Efficient Polymer-Mediated Delivery of Gene-Editing Ribonucleoprotein Payloads through Combinatorial Design, Parallelized Experimentation, and Machine Learning

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.

Evaluation of culture methods and chemical reagent combinations on CRISPR/Cas9 gene editing systems by lipofection in pig zygotes

The delivery of CRISPR/Cas ribonucleoprotein (RNP) complexes is gaining attention owing to its high cleavage efficiency and reduced off-target effects. Although RNPs can be delivered into porcine zygotes via electroporation with relatively high efficiency, lipofection-mediated transfection appears to be versatile because of its ease of use, low cost, and adaptation to high-throughput systems. However, this system requires improvements in terms of embryo development and mutation rates. Therefore, this study elucidated the effects of culture methods and reagent combinations on the CRISPR/Cas9 gene editing systems by using three lipofection reagents: Lipofectamine™ CRISPRMAX™ Cas9 Transfection Reagent (CM), Lipofectamine™ 2000 Transfection Reagent (LP), and jetCRISPR™ RNP Transfection Reagent (Jet). Porcine zona pellucida-free zygotes were incubated for 5 h with Cas9, a guide RNA targeting CD163, and the above lipofection reagents. When examining the effect of culture methods using 4-well (multiple embryo culture) and 25-well plates (single embryo culture) on the efficiency of CM-mediated zygote transfection, the culture of embryos in 25-well plates significantly increased the blastocyst formation rate; however, there was no difference in mutation rates between the 4-well and 25-well plates. When assessing the effects of individual or combined reagents on the efficiency of zygote transfection, the mutation rate was significantly lower for individual LP compared to individual CM- and Jet-mediated transfections. Moreover, combinations of lipofection transfection reagents did not significantly increase the mutation rate or mutation efficiency.

Streamlining the Generation of Advanced Polymer Materials Through the Marriage of Automation and Multiblock Copolymer Synthesis in Emulsion

Synthetic polymers are of paramount importance in modern life - an incredibly wide range of polymeric materials possessing an impressive variety of properties have been developed to date. The recent emergence of artificial intelligence and automation presents a great opportunity to significantly speed up discovery and development of the next generation of advanced polymeric materials. We have focused on the high-throughput automated synthesis of multiblock copolymers that comprise three or more distinct polymer segments of different monomer composition bonded in linear sequence. The present work has exploited automation to prepare high molar mass multiblock copolymers (typically>100,000 g mol-1) using reversible addition-fragmentation chain transfer (RAFT) polymerization in aqueous emulsion. A variety of original multiblock copolymers have been synthesised via a Chemspeed robot, exemplified by a multiblock copolymer comprising thirteen constituent blocks. Moreover, libraries of copolymers of randomized monomer compositions (acrylates, acrylamides, methacrylates, and styrenes), block orders, and block lengths were also generated, thereby demonstrating the robustness of our synthetic approach. One multiblock copolymer contained all four monomer families listed in the pool, which is unprecedented in the literature. The present work demonstrates that automation has the power to render complex and laborious syntheses of such unprecedented materials not just possible, but facile and straightforward, thus representing the way forward to the next generation of complex macromolecular architectures.

Poly(l-glutamic acid) augments the transfection performance of lipophilic polycations by overcoming tradeoffs among cytotoxicity, pDNA delivery efficiency, and serum stability

Polycations are scalable and affordable nanocarriers for delivering therapeutic nucleic acids. Yet, cationicity-dependent tradeoffs between nucleic acid delivery efficiency, cytotoxicity, and serum stability hinder clinical translation. Typically, the most efficient polycationic vehicles also tend to be the most toxic. For lipophilic polycations-which recruit hydrophobic interactions in addition to electrostatic interactions to bind and deliver nucleic acids-extensive chemical or architectural modifications sometimes fail to resolve intractable toxicity-efficiency tradeoffs. Here, we employ a facile post-synthetic polyplex surface modification strategy wherein poly(l-glutamic acid) (PGA) rescues toxicity, inhibits hemolysis, and prevents serum inhibition of lipophilic polycation-mediated plasmid (pDNA) delivery. Importantly, the sequence in which polycations, pDNA, and PGA are combined dictates pDNA conformations and spatial distribution. Circular dichroism spectroscopy reveals that PGA must be added last to polyplexes assembled from lipophilic polycations and pDNA; else, PGA will disrupt polycation-mediated pDNA condensation. Although PGA did not mitigate toxicity caused by hydrophilic PEI-based polycations, PGA tripled the population of transfected viable cells for lipophilic polycations. Non-specific adsorption of serum proteins abrogated pDNA delivery mediated by lipophilic polycations; however, PGA-coated polyplexes proved more serum-tolerant than uncoated polyplexes. Despite lower cellular uptake than uncoated polyplexes, PGA-coated polyplexes were imported into nuclei at higher rates. PGA also silenced the hemolytic activity of lipophilic polycations. Our work provides fundamental insights into how polyanionic coatings such as PGA transform intermolecular interactions between lipophilic polycations, nucleic acids, and serum proteins, and facilitate gentle yet efficient transgene delivery.

Synergistic Polymer Blending Informs Efficient Terpolymer Design and Machine Learning Discerns Performance Trends for pDNA Delivery

Cationic polymers offer an alternative to viral vectors in nucleic acid delivery. However, the development of polymer vehicles capable of high transfection efficiency and minimal toxicity has remained elusive, and continued exploration of the vast design space is required. Traditional single polymer syntheses with large monomer bases are very time-intensive, limiting the speed at which new formulations are identified. In this work, we present an experimental method for the quick probing of the design space, utilizing a combinatorial set of 90 polymer blends, derived from 6 statistical copolymers, to deliver pDNA. This workflow facilitated rapid screening of polyplex compositions, successfully tailoring polyplex hydrophobicity, particle size, and payload binding affinity. This workflow identified blended polyplexes with high levels of transfection efficiency and cell viability relative to single copolymer controls and commercial JetPEI, indicating synergistic benefits from copolymer blending. Polyplex composition was coupled with biological outputs to guide the synthesis of single terpolymer vehicles, with high-performing polymers P10 and M20, providing superior transfection of HEK293T cells in serum-free and serum-containing media, respectively. Machine learning coupled with SHapley Additive exPlanations (SHAP) was used to identify polymer/polyplex attributes that most impact transfection efficiency, viability, and overall effective efficiency. Subsequent transfections on ARPE-19 and HDFn cells found that P10 and M20 were surpassed in performance by M10, contrasting with results in HEK293T cells. This cell type dependency reinforced the need to evaluate transfection conditions with multiple cell models to potentially identify moieties more beneficial to delivery in certain tissues. Overall, the workflow employed can be used to expedite the exploration of the polymer design space, bypassing extensive synthesis, and to develop improved polymer delivery vehicles more readily for nucleic acid therapies.

Dynamic Covalent Boronate Chemistry Accelerates the Screening of Polymeric Gene Delivery Vectors via In Situ Complexation of Nucleic Acids

Gene therapy provides exciting new therapeutic opportunities beyond the reach of traditional treatments. Despite the tremendous progress of viral vectors, their high cost, complex manufacturing, and side effects have encouraged the development of nonviral alternatives, including cationic polymers. However, these are less efficient in overcoming cellular barriers, resulting in lower transfection efficiencies. Although the exquisite structural tunability of polymers might be envisaged as a versatile tool for improving transfection, the need to fine-tune several structural parameters represents a bottleneck in current screening technologies. By taking advantage of the fast-forming and strong boronate ester bond, an archetypal example of dynamic covalent chemistry, a highly adaptable gene delivery platform is presented, in which the polycation synthesis and pDNA complexation occur in situ. The robustness of the strategy entitles the simultaneous evaluation of several structural parameters at will, enabling the accelerated screening and adaptive optimization of lead polymeric vectors using dynamic covalent libraries.

Polymer design via SHAP and Bayesian machine learning optimizes pDNA and CRISPR ribonucleoprotein delivery

We present the facile synthesis of a clickable polymer library with systematic variations in length, binary composition, pKa, and hydrophobicity (clog P) to optimize intracellular pDNA and CRISPR-Cas9 ribonucleoprotein (RNP) performance. We couple physicochemical characterization and machine learning to interpret quantitative structure-property relationships within the combinatorial design space. For the first time, we reveal unexpected disparate design parameters for nucleic acid carriers; via explainable machine learning on 432 formulations, we discover that lower polymer pKa and higher percentages of benzimidazole ethanethiol enhance pDNA delivery, yet polymer length and captamine cation identity improve RNP delivery. Closed-loop Bayesian optimization of 552 formulation ratios further enhances in vitro performance. The top three polymers yield a higher signal and stable transgene expression over 20 days in vivo, and a 1.7-fold enhancement over controls. Our facile coupling of synthesis, characterization, and machine analysis provides powerful tools to quantitate performance parameters accelerating next-generation vehicles for nucleic acid medicines.

Poly(β-aminoester) Physicochemical Properties Govern the Delivery of siRNA from Electrostatically Assembled Coatings

Localized short interfering RNA (siRNA) therapy has the potential to drive high-specificity molecular-level treatment of a variety of disease states. Unfortunately, effective siRNA therapy suffers from several barriers to its intracellular delivery. Thus, drug delivery systems that package and control the release of therapeutic siRNAs are necessary to overcome these obstacles to clinical translation. Layer-by-layer (LbL) electrostatic assembly of thin film coatings containing siRNA and protonatable, hydrolyzable poly(β-aminoester) (PBAE) polymers is one such drug delivery strategy. However, the impact of PBAE physicochemical properties on the transfection efficacy of siRNA released from LbL thin film coatings has not been systematically characterized. In this study, we investigate the siRNA transfection efficacy of four structurally similar PBAEs in vitro. We demonstrate that small changes in structure yield large changes in physicochemical properties, such as hydrophobicity, pKa, and amine chemical structure, driving differences in the interactions between PBAEs and siRNA in polyplexes and in LbL thin film coatings for wound dressings. In our polymer set, Poly3 forms the most stable interactions with siRNA (Keff,w/w = 0.298) to slow release kinetics and enhance transfection of reporter cells in both colloidal and thin film coating approaches. This is due to its unique physiochemical properties: high hydrophobicity (clog P = 7.86), effective pKa closest to endosomal pH (pKa = 6.21), and high cooperativity in buffering (nhill = 7.2). These properties bestow Poly3 with enhanced endosomal buffering and escape properties. Taken together, this work elucidates the connections between small changes in polymer structure, emergent properties, and polyelectrolyte theory to better understand PBAE transfection efficacy.

Artificial Intelligence and Computational Biology in Gene Therapy: A Review

One of the trending fields in almost all areas of science and technology is artificial intelligence. Computational biology and artificial intelligence can help gene therapy in many steps including: gene identification, gene editing, vector design, development of new macromolecules and modeling of gene delivery. There are various tools used by computational biology and artificial intelligence in this field, such as genomics, transcriptomic and proteomics data analysis, machine learning algorithms and molecular interaction studies. These tools can introduce new gene targets, novel vectors, optimized experiment conditions, predict the outcomes and suggest the best solutions to avoid undesired immune responses following gene therapy treatment.

Combinatorial High-Throughput Screening of Complex Polymeric Enzyme Immobilization Supports

Recent advances have demonstrated the promise of complex multicomponent polymeric supports to enable supra-biological enzyme performance. However, the discovery of such supports has been limited by time-consuming, low-throughput synthesis and screening. Here, we describe a novel combinatorial and high-throughput platform that enables rapid screening of complex and heterogeneous copolymer brushes as enzyme immobilization supports, named combinatorial high-throughput enzyme support screening (CHESS). Using a 384-well plate format, we synthesized arrays of three-component polymer brushes in the microwells using photoactivated surface-initiated polymerization and immobilized enzymes in situ. The utility of CHESS to identify optimal immobilization supports under thermally and chemically denaturing conditions was demonstrated usingBacillus subtilisLipase A (LipA). The identification of supports with optimal compositions was validated by immobilizing LipA on polymer-brush-modified biocatalyst particles. We further demonstrated that CHESS could be used to predict the optimal composition of polymer brushes a priori for the previously unexplored enzyme, alkaline phosphatase (AlkP). Our findings demonstrate that CHESS represents a predictable and reliable platform for dramatically accelerating the search of chemical compositions for immobilization supports and further facilitates the discovery of biocompatible and stabilizing materials.

Mapping Composition Evolution through Synthesis, Purification, and Depolymerization of Random Heteropolymers

Random heteropolymers (RHPs) consisting of three or more comonomers have been routinely used to synthesize functional materials. While increasing the monomer variety diversifies the side-chain chemistry, this substantially expands the sequence space and leads to ensemble-level sequence heterogeneity. Most studies have relied on monomer composition and simulated sequences to design RHPs, but the questions remain unanswered regarding heterogeneities within each RHP ensemble and how closely these simulated sequences reflect the experimental outcomes. Here, we quantitatively mapped out the evolution of monomer compositions in four-monomer-based RHPs throughout a design-synthesis-purification-depolymerization process. By adopting a Jaacks method, we first determined 12 reactivity ratios directly from quaternary methacrylate RAFT copolymerization experiments to account for the influences of competitive monomer addition and the reversible activation/deactivation equilibria. The reliability of in silico analysis was affirmed by a quantitative agreement (<4% difference) between the simulated RHP compositions and the experimental results. Furthermore, we mapped out the conformation distribution within each ensemble in different solvents as a function of monomer chemistry, composition, and segmental characteristics via high-throughput computation based on self-consistent field theory (SCFT). These comprehensive studies confirmed monomer composition as a viable design parameter to engineer RHP-based functional materials as long as the reactivity ratios are accurately determined and the livingness of RHP synthesis is ensured.

Data-Driven Design of Polymer-Based Biomaterials: High-throughput Simulation, Experimentation, and Machine Learning

Polymers, with the capacity to tunably alter properties and response based on manipulation of their chemical characteristics, are attractive components in biomaterials. Nevertheless, their potential as functional materials is also inhibited by their complexity, which complicates rational or brute-force design and realization. In recent years, machine learning has emerged as a useful tool for facilitating materials design via efficient modeling of structure-property relationships in the chemical domain of interest. In this Spotlight, we discuss the emergence of data-driven design of polymers that can be deployed in biomaterials with particular emphasis on complex copolymer systems. We outline recent developments, as well as our own contributions and takeaways, related to high-throughput data generation for polymer systems, methods for surrogate modeling by machine learning, and paradigms for property optimization and design. Throughout this discussion, we highlight key aspects of successful strategies and other considerations that will be relevant to the future design of polymer-based biomaterials with target properties.

Cinchona Alkaloid Polymers Demonstrate Highly Efficient Gene Delivery Dependent on Stereochemistry, Methoxy Substitution, and Length

Nucleic acid delivery with cationic polymers is a promising alternative to expensive viral-based methods; however, it often suffers from a lower performance. Herein, we present a highly efficient delivery system based on cinchona alkaloid natural products copolymerized with 2-hydroxyethyl acrylate. Cinchona alkaloids are an attractive monomer class for gene delivery applications, given their ability to bind to DNA via both electrostatics and intercalation. To uncover the structure-activity profile of the system, four structurally similar cinchona alkaloids were incorporated into polymers: quinine, quinidine, cinchonine, and cinchonidine. These polymers differed in the chain length, the presence or absence of a pendant methoxy group, and stereochemistry, all of which were found to alter gene delivery performance and the ways in which the polymers overcome biological barriers to transfection. Longer polymers that contained the methoxy-bearing cinchona alkaloids (i.e., quinine and quinidine) were found to have the best performance. These polymers exhibited the tightest DNA binding, largest and most abundant DNA-polymer complexes, and best endosomal escape thanks to their increased buffering capacity and closest nuclear proximity of the payload. Overall, this work highlights the remarkable efficiency of polymer systems that incorporate cinchona alkaloid natural products while demonstrating the profound impact that small structural changes can have on overcoming biological hurdles associated with gene delivery.

Blended Block Polycation Micelles Enhance Antisense Oligonucleotide Delivery

Nucleic acid-based medicines and vaccines are becoming an important part of our therapeutic toolbox. One key genetic medicine is antisense oligonucleotides (ASOs), which are short single-stranded nucleic acids that downregulate protein production by binding to mRNA. However, ASOs cannot enter the cell without a delivery vehicle. Diblock polymers containing cationic and hydrophobic blocks self-assemble into micelles that have shown improved delivery compared to linear nonmicelle variants. Yet synthetic and characterization bottlenecks have hindered rapid screening and optimization. In this study, we aim to develop a method to increase throughput and discovery of new micelle systems by mixing diblock polymers together to rapidly form new micelle formulations. We synthesized diblocks containing an n-butyl acrylate block chain extended with cationic moieties amino ethyl acrylamide (A), dimethyl amino ethyl acrylamide (D), or morpholino ethyl acrylamide (M). These diblocks were then self-assembled into homomicelles (A100, D100, and M100)), mixed micelles comprising 2 homomicelles (MixR%+R'%), and blended diblock micelles comprising 2 diblocks blended into one micelle (BldR%R'%) and tested for ASO delivery. Interestingly, we observed that mixing or blending M with A (BldA50M50 and MixA50+M50) did not improve transfection efficiency compared to A100; however, when M was mixed with D, there was a significant increase in transfection efficacy for the mixed micelle MixD50+M50 compared to D100. We further examined mixed and blended D systems at different ratios. We observed a large increase in transfection and minimal change in toxicity when M was mixed with D at a low percentage of D incorporation in mixed diblock micelles (i.e., BldD20M80) compared to D100 and MixD20+M80. To understand the cellular mechanisms that may result in these differences, we added proton pump inhibitor Bafilomycin-A1 (Baf-A1) to the transfection experiments. Formulations that contain D decreased in performance in the presence of Baf-A1, indicating that micelles with D rely on the proton sponge effect for endosomal escape more than micelles with A. This result supports our conclusion that M is able to modulate transfection of D, but not with A. This research shows that polymer blending in a manner similar to that of lipids can significantly boost transfection efficiency and is a facile way to increase throughput of testing, optimization, and successful formulation identification for polymeric nucleic acid delivery systems.

Applied machine learning as a driver for polymeric biomaterials design

Polymers are ubiquitous to almost every aspect of modern society and their use in medical products is similarly pervasive. Despite this, the diversity in commercial polymers used in medicine is stunningly low. Considerable time and resources have been extended over the years towards the development of new polymeric biomaterials which address unmet needs left by the current generation of medical-grade polymers. Machine learning (ML) presents an unprecedented opportunity in this field to bypass the need for trial-and-error synthesis, thus reducing the time and resources invested into new discoveries critical for advancing medical treatments. Current efforts pioneering applied ML in polymer design have employed combinatorial and high throughput experimental design to address data availability concerns. However, the lack of available and standardized characterization of parameters relevant to medicine, including degradation time and biocompatibility, represents a nearly insurmountable obstacle to ML-aided design of biomaterials. Herein, we identify a gap at the intersection of applied ML and biomedical polymer design, highlight current works at this junction more broadly and provide an outlook on challenges and future directions.

3D Macrocyclic Structure Boosted Gene Delivery: Multi-Cyclic Poly(β-Amino Ester)s from Step Growth Polymerization

The topological structures of polymers play a critical role in determining their gene delivery efficiency. Exploring novel polymeric structures as gene delivery vectors is thus of great interest. In this work, a new generation of multi-cyclic poly(β-amino ester)s (CPAEs) with unique topology structure was synthesized for the first time via step growth polymerization. Through controlling the occurrence stage of cyclization, three types of CPAEs with rings of different sizes and topologies were obtained. In vitro experiments demonstrated that the CPAEs with macro rings (MCPAEs) significantly boosted the transgene expression comparing to their branched counterparts. Moreover, the MCPAE vector with optimized terminal group efficiently delivered the CRISPR plasmid coding both Staphylococcus aureus Cas9 nuclease and dual guide sgRNAs for gene editing therapy.

Enhancing pDNA Delivery with Hydroquinine Polymers by Modulating Structure and Composition

Quinine is a promising natural product building block for polymer-based nucleic acid delivery vehicles as its structure enables DNA binding through both intercalation and electrostatic interactions. However, studies exploring the potential of quinine-based polymers for nucleic acid delivery applications (transfection) are limited. In this work, we used a hydroquinine-functionalized monomer, HQ, with 2-hydroxyethyl acrylate to create a family of seven polymers (HQ-X, X = mole percentage of HQ), with mole percentages of HQ ranging from 12 to 100%. We developed a flow cytometer-based assay for studying the polymer-pDNA complexes (polyplex particles) directly and demonstrate that polymer composition and monomer structure influence polyplex characteristics such as the pDNA loading and the extent of adsorption of serum proteins on polyplex particles. Biological delivery experiments revealed that maximum transgene expression, outperforming commercial controls, was achieved with HQ-25 and HQ-35 as these two variants sustained gene expression over 96 h. HQ-44, HQ-60, and HQ-100 were not successful in inducing transgene expression, despite being able to deliver pDNA into the cells, highlighting that the release of pDNA is likely the bottleneck in transfection for polymers with higher HQ content. Using confocal imaging, we quantified the extent of colocalization between pDNA and lysosomes, proving the remarkable endosomal escape capabilities of the HQ-X polymers. Overall, this study demonstrates the advantages of HQ-X polymers as well as provides guiding principles for improving the monomer structure and polymer composition, supporting the development of the next generation of polymer-based nucleic acid delivery vehicles harnessing the power of natural products.

Single-dose injectable nanovaccine-in-hydrogel for robust immunotherapy of large tumors with abscopal effect

Current cancer immunotherapy [e.g., immune checkpoint blockade (ICB)] only benefits small subsets of patients, largely due to immunosuppressive tumor microenvironment (TME). In situ tumor vaccination can reduce TME immunosuppression and thereby improve cancer immunotherapy. Here, we present single-dose injectable (nanovaccines + ICBs)-in-hydrogel (NvIH) for robust immunotherapy of large tumors with abscopal effect. NvIH is thermo-responsive hydrogel co-encapsulated with ICB antibodies and novel polymeric nanoparticles loaded with three immunostimulatory agonists for Toll-like receptors 7/8/9 (TLR7/8/9) and stimulator of interferon genes (STING). Upon in situ tumor vaccination, NvIH undergoes rapid sol-to-gel transformation, prolongs tumor retention, sustains the release of immunotherapeutics, and reduces acute systemic inflammation. In multiple poorly immunogenic tumor models, single-dose NvIH reduces multitier TME immunosuppression, elicits potent TME and systemic innate and adaptive antitumor immunity with memory, and regresses both local (vaccinated) and distant large tumors with abscopal effect, including distant orthotopic glioblastoma. Overall, NvIH holds great potential for tumor immunotherapy.

Enhanced ASO-Mediated Gene Silencing with Lipophilic pH-Responsive Micelles

Herein, we examine the ASO-mediated gene silencing efficiency of pH-responsive micelles, by incorporating 2-(diisopropylamino)ethyl methacrylate (DIP) into the micelle core and comparing physical and biological properties with non-pH-responsive micelles. Additionally, the lipophilic effect of the micelle cores was examined in both types of micelles. Varying lipophilicity was achieved by varying alkyl monomer chain lengths─butyl (4), lauryl (12), and stearyl (18) methacrylate. Each of the micelles formed within our family offered the added benefit of well-defined and uniform templates for loading antisense oligonucleotide (ASO) payloads. Overall, the micelles followed previously established trends of outperforming their linear polymer (nonmicelle) analogs and ASO only control. More specifically, the highest performing micelles were the pH-responsive micelles with longer alkyl chains or higher lipophilicity─D-DIP+LMA and D-DIP+SMA (∼90% silencing). These two micelles demonstrated silencing efficiencies similar to Jet-PEI and Lipofectamine 2000 and caused lower toxicity than Lipofectamine 2000. The shortest alkyl chain pH-responsive micelle, D-DIP+BMA (64%), displayed strong gene silencing similar to that about that of its non-pH-responsive micelle, D-BMA (68%), and the pH-responsive micelle without an alkyl chain incorporated, D-DIP (59%). This work illuminates a minimum alkyl chain length dependence to allow gene silencing within our micelle family. However, including only longer alkyl chains into the micelle core without the pH-responsive unit DIP had a hindering effect, thus demonstrating the requirement of the DIP unit when including longer alkyl chain lengths. This work demonstrates the exemplary gene silencing efficiencies of polymeric micelles and uncovers the relationship between pH responsiveness and performance with lipophilic polymer micelles for enhancing ASO-mediated gene silencing.

Towards artificial intelligence-enabled extracellular vesicle precision drug delivery

Extracellular Vesicles (EVs), particularly exosomes, recently exploded into nanomedicine as an emerging drug delivery approach due to their superior biocompatibility, circulating stability, and bioavailability in vivo. However, EV heterogeneity makes molecular targeting precision a critical challenge. Deciphering key molecular drivers for controlling EV tissue targeting specificity is in great need. Artificial intelligence (AI) brings powerful prediction ability for guiding the rational design of engineered EVs in precision control for drug delivery. This review focuses on cutting-edge nano-delivery via integrating large-scale EV data with AI to develop AI-directed EV therapies and illuminate the clinical translation potential. We briefly review the current status of EVs in drug delivery, including the current frontier, limitations, and considerations to advance the field. Subsequently, we detail the future of AI in drug delivery and its impact on precision EV delivery. Our review discusses the current universal challenge of standardization and critical considerations when using AI combined with EVs for precision drug delivery. Finally, we will conclude this review with a perspective on future clinical translation led by a combined effort of AI and EV research.

A User's Guide to Machine Learning for Polymeric Biomaterials

The development of novel biomaterials is a challenging process, complicated by a design space with high dimensionality. Requirements for performance in the complex biological environment lead to difficult a priori rational design choices and time-consuming empirical trial-and-error experimentation. Modern data science practices, especially artificial intelligence (AI)/machine learning (ML), offer the promise to help accelerate the identification and testing of next-generation biomaterials. However, it can be a daunting task for biomaterial scientists unfamiliar with modern ML techniques to begin incorporating these useful tools into their development pipeline. This Perspective lays the foundation for a basic understanding of ML while providing a step-by-step guide to new users on how to begin implementing these techniques. A tutorial Python script has been developed walking users through the application of an ML pipeline using data from a real biomaterial design challenge based on group's research. This tutorial provides an opportunity for readers to see and experiment with ML and its syntax in Python. The Google Colab notebook can be easily accessed and copied from the following URL: www.gormleylab.com/MLcolab.

The steep road to nonviral nanomedicines: Frequent challenges and culprits in designing nanoparticles for gene therapy

The potential of therapeutically loaded nanoparticles (NPs) has been successfully demonstrated during the last decade, with NP-mediated nonviral gene delivery gathering significant attention as highlighted by the broad clinical acceptance of mRNA-based COVID-19 vaccines. A significant barrier to progress in this emerging area is the wild variability of approaches reported in published literature regarding nanoparticle characterizations. Here, we provide a brief overview of the current status and outline important concerns regarding the need for standardized protocols to evaluate NP uptake, NP transfection efficacy, drug dose determination, and variability of nonviral gene delivery systems. Based on these concerns, we propose wide adherence to multimodal, multiparameter, and multistudy analysis of NP systems. Adoption of these proposed approaches will ensure improved transparency, provide a better basis for interlaboratory comparisons, and will simplify judging the significance of new findings in a broader context, all critical requirements for advancing the field of nonviral gene delivery.

Enzyme-Polymer Conjugates for Tuning, Enhancing, and Expanding Biocatalytic Activity

Combining polymers with functional proteins is an approach that has brought several successful stories in the field of biomedicine with PEGylated therapeutic proteins. The latest advances in polymer chemistry have facilitated the expansion of protein-polymer hybrids to other research areas such as biocatalysis. Polymers can impart stability and novel functionalities to the enzyme of interest, thereby improving the catalytic performance of a given reaction. In this review, we have revisited the main methodologies currently used for the synthesis of enzyme-polymer hybrids, unveiling the interplay between the configuration and the composition of the assembled structure and the eventual traits of the hybrid. Finally, the latest advances, such as the assembly of polymer-based chemoenzymatic nanoreactors and the use of deep learning methodologies to achieve the most suitable polymer compositions for catalysis, are discussed.

Sequence Design of Random Heteropolymers as Protein Mimics

Random heteropolymers (RHPs) have been computationally designed and experimentally shown to recapitulate protein-like phase behavior and function. However, unlike proteins, RHP sequences are only statistically defined and cannot be sequenced. Recent developments in reversible-deactivation radical polymerization allowed simulated polymer sequences based on the well-established Mayo-Lewis equation to more accurately reflect ground-truth sequences that are experimentally synthesized. This led to opportunities to perform bioinformatics-inspired analysis on simulated sequences to guide the design, synthesis, and interpretation of RHPs. We compared batches on the order of 10000 simulated RHP sequences that vary by synthetically controllable and measurable RHP characteristics such as chemical heterogeneity and average degree of polymerization. Our analysis spans across 3 levels: segments along a single chain, sequences within a batch, and batch-averaged statistics. We discuss simulator fidelity and highlight the importance of robust segment definition. Examples are presented that demonstrate the use of simulated sequence analysis for in-silico iterative design to mimic protein hydrophobic/hydrophilic segment distributions in RHPs and compare RHP and protein sequence segments to explain experimental results of RHPs that mimic protein function. To facilitate the community use of this workflow, the simulator and analysis modules have been made available through an open source toolkit, the RHPapp.

Structure-Activity Relationships in Nucleic-Acid-Templated Vectors Based on Peptidic Dynamic Covalent Polymers

The use of nucleic acids as templates, which can trigger the self-assembly of their own vectors represent an emerging, simple and versatile, approach toward the self-fabrication of tailored nucleic acids delivery vectors. However, the structure-activity relationships governing this complex templated self-assembly process that accompanies the complexation of nucleic acids remains poorly understood. Herein, the class of arginine-rich dynamic covalent polymers (DCPs) composed of different monomers varying the number and position of arginines were studied. The combinations that lead to nucleic acid complexation, in saline buffer, using different templates, from short siRNA to long DNA, are described. Finally, a successful peptidic DCP featuring six-arginine repeating unit that promote the safe and effective delivery of siRNA in live cancer cells was identified.

Efficient Transfection via an Unexpected Mechanism by Near Neutral Polypiperazines with Tailored Response to Endosomal pH

Cationic pH-responsive polymers promise to overcome critical challenges in cellular delivery. Ideally, the polymers become selectively charged along the endosomal pathway disturbing only the local membrane and avoiding unintended interactions or cytotoxic side effects at physiological conditions. Polypiperazines represent a novel, hydrophilic class of pH-responsive polymers whose response can be tuned within the relevant pH range (5-7.4). The authors discovered that the polypiperazines are effectively binding plasmid DNA (pDNA) and demonstrate high efficiency in transfection. By design of experiments (DoE), a wide parameter space (pDNA and polymer concentration) is screened to identify the range of effective concentrations for transfection. An isopropyl modified polypiperazine is highly efficient over a wide range of concentrations outperforming linear polyethylenimine (l-PEI, 25 kDa) in regions of low N*/P ratios. A quantitative polymerase chain reaction (qPCR) surprisingly revealed that the pDNA within the piperazine-based polyplexes can be amplified in contrast to polyplexes based on l-PEI. The pDNA must therefore be more accessible and bound differently than for other known transfection polymers. Considering the various opportunities to further optimize their structure, polypiperazines represent a promising platform for designing effective soluble polymeric vectors, which are charge-neutral at physiological conditions.

Protein charge parameters that influence stability and cellular internalization of polyelectrolyte complex micelles

Proteins are an important class of biologics, but there are several recurring challenges to address when designing protein-based therapeutics. These challenges include: the propensity of proteins to aggregate during formulation, relatively low loading in traditional hydrophobic delivery vehicles, and inefficient cellular uptake. This last criterion is particularly challenging for anionic proteins as they cannot cross the anionic plasma membrane. Here we investigated the complex coacervation of anionic proteins with a block copolymer of opposite charge to form polyelectrolyte complex (PEC) micelles for use as a protein delivery vehicle. Using genetically modified variants of the model protein green fluorescent protein (GFP), we evaluated the role of protein charge and charge localization in the formation and stability of PEC micelles. A neutral-cationic block copolymer, poly(oligoethylene glycol methacrylate-block-quaternized 4-vinylpyridine), POEGMA79-b-qP4VP175, was prepared via RAFT polymerization for complexation and microphase separation with the panel of engineered anionic GFPs. We found that isotropically supercharged proteins formed micelles at higher ionic strength relative to protein variants with charge localized to a polypeptide tag. We then studied GFP delivery by PEC micelles and found that they effectively delivered the protein cargo to mammalian cells. However, cellular delivery varied as a function of protein charge and charge distribution and we found an inverse relationship between the PEC micelle critical salt concentration and delivery efficiency. This model system has highlighted the potential of polyelectrolyte complexes to deliver anionic proteins intracellularly. Using this model system, we have identified requirements for the formation of PEC micelles that are stable at physiological ionic strength and that smaller protein-polyelectrolyte complexes effectively deliver proteins to Jurkat cells.

Hydrophilic Surface Modification of Cationic Unimolecular Bottlebrush Vectors Moderate pDNA and RNP Bottleplex Stability and Delivery Efficacy

A cationic unimolecular bottlebrush polymer with chemically modified end-groups was synthesized to understand the impact of hydrophilicity on colloidal stability, nucleic acid delivery performance, and toxicity. The bottlebrush polymer template was synthesized using grafting-through techniques and was therefore composed of a polynorbornene backbone with poly(2-(dimethylamino)ethyl methacrylate) side chains with dodecyl trithiocarbonate end-groups. Postpolymerization modification was performed to fully remove the end-groups or install hydroxy and methoxy poly(ethylene glycol) functional groups on the bottlebrush exterior. The bottlebrush family was preformulated with biological payloads of pDNA and CRISPR-Cas9 RNP in both water and PBS to understand binding, aggregation kinetics, cytotoxicity, and delivery efficacy. Increasing end-group hydrophilicity and preformulation of bottleplexes in PBS increased colloidal stability and cellular viability; however, this did not always result in increased transfection efficiency. The bottlebrush family exemplifies how formulation conditions, polymer loading, and end-group functionality of bottlebrushes can be tuned to balance expression with cytotoxicity ratios and result in enhanced overall performance.

Could artificial intelligence revolutionize the development of nanovectors for gene therapy and mRNA vaccines?

Gene therapy enables the introduction of nucleic acids like DNA and RNA into host cells, and is expected to revolutionize the treatment of a wide range of diseases. This growth has been further accelerated by the discovery of CRISPR/Cas technology, which allows accurate genomic editing in a broad range of cells and organisms in vitro and in vivo. Despite many advances in gene delivery and the development of various viral and non-viral gene delivery vectors, the lack of highly efficient non-viral systems with low cellular toxicity remains a challenge. The application of cutting-edge technologies such as artificial intelligence (AI) has great potential to find new paradigms to solve this issue. Herein, we review AI and its major subfields including machine learning (ML), neural networks (NNs), expert systems, deep learning (DL), computer vision and robotics. We discuss the potential of AI-based models and algorithms in the design of targeted gene delivery vehicles capable of crossing extracellular and intracellular barriers by viral mimicry strategies. We finally discuss the role of AI in improving the function of CRISPR/Cas systems, developing novel nanobots, and mRNA vaccine carriers.

Automated Parallel Dialysis for Purification of Polymers

The implementation of a dialysis method for the simultaneous purification of different polymer materials in a commercially available automated parallel synthesizer (APS) is discussed. The efficiency of this "unattended" automated parallel dialysis (APD) method was investigated by means of proton nuclear magnetic resonance (¹H-NMR) measurements, which confirmed that the method enables the removal of up to 99% of the unreacted monomer derived from the synthesis of the corresponding polymers in the APS. Size-exclusion chromatography (SEC) revealed that the molar mass and molar mass distribution of the investigated polymers did not undergo significant changes after the application of the APD method. The method discussed herein can be regarded as a good alternative to the "unattended" and reliable purification of polymer libraries prepared in APS. This method may be useful for overcoming current limitations of high-throughput/-output (HT/O) synthesis of polymer libraries, where purification of the generated materials currently represents a significant constraint for establishing fully automated experimental workflows necessary to advance towards a full digitalization of research and development of new polymers for diverse applications.

Hyperbranched Poly(β-amino ester)s (HPAEs) Structure Optimisation for Enhanced Gene Delivery: Non-Ideal Termination Elimination

Many polymeric gene delivery nano-vectors with hyperbranched structures have been demonstrated to be superior to their linear counterparts. The higher delivery efficacy is commonly attributed to the abundant terminal groups of branched polymers, which play critical roles in cargo entrapment, material-cell interaction, and endosome escape. Hyperbranched poly(β-amino ester)s (HPAEs) have developed as a class of safe and efficient gene delivery vectors. Although numerous research has been conducted to optimise the HPAE structure for gene delivery, the effect of the secondary amine residue on its backbone monomer, which is considered the non-ideal termination, has never been optimised. In this work, the effect of the non-ideal termination was carefully evaluated. Moreover, a series of HPAEs with only ideal terminations were synthesised by adjusting the backbone synthesis strategy to further explore the merits of hyperbranched structures. The HPAE obtained from modified synthesis methods exhibited more than twice the amounts of the ideal terminal groups compared to the conventional ones, determined by NMR. Their transfection performance enhanced significantly, where the optimal HPAE candidates developed in this study outperformed leading commercial benchmarks for DNA delivery, including Lipofectamine 3000, jetPEI, and jetOPTIMUS.

Designing Synthetic Polymers for Nucleic Acid Complexation and Delivery: From Polyplexes to Micelleplexes to Triggered Degradation

Gene delivery as a therapeutic tool continues to advance toward impacting human health, with several gene therapy products receiving FDA approval over the past 5 years. Despite this important progress, the safety and efficacy of gene therapy methodology requires further improvement to ensure that nucleic acid therapeutics reach the desired targets while minimizing adverse effects. Synthetic polymers offer several enticing features as nucleic acid delivery vectors due to their versatile functionalities and architectures and the ability of synthetic chemists to rapidly build large libraries of polymeric candidates equipped for DNA/RNA complexation and transport. Current synthetic designs are pursuing challenging objectives that seek to improve transfection efficiency and, at the same time, mitigate cytotoxicity. This Perspective will describe recent work in polymer-based gene complexation and delivery vectors in which cationic polyelectrolytes are modified synthetically by introduction of additional components─including hydrophobic, hydrophilic, and fluorinated units─as well as embedding of degradable linkages within the macromolecular structure. As will be seen, recent advances employing these emerging design strategies are promising with respect to their excellent biocompatibility and transfection capability, suggesting continued promise of synthetic polymer gene delivery vectors going forward.

Self-assembly of globular proteins with intrinsically disordered protein polyelectrolytes and block copolymers

Intrinsically disordered polypeptides are a versatile class of materials, combining the biocompatibility of peptides with the disordered structure and diverse phase behaviors of synthetic polymers. Synthetic polyelectrolytes are capable of complex phase behavior when mixed with oppositely charged polyelectrolytes, facilitating nanoparticle formation and bulk phase separation. However, there has been limited exploration of intrinsically disordered protein polyelectrolytes as potential bio-based replacements for synthetic polyelectrolytes. Here, we produce negatively charged, intrinsically disordered polypeptides, capable of high-yield expression in E. coli and use this intrinsically disordered peptide to produce entirely protein-based polyelectrolyte complexes. The complexes display rich phase behavior, showing sensitivity to charge density, salt concentration, temperature, and charge fraction. We characterize this behavior through a combination of turbidity assays, dynamic light scattering, and transmission electron microscopy. The robust expression profile and stimuli-responsive phase behavior of the intrinsically disordered peptides demonstrates their potential as easily producible, biocompatible substitutes for synthetic polyelectrolytes.

Machine learning makes magnificent macromolecules for medicine

At the University of Minnesota, scientists explore the application of machine learning to screen a multiparametric library of polymers to investigate the relationship between polymer attributes, payload type, and biological outcomes to optimize polymeric vector development for delivery of nucleic acid payloads.

Polycation Architecture Affects Complexation and Delivery of Short Antisense Oligonucleotides: Micelleplexes Outperform Polyplexes

Herein, we examine the complexation and biological delivery of a short single-stranded antisense oligonucleotide (ASO) payload with four polymer derivatives that form two architectural variants (polyplexes and micelleplexes): a homopolymer poly(2-dimethylaminoethyl methacrylate) (D), a diblock polymer poly(ethylene glycol)methylether methacrylate-block-poly(2-dimethylaminoethyl methacrylate) (O_bD), and two micelle-forming variants, poly(2-dimethylaminoethyl methacrylate)-block-poly(n-butyl methacrylate) (DB) and poly(ethylene glycol)methylether methacrylate-block-poly(2-dimethylaminoethyl methacrylate)-block-poly(n-butyl methacrylate) (O_bDB). Both polyplexes and micelleplexes complexed ASOs, and the incorporation of an O_b brush enhances colloidal stability. Micellplexes are templated by the size and shape of the unloaded micelle and that micelle-ASO complexation is not sensitive to formulation/mixing order, allowing ease, versatility, and reproducibility in packaging short oligonucleotides. The DB micelleplexes promoted the largest gene silencing, internalization, and tolerable toxicity while the O_bDB micelleplexes displayed enhanced colloidal stability and highly efficient payload trafficking despite having lower cellular uptake. Overall, this work demonstrates that cationic micelles are superior delivery vehicles for ASOs denoting the importance of vehicle architecture in biological performance.

A Microfluidic System of Gene Transfer by Ultrasound

Ultrasonic gene transfer has advantages beyond other cell transfer techniques because ultrasound does not directly act on cells, but rather pushes the gene fragments around the cells into cells through an acoustic hole effect. Most examples reported were carried out in macro volumes with conventional ultrasonic equipment. In the present study, a MEMS focused ultrasonic transducer based on piezoelectric thin film with flexible substrate was integrated with microchannels to form a microfluidic system of gene transfer. The core part of the system is a bowl-shaped curved piezoelectric film structure that functions to focus ultrasonic waves automatically. Therefore, the low input voltage and power can obtain the sound pressure exceeding the cavitation threshold in the local area of the microchannel in order to reduce the damage to cells. The feasibility of the system is demonstrated by finite element simulation and an integrated system of MEMS ultrasonic devices and microchannels are developed to successfully carry out the ultrasonic gene transfection experiments for HeLa cells. The results show that having more ultrasonic transducers leads a higher transfection rate. The system is of great significance to the development of single-cell biochip platforms for early cancer diagnosis and assessment of cancer treatment.

Machine Learning on a Robotic Platform for the Design of Polymer-Protein Hybrids

Polymer-protein hybrids are intriguing materials that can bolster protein stability in non-native environments, thereby enhancing their utility in diverse medicinal, commercial, and industrial applications. One stabilization strategy involves designing synthetic random copolymers with compositions attuned to the protein surface, but rational design is complicated by the vast chemical and composition space. Here, a strategy is reported to design protein-stabilizing copolymers based on active machine learning, facilitated by automated material synthesis and characterization platforms. The versatility and robustness of the approach is demonstrated by the successful identification of copolymers that preserve, or even enhance, the activity of three chemically distinct enzymes following exposure to thermal denaturing conditions. Although systematic screening results in mixed success, active learning appropriately identifies unique and effective copolymer chemistries for the stabilization of each enzyme. Overall, this work broadens the capabilities to design fit-for-purpose synthetic copolymers that promote or otherwise manipulate protein activity, with extensions toward the design of robust polymer-protein hybrid materials.

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.

Machine learning approaches for biomolecular, biophysical, and biomaterials research

A fluent conversation with a virtual assistant, person-tailored news feeds, and deep-fake images created within seconds-all those things that have been unthinkable for a long time are now a part of our everyday lives. What these examples have in common is that they are realized by different means of machine learning (ML), a technology that has fundamentally changed many aspects of the modern world. The possibility to process enormous amount of data in multi-hierarchical, digital constructs has paved the way not only for creating intelligent systems but also for obtaining surprising new insight into many scientific problems. However, in the different areas of biosciences, which typically rely heavily on the collection of time-consuming experimental data, applying ML methods is a bit more challenging: Here, difficulties can arise from small datasets and the inherent, broad variability, and complexity associated with studying biological objects and phenomena. In this Review, we give an overview of commonly used ML algorithms (which are often referred to as "machines") and learning strategies as well as their applications in different bio-disciplines such as molecular biology, drug development, biophysics, and biomaterials science. We highlight how selected research questions from those fields were successfully translated into machine readable formats, discuss typical problems that can arise in this context, and provide an overview of how to resolve those encountered difficulties.

Merging data curation and machine learning to improve nanomedicines

Nanomedicine design is often a trial-and-error process, and the optimization of formulations and in vivo properties requires tremendous benchwork. To expedite the nanomedicine research progress, data science is steadily gaining importance in the field of nanomedicine. Recently, efforts have explored the potential to predict nanomaterials synthesis and biological behaviors via advanced data analytics. Machine learning algorithms process large datasets to understand and predict various material properties in nanomedicine synthesis, pharmacologic parameters, and efficacy. "Big data" approaches may enable even larger advances, especially if researchers capitalize on data curation methods. However, the concomitant use of data curation processes needed to facilitate the acquisition and standardization of large, heterogeneous data sets, to support advanced data analytics methods such as machine learning has yet to be leveraged. Currently, data curation and data analytics areas of nanotechnology-focused data science, or 'nanoinformatics', have been proceeding largely independently. This review highlights the current efforts in both areas and the potential opportunities for coordination to advance the capabilities of data analytics in nanomedicine.

Combinatorial Polycation Synthesis and Causal Machine Learning Reveal Divergent Polymer Design Rules for Effective pDNA and Ribonucleoprotein Delivery

The development of polymers that can replace engineered viral vectors in clinical gene therapy has proven elusive despite the vast portfolios of multifunctional polymers generated by advances in polymer synthesis. Functional delivery of payloads such as plasmids (pDNA) and ribonucleoproteins (RNP) to various cellular populations and tissue types requires design precision. Herein, we systematically screen a combinatorially designed library of 43 well-defined polymers, ultimately identifying a lead polycationic vehicle (P38) for efficient pDNA delivery. Further, we demonstrate the versatility of P38 in codelivering spCas9 RNP and pDNA payloads to mediate homology-directed repair as well as in facilitating efficient pDNA delivery in ARPE-19 cells. P38 achieves nuclear import of pDNA and eludes lysosomal processing far more effectively than a structural analogue that does not deliver pDNA as efficiently. To reveal the physicochemical drivers of P38's gene delivery performance, SHapley Additive exPlanations (SHAP) are computed for nine polyplex features, and a causal model is applied to evaluate the average treatment effect of the most important features selected by SHAP. Our machine learning interpretability and causal inference approach derives structure-function relationships underlying delivery efficiency, polyplex uptake, and cellular viability and probes the overlap in polymer design criteria between RNP and pDNA payloads. Together, combinatorial polymer synthesis, parallelized biological screening, and machine learning establish that pDNA delivery demands careful tuning of polycation protonation equilibria while RNP payloads are delivered most efficaciously by polymers that deprotonate cooperatively via hydrophobic interactions. These payload-specific design guidelines will inform further design of bespoke polymers for specific therapeutic contexts.

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

Nanocarriers have significant potential to advance personalized medicine through targeted drug delivery. However, to date, efforts to improve nanoparticle accumulation at target disease sites have largely failed to translate clinically, stemming from an incomplete understanding of nano-bio interactions. While progress has been made to evaluate the effects of specific physical and chemical nanoparticle properties on trafficking and uptake, there is much to be gained from controlling these properties singularly and in combination to determine their interactions with different cell types. We and others have recently begun leveraging library-based nanoparticle screens to study structure-function relationships of lipid- and polymer-based drug delivery systems to guide nanoparticle design. These combinatorial screening efforts are showing promise in leading to the successful identification of critical characteristics that yield improved and specific accumulation at target sites. However, there is a crucial need to equally consider the influence of biological complexity on nanoparticle delivery, particularly in the context of clinical translation. For example, tissue and cellular heterogeneity presents an additional dimension to nanoparticle trafficking, uptake, and accumulation; applying imaging and screening tools as well as bioinformatics may further expand our understanding of how nanoparticles engage with cells and tissues. Given recent advances in the fields of omics and machine learning, there is substantial promise to revolutionize nanocarrier development through the use of integrated screens, harnessing the combinatorial parameter space afforded both by nanoparticle libraries and clinically annotated biological data sets in combination with high throughput in vivo studies.

Non-viral delivery of the CRISPR/Cas system: DNA versus RNA versus RNP

Since its discovery, the CRISPR/Cas technology has rapidly become an essential tool in modern biomedical research. The opportunities to specifically modify and correct genomic DNA has also raised big hope...

A graph representation of molecular ensembles for polymer property prediction

Synthetic polymers are versatile and widely used materials. Similar to small organic molecules, a large chemical space of such materials is hypothetically accessible. Computational property prediction and virtual screening can accelerate polymer design by prioritizing candidates expected to have favorable properties. However, in contrast to organic molecules, polymers are often not well-defined single structures but an ensemble of similar molecules, which poses unique challenges to traditional chemical representations and machine learning approaches. Here, we introduce a graph representation of molecular ensembles and an associated graph neural network architecture that is tailored to polymer property prediction. We demonstrate that this approach captures critical features of polymeric materials, like chain architecture, monomer stoichiometry, and degree of polymerization, and achieves superior accuracy to off-the-shelf cheminformatics methodologies. While doing so, we built a dataset of simulated electron affinity and ionization potential values for >40k polymers with varying monomer composition, stoichiometry, and chain architecture, which may be used in the development of other tailored machine learning approaches. The dataset and machine learning models presented in this work pave the path toward new classes of algorithms for polymer informatics and, more broadly, introduce a framework for the modeling of molecular ensembles.

A graph representation that captures critical features of polymeric materials and an associated graph neural network achieve superior accuracy to off-the-shelf cheminformatics methodologies.

Generation of mutant pigs by lipofection-mediated genome editing in embryos

The specificity and efficiency of CRISPR/Cas9 gene-editing systems are determined by several factors, including the mode of delivery, when applied to mammalian embryos. Given the limited time window for delivery, faster and more reliable methods to introduce Cas9-gRNA ribonucleoprotein complexes (RNPs) into target embryos are needed. In pigs, somatic cell nuclear transfer using gene-modified somatic cells and the direct introduction of gene editors into the cytoplasm of zygotes/embryos by microinjection or electroporation have been used to generate gene-edited embryos; however, these strategies require expensive equipment and sophisticated techniques. In this study, we developed a novel lipofection-mediated RNP transfection technique that does not require specialized equipment for the generation of gene-edited pigs and produced no detectable off-target events. In particular, we determined the concentration of lipofection reagent for efficient RNP delivery into embryos and successfully generated MSTN gene-edited pigs (with mutations in 7 of 9 piglets) after blastocyst transfer to a recipient gilt. This newly established lipofection-based technique is still in its early stages and requires improvements, particularly in terms of editing efficiency. Nonetheless, this practical method for rapid and large-scale lipofection-mediated gene editing in pigs has important agricultural and biomedical applications.

Synthetic molecule libraries for nucleic acid delivery: Design parameters in cationic/ionizable lipids and polymers

Recent progress in the design of cationic lipids and polymers has successfully translated nucleic acid drugs into clinical applications, such as the treatment of liver diseases and the prevention of virus infection. Small or large libraries of delivery molecules have been used to find the key chemical structures to protect nucleic acids from nucleases in the extracellular milieu and to facilitate the endosomal escape after endocytosis. This review introduces three essential design parameters (i.e., acid dissociation constant, hydrophobicity, and biodegradability) to develop synthetic molecules for nucleic acid delivery. The significance and mechanism of each parameter are described based on the results obtained from in vitro and in vivo evaluations. Other design parameters were then discussed to create the next generation of delivery molecules for future nucleic acid therapeutics.

Machine-Learning-Guided Discovery of 19F MRI Agents Enabled by Automated Copolymer Synthesis

Modern polymer science suffers from the curse of multidimensionality. The large chemical space imposed by including combinations of monomers into a statistical copolymer overwhelms polymer synthesis and characterization technology and limits the ability to systematically study structure-property relationships. To tackle this challenge in the context of 19F magnetic resonance imaging (MRI) agents, we pursued a computer-guided materials discovery approach that combines synergistic innovations in automated flow synthesis and machine learning (ML) method development. A software-controlled, continuous polymer synthesis platform was developed to enable iterative experimental-computational cycles that resulted in the synthesis of 397 unique copolymer compositions within a six-variable compositional space. The nonintuitive design criteria identified by ML, which were accomplished by exploring <0.9% of the overall compositional space, lead to the identification of >10 copolymer compositions that outperformed state-of-the-art materials.