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.

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.

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.

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.

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.

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.

Continuous microfluidic assembly of biodegradable poly(beta-amino ester)/DNA nanoparticles for enhanced gene delivery.

Translation of biomaterial-based nanoparticle formulations to the clinic faces significant challenges including efficacy, safety, consistency and scale-up of manufacturing, and stability during long-term storage. Continuous microfluidic fabrication of polymeric nanoparticles has the potential to alleviate the challenges associated with manufacture, while offering a scalable solution for clinical level production. Poly(beta-amino esters) (PBAE)s are a class of biodegradable cationic polymers that self-assemble with anionic plasmid DNA to form polyplex nanoparticles that have been shown to be effective for transfecting cancer cells specifically in vitro and in vivo. Here, we demonstrate the use of a microfluidic device for the continuous and scalable production of PBAE/DNA nanoparticles followed by lyophilization and long term storage that results in improved in vitro efficacy in multiple cancer cell lines compared to nanoparticles produced by bulk mixing as well as in comparison to widely used commercially available transfection reagents polyethylenimine and Lipofectamine® 2000. We further characterized the nanoparticles using nanoparticle tracking analysis (NTA) to show that microfluidic mixing resulted in fewer DNA-free polymeric nanoparticles compared to those produced by bulk mixing. © 2017 Wiley Periodicals, Inc. J Biomed Mater Res Part A: 105A: 1813-1825, 2017.