AAV6 mediated Gsx1 expression in neural stem progenitor cells promotes neurogenesis and restores locomotor function after contusion spinal cord injury.

Genomic screened homeobox 1 (Gsx1 or Gsh1) is a neurogenic transcription factor required for the generation of excitatory and inhibitory interneurons during spinal cord development. In the adult, lentivirus (LV) mediated Gsx1 expression promotes neural regeneration and functional locomotor recovery in a mouse model of lateral hemisection spinal cord injury (SCI). The LV delivery method is clinically unsafe due to insertional mutations to the host DNA. In addition, the most common clinical case of SCI is contusion/compression. In this study, we identify that adeno-associated virus serotype 6 (AAV6) preferentially infects neural stem/progenitor cells (NSPCs) in the injured spinal cord. Using a rat model of contusion SCI, we demonstrate that AAV6 mediated Gsx1 expression promotes neurogenesis, increases the number of neuroblasts/immature neurons, restores excitatory/inhibitory neuron balance and serotonergic neuronal activity through the lesion core, and promotes locomotor functional recovery. Our findings support that AAV6 preferentially targets NSPCs for gene delivery and confirmed Gsx1 efficacy in clinically relevant rat model of contusion SCI.

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.

Examining polymer-protein biophysical interactions with small-angle x-ray scattering and quartz crystal microbalance with dissipation.

Polymer-protein hybrids can be deployed to improve protein solubility and stability in denaturing environments. While previous work used robotics and active machine learning to inform new designs, further biophysical information is required to ascertain structure-function behavior. Here, we show the value of tandem small-angle x-ray scattering (SAXS) and quartz crystal microbalance with dissipation (QCMD) experiments to reveal detailed polymer-protein interactions with horseradish peroxidase (HRP) as a test case. Of particular interest was the process of polymer-protein complex formation under thermal stress whereby SAXS monitors formation in solution while QCMD follows these dynamics at an interface. The radius of gyration (Rg ) of the protein as measured by SAXS does not change significantly in the presence of polymer under denaturing conditions, but thickness and dissipation changes were observed in QCMD data. SAXS data with and without thermal stress were utilized to create bead models of the potential complexes and denatured enzyme, and each model fit provided insight into the degree of interactions. Additionally, QCMD data demonstrated that HRP deforms by spreading upon surface adsorption at low concentration as shown by longer adsorption times and smaller frequency shifts. In contrast, thermally stressed and highly inactive HRP had faster adsorption kinetics. The combination of SAXS and QCMD serves as a framework for biophysical characterization of interactions between proteins and polymers which could be useful in designing polymer-protein hybrids.

Martini 3 Coarse-Grained Force Field for Carbohydrates.

The Martini 3 force field is a full reparametrization of the Martini coarse-grained model for biomolecular simulations. Due to the improved interaction balance, it allows for a more accurate description of condensed phase systems. In the present work, we develop a consistent strategy to parametrize carbohydrate molecules accurately within the framework of Martini 3. In particular, we develop a canonical mapping scheme which decomposes arbitrarily large carbohydrates into a limited number of fragments. Bead types for these fragments have been assigned by matching physicochemical properties of mono- and disaccharides. In addition, guidelines for assigning bonds, angles, and dihedrals were developed. These guidelines enable a more accurate description of carbohydrate conformations than in the Martini 2 force field. We show that models obtained with this approach are able to accurately reproduce osmotic pressures of carbohydrate water solutions. Furthermore, we provide evidence that the model differentiates correctly the solubility of the polyglucoses dextran (water-soluble) and cellulose (water insoluble but soluble in ionic liquids). Finally, we demonstrate that the new building blocks can be applied to glycolipids. We show they are able to reproduce membrane properties and induce binding of peripheral membrane proteins. These test cases demonstrate the validity and transferability of our approach.

Polymer Texture Influences Cell Responses in Osteogenic Microparticles.

Introduction: Polymer materials used in medical devices and treatments invariably encounter cellular networks. For the device to succeed in tissue engineering applications, the polymer must promote cellular interactions through adhesion and proliferation. To predict how a polymer will behave in vitro, these material-cell interactions need to be well understood. Methods: To study polymer structure-property relationships, microparticles of four chemically distinct tyrosol-derived poly(ester-arylate) polymers and a commercially available poly(lactic acid-co-glycolic acid) (PLGA) copolymer were prepared and their interactions with cells investigated. Cell loading concentration was optimized and cell adhesion and proliferation evaluated. Particles were also tested for their ability to adsorb bone morphogenetic protein-2 (BMP-2) and differentiate a myoblast cell line towards an osteoblast lineage through BMP-2 loading and release. Results: While cell adhesion was observed on all particles after 24 h of incubation, the highest degree of cell adhesion occurred on polymers with smaller crystallites. At longer incubation times, cells proliferated on all particle formulations, regardless of the differences in polymer properties. High BMP-2 loading was achieved for all particle formulations and all formulations showed a burst release. Even with the burst release, cells cultured on all formulations showed an upregulation in alkaline phosphatase (ALP) activity, a measure of osteoblast differentiation. Conclusions: As with cell adhesion, the polymer with the smaller crystallite showed the most ALP activity. We suggest that smaller crystallites serve as a proxy for topographical roughness to elicit the observed responses from cells. Furthermore, we have drawn a correlation between the polymer crystallite with the hydration potential using surface analysis techniques. Supplementary Information: The online version contains supplementary material available at 10.1007/s12195-022-00729-9.

In vitro and in vivo investigation of a zonal microstructured scaffold for osteochondral defect repair.

Articular cartilage is comprised of zones that vary in architecture, extracellular matrix composition, and mechanical properties. Here, we designed and engineered a porous zonal microstructured scaffold from a single biocompatible polymer (poly [ϵ-caprolactone]) using multiple fabrication strategies: electrospinning, spherical porogen leaching, directional freezing, and melt electrowriting. With this approach we mimicked the zonal structure of articular cartilage and produced a stiffness gradient through the scaffold which aligns with the mechanics of the native tissue. Chondrocyte-seeded scaffolds accumulated extracellular matrix including glycosaminoglycans and collagen II over four weeks in vitro. This prompted us to further study the repair efficacy in a skeletally mature porcine model. Two osteochondral lesions were produced in the trochlear groove of 12 animals and repaired using four treatment conditions: (1) microstructured scaffold, (2) chondrocyte seeded microstructured scaffold, (3) MaioRegen™, and (4) empty defect. After 6 months the defect sites were harvested and analyzed using histology, micro computed tomography, and Raman microspectroscopy mapping. Overall, the scaffolds were retained in the defect space, repair quality was repeatable, and there was clear evidence of osteointegration. The repair quality of the microstructured scaffolds was not superior to the control based on histological scoring; however, the lower score was biased by the lack of histological staining due to the limited degradation of the implant at 6 months. Longer follow up studies (e.g., 1 yr) will be required to fully evaluate the efficacy of the microstructured scaffold. In conclusion, we found consistent scaffold retention, osteointegration, and prolonged degradation of the microstructured scaffold, which we propose may have beneficial effects for the long-term repair of osteochondral defects.

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.

Machine-Assisted Discovery of Chondroitinase ABC Complexes toward Sustained Neural Regeneration.

Among the many molecules that contribute to glial scarring, chondroitin sulfate proteoglycans (CSPGs) are known to be potent inhibitors of neuronal regeneration. Chondroitinase ABC (ChABC), a bacterial lyase, degrades the glycosaminoglycan (GAG) side chains of CSPGs and promotes tissue regeneration. However, ChABC is thermally unstable and loses all activity within a few hours at 37 °C under dilute conditions. To overcome this limitation, the discovery of a diverse set of tailor-made random copolymers that complex and stabilize ChABC at physiological temperature is reported. The copolymer designs, which are based on chain length and composition of the copolymers, are identified using an active machine learning paradigm, which involves iterative copolymer synthesis, testing for ChABC thermostability upon copolymer complexation, Gaussian process regression modeling, and Bayesian optimization. Copolymers are synthesized by automated PET-RAFT and thermostability of ChABC is assessed by retained enzyme activity (REA) after 24 h at 37 °C. Significant improvements in REA in three iterations of active learning are demonstrated while identifying exceptionally high-performing copolymers. Most remarkably, one designed copolymer promotes residual ChABC activity near 30%, even after one week and notably outperforms other common stabilization methods for ChABC. Together, these results highlight a promising pathway toward sustained tissue regeneration.

Machine learning in combinatorial polymer chemistry.

The design of new functional polymers depends on the successful navigation of their structure-function landscapes. Advances in combinatorial polymer chemistry and machine learning provide exciting opportunities for the engineering of fit-for-purpose polymeric materials.

Automated PET-RAFT Polymerization Towards Pharmaceutical Amorphous Solid Dispersion Development.

In pharmaceutical oral drug delivery development, about 90% of drugs in the pipeline have poor aqueous solubility leading to severe challenges with oral bioavailability and translation to effective and safe drug products. Amorphous solid dispersions (ASDs) have been utilized to enhance the oral bioavailability of poorly soluble active pharmaceutical ingredients (APIs). However, a limited selection of regulatory-approved polymer excipients exists for the development and further understanding of tailor-made ASDs. Thus, a significant need exists to better understand how polymers can be designed to interact with specific API moieties. Here, we demonstrate how an automated combinatorial library approach can be applied to the synthesis and screening of polymer excipients for the model drug probucol. We synthesized a library of 25 random heteropolymers containing one hydrophilic monomer (2-hydroxypropyl acrylate (HPA)) and four hydrophobic monomers at varied incorporation. The performance of ASDs made by a rapid film casting method was evaluated by dissolution using ultra-performance liquid chromatography (UPLC) sampling at various time points. This combinatorial library and rapid screening strategy enabled us to identify a relationship between polymer hydrophobicity, monomer hydrophobic side group geometry, and API dissolution performance. Remarkably, the most effective synthesized polymers displayed slower drug release kinetics compared to industry standard polymer excipients, showing the ability to modulate the drug release profile. Future coupling of high throughput polymer synthesis, high throughput screening (HTS), and quantitative modeling would enable specification of designer polymer excipients for specific API functionalities.

Tyrosol-Derived Biodegradable Inks with Tunable Properties for 3D Printing.

Three-dimensional (3D) printing has emerged as a valuable tool in medicine over the past few decades. With a growing number of applications using this advanced processing technique, new polymer libraries with varied properties are required. Herein, we investigate tyrosol-based poly(ester-arylate)s as biodegradable inks in fused deposition modeling (FDM). Tyrosol-based polycarbonates and polyesters have proven to be useful biomaterials due to their excellent tunability, nonacidic degradation components, and the ability to be functionalized. Polymers are synthesized by polycondensation between a custom diphenol and commercially available diacids. Thermal properties, degradation rates, and mechanical properties are all tunable based on the diphenol and diacid chosen. Evaluation of material print as it relates to chemical structure, molecular weight, and thermal properties was explored. Higher-molecular-weight polymers greater than 50 kDa exhibit thermal degradation during printing and at some points are too viscous to print. It was determined that polymers with lower processing temperatures and molecular weights were printable regardless of the structure. An exception to this was pHTy6 that was printed at 65 kDa with minimal degradation. This is most likely due to its low melting temperature and, as a result, lower printing temperatures. Additionally, chemical improvements were made to incorporate thiol-alkene click chemistry as a means for postprint curing. Low-molecular-weight pHTy6 was end-capped with alkene functionality. This material was then formulated with either a dithiol for chain extension or tetrathiol for cross-linking. Scaffolds were cured after printing for 5, 15, 30 and 60 min intervals where longer cure times resulted in a tougher material. This design builds on the library of biologically active materials previously explored and aims to bring new biomaterials to the field of 3D-printed personal medicine.

Tyrosol Derived Poly(ester-arylate)s for Sustained Drug Delivery from Microparticles.

New biodegradable polymers are needed for use in drug delivery systems to overcome the high burst release, lack of sustained drug release, and acidic degradation products frequently observed in current formulations. Commercially available poly(lactide-co-glycolide) (PLGA) is often used for particle drug release formulations; however, it is often limited by its large burst release and acidic degradation products. Therefore, a biocompatible and biodegradable tyrosol-derived poly(ester-arylate) library has been used to prepare a microparticle drug delivery system which shows sustained delivery of hydrophobic drugs. Studies were performed using polymers with varying hydrophilicity and thermal properties and compared to PLGA. Various drug solubilizing cosolvents were used to load model drugs curcumin, dexamethasone, nicotinamide, and acyclovir. Hydrophobic drugs curcumin and dexamethasone were successfully loaded up to 50 weight percent (wt %), and a linear correlation between drug wt % loaded and the particle glass transition temperature (Tg) was observed. Both curcumin and dexamethasone were visible on the particle surface at 20 wt % loading and higher. By adjusting the polymer concentration during particle formation, release rates were able to be controlled. Release studies of dexamethasone loaded particles with a lower polymer concentration showed a biphasic release profile and complete release after 47 days. Particles prepared using a higher polymer concentration showed sustained release for up to 77 days. Comparably, PLGA showed a traditional triphasic release profile and complete release after 63 days. This novel tyrosol-derived poly(ester-arylate) library can be used to develop injectable, long-term release formulations capable of providing sustained drug delivery.

Automation and data-driven design of polymer therapeutics.

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

Automation of Controlled/Living Radical Polymerization.

Controlled/living radical polymerization (CLRP) techniques are widely utilized to synthesize advanced and controlled synthetic polymers for chemical and biological applications. While automation has long stood as a high-throughput (HTP) research tool to increase productivity as well as synthetic/analytical reliability and precision, oxygen intolerance of CLRP has limited the widespread adoption of these systems. Recently, however, oxygen-tolerant CLRP techniques, such as oxygen-tolerant photoinduced electron/energy transfer-reversible addition-fragmentation chain transfer (PET-RAFT), enzyme degassing of RAFT (Enz-RAFT), and atom-transfer radical polymerization (ATRP), have emerged. Herein, the use of a Hamilton MLSTARlet liquid handling robot for automating CLRP reactions is demonstrated. Synthesis processes are developed using Python and used to automate reagent handling, dispensing sequences, and synthesis steps required to create homopolymers, random heteropolymers, and block copolymers in 96-well plates, as well as postpolymerization modifications. Using this approach, the synergy between highly customizable liquid handling robotics and oxygen-tolerant CLRP to automate advanced polymer synthesis for HTP and combinatorial polymer research is demonstrated.

Purifying Low-Volume Combinatorial Polymer Libraries with Gel Filtration Columns.

Recent advances in oxygen-tolerant controlled/living radical polymer chemistry now enable efficient synthesis of diverse and combinatorial polymer libraries. While library synthesis has been dramatically simplified, equally efficient purification strategies for removal of small-molecule impurities are not yet established in high throughput settings. It is shown that gel filtration columns for chromatography frequently used in the protein science community are well suited for high throughput polymer purification. Using either single-use columns or gel filtration plates, the purification of 32 diverse polymers is demonstrated in a library with >95% removal of small molecule impurities and >85% polymer retention in a single purification step. Doing so replaces the typical procedure of polymer precipitation, which requires solvent optimization for each polymer in a complex library. Overall, this work raises awareness in the polymer science community that gel filtration is amenable to purification of large polymer libraries and can speed up the progress of combinatorial polymer chemistry.

A Dual Wavelength Polymerization and Bioconjugation Strategy for High Throughput Synthesis of Multivalent Ligands.

Structure-function relationships for multivalent polymer scaffolds are highly complex due to the wide diversity of architectures offered by such macromolecules. Evaluation of this landscape has traditionally been accomplished case-by-case due to the experimental difficulty associated with making these complex conjugates. Here, we introduce a simple dual-wavelength, two-step polymerize and click approach for making combinatorial conjugate libraries. It proceeds by incorporation of a polymerization friendly cyclopropenone-masked dibenzocyclooctyne into the side chain of linear polymers or the α-chain end of star polymers. Polymerizations are performed under visible light using an oxygen tolerant porphyrin-catalyzed photoinduced electron/energy transfer-reversible addition-fragmentation chain-transfer (PET-RAFT) process, after which the deprotection and click reaction is triggered by UV light. Using this approach, we are able to precisely control the valency and position of ligands on a polymer scaffold in a manner conducive to high throughput synthesis.

Free radical-mediated targeting and immobilization of coupled payloads.

Targeted drug delivery is a promising approach to enhance the accumulation of therapies in diseased tissues while limiting off-site effects. Ligand-receptor interactions are traditionally identified to deliver therapies, and although specific, this can be costly and often suffers from limited sensitivity. An emerging approach is to target intermediary species that modulate disease progression. Here, we propose novel methods of targeting therapies by using native free radicals as a homing signal. Elevated concentrations of free radicals are a characteristic comorbidity of many different diseases. In polymer chemistry, free radicals are frequently used to initiate crosslinking reactions. We proposed that free radicals elevated in injury sites are capable of inducing crosslinking of acrylate groups on polymer chains. Coupling payloads to the polymer then allow for specific targeting of therapies to areas with elevated free radicals. We demonstrate in vitro proof-of-principle of this approach. Reactive oxygen species (ROS) initiated crosslinking of acrylated PEGs, which immobilized a fluorescent payload within tissue mimics. The cross-linking efficiency and immobilization potential varied with the polymer chain length, suggesting that a tuneable platform can be achieved. Together these results provide promising proof-of-concept for using free radicals to specifically target and sustain nearly endless payloads to disease sites.

PET-RAFT and SAXS: High Throughput Tools to Study Compactness and Flexibility of Single-Chain Polymer Nanoparticles.

From protein science, it is well understood that ordered folding and 3D structure mainly arises from balanced and noncovalent polar and nonpolar interactions, such as hydrogen bonding. Similarly, it is understood that single-chain polymer nanoparticles (SCNPs) will also compact and become more rigid with greater hydrophobicity and intrachain hydrogen bonding. Here, we couple high throughput photoinduced electron/energy transfer reversible addition-fragmentation chain-transfer (PET-RAFT) polymerization with high throughput small-angle X-ray scattering (SAXS) to characterize a large combinatorial library (>450) of several homopolymers, random heteropolymers, block copolymers, PEG-conjugated polymers, and other polymer-functionalized polymers. Coupling these two high throughput tools enables us to study the major influence(s) for compactness and flexibility in higher breadth than ever before possible. Not surprisingly, we found that many were either highly disordered in solution, in the case of a highly hydrophilic polymer, or insoluble if too hydrophobic. Remarkably, we also found a small group (9/457) of PEG-functionalized random heteropolymers and block copolymers that exhibited compactness and flexibility similar to that of bovine serum albumin (BSA) by dynamic light scattering (DLS), NMR, and SAXS. In general, we found that describing a rough association between compactness and flexibility parameters (R g /R h and Porod Exponent, respectively) with logP, a quantity that describes hydrophobicity, helps to demonstrate and predict material parameters that lead to SCNPs with greater compactness, rigidity, and stability. Future implementation of this combinatorial and high throughput approach for characterizing SCNPs will allow for the creation of detailed design parameters for well-defined macromolecular chemistry.

Up in the air: oxygen tolerance in controlled/living radical polymerisation.

The requirement for deoxygenation in controlled/living radical polymerisation (CLRP) places significant limitations on its widespread implementation by necessitating the use of large reaction volumes, sealed reaction vessels as well as requiring access to specialised equipment such as a glove box and/or inert gas source. As a result, in recent years there has been intense interest in developing strategies for overcoming the effects of oxygen inhibition in CLRP and therefore remove the necessity for deoxygenation. In this review, we highlight several strategies for achieving oxygen tolerant CLRP including: "polymerising through" oxygen, enzyme mediated deoxygenation and the continuous regeneration of a redox-active catalyst. In order to provide further clarity to the field, we also establish some basic parameters for evaluating the degree of "oxygen tolerance" that can be achieved using a given oxygen scrubbing strategy. Finally, we propose some applications that could most benefit from the implementation of oxygen tolerant CLRP and provide a perspective on the future direction of this field.