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

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.

Recent Progress in the Endosomal Escape Mechanism and Chemical Structures of Polycations for Nucleic Acid Delivery

Nucleic acid-based therapies are seeing a spiralling surge. Stimuli-responsive polymers, especially pH-responsive ones, are gaining widespread attention because of their ability to efficiently deliver nucleic acids. These polymers can be synthesized and modified according to target requirements, such as delivery sites and the nature of nucleic acids. In this regard, the endosomal escape mechanism of polymer-nucleic acid complexes (polyplexes) remains a topic of considerable interest owing to various plausible escape mechanisms. This review describes current progress in the endosomal escape mechanism of polyplexes and state-of-the-art chemical designs for pH-responsive polymers. The importance is also discussed of the acid dissociation constant (i.e., pKa) in designing the new generation of pH-responsive polymers, along with assays to monitor and quantify the endosomal escape behavior. Further, the use of machine learning is addressed in pKa prediction and polymer design to find novel chemical structures for pH responsiveness. This review will facilitate the design of new pH-responsive polymers for advanced and efficient nucleic acid delivery.

Dynamic carriers for therapeutic RNA delivery

Carriers for RNA delivery must be dynamic, first stabilizing and protecting therapeutic RNA during delivery to the target tissue and across cellular membrane barriers and then releasing the cargo in bioactive form. The chemical space of carriers ranges from small cationic lipids applied in lipoplexes and lipid nanoparticles, over medium-sized sequence-defined xenopeptides, to macromolecular polycations applied in polyplexes and polymer micelles. This perspective highlights the discovery of distinct virus-inspired dynamic processes that capitalize on mutual nanoparticle-host interactions to achieve potent RNA delivery. From the host side, subtle alterations of pH, ion concentration, redox potential, presence of specific proteins, receptors, or enzymes are cues, which must be recognized by the RNA nanocarrier via dynamic chemical designs including cleavable bonds, alterable physicochemical properties, and supramolecular assembly-disassembly processes to respond to changing biological microenvironment during delivery.

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.

Machine learning coupled with causal inference to identify COVID-19 related chemicals that pose a high concern to drinking water

Various synthetic substances were utilized in large quantities during the recent coronavirus pandemic, COVID-19. Some of these chemicals could potentially enter drinking water sources. Persistent, mobile, and toxic (PMT) substances have been recognized as a threat to drinking water resources. It has not yet been assessed how many COVID-19 related substances could be considered PMT substances. One reason is the lack of high-quality experimental data for the identification of PMT substances. To solve this problem, we applied a machine learning model to identify the PMT substances among COVID-19 related chemicals. The optimal model achieved an accuracy of 90.6% based on external test data. The model interpretation and causal inference indicated that our approach understood causation between PMT properties and molecular descriptors. Notably, the screening results showed that over 60% of the COVID-19 chemicals considered are candidate PMT substances, which should be prioritized to prevent undue pollution of water resources.

Self-Assembly and In Situ Quaternization of Triblock Bottlebrush Block Copolymers via Organized Spontaneous Emulsification for Effective Loading of DNA

Microspheres bearing large pores are useful in the capture and separation of biomolecules. However, pore size is typically poorly controlled, leading to disordered porous structures with limited performances. Herein, ordered porous spheres with a layer of cations on the internal surface of the nanopores are facilely fabricated in a single step for effective loading of DNA bearing negative charges. Triblock bottlebrush copolymers (BBCPs), (polynorbornene-g-polystyrene)-b-(polynorbornene-g-polyethylene oxide)-b-(polynorbornene-g-bromoethane) (PNPS-b-PNPEO-b-PNBr), are designed and synthesized for fabrication of the positively charged porous spheres through self-assembly and in situ quaternization during an organized spontaneous emulsification (OSE) process. Pore diameter as well as charge density increase with the increase of PNBr content, resulting in a significant increase of loading density from 4.79 to 22.5 ng µg-1 within the spheres. This work provides a general strategy for efficient loading and encapsulation of DNA, which may be extended to a variety of different areas for different real applications.

Frontiers in nonviral delivery of small molecule and genetic drugs, driven by polymer chemistry and machine learning for materials informatics

Materials informatics (MI) has immense potential to accelerate the pace of innovation and new product development in biotechnology. Close collaborations between skilled physical and life scientists with data scientists are being established in pursuit of leveraging MI tools in automation and artificial intelligence (AI) to predict material properties in vitro and in vivo. However, the scarcity of large, standardized, and labeled materials data for connecting structure-function relationships represents one of the largest hurdles to overcome. In this Highlight, focus is brought to emerging developments in polymer-based therapeutic delivery platforms, where teams generate large experimental datasets around specific therapeutics and successfully establish a design-to-deployment cycle of specialized nanocarriers. Three select collaborations demonstrate how custom-built polymers protect and deliver small molecules, nucleic acids, and proteins, representing ideal use-cases for machine learning to understand how molecular-level interactions impact drug stabilization and release. We conclude with our perspectives on how MI innovations in automation efficiencies and digitalization of data-coupled with fundamental insight and creativity from the polymer science community-can accelerate translation of more gene therapies into lifesaving medicines.

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.

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.

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.

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.

Engineering poly- and micelleplexes for nucleic acid delivery - A reflection on their endosomal escape

For the longest time, the field of nucleic acid delivery has remained skeptical whether or not polycationic drug carrier systems would ever make it into clinical practice. Yet, with the disclosure of patents on polyethyleneimine-based RNA carriers through leading companies in the field of nucleic acid therapeutics such as BioNTech SE and the progress in clinical studies beyond phase I trials, this aloofness seems to regress. As one of the most striking characteristics of polymer-based vectors, the extraordinary tunability can be both a blessing and a curse. Yet, knowing about the adjustment screws and how they impact the performance of the drug carrier provides the formulation scientist committed to its development with a head start. Here, we equip the reader with a toolbox - a toolbox that should advise and support the developer to conceptualize a cutting-edge poly- or micelleplex system for the delivery of therapeutic nucleic acids; to be specific, to engineer the vector towards maximum endosomal escape performance at minimum toxicity. Therefore, after briefly sketching the boundary conditions of polymeric vector design, we will dive into the topic of endosomal trafficking. We will not only discuss the most recent knowledge of the endo-lysosomal compartment but further depict different hypotheses and mechanisms that facilitate the endosomal escape of polyplex systems. Finally, we will combine the different facets introduced in the previous chapters with the fundamental building blocks of polymer vector design and evaluate the advantages and drawbacks. Throughout the article, a particular focus will be placed on cellular peculiarities, not only as an additional barrier, but also to give inspiration to how such cell-specific traits might be capitalized on.

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.

Cationic Micelles Outperform Linear Polymers for Delivery of Antisense Oligonucleotides in Serum: An Exploration of Polymer Architecture, Cationic Moieties, and Cell Addition Order

Antisense oligonucleotides (ASOs) are an important emerging therapeutic; however, they struggle to enter cells without a delivery vehicle, such as a cationic polymer. To understand the role of polymer architecture for ASO delivery, five linear polymers and five diblock polymers (capable of self-assembly into micelles) were synthesized with varying cationic groups. After complexation of each polymer/micelle with ASO, it was found that less bulky cationic moieties transfected the ASO more effectively. Interestingly, however the ASO internalization trend was the opposite of the transfection trend for cationic moiety, indicating internalization is not the major factor in determining transfection efficiency for this series. Micelleplexes (micelle-ASO complexes) generally enable higher transfection efficacy as compared to polyplexes (linear polymer-ASO complexes). Additionally, the order of addition of cells and complexes was explored. Linear polyplexes showed better transfection efficiency in adhered cells, whereas micelleplexes delivered the ASO more efficiently when the cells and micelleplexes were added simultaneously. This phenomenon may be due to increased cell-complex interactions as micelleplexes have increased colloidal stability compared to polyplexes. These findings emphasize the importance of polymer composition and architecture in governing the cellular interactions necessary for transfection, thus allowing advancement in the design principles for nonviral nucleic acid delivery formulations.

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.

Materials Data toward Machine Learning: Advances and Challenges

Machine learning (ML) is believed to have enabled a paradigm shift in materials research, and in practice, ML has demonstrated its power in speeding up the cost-efficient discovery of new materials and autonomizing materials laboratories. In this Perspective, current research progress in materials data which are the backbones of ML are reviewed, focusing on high-throughput data generation, standardized data storage, and data representation. More importantly, the challenging issues in materials data that should be overcome to unlock the full potential of ML in materials research and development, including classic 5V (volume, velocity, variety, veracity, and value) issues, 3M (multicomponent, multiscale, and multistage) challenges, co-mining of experimental and computational data, and materials data toward transferable/explainable ML or causal ML, are discussed.

Can pulmonary RNA delivery improve our pandemic preparedness?

The coronavirus pandemic has changed our perception of RNA medicines, and RNA vaccines have revolutionized our pandemic preparedness. But are we indeed prepared for the next variant or the next emerging virus? How can we prepare? And what does the role of inhaled antiviral RNA play in this regard? When the pandemic started, I rerouted much of the ongoing inhaled RNA delivery research in my group towards the inhibition and treatment of respiratory viral infections. Two years later, I have taken the literature, past and ongoing clinical trials into consideration and have gained new insights based on our collaborative research which I will discuss in this oration.

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.