impacting small molecule drug discovery and showing signs of emerging in drug delivery. In this review, we present state-of-the-art research in drug delivery, gene delivery, antimicrobial polymers, and bioactive polymers alongside data-driven developments in drug design and organic synthesis. From this insight, important lessons are revealed for the polymer therapeutics community including the value of a closed loop design-build-test-learn workflow. This is an exciting time as researchers will gain the ability to fully explore the polymer structural landscape and establish quantitative structure-property relationships (QSPRs) with biological significance.