1
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Di Mare EJ, Punia A, Lamm MS, Rhodes TA, Gormley AJ. Data-Driven Design of Novel Polymer Excipients for Pharmaceutical Amorphous Solid Dispersions. Bioconjug Chem 2024. [PMID: 39150455 DOI: 10.1021/acs.bioconjchem.4c00294] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 08/17/2024]
Abstract
About 90% of active pharmaceutical ingredients (APIs) in the oral drug delivery system pipeline have poor aqueous solubility and low bioavailability. To address this problem, amorphous solid dispersions (ASDs) embed hydrophobic APIs within polymer excipients to prevent drug crystallization, improve solubility, and increase bioavailability. There are a limited number of commercial polymer excipients, and the structure-function relationships which lead to successful ASD formulations are not well-documented. There are, however, certain solid-state ASD characteristics that inform ASD performance. One characteristic shared by successful ASDs is a high glass transition temperature (Tg), which correlates with higher shelf stability and decreased drug crystallization. We aim to identify how polymer features such as side chain geometry, backbone methylation, and hydrophilic-lipophilic balance impact Tg to design copolymers capable of forming high-Tg ASDs. We tested a library of 50 ASD formulations (18 previously studied and 32 newly synthesized) of the model drug probucol with copolymers synthesized through automated photoinduced electron/energy transfer-reversible addition-fragmentation chain-transfer (PET-RAFT) polymerization. A machine learning (ML) algorithm was trained on the Tg data to identify the major factors influencing Tg, including backbone methylation and nonlinear side chain geometry. In both polymer alone and probucol-loaded ASDs, a Random Forest Regressor captured structure-function trends in the data set and accurately predicted Tg with an average R2 > 0.83 across a 10-fold cross validation. This ML model will be used to predict novel copolymers to design ASDs with high Tg, a crucial factor in predicting ASD success.
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Affiliation(s)
- Elena J Di Mare
- Department of Biomedical Engineering, Rutgers, The State University of New Jersey, Piscataway, New Jersey 08854, United States
| | - Ashish Punia
- Analytical Research and Development, MRL, Merck & Co., Inc., Rahway, New Jersey 07065, United States
| | - Matthew S Lamm
- Analytical Research and Development, MRL, Merck & Co., Inc., Rahway, New Jersey 07065, United States
| | - Timothy A Rhodes
- Analytical Research and Development, MRL, Merck & Co., Inc., Rahway, New Jersey 07065, United States
| | - Adam J Gormley
- Department of Biomedical Engineering, Rutgers, The State University of New Jersey, Piscataway, New Jersey 08854, United States
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2
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Clothier GKK, Guimarães TR, Thompson SW, Howard SC, Muir BW, Moad G, Zetterlund PB. Streamlining the Generation of Advanced Polymer Materials Through the Marriage of Automation and Multiblock Copolymer Synthesis in Emulsion. Angew Chem Int Ed Engl 2024; 63:e202320154. [PMID: 38400586 DOI: 10.1002/anie.202320154] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/08/2024] [Revised: 02/22/2024] [Accepted: 02/23/2024] [Indexed: 02/25/2024]
Abstract
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.
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Affiliation(s)
- Glenn K K Clothier
- Cluster for Advanced Macromolecular Design (CAMD), School of Chemical Engineering, The University of New South Wales, Sydney, NSW 2052, Australia
| | - Thiago R Guimarães
- Laboratoire de Chimie des Polymères Organiques (LCPO), CNRS (UMR 5629), ENSCPB, Université de Bordeaux, 16 avenue Pey Berland, 33607, Pessac, France
| | - Steven W Thompson
- Cluster for Advanced Macromolecular Design (CAMD), School of Chemical Engineering, The University of New South Wales, Sydney, NSW 2052, Australia
| | - Shaun C Howard
- CSIRO Manufacturing, Bag 10, Clayton South, VIC, 3169, Australia
| | - Benjamin W Muir
- CSIRO Manufacturing, Bag 10, Clayton South, VIC, 3169, Australia
| | - Graeme Moad
- CSIRO Manufacturing, Bag 10, Clayton South, VIC, 3169, Australia
| | - Per B Zetterlund
- Cluster for Advanced Macromolecular Design (CAMD), School of Chemical Engineering, The University of New South Wales, Sydney, NSW 2052, Australia
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3
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Chen J, Bhat V, Hawker CJ. High-Throughput Synthesis, Purification, and Application of Alkyne-Functionalized Discrete Oligomers. J Am Chem Soc 2024; 146:8650-8658. [PMID: 38489842 PMCID: PMC10979451 DOI: 10.1021/jacs.4c00751] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/17/2024] [Revised: 02/29/2024] [Accepted: 03/01/2024] [Indexed: 03/17/2024]
Abstract
The development of synthetic oligomers as discrete single molecular entities with accurate control over the number and nature of functional groups along the backbone has enabled a variety of new research opportunities. From fundamental studies of self-assembly in materials science to understanding efficacy and safety profiles in biology and pharmaceuticals, future directions are significantly impacted by the availability of discrete, multifunctional oligomers. However, the preparation of diverse libraries of discrete and stereospecific oligomers remains a significant challenge. We report a novel strategy for accelerating the synthesis and isolation of discrete oligomers in a high-throughput manner based on click chemistry and simplified bead-based purification. The resulting synthetic platform allows libraries of discrete polyether oligomers to be prepared and the impact of variables such as chain length, number, and nature of side chain functionalities and molecular dispersity on antibacterial behavior examined. Significantly, discrete oligomers were shown to exhibit enhanced activity with lower toxicity compared with traditional disperse samples. This work provides a practical and scalable methodology for nonexperts to prepare libraries of multifunctional discrete oligomers and demonstrates the advantages of discrete materials in biological applications.
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Affiliation(s)
- Junfeng Chen
- Materials
Department, Materials Research Laboratory, and Department of Chemistry
and Biochemistry, University of California, Santa Barbara, California 93106, United States
| | - Vittal Bhat
- Materials
Department, Materials Research Laboratory, and Department of Chemistry
and Biochemistry, University of California, Santa Barbara, California 93106, United States
- Department
of Chemistry, University of North Carolina, Chapel Hill, North Carolina 27599, United States
| | - Craig J. Hawker
- Materials
Department, Materials Research Laboratory, and Department of Chemistry
and Biochemistry, University of California, Santa Barbara, California 93106, United States
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4
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Patel RA, Webb MA. Data-Driven Design of Polymer-Based Biomaterials: High-throughput Simulation, Experimentation, and Machine Learning. ACS APPLIED BIO MATERIALS 2024; 7:510-527. [PMID: 36701125 DOI: 10.1021/acsabm.2c00962] [Citation(s) in RCA: 5] [Impact Index Per Article: 5.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 01/27/2023]
Abstract
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.
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Affiliation(s)
- Roshan A Patel
- Department of Chemical and Biological Engineering, Princeton University, Princeton, New Jersey 08540, United States
| | - Michael A Webb
- Department of Chemical and Biological Engineering, Princeton University, Princeton, New Jersey 08540, United States
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5
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Ting JM, Tamayo-Mendoza T, Petersen SR, Van Reet J, Ahmed UA, Snell NJ, Fisher JD, Stern M, Oviedo F. Frontiers in nonviral delivery of small molecule and genetic drugs, driven by polymer chemistry and machine learning for materials informatics. Chem Commun (Camb) 2023; 59:14197-14209. [PMID: 37955165 DOI: 10.1039/d3cc04705a] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/14/2023]
Abstract
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.
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6
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Hu X, Szczepaniak G, Lewandowska-Andralojc A, Jeong J, Li B, Murata H, Yin R, Jazani AM, Das SR, Matyjaszewski K. Red-Light-Driven Atom Transfer Radical Polymerization for High-Throughput Polymer Synthesis in Open Air. J Am Chem Soc 2023; 145:24315-24327. [PMID: 37878520 PMCID: PMC10636753 DOI: 10.1021/jacs.3c09181] [Citation(s) in RCA: 3] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/22/2023] [Revised: 10/08/2023] [Accepted: 10/09/2023] [Indexed: 10/27/2023]
Abstract
Photoinduced reversible-deactivation radical polymerization (photo-RDRP) techniques offer exceptional control over polymerization, providing access to well-defined polymers and hybrid materials with complex architectures. However, most photo-RDRP methods rely on UV/visible light or photoredox catalysts (PCs), which require complex multistep synthesis. Herein, we present the first example of fully oxygen-tolerant red/NIR-light-mediated photoinduced atom transfer radical polymerization (photo-ATRP) in a high-throughput manner under biologically relevant conditions. The method uses commercially available methylene blue (MB+) as the PC and [X-CuII/TPMA]+ (TPMA = tris(2-pyridylmethyl)amine) complex as the deactivator. The mechanistic study revealed that MB+ undergoes a reductive quenching cycle in the presence of the TPMA ligand used in excess. The formed semireduced MB (MB•) sustains polymerization by regenerating the [CuI/TPMA]+ activator and together with [X-CuII/TPMA]+ provides control over the polymerization. This dual catalytic system exhibited excellent oxygen tolerance, enabling polymerizations with high monomer conversions (>90%) in less than 60 min at low volumes (50-250 μL) and high-throughput synthesis of a library of well-defined polymers and DNA-polymer bioconjugates with narrow molecular weight distributions (Đ < 1.30) in an open-air 96-well plate. In addition, the broad absorption spectrum of MB+ allowed ATRP to be triggered under UV to NIR irradiation (395-730 nm). This opens avenues for the integration of orthogonal photoinduced reactions. Finally, the MB+/Cu catalysis showed good biocompatibility during polymerization in the presence of cells, which expands the potential applications of this method.
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Affiliation(s)
- Xiaolei Hu
- Department
of Chemistry, Carnegie Mellon University, Pittsburgh, Pennsylvania 15213, United States
| | - Grzegorz Szczepaniak
- Department
of Chemistry, Carnegie Mellon University, Pittsburgh, Pennsylvania 15213, United States
- Faculty
of Chemistry, University of Warsaw, Pasteura 1, 02-093 Warsaw, Poland
| | - Anna Lewandowska-Andralojc
- Faculty
of Chemistry, Adam Mickiewicz University, Uniwersytetu Poznanskiego 8, 61-614 Poznan, Poland
- Center
for Advanced Technology, Adam Mickiewicz
University, Uniwersytetu
Poznanskiego 10, 61-614 Poznan, Poland
| | - Jaepil Jeong
- Department
of Chemistry, Carnegie Mellon University, Pittsburgh, Pennsylvania 15213, United States
- Center
for Nucleic Acids Science & Technology, Carnegie Mellon University, Pittsburgh, Pennsylvania 15213, United States
| | - Bingda Li
- Department
of Biomedical Engineering, Carnegie Mellon
University, Pittsburgh, Pennsylvania 15213, United States
| | - Hironobu Murata
- Department
of Chemistry, Carnegie Mellon University, Pittsburgh, Pennsylvania 15213, United States
| | - Rongguan Yin
- Department
of Chemistry, Carnegie Mellon University, Pittsburgh, Pennsylvania 15213, United States
| | - Arman Moini Jazani
- Department
of Chemistry, Carnegie Mellon University, Pittsburgh, Pennsylvania 15213, United States
| | - Subha R. Das
- Department
of Chemistry, Carnegie Mellon University, Pittsburgh, Pennsylvania 15213, United States
- Center
for Nucleic Acids Science & Technology, Carnegie Mellon University, Pittsburgh, Pennsylvania 15213, United States
| | - Krzysztof Matyjaszewski
- Department
of Chemistry, Carnegie Mellon University, Pittsburgh, Pennsylvania 15213, United States
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7
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Jafari VF, Mossayebi Z, Allison-Logan S, Shabani S, Qiao GG. The Power of Automation in Polymer Chemistry: Precision Synthesis of Multiblock Copolymers with Block Sequence Control. Chemistry 2023; 29:e202301767. [PMID: 37401148 DOI: 10.1002/chem.202301767] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/02/2023] [Revised: 07/01/2023] [Accepted: 07/03/2023] [Indexed: 07/05/2023]
Abstract
Machines can revolutionize the field of chemistry and material science, driving the development of new chemistries, increasing productivity, and facilitating reaction scale up. The incorporation of automated systems in the field of polymer chemistry has however proven challenging owing to the demanding reaction conditions, rendering the automation setup complex and costly. There is an imminent need for an automation platform which uses fast and simple polymerization protocols, while providing a high level of control on the structure of macromolecules via precision synthesis. This work combines an oxygen tolerant, room temperature polymerization method with a simple liquid handling robot to automatically prepare precise and high order multiblock copolymers with unprecedented livingness even after many chain extensions. The highest number of blocks synthesized in such a system is reported, demonstrating the capabilities of this automated platform for the rapid synthesis and complex polymer structure formation.
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Affiliation(s)
- Vianna F Jafari
- Department of Chemical Engineering, The University of Melbourne, Parkville, VIC 3010, Australia
| | - Zahra Mossayebi
- Department of Chemical Engineering, The University of Melbourne, Parkville, VIC 3010, Australia
| | - Stephanie Allison-Logan
- Department of Chemical Engineering, The University of Melbourne, Parkville, VIC 3010, Australia
| | - Sadegh Shabani
- Department of Chemical Engineering, The University of Melbourne, Parkville, VIC 3010, Australia
| | - Greg G Qiao
- Department of Chemical Engineering, The University of Melbourne, Parkville, VIC 3010, Australia
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8
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McDonald SM, Augustine EK, Lanners Q, Rudin C, Catherine Brinson L, Becker ML. Applied machine learning as a driver for polymeric biomaterials design. Nat Commun 2023; 14:4838. [PMID: 37563117 PMCID: PMC10415291 DOI: 10.1038/s41467-023-40459-8] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/08/2023] [Accepted: 07/24/2023] [Indexed: 08/12/2023] Open
Abstract
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.
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Affiliation(s)
| | - Emily K Augustine
- Thomas Lord Department of Mechanical Engineering and Materials Science, Duke University, Durham, NC, USA
| | - Quinn Lanners
- Department of Biostatistics and Bioinformatics, Duke University, Durham, NC, USA
| | - Cynthia Rudin
- Department of Biostatistics and Bioinformatics, Duke University, Durham, NC, USA
| | - L Catherine Brinson
- Thomas Lord Department of Mechanical Engineering and Materials Science, Duke University, Durham, NC, USA
| | - Matthew L Becker
- Department of Chemistry, Duke University, Durham, NC, USA.
- Thomas Lord Department of Mechanical Engineering and Materials Science, Duke University, Durham, NC, USA.
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9
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Meyer T, Ramirez C, Tamasi MJ, Gormley AJ. A User's Guide to Machine Learning for Polymeric Biomaterials. ACS POLYMERS AU 2023; 3:141-157. [PMID: 37065715 PMCID: PMC10103193 DOI: 10.1021/acspolymersau.2c00037] [Citation(s) in RCA: 7] [Impact Index Per Article: 7.0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 08/05/2022] [Revised: 10/27/2022] [Accepted: 10/27/2022] [Indexed: 11/18/2022]
Abstract
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.
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Affiliation(s)
- Travis
A. Meyer
- Department of Biomedical
Engineering, Rutgers, The State University
of New Jersey, Piscataway, New Jersey 08854, United States
| | - Cesar Ramirez
- Department of Biomedical
Engineering, Rutgers, The State University
of New Jersey, Piscataway, New Jersey 08854, United States
| | - Matthew J. Tamasi
- Department of Biomedical
Engineering, Rutgers, The State University
of New Jersey, Piscataway, New Jersey 08854, United States
| | - Adam J. Gormley
- Department of Biomedical
Engineering, Rutgers, The State University
of New Jersey, Piscataway, New Jersey 08854, United States
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10
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Upadhya R, Di Mare E, Tamasi MJ, Kosuri S, Murthy NS, Gormley AJ. Examining polymer-protein biophysical interactions with small-angle x-ray scattering and quartz crystal microbalance with dissipation. J Biomed Mater Res A 2023; 111:440-450. [PMID: 36537182 PMCID: PMC9908847 DOI: 10.1002/jbm.a.37479] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/18/2022] [Revised: 11/29/2022] [Accepted: 12/05/2022] [Indexed: 12/24/2022]
Abstract
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.
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Affiliation(s)
- Rahul Upadhya
- Department of Biomedical Engineering, RutgersThe State University of New JerseyPiscatawayNew JerseyUSA
| | - Elena Di Mare
- Department of Biomedical Engineering, RutgersThe State University of New JerseyPiscatawayNew JerseyUSA
| | - Matthew J. Tamasi
- Department of Biomedical Engineering, RutgersThe State University of New JerseyPiscatawayNew JerseyUSA
| | - Shashank Kosuri
- Department of Biomedical Engineering, RutgersThe State University of New JerseyPiscatawayNew JerseyUSA
| | - N. Sanjeeva Murthy
- Department of Biomedical Engineering, RutgersThe State University of New JerseyPiscatawayNew JerseyUSA
| | - Adam J. Gormley
- Department of Biomedical Engineering, RutgersThe State University of New JerseyPiscatawayNew JerseyUSA
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11
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Förster C, Lehn R, Andrieu-Brunsen A. Automated Multi- and Block-Copolymer Writing in Mesoporous Films Using Visible-Light PET-RAFT and a Microscope. SMALL (WEINHEIM AN DER BERGSTRASSE, GERMANY) 2023; 19:e2207762. [PMID: 36651003 DOI: 10.1002/smll.202207762] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 12/16/2022] [Indexed: 06/17/2023]
Abstract
For high throughput applications, e.g., in the context of sensing especially when being combined with machine learning, large sample numbers in acceptable production time are required. This needs automated synthesis and material functionalization concepts ideally combined with high precision. To automate sensing relevant mesopore polymer functionalization while being highly precise in polymer placement, polymer amount control, and polymer sequence design, a process for polymer writing in mesoporous silica films with pore diameter in the range of 13 nm is developed. Mesoporous films are functionalized with different polymers in adjustable polymer amount including block-copolymer functionalization in an automated process using a visible-light induced, controlled photo electron/energy transfer-reversible addition-fragmentation chain-transfer (PET-RAFT) polymerization. While transferring this PET-RAFT to a commercially available microscope, direct, automated laser writing of three different polymers, as well as polymer re-initiation is demonstrated. Using a laser diameter of ≈72 µm, significantly smaller polymer spots of ≈7 µm in diameter are realized. Micrometerscale resolved polymer images including block-copolymers are written into mesoporous layers covering millimeter scale areas requiring a writing time in the range of one second per polymer spot.
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Affiliation(s)
- Claire Förster
- Macromolecular Chemistry - Smart Membranes, Technische Universität Darmstadt, 64287, Darmstadt, Germany
| | - Robert Lehn
- Macromolecular Chemistry - Smart Membranes, Technische Universität Darmstadt, 64287, Darmstadt, Germany
| | - Annette Andrieu-Brunsen
- Macromolecular Chemistry - Smart Membranes, Technische Universität Darmstadt, 64287, Darmstadt, Germany
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12
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Kapil K, Jazani AM, Szczepaniak G, Murata H, Olszewski M, Matyjaszewski K. Fully Oxygen-Tolerant Visible-Light-Induced ATRP of Acrylates in Water: Toward Synthesis of Protein-Polymer Hybrids. Macromolecules 2023; 56:2017-2026. [PMID: 36938511 PMCID: PMC10019465 DOI: 10.1021/acs.macromol.2c02537] [Citation(s) in RCA: 5] [Impact Index Per Article: 5.0] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/18/2022] [Revised: 02/05/2023] [Indexed: 02/22/2023]
Abstract
Over the last decade, photoinduced ATRP techniques have been developed to harness the energy of light to generate radicals. Most of these methods require the use of UV light to initiate polymerization. However, UV light has several disadvantages: it can degrade proteins, damage DNA, cause undesirable side reactions, and has low penetration depth in reaction media. Recently, we demonstrated green-light-induced ATRP with dual catalysis, where eosin Y (EYH2) was used as an organic photoredox catalyst in conjunction with a copper complex. This dual catalysis proved to be highly efficient, allowing rapid and well-controlled aqueous polymerization of oligo(ethylene oxide) methyl ether methacrylate without the need for deoxygenation. Herein, we expanded this system to synthesize polyacrylates under biologically relevant conditions using CuII/Me6TREN (Me6TREN = tris[2-(dimethylamino)ethyl]amine) and EYH2 at ppm levels. Water-soluble oligo(ethylene oxide) methyl ether acrylate (average M n = 480, OEOA480) was polymerized in open reaction vessels under green light irradiation (520 nm). Despite continuous oxygen diffusion, high monomer conversions were achieved within 40 min, yielding polymers with narrow molecular weight distributions (1.17 ≤ D̵ ≤ 1.23) for a wide targeted DP range (50-800). In situ chain extension and block copolymerization confirmed the preserved chain end functionality. In addition, polymerization was triggered/halted by turning on/off a green light, showing temporal control. The optimized conditions also enabled controlled polymerization of various hydrophilic acrylate monomers, such as 2-hydroxyethyl acrylate, 2-(methylsulfinyl)ethyl acrylate), and zwitterionic carboxy betaine acrylate. Notably, the method allowed the synthesis of well-defined acrylate-based protein-polymer hybrids using a straightforward reaction setup without rigorous deoxygenation.
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13
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Martin TB, Audus DJ. Emerging Trends in Machine Learning: A Polymer Perspective. ACS POLYMERS AU 2023. [DOI: 10.1021/acspolymersau.2c00053] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 01/19/2023]
Affiliation(s)
- Tyler B. Martin
- National Institute of Standards and Technology, Gaithersburg, Maryland20899, United States
| | - Debra J. Audus
- National Institute of Standards and Technology, Gaithersburg, Maryland20899, United States
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14
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Weiss AM, Hossainy S, Rowan SJ, Hubbell JA, Esser-Kahn AP. Immunostimulatory Polymers as Adjuvants, Immunotherapies, and Delivery Systems. Macromolecules 2022; 55:6913-6937. [PMID: 36034324 PMCID: PMC9404695 DOI: 10.1021/acs.macromol.2c00854] [Citation(s) in RCA: 22] [Impact Index Per Article: 11.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/25/2022] [Revised: 07/16/2022] [Indexed: 12/14/2022]
Abstract
![]()
Activating innate immunity in a controlled manner is
necessary
for the development of next-generation therapeutics. Adjuvants, or
molecules that modulate the immune response, are critical components
of vaccines and immunotherapies. While small molecules and biologics
dominate the adjuvant market, emerging evidence supports the use of
immunostimulatory polymers in therapeutics. Such polymers can stabilize
and deliver cargo while stimulating the immune system by functioning
as pattern recognition receptor (PRR) agonists. At the same time,
in designing polymers that engage the immune system, it is important
to consider any unintended initiation of an immune response that results
in adverse immune-related events. Here, we highlight biologically
derived and synthetic polymer scaffolds, as well as polymer–adjuvant
systems and stimuli-responsive polymers loaded with adjuvants, that
can invoke an immune response. We present synthetic considerations
for the design of such immunostimulatory polymers, outline methods
to target their delivery, and discuss their application in therapeutics.
Finally, we conclude with our opinions on the design of next-generation
immunostimulatory polymers, new applications of immunostimulatory
polymers, and the development of improved preclinical immunocompatibility
tests for new polymers.
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Affiliation(s)
- Adam M. Weiss
- Pritzker School of Molecular Engineering, University of Chicago 5640 S. Ellis Ave., Chicago, Illinois 60637, United States
- Department of Chemistry, University of Chicago 5735 S Ellis Ave., Chicago, Illinois 60637, United States
| | - Samir Hossainy
- Pritzker School of Molecular Engineering, University of Chicago 5640 S. Ellis Ave., Chicago, Illinois 60637, United States
| | - Stuart J. Rowan
- Pritzker School of Molecular Engineering, University of Chicago 5640 S. Ellis Ave., Chicago, Illinois 60637, United States
- Department of Chemistry, University of Chicago 5735 S Ellis Ave., Chicago, Illinois 60637, United States
| | - Jeffrey A. Hubbell
- Pritzker School of Molecular Engineering, University of Chicago 5640 S. Ellis Ave., Chicago, Illinois 60637, United States
| | - Aaron P. Esser-Kahn
- Pritzker School of Molecular Engineering, University of Chicago 5640 S. Ellis Ave., Chicago, Illinois 60637, United States
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15
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Dworakowska S, Lorandi F, Gorczyński A, Matyjaszewski K. Toward Green Atom Transfer Radical Polymerization: Current Status and Future Challenges. ADVANCED SCIENCE (WEINHEIM, BADEN-WURTTEMBERG, GERMANY) 2022; 9:e2106076. [PMID: 35175001 PMCID: PMC9259732 DOI: 10.1002/advs.202106076] [Citation(s) in RCA: 47] [Impact Index Per Article: 23.5] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 01/03/2022] [Indexed: 05/13/2023]
Abstract
Reversible-deactivation radical polymerizations (RDRPs) have revolutionized synthetic polymer chemistry. Nowadays, RDRPs facilitate design and preparation of materials with controlled architecture, composition, and functionality. Atom transfer radical polymerization (ATRP) has evolved beyond traditional polymer field, enabling synthesis of organic-inorganic hybrids, bioconjugates, advanced polymers for electronics, energy, and environmentally relevant polymeric materials for broad applications in various fields. This review focuses on the relation between ATRP technology and the 12 principles of green chemistry, which are paramount guidelines in sustainable research and implementation. The green features of ATRP are presented, discussing the environmental and/or health issues and the challenges that remain to be overcome. Key discoveries and recent developments in green ATRP are highlighted, while providing a perspective for future opportunities in this area.
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Affiliation(s)
- Sylwia Dworakowska
- Department of ChemistryCarnegie Mellon University4400 Fifth AvenuePittsburghPA15213USA
- Faculty of Chemical Engineering and TechnologyCracow University of TechnologyWarszawska 24Cracow31‐155Poland
| | - Francesca Lorandi
- Department of ChemistryCarnegie Mellon University4400 Fifth AvenuePittsburghPA15213USA
- Department of Industrial EngineeringUniversity of Padovavia Marzolo 9Padova35131Italy
| | - Adam Gorczyński
- Department of ChemistryCarnegie Mellon University4400 Fifth AvenuePittsburghPA15213USA
- Faculty of ChemistryAdam Mickiewicz UniversityUniwersytetu Poznańskiego 8Poznań61‐614Poland
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16
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Tamasi MJ, Patel RA, Borca CH, Kosuri S, Mugnier H, Upadhya R, Murthy NS, Webb MA, Gormley AJ. Machine Learning on a Robotic Platform for the Design of Polymer-Protein Hybrids. ADVANCED MATERIALS (DEERFIELD BEACH, FLA.) 2022. [PMID: 35593444 DOI: 10.34770/h938-nn26] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Subscribe] [Scholar Register] [Indexed: 05/06/2023]
Abstract
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.
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Affiliation(s)
- Matthew J Tamasi
- Department of Biomedical Engineering, Rutgers, The State University of New Jersey, Piscataway, NJ, 08854, USA
| | - Roshan A Patel
- Department of Chemical and Biological Engineering, Princeton University, Princeton, NJ, 08544, USA
| | - Carlos H Borca
- Department of Chemical and Biological Engineering, Princeton University, Princeton, NJ, 08544, USA
| | - Shashank Kosuri
- Department of Biomedical Engineering, Rutgers, The State University of New Jersey, Piscataway, NJ, 08854, USA
| | - Heloise Mugnier
- Department of Biomedical Engineering, Rutgers, The State University of New Jersey, Piscataway, NJ, 08854, USA
| | - Rahul Upadhya
- Department of Biomedical Engineering, Rutgers, The State University of New Jersey, Piscataway, NJ, 08854, USA
| | - N Sanjeeva Murthy
- Department of Biomedical Engineering, Rutgers, The State University of New Jersey, Piscataway, NJ, 08854, USA
| | - Michael A Webb
- Department of Chemical and Biological Engineering, Princeton University, Princeton, NJ, 08544, USA
| | - Adam J Gormley
- Department of Biomedical Engineering, Rutgers, The State University of New Jersey, Piscataway, NJ, 08854, USA
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17
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Tamasi MJ, Patel RA, Borca CH, Kosuri S, Mugnier H, Upadhya R, Murthy NS, Webb MA, Gormley AJ. Machine Learning on a Robotic Platform for the Design of Polymer-Protein Hybrids. ADVANCED MATERIALS (DEERFIELD BEACH, FLA.) 2022; 34:e2201809. [PMID: 35593444 PMCID: PMC9339531 DOI: 10.1002/adma.202201809] [Citation(s) in RCA: 35] [Impact Index Per Article: 17.5] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 02/24/2022] [Revised: 04/26/2022] [Indexed: 06/04/2023]
Abstract
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.
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Affiliation(s)
- Matthew J Tamasi
- Department of Biomedical Engineering, Rutgers, The State University of New Jersey, Piscataway, NJ, 08854, USA
| | - Roshan A Patel
- Department of Chemical and Biological Engineering, Princeton University, Princeton, NJ, 08544, USA
| | - Carlos H Borca
- Department of Chemical and Biological Engineering, Princeton University, Princeton, NJ, 08544, USA
| | - Shashank Kosuri
- Department of Biomedical Engineering, Rutgers, The State University of New Jersey, Piscataway, NJ, 08854, USA
| | - Heloise Mugnier
- Department of Biomedical Engineering, Rutgers, The State University of New Jersey, Piscataway, NJ, 08854, USA
| | - Rahul Upadhya
- Department of Biomedical Engineering, Rutgers, The State University of New Jersey, Piscataway, NJ, 08854, USA
| | - N Sanjeeva Murthy
- Department of Biomedical Engineering, Rutgers, The State University of New Jersey, Piscataway, NJ, 08854, USA
| | - Michael A Webb
- Department of Chemical and Biological Engineering, Princeton University, Princeton, NJ, 08544, USA
| | - Adam J Gormley
- Department of Biomedical Engineering, Rutgers, The State University of New Jersey, Piscataway, NJ, 08854, USA
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18
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Kumar R. Materiomically Designed Polymeric Vehicles for Nucleic Acids: Quo Vadis? ACS APPLIED BIO MATERIALS 2022; 5:2507-2535. [PMID: 35642794 DOI: 10.1021/acsabm.2c00346] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/28/2022]
Abstract
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.
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Affiliation(s)
- Ramya Kumar
- Department of Chemical & Biological Engineering, Colorado School of Mines, 1613 Illinois St, Golden, Colorado 80401, United States
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19
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Kosuri S, Borca CH, Mugnier H, Tamasi M, Patel RA, Perez I, Kumar S, Finkel Z, Schloss R, Cai L, Yarmush ML, Webb MA, Gormley AJ. Machine-Assisted Discovery of Chondroitinase ABC Complexes toward Sustained Neural Regeneration. Adv Healthc Mater 2022; 11:e2102101. [PMID: 35112508 PMCID: PMC9119153 DOI: 10.1002/adhm.202102101] [Citation(s) in RCA: 20] [Impact Index Per Article: 10.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/01/2021] [Revised: 12/17/2021] [Indexed: 12/26/2022]
Abstract
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.
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Affiliation(s)
- Shashank Kosuri
- Biomedical Engineering, Rutgers, The State University of New Jersey, Piscataway, New Jersey 08854, USA
| | - Carlos H. Borca
- Chemical and Biological Engineering, Princeton University, Princeton, New Jersey 08544, USA
| | - Heloise Mugnier
- Biomedical Engineering, Rutgers, The State University of New Jersey, Piscataway, New Jersey 08854, USA
| | - Matthew Tamasi
- Biomedical Engineering, Rutgers, The State University of New Jersey, Piscataway, New Jersey 08854, USA
| | - Roshan A. Patel
- Chemical and Biological Engineering, Princeton University, Princeton, New Jersey 08544, USA
| | - Isabel Perez
- Biomedical Engineering, Rutgers, The State University of New Jersey, Piscataway, New Jersey 08854, USA
| | - Suneel Kumar
- Biomedical Engineering, Rutgers, The State University of New Jersey, Piscataway, New Jersey 08854, USA
| | - Zachary Finkel
- Biomedical Engineering, Rutgers, The State University of New Jersey, Piscataway, New Jersey 08854, USA
| | - Rene Schloss
- Biomedical Engineering, Rutgers, The State University of New Jersey, Piscataway, New Jersey 08854, USA
| | - Li Cai
- Biomedical Engineering, Rutgers, The State University of New Jersey, Piscataway, New Jersey 08854, USA
| | - Martin L. Yarmush
- Biomedical Engineering, Rutgers, The State University of New Jersey, Piscataway, New Jersey 08854, USA
| | - Michael A. Webb
- Chemical and Biological Engineering, Princeton University, Princeton, New Jersey 08544, USA
| | - Adam J. Gormley
- Biomedical Engineering, Rutgers, The State University of New Jersey, Piscataway, New Jersey 08854, USA
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20
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Schuett T, Kimmig J, Zechel S, Schubert US. Fully Automated Multi-Step Synthesis of Block Copolymers. Polymers (Basel) 2022; 14:292. [PMID: 35054696 PMCID: PMC8780857 DOI: 10.3390/polym14020292] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/13/2021] [Revised: 12/30/2021] [Accepted: 01/04/2022] [Indexed: 11/24/2022] Open
Abstract
An automated synthesis protocol is developed for the synthesis of block copolymers in a multi-step approach in a fully automated manner. For this purpose, an automated dialysis setup is combined with robot-based synthesis protocols. Consequently, several block copolymerizations are executed completely automated and compared to the respective manual synthesis. As a result, this study opens up the field of autonomous multi-step reactions without any human interactions.
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Affiliation(s)
- Timo Schuett
- Laboratory of Organic and Macromolecular Chemistry (IOMC), Friedrich Schiller University Jena, Humboldtstr. 10, 07743 Jena, Germany; (T.S.); (J.K.); (S.Z.); (U.S.S.)
- Jena Center for Soft Matter (JCSM), Friedrich Schiller University Jena, Philosophenweg 7, 07743 Jena, Germany
| | - Julian Kimmig
- Laboratory of Organic and Macromolecular Chemistry (IOMC), Friedrich Schiller University Jena, Humboldtstr. 10, 07743 Jena, Germany; (T.S.); (J.K.); (S.Z.); (U.S.S.)
- Jena Center for Soft Matter (JCSM), Friedrich Schiller University Jena, Philosophenweg 7, 07743 Jena, Germany
| | - Stefan Zechel
- Laboratory of Organic and Macromolecular Chemistry (IOMC), Friedrich Schiller University Jena, Humboldtstr. 10, 07743 Jena, Germany; (T.S.); (J.K.); (S.Z.); (U.S.S.)
- Jena Center for Soft Matter (JCSM), Friedrich Schiller University Jena, Philosophenweg 7, 07743 Jena, Germany
| | - Ulrich S. Schubert
- Laboratory of Organic and Macromolecular Chemistry (IOMC), Friedrich Schiller University Jena, Humboldtstr. 10, 07743 Jena, Germany; (T.S.); (J.K.); (S.Z.); (U.S.S.)
- Jena Center for Soft Matter (JCSM), Friedrich Schiller University Jena, Philosophenweg 7, 07743 Jena, Germany
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21
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Pu Z, Fan X, Su J, Zhu M, Jiang Z. Aqueous dispersing mechanism study of nonionic polymeric dispersant for organic pigments. Colloid Polym Sci 2022. [DOI: 10.1007/s00396-021-04937-z] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/29/2022]
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22
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Hakobyan K, Xu J, Müllner M. The challenges of controlling polymer synthesis at the molecular and macromolecular level. Polym Chem 2022. [DOI: 10.1039/d1py01581h] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/21/2022]
Abstract
In this Perspective, we outline advances and challenges in controlling the structure of polymers at various size regimes in the context of structural features such as molecular weight distribution, end groups, architecture, composition and sequence.
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Affiliation(s)
- Karen Hakobyan
- Key Centre for Polymers and Colloids, School of Chemistry, The University of Sydney, Sydney, NSW 2006, Australia
- The University of Sydney Nano Institute (Sydney Nano), Sydney, NSW 2006, Australia
- School of Chemical Engineering, UNSW Sydney, NSW 2052, Australia
| | - Jiangtao Xu
- School of Chemical Engineering, UNSW Sydney, NSW 2052, Australia
| | - Markus Müllner
- Key Centre for Polymers and Colloids, School of Chemistry, The University of Sydney, Sydney, NSW 2006, Australia
- The University of Sydney Nano Institute (Sydney Nano), Sydney, NSW 2006, Australia
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23
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Szczepaniak G, Jeong J, Kapil K, Dadashi-Silab S, Yerneni SS, Ratajczyk P, Lathwal S, Schild DJ, Das SR, Matyjaszewski K. Open-air green-light-driven ATRP enabled by dual photoredox/copper catalysis. Chem Sci 2022; 13:11540-11550. [PMID: 36320395 PMCID: PMC9557244 DOI: 10.1039/d2sc04210j] [Citation(s) in RCA: 17] [Impact Index Per Article: 8.5] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/28/2022] [Accepted: 09/19/2022] [Indexed: 11/24/2022] Open
Abstract
Photoinduced atom transfer radical polymerization (photo-ATRP) has risen to the forefront of modern polymer chemistry as a powerful tool giving access to well-defined materials with complex architecture. However, most photo-ATRP systems can only generate radicals under biocidal UV light and are oxygen-sensitive, hindering their practical use in the synthesis of polymer biohybrids. Herein, inspired by the photoinduced electron transfer-reversible addition–fragmentation chain transfer (PET-RAFT) polymerization, we demonstrate a dual photoredox/copper catalysis that allows open-air ATRP under green light irradiation. Eosin Y was used as an organic photoredox catalyst (PC) in combination with a copper complex (X–CuII/L). The role of PC was to trigger and drive the polymerization, while X–CuII/L acted as a deactivator, providing a well-controlled polymerization. The excited PC was oxidatively quenched by X–CuII/L, generating CuI/L activator and PC˙+. The ATRP ligand (L) used in excess then reduced the PC˙+, closing the photocatalytic cycle. The continuous reduction of X–CuII/L back to CuI/L by excited PC provided high oxygen tolerance. As a result, a well-controlled and rapid ATRP could proceed even in an open vessel despite continuous oxygen diffusion. This method allowed the synthesis of polymers with narrow molecular weight distributions and controlled molecular weights using Cu catalyst and PC at ppm levels in both aqueous and organic media. A detailed comparison of photo-ATRP with PET-RAFT polymerization revealed the superiority of dual photoredox/copper catalysis under biologically relevant conditions. The kinetic studies and fluorescence measurements indicated that in the absence of the X–CuII/L complex, green light irradiation caused faster photobleaching of eosin Y, leading to inhibition of PET-RAFT polymerization. Importantly, PET-RAFT polymerizations showed significantly higher dispersity values (1.14 ≤ Đ ≤ 4.01) in contrast to photo-ATRP (1.15 ≤ Đ ≤ 1.22) under identical conditions. Fully oxygen-tolerant photoinduced atom transfer radical polymerization (photo-ATRP) allowed the synthesis of well-defined polymers using a Cu catalyst and eosin Y at ppm levels in both aqueous and organic media.![]()
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Affiliation(s)
- Grzegorz Szczepaniak
- Department of Chemistry, Carnegie Mellon University, Pittsburgh, Pennsylvania 15213, USA
- Faculty of Chemistry, University of Warsaw, Pasteura 1, 02-093 Warsaw, Poland
| | - Jaepil Jeong
- Department of Chemistry, Carnegie Mellon University, Pittsburgh, Pennsylvania 15213, USA
| | - Kriti Kapil
- Department of Chemistry, Carnegie Mellon University, Pittsburgh, Pennsylvania 15213, USA
| | - Sajjad Dadashi-Silab
- Department of Chemistry, Carnegie Mellon University, Pittsburgh, Pennsylvania 15213, USA
| | | | - Paulina Ratajczyk
- Department of Chemistry, Carnegie Mellon University, Pittsburgh, Pennsylvania 15213, USA
- Faculty of Chemistry, Adam Mickiewicz University, Uniwersytetu Poznańskiego 8, 61-614 Poznań, Poland
| | - Sushil Lathwal
- Department of Chemistry, Carnegie Mellon University, Pittsburgh, Pennsylvania 15213, USA
| | - Dirk J. Schild
- Department of Chemistry, Carnegie Mellon University, Pittsburgh, Pennsylvania 15213, USA
| | - Subha R. Das
- Department of Chemistry, Carnegie Mellon University, Pittsburgh, Pennsylvania 15213, USA
- Center for Nucleic Acids Science & Technology, Carnegie Mellon University, Pittsburgh, Pennsylvania 15213, USA
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24
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Esen C, Antonietti M, Kumru B. On the photopolymerization of mevalonic lactone methacrylate: exposing the potential of an overlooked monomer. Polym Chem 2022. [DOI: 10.1039/d1py01497h] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 01/09/2023]
Abstract
This manuscript will exhibit the photopolymerization of mevalonic lactone methacrylate, an overlooked monomer, and how functional polymers with lactone pendant units can be synthesized.
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Affiliation(s)
- Cansu Esen
- Max Planck Institute of Colloids and Interfaces, Department of Colloid Chemistry, Am Mühlenberg 1, 14424 Potsdam, Germany
| | - Markus Antonietti
- Max Planck Institute of Colloids and Interfaces, Department of Colloid Chemistry, Am Mühlenberg 1, 14424 Potsdam, Germany
| | - Baris Kumru
- Max Planck Institute of Colloids and Interfaces, Department of Colloid Chemistry, Am Mühlenberg 1, 14424 Potsdam, Germany
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25
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Sattari K, Xie Y, Lin J. Data-driven algorithms for inverse design of polymers. SOFT MATTER 2021; 17:7607-7622. [PMID: 34397078 DOI: 10.1039/d1sm00725d] [Citation(s) in RCA: 22] [Impact Index Per Article: 7.3] [Reference Citation Analysis] [Abstract] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 06/13/2023]
Abstract
The ever-increasing demand for novel polymers with superior properties requires a deeper understanding and exploration of the chemical space. Recently, data-driven approaches to explore the chemical space for polymer design have emerged. Among them, inverse design strategies for designing polymers with specific properties have evolved to be a significant materials informatics platform by learning hidden knowledge from materials data as well as smartly navigating the chemical space in an optimized way. In this review, we first summarize the progress in the representation of polymers, a prerequisite step for the inverse design of polymers. Then, we systematically introduce three data-driven strategies implemented for the inverse design of polymers, i.e., high-throughput virtual screening, global optimization, and generative models. Finally, we discuss the challenges and opportunities of the data-driven strategies as well as optimization algorithms employed in the inverse design of polymers.
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Affiliation(s)
- Kianoosh Sattari
- Department of Mechanical and Aerospace Engineering, University of Missouri, Columbia, MO 65211, USA.
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26
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Gormley AJ, Webb MA. Machine learning in combinatorial polymer chemistry. NATURE REVIEWS. MATERIALS 2021; 6:642-644. [PMID: 34394961 PMCID: PMC8356908 DOI: 10.1038/s41578-021-00282-3] [Citation(s) in RCA: 55] [Impact Index Per Article: 18.3] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 05/05/2023]
Abstract
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.
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Affiliation(s)
- Adam J. Gormley
- Department of Biomedical Engineering, Rutgers, The State University of New Jersey, Piscataway, NJ, USA
| | - Michael A. Webb
- Department of Chemical and Biological Engineering, Princeton University, Princeton, NJ, USA
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27
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Soheilmoghaddam F, Rumble M, Cooper-White J. High-Throughput Routes to Biomaterials Discovery. Chem Rev 2021; 121:10792-10864. [PMID: 34213880 DOI: 10.1021/acs.chemrev.0c01026] [Citation(s) in RCA: 12] [Impact Index Per Article: 4.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/12/2022]
Abstract
Many existing clinical treatments are limited in their ability to completely restore decreased or lost tissue and organ function, an unenviable situation only further exacerbated by a globally aging population. As a result, the demand for new medical interventions has increased substantially over the past 20 years, with the burgeoning fields of gene therapy, tissue engineering, and regenerative medicine showing promise to offer solutions for full repair or replacement of damaged or aging tissues. Success in these fields, however, inherently relies on biomaterials that are engendered with the ability to provide the necessary biological cues mimicking native extracellular matrixes that support cell fate. Accelerating the development of such "directive" biomaterials requires a shift in current design practices toward those that enable rapid synthesis and characterization of polymeric materials and the coupling of these processes with techniques that enable similarly rapid quantification and optimization of the interactions between these new material systems and target cells and tissues. This manuscript reviews recent advances in combinatorial and high-throughput (HT) technologies applied to polymeric biomaterial synthesis, fabrication, and chemical, physical, and biological screening with targeted end-point applications in the fields of gene therapy, tissue engineering, and regenerative medicine. Limitations of, and future opportunities for, the further application of these research tools and methodologies are also discussed.
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Affiliation(s)
- Farhad Soheilmoghaddam
- Tissue Engineering and Microfluidics Laboratory (TEaM), Australian Institute for Bioengineering and Nanotechnology (AIBN), University Of Queensland, St. Lucia, Queensland, Australia 4072.,School of Chemical Engineering, University Of Queensland, St. Lucia, Queensland, Australia 4072
| | - Madeleine Rumble
- Tissue Engineering and Microfluidics Laboratory (TEaM), Australian Institute for Bioengineering and Nanotechnology (AIBN), University Of Queensland, St. Lucia, Queensland, Australia 4072.,School of Chemical Engineering, University Of Queensland, St. Lucia, Queensland, Australia 4072
| | - Justin Cooper-White
- Tissue Engineering and Microfluidics Laboratory (TEaM), Australian Institute for Bioengineering and Nanotechnology (AIBN), University Of Queensland, St. Lucia, Queensland, Australia 4072.,School of Chemical Engineering, University Of Queensland, St. Lucia, Queensland, Australia 4072
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28
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Upadhya R, Kosuri S, Tamasi M, Meyer TA, Atta S, Webb MA, Gormley AJ. Automation and data-driven design of polymer therapeutics. Adv Drug Deliv Rev 2021; 171:1-28. [PMID: 33242537 PMCID: PMC8127395 DOI: 10.1016/j.addr.2020.11.009] [Citation(s) in RCA: 23] [Impact Index Per Article: 7.7] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/30/2020] [Revised: 11/10/2020] [Accepted: 11/12/2020] [Indexed: 01/01/2023]
Abstract
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.
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Affiliation(s)
| | | | | | | | - Supriya Atta
- Rutgers, The State University of New Jersey, USA
| | - Michael A Webb
- Department of Chemical and Biological Engineering, Princeton University, Princeton, NJ 08540, USA
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Upadhya R, Punia A, Kanagala MJ, Liu L, Lamm M, Rhodes TA, Gormley AJ. Automated PET-RAFT Polymerization Towards Pharmaceutical Amorphous Solid Dispersion Development. ACS APPLIED POLYMER MATERIALS 2021; 3:1525-1536. [PMID: 34368765 PMCID: PMC8336633 DOI: 10.1021/acsapm.0c01376] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 06/13/2023]
Abstract
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.
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Affiliation(s)
- Rahul Upadhya
- Department of Biomedical Engineering, Rutgers, The State University of New Jersey, Piscataway, NJ 08854, USA
| | - Ashish Punia
- Preformulation Sciences, MRL, Merck & Co., Inc., Rahway, NJ 07065, USA
| | - Mythili J. Kanagala
- Department of Biomedical Engineering, Rutgers, The State University of New Jersey, Piscataway, NJ 08854, USA
| | - Lina Liu
- Preformulation Sciences, MRL, Merck & Co., Inc., Rahway, NJ 07065, USA
| | - Matthew Lamm
- Preformulation Sciences, MRL, Merck & Co., Inc., Rahway, NJ 07065, USA
| | - Timothy A. Rhodes
- Preformulation Sciences, MRL, Merck & Co., Inc., Rahway, NJ 07065, USA
| | - Adam J. Gormley
- Department of Biomedical Engineering, Rutgers, The State University of New Jersey, Piscataway, NJ 08854, USA
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Atta S, Cohen J, Kohn J, Gormley AJ. Ring opening polymerization of ε-caprolactone through water. Polym Chem 2021. [DOI: 10.1039/d0py01481h] [Citation(s) in RCA: 6] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/21/2022]
Abstract
Ring opening polymerization (ROP) through water is used to synthesize biodegradable polymers such as polycaprolactone (PCL).
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Affiliation(s)
- Supriya Atta
- Department of Biomedical Engineering Rutgers
- The State University of New Jersey
- Piscataway
- USA
| | - Jarrod Cohen
- Department of Chemistry and Chemical Biology Rutgers
- The State University of New Jersey
- Piscataway
- USA
| | - Joachim Kohn
- Department of Chemistry and Chemical Biology Rutgers
- The State University of New Jersey
- Piscataway
- USA
| | - Adam J. Gormley
- Department of Biomedical Engineering Rutgers
- The State University of New Jersey
- Piscataway
- USA
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31
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Schuett T, Kimmig J, Zechel S, Schubert US. Automated Polymer Purification Using Dialysis. Polymers (Basel) 2020; 12:E2095. [PMID: 32942646 PMCID: PMC7569804 DOI: 10.3390/polym12092095] [Citation(s) in RCA: 9] [Impact Index Per Article: 2.3] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/20/2020] [Revised: 09/03/2020] [Accepted: 09/09/2020] [Indexed: 01/05/2023] Open
Abstract
The automated dialysis of polymers in synthetic robots is described as a first approach for the purification of polymers using an automated protocol. For this purpose, a dialysis apparatus was installed within a synthesis robot. Therein, the polymer solution could be transferred automatically into the dialysis tube. Afterwards, a permanent running dialysis could be started, enabling the removal of residual monomer. Purification efficiency was studied using chromatography and NMR spectroscopy, showing that the automated dialysis requires less solvent and is faster compared to the classical manual approach.
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Affiliation(s)
- Timo Schuett
- Laboratory of Organic and Macromolecular Chemistry (IOMC), Friedrich Schiller University Jena, Humboldtstr. 10, 07743 Jena, Germany; (T.S.); (J.K.); (S.Z.)
- Jena Center of Soft Matter (JCSM), Friedrich Schiller University Jena, Philosophenweg 7, 07743 Jena, Germany
| | - Julian Kimmig
- Laboratory of Organic and Macromolecular Chemistry (IOMC), Friedrich Schiller University Jena, Humboldtstr. 10, 07743 Jena, Germany; (T.S.); (J.K.); (S.Z.)
- Jena Center of Soft Matter (JCSM), Friedrich Schiller University Jena, Philosophenweg 7, 07743 Jena, Germany
| | - Stefan Zechel
- Laboratory of Organic and Macromolecular Chemistry (IOMC), Friedrich Schiller University Jena, Humboldtstr. 10, 07743 Jena, Germany; (T.S.); (J.K.); (S.Z.)
- Jena Center of Soft Matter (JCSM), Friedrich Schiller University Jena, Philosophenweg 7, 07743 Jena, Germany
| | - Ulrich S. Schubert
- Laboratory of Organic and Macromolecular Chemistry (IOMC), Friedrich Schiller University Jena, Humboldtstr. 10, 07743 Jena, Germany; (T.S.); (J.K.); (S.Z.)
- Jena Center of Soft Matter (JCSM), Friedrich Schiller University Jena, Philosophenweg 7, 07743 Jena, Germany
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Abstract
With the rapid development of high technology, chemical science is not as it used to be a century ago. Many chemists acquire and utilize skills that are well beyond the traditional definition of chemistry. The digital age has transformed chemistry laboratories. One aspect of this transformation is the progressing implementation of electronics and computer science in chemistry research. In the past decade, numerous chemistry-oriented studies have benefited from the implementation of electronic modules, including microcontroller boards (MCBs), single-board computers (SBCs), professional grade control and data acquisition systems, as well as field-programmable gate arrays (FPGAs). In particular, MCBs and SBCs provide good value for money. The application areas for electronic modules in chemistry research include construction of simple detection systems based on spectrophotometry and spectrofluorometry principles, customizing laboratory devices for automation of common laboratory practices, control of reaction systems (batch- and flow-based), extraction systems, chromatographic and electrophoretic systems, microfluidic systems (classical and nonclassical), custom-built polymerase chain reaction devices, gas-phase analyte detection systems, chemical robots and drones, construction of FPGA-based imaging systems, and the Internet-of-Chemical-Things. The technology is easy to handle, and many chemists have managed to train themselves in its implementation. The only major obstacle in its implementation is probably one's imagination.
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Affiliation(s)
- Gurpur Rakesh D Prabhu
- Department of Chemistry, National Tsing Hua University, 101, Section 2, Kuang-Fu Road, Hsinchu, 30013, Taiwan.,Department of Applied Chemistry, National Chiao Tung University, 1001 University Road, Hsinchu, 300, Taiwan
| | - Pawel L Urban
- Department of Chemistry, National Tsing Hua University, 101, Section 2, Kuang-Fu Road, Hsinchu, 30013, Taiwan.,Frontier Research Center on Fundamental and Applied Sciences of Matters, National Tsing Hua University, 101, Section 2, Kuang-Fu Road, Hsinchu, 30013, Taiwan
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