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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 (R

Polymers are uniquely suited for drug delivery and biomaterials 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.

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.

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 a vast chemical and composition space. Here, we report a strategy 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 chemistries for each enzyme. Overall, this work broadens our capabilities to design fit-for-purpose synthetic copolymers that promote or otherwise manipulate protein activity, with extensions towards the design of robust polymer-protein hybrid materials.

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.

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.

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, is known to degrade the glycosaminoglycan (GAG) side chains of CSPGs and promote 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, we report the discovery of a diverse set of tailor-made random copolymers that complex and stabilize ChABC at physiological temperature. The copolymer designs, which are based on chain length and composition of the copolymers, were 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 hrs at 37°C. We demonstrated significant improvements in REA in three iterations of active learning while identifying exceptionally high-performing copolymers. Most remarkably, one designed copolymer promoted residual ChABC activity near 30%, even after one week and notably outperformed other common stabilization methods for ChABC. Together, these results highlight a promising pathway towards sustained tissue regeneration.

Checkpoint immunotherapy, through the reversal of tumor-mediated inactivation of the immune system, has shown promise in the treatment of several types of cancer. This has culminated in the approval of seven immune checkpoint inhibitors (ICIs). However, only a small population of patients respond to these drugs. Because of the physical and economic burden of ICIs on the patient, there is a critical need to identify biomarkers that can inform on the potential response to ICIs. The presence of tumor infiltrating lymphocytes (TILs) has demonstrated good prognostic value in determining if a patient should receive ICIs. Current clinical methods to assess TILs involve invasive biopsies and immunohistochemistry, which suffer from intratumoral heterogeneity, observer variability, and a lack of real-time feedback. Here, we report on near infrared light excitable rare earth metal-based nanoparticles, termed rare earth albumin nanocomposites (ReANCs), that emit shortwave infrared (SWIR) light, allowing for deep tissue imaging and high signal-to-noise ratios compared to visible or near infrared fluorescence probes. Tumor-targeted ReANCs have been previously employed to monitor tumor progression and response to chemotherapy in mouse models of breast cancer metastasis. In this study, to target CD3+ T cells, ReANCs were conjugated using the zero-length cross-linker 1-ethyl-3-(3-dimethylaminopropyl)carbodiimide (EDC) to a peptide derived from the sequence of the CD3-ϵ receptor sub-unit. Target specific binding was validated by flow cytometry as a measure of increased uptake of peptide-conjugated ReANCs by Jurkat cells. To specifically target cytotoxic T lymphocytes, we employed the fragment antigen binding (Fab) derived from enzymatic digestion of a CD8 antibody (clone 53-6.7) with papain. The Fab fragments were conjugated to ReANCs with sulfo-succinimidyl 4-(N-maleimidomethyl)cyclohexane-1-carboxylate (sulfo-SMCC). Conjugation was confirmed by non-reducing gel electrophoresis and high performance liquid chromatography (HPLC). A loading efficiency of approximately 60% was achieved. Target specific binding was validated by flow cytometry as a measure of increased uptake of Fab-conjugated ReANCs by T cells isolated from splenocytes. We generated a metric for measuring immune burden around tumor spheroids by pre-labeling T cells with ReANCs and co-culturing them with tumor cell spheroids in vitro. Imaging of T cells with CD3 and CD8-targeted ReANCs provides a basis for future in vivo small animal imaging studies where we will investigate the potential of this technology to track immune cells in relation to a tumor in real time. Metrics of immune cell imaging will then inform on the potential of immunotherapy and monitor response to treatment in a longitudinal study. Citation Format: Jay V. Shah, Jake N. Siebert, Amber Gonda, Rahul Pemmaraju, Shashank Kosuri, Carolina Bobadilla Mendez, Xinyu Zhao, Shuqing He, Richard E. Riman, Mei Chee Tan, Mark C. Pierce, Edmund C. Lattime, Prabhas V. Moghe, Vidya Ganapathy. Rare earth albumin nanoparticles engineered to target cytotoxic T cells to evaluate response to immunotherapy [abstract]. In: Proceedings of the American Association for Cancer Research Annual Meeting 2021; 2021 Apr 10-15 and May 17-21. Philadelphia (PA): AACR; Cancer Res 2021;81(13_Suppl):Abstract nr 2802.