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Borges J, Zeng J, Liu XQ, Chang H, Monge C, Garot C, Ren KF, Machillot P, Vrana NE, Lavalle P, Akagi T, Matsusaki M, Ji J, Akashi M, Mano JF, Gribova V, Picart C. Recent Developments in Layer-by-Layer Assembly for Drug Delivery and Tissue Engineering Applications. Adv Healthc Mater 2024; 13:e2302713. [PMID: 38116714 DOI: 10.1002/adhm.202302713] [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: 08/17/2023] [Revised: 11/27/2023] [Indexed: 12/21/2023]
Abstract
Surfaces with biological functionalities are of great interest for biomaterials, tissue engineering, biophysics, and for controlling biological processes. The layer-by-layer (LbL) assembly is a highly versatile methodology introduced 30 years ago, which consists of assembling complementary polyelectrolytes or biomolecules in a stepwise manner to form thin self-assembled films. In view of its simplicity, compatibility with biological molecules, and adaptability to any kind of supporting material carrier, this technology has undergone major developments over the past decades. Specific applications have emerged in different biomedical fields owing to the possibility to load or immobilize biomolecules with preserved bioactivity, to use an extremely broad range of biomolecules and supporting carriers, and to modify the film's mechanical properties via crosslinking. In this review, the focus is on the recent developments regarding LbL films formed as 2D or 3D objects for applications in drug delivery and tissue engineering. Possible applications in the fields of vaccinology, 3D biomimetic tissue models, as well as bone and cardiovascular tissue engineering are highlighted. In addition, the most recent technological developments in the field of film construction, such as high-content liquid handling or machine learning, which are expected to open new perspectives in the future developments of LbL, are presented.
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Grants
- GA259370 ERC "BIOMIM"
- GA692924 ERC "BioactiveCoatings"
- GA790435 ERC "Regenerbone"
- ANR-17-CE13-022 Agence Nationale de la Recherche "CODECIDE", "OBOE", "BuccaVac"
- ANR-18-CE17-0016 Agence Nationale de la Recherche "CODECIDE", "OBOE", "BuccaVac"
- 192974 Agence Nationale de la Recherche "CODECIDE", "OBOE", "BuccaVac"
- ANR-20-CE19-022 BIOFISS Agence Nationale de la Recherche "CODECIDE", "OBOE", "BuccaVac"
- ANR22-CE19-0024 SAFEST Agence Nationale de la Recherche "CODECIDE", "OBOE", "BuccaVac"
- DOS0062033/0 FUI-BPI France
- 883370 European Research Council "REBORN"
- 2020.00758.CEECIND Portuguese Foundation for Science and Technology
- UIDB/50011/2020,UIDP/50011/2020,LA/P/0006/2020 FCT/MCTES (PIDDAC)
- 751061 European Union's Horizon 2020 "PolyVac"
- 11623 Sidaction
- 20H00665 JSPS Grant-in-Aid for Scientific Research
- 3981662 BPI France Aide Deep Tech programme
- ECTZ60600 Agence Nationale de Recherches sur le Sida et les Hépatites Virales
- 101079482 HORIZON EUROPE Framework Programme "SUPRALIFE"
- 101058554 Horizon Europe EIC Accelerator "SPARTHACUS"
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Affiliation(s)
- João Borges
- CICECO - Aveiro Institute of Materials, Department of Chemistry, University of Aveiro, Campus Universitário de Santiago, Aveiro, 3810-193, Portugal
| | - Jinfeng Zeng
- Division of Applied Chemistry, Graduate School of Engineering, Osaka University, 2-1 Yamadaoka, Suita, Osaka, 565-0871, Japan
| | - Xi Qiu Liu
- School of Pharmacy, Tongji Medical College, Huazhong University of Science and Technology, Wuhan, 430030, China
| | - Hao Chang
- Hangzhou Institute of Medicine, Chinese Academy of Sciences, Hangzhou, Zhejiang, 310022, China
| | - Claire Monge
- Laboratory of Tissue Biology and Therapeutic Engineering (LBTI), UMR5305 CNRS/Universite Claude Bernard Lyon 1, 7 Passage du Vercors, Lyon, 69367, France
| | - Charlotte Garot
- Université de Grenoble Alpes, CEA, INSERM U1292 Biosanté, CNRS EMR 5000 Biomimetism and Regenerative Medicine (BRM), 17 avenue des Martyrs, Grenoble, F-38054, France
| | - Ke-Feng Ren
- Department of Polymer Science and Engineering, Zhejiang University, Hangzhou, 310027, China
| | - Paul Machillot
- Université de Grenoble Alpes, CEA, INSERM U1292 Biosanté, CNRS EMR 5000 Biomimetism and Regenerative Medicine (BRM), 17 avenue des Martyrs, Grenoble, F-38054, France
| | - Nihal E Vrana
- SPARTHA Medical, 1 Rue Eugène Boeckel, Strasbourg, 67000, France
| | - Philippe Lavalle
- SPARTHA Medical, 1 Rue Eugène Boeckel, Strasbourg, 67000, France
- Institut National de la Santé et de la Recherche Médicale, Inserm UMR_S 1121 Biomaterials and Bioengineering, Centre de Recherche en Biomédecine de Strasbourg, 1 rue Eugène Boeckel, Strasbourg, 67000, France
- Université de Strasbourg, Faculté de Chirurgie Dentaire, 1 place de l'Hôpital, Strasbourg, 67000, France
| | - Takami Akagi
- Building Block Science Joint Research Chair, Graduate School of Frontier Biosciences, Osaka University, 1-3 Yamadaoka, Suita, Osaka, 565-0871, Japan
| | - Michiya Matsusaki
- Division of Applied Chemistry, Graduate School of Engineering, Osaka University, 2-1 Yamadaoka, Suita, Osaka, 565-0871, Japan
| | - Jian Ji
- Department of Polymer Science and Engineering, Zhejiang University, Hangzhou, 310027, China
| | - Mitsuru Akashi
- Building Block Science Joint Research Chair, Graduate School of Frontier Biosciences, Osaka University, 1-3 Yamadaoka, Suita, Osaka, 565-0871, Japan
| | - João F Mano
- CICECO - Aveiro Institute of Materials, Department of Chemistry, University of Aveiro, Campus Universitário de Santiago, Aveiro, 3810-193, Portugal
| | - Varvara Gribova
- Institut National de la Santé et de la Recherche Médicale, Inserm UMR_S 1121 Biomaterials and Bioengineering, Centre de Recherche en Biomédecine de Strasbourg, 1 rue Eugène Boeckel, Strasbourg, 67000, France
- Université de Strasbourg, Faculté de Chirurgie Dentaire, 1 place de l'Hôpital, Strasbourg, 67000, France
| | - Catherine Picart
- Université de Grenoble Alpes, CEA, INSERM U1292 Biosanté, CNRS EMR 5000 Biomimetism and Regenerative Medicine (BRM), 17 avenue des Martyrs, Grenoble, F-38054, France
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Falcone N, Ermis M, Tamay DG, Mecwan M, Monirizad M, Mathes TG, Jucaud V, Choroomi A, de Barros NR, Zhu Y, Vrana NE, Kraatz HB, Kim HJ, Khademhosseini A. Peptide Hydrogels as Immunomaterials and Their Use in Cancer Immunotherapy Delivery. Adv Healthc Mater 2023; 12:e2301096. [PMID: 37256647 PMCID: PMC10615713 DOI: 10.1002/adhm.202301096] [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: 04/06/2023] [Revised: 05/15/2023] [Indexed: 06/01/2023]
Abstract
Peptide-based hydrogel biomaterials have emerged as an excellent strategy for immune system modulation. Peptide-based hydrogels are supramolecular materials that self-assemble into various nanostructures through various interactive forces (i.e., hydrogen bonding and hydrophobic interactions) and respond to microenvironmental stimuli (i.e., pH, temperature). While they have been reported in numerous biomedical applications, they have recently been deemed promising candidates to improve the efficacy of cancer immunotherapies and treatments. Immunotherapies seek to harness the body's immune system to preemptively protect against and treat various diseases, such as cancer. However, their low efficacy rates result in limited patient responses to treatment. Here, the immunomaterial's potential to improve these efficacy rates by either functioning as immune stimulators through direct immune system interactions and/or delivering a range of immune agents is highlighted. The chemical and physical properties of these peptide-based materials that lead to immuno modulation and how one may design a system to achieve desired immune responses in a controllable manner are discussed. Works in the literature that reports peptide hydrogels as adjuvant systems and for the delivery of immunotherapies are highlighted. Finally, the future trends and possible developments based on peptide hydrogels for cancer immunotherapy applications are discussed.
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Affiliation(s)
- Natashya Falcone
- Terasaki Institute for Biomedical Innovation, 1018 Westwood Blvd, Los Angeles, CA, 90034, USA
| | - Menekse Ermis
- Terasaki Institute for Biomedical Innovation, 1018 Westwood Blvd, Los Angeles, CA, 90034, USA
- BIOMATEN, Center of Excellence in Biomaterials and Tissue Engineering, Middle East Technical University, Ankara, 06800, Turkey
| | - Dilara Goksu Tamay
- BIOMATEN, Center of Excellence in Biomaterials and Tissue Engineering, Middle East Technical University, Ankara, 06800, Turkey
- Department of Biotechnology, Middle East Technical University, Ankara, 06800, Turkey
| | - Marvin Mecwan
- Terasaki Institute for Biomedical Innovation, 1018 Westwood Blvd, Los Angeles, CA, 90034, USA
| | - Mahsa Monirizad
- Terasaki Institute for Biomedical Innovation, 1018 Westwood Blvd, Los Angeles, CA, 90034, USA
| | - Tess Grett Mathes
- Terasaki Institute for Biomedical Innovation, 1018 Westwood Blvd, Los Angeles, CA, 90034, USA
| | - Vadim Jucaud
- Terasaki Institute for Biomedical Innovation, 1018 Westwood Blvd, Los Angeles, CA, 90034, USA
| | - Auveen Choroomi
- Terasaki Institute for Biomedical Innovation, 1018 Westwood Blvd, Los Angeles, CA, 90034, USA
| | - Natan Roberto de Barros
- Terasaki Institute for Biomedical Innovation, 1018 Westwood Blvd, Los Angeles, CA, 90034, USA
| | - Yangzhi Zhu
- Terasaki Institute for Biomedical Innovation, 1018 Westwood Blvd, Los Angeles, CA, 90034, USA
| | - Nihal Engin Vrana
- SPARTHA Medical, CRBS 1 Rue Eugene Boeckel, Strasbourg, 67000, France
| | - Heinz-Bernhard Kraatz
- Department of Chemical Engineering and Applied Chemistry, University of Toronto, Toronto, ON, M5S 3E5, Canada
- Department of Physical and Environmental Sciences, University of Toronto Scarborough, Toronto, ON, M1C 1A4, Canada
| | - Han-Jun Kim
- Terasaki Institute for Biomedical Innovation, 1018 Westwood Blvd, Los Angeles, CA, 90034, USA
- College of Pharmacy, Korea University, Sejong, 30019, Republic of Korea
| | - Ali Khademhosseini
- Terasaki Institute for Biomedical Innovation, 1018 Westwood Blvd, Los Angeles, CA, 90034, USA
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Frisch E, Clavier L, Belhamdi A, Vrana NE, Lavalle P, Frisch B, Heurtault B, Gribova V. Preclinical in vitro evaluation of implantable materials: conventional approaches, new models and future directions. Front Bioeng Biotechnol 2023; 11:1193204. [PMID: 37576997 PMCID: PMC10416115 DOI: 10.3389/fbioe.2023.1193204] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/24/2023] [Accepted: 07/12/2023] [Indexed: 08/15/2023] Open
Abstract
Nowadays, implants and prostheses are widely used to repair damaged tissues or to treat different diseases, but their use is associated with the risk of infection, inflammation and finally rejection. To address these issues, new antimicrobial and anti-inflammatory materials are being developed. Aforementioned materials require their thorough preclinical testing before clinical applications can be envisaged. Although many researchers are currently working on new in vitro tissues for drug screening and tissue replacement, in vitro models for evaluation of new biomaterials are just emerging and are extremely rare. In this context, there is an increased need for advanced in vitro models, which would best recapitulate the in vivo environment, limiting animal experimentation and adapted to the multitude of these materials. Here, we overview currently available preclinical methods and models for biological in vitro evaluation of new biomaterials. We describe several biological tests used in biocompatibility assessment, which is a primordial step in new material's development, and discuss existing challenges in this field. In the second part, the emphasis is made on the development of new 3D models and approaches for preclinical evaluation of biomaterials. The third part focuses on the main parameters to consider to achieve the optimal conditions for evaluating biocompatibility; we also overview differences in regulations across different geographical regions and regulatory systems. Finally, we discuss future directions for the development of innovative biomaterial-related assays: in silico models, dynamic testing models, complex multicellular and multiple organ systems, as well as patient-specific personalized testing approaches.
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Affiliation(s)
- Emilie Frisch
- Université de Strasbourg, CNRS UMR 7199, 3Bio Team, Laboratoire de Conception et Application de Molécules Bioactives, Faculté de Pharmacie, Strasbourg, France
| | - Lisa Clavier
- Institut National de la Santé et de la Recherche Médicale, Inserm UMR_S 1121 Biomaterials and Bioengineering, Centre de Recherche en Biomédecine de Strasbourg, Strasbourg, France
- Université de Strasbourg, Faculté de Chirurgie Dentaire, Strasbourg, France
| | | | | | - Philippe Lavalle
- Institut National de la Santé et de la Recherche Médicale, Inserm UMR_S 1121 Biomaterials and Bioengineering, Centre de Recherche en Biomédecine de Strasbourg, Strasbourg, France
- Université de Strasbourg, Faculté de Chirurgie Dentaire, Strasbourg, France
- SPARTHA Medical, Strasbourg, France
| | - Benoît Frisch
- Université de Strasbourg, CNRS UMR 7199, 3Bio Team, Laboratoire de Conception et Application de Molécules Bioactives, Faculté de Pharmacie, Strasbourg, France
| | - Béatrice Heurtault
- Université de Strasbourg, CNRS UMR 7199, 3Bio Team, Laboratoire de Conception et Application de Molécules Bioactives, Faculté de Pharmacie, Strasbourg, France
| | - Varvara Gribova
- Institut National de la Santé et de la Recherche Médicale, Inserm UMR_S 1121 Biomaterials and Bioengineering, Centre de Recherche en Biomédecine de Strasbourg, Strasbourg, France
- Université de Strasbourg, Faculté de Chirurgie Dentaire, Strasbourg, France
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Trzepieciński T, Najm SM, Ibrahim OM, Kowalik M. Analysis of the Frictional Performance of AW-5251 Aluminium Alloy Sheets Using the Random Forest Machine Learning Algorithm and Multilayer Perceptron. MATERIALS (BASEL, SWITZERLAND) 2023; 16:5207. [PMID: 37569911 PMCID: PMC10420024 DOI: 10.3390/ma16155207] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Subscribe] [Scholar Register] [Received: 06/05/2023] [Revised: 07/18/2023] [Accepted: 07/20/2023] [Indexed: 08/13/2023]
Abstract
This paper is devoted to the determination of the coefficient of friction (COF) in the drawbead region in metal forming processes. As the test material, AW-5251 aluminium alloys sheets fabricated under various hardening conditions (AW-5251-O, AW-5251-H14, AW-5251-H16 and AW-5251H22) were used. The sheets were tested using a drawbead simulator with different countersample roughness and different orientations of the specimens in relation to the sheet rolling direction. A drawbead simulator was designed to model the friction conditions when the sheet metal passed through the drawbead in sheet metal forming. The experimental tests were carried out under conditions of dry friction and lubrication of the sheet metal surfaces with three lubricants: machine oil, hydraulic oil, and engine oil. Based on the results of the experimental tests, the value of the COF was determined. The Random Forest (RF) machine learning algorithm and artificial neural networks (ANNs) were used to identify the parameters affecting the COF. The R statistical package software version 4.1.0 was used for running the RF model and neural network. The relative importance of the inputs was analysed using 12 different activation functions in ANNs and nine different loss functions in the RF. Based on the experimental tests, it was concluded that the COF for samples cut along the sheet rolling direction was greater than for samples cut in the transverse direction. However, the COF's most relevant input was oil viscosity (0.59), followed by the average counter sample roughness Ra (0.30) and the yield stress Rp0.2 and strength coefficient K (0.05 and 0.06, respectively). The hard sigmoid activation function had the poorest R2 (0.25) and nRMSE (0.30). The ideal run was found after training and testing the RF model (R2 = 0.90 ± 0.028). Ra values greater than 1.1 and Rp0.2 values between 105 and 190 resulted in a decreased COF. The COF values dropped to 9-35 for viscosity and 105-190 for Rp0.2, with a gap between 110 and 130 when the oil viscosity was added. The COF was low when the oil viscosity was 9-35, and the Ra was 0.95-1.25. The interaction between K and the other inputs, which produces a relatively limited range of reduced COF values, was the least relevant. The COF was reduced by setting the Rp0.2 between 105 and 190, the Ra between 0.95 and 1.25, and the oil viscosity between 9 and 35.
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Affiliation(s)
- Tomasz Trzepieciński
- Department of Manufacturing Processes and Production Engineering, Faculty of Mechanical Engineering and Aeronautics, Rzeszow University of Technology, al. Powst. Warszawy 8, 35-959 Rzeszów, Poland
| | - Sherwan Mohammed Najm
- Kirkuk Technical Institute, Northern Technical University, 36001 Kirkuk, Iraq;
- Department of Manufacturing Science and Engineering, Budapest University of Technology and Economics, Műegyetemrkp 3, H-1111 Budapest, Hungary
| | - Omar Maghawry Ibrahim
- Plant Production Department, Arid Land Cultivation Research Institute, City of Scientific Research and Technological Applications SRTA-City, Borg Al-Arab 21934, Egypt;
| | - Marek Kowalik
- Faculty of Mechanical Engineering, Kazimierz Pulaski University of Technology and Humanities in Radom, 54 Stasieckiego Street, 26-600 Radom, Poland;
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