Peptidic biomaterials represent a particularly exciting topic in regenerative medicine. Peptidic scaffolds can be specifically designed for biomimetic customization for targeted therapy. The field is ...at a pivotal point where preclinical research is being translated into clinics, so it is crucial to understand the theory and describe the status of this rapidly developing technology. In this review, we highlight major advantages and current limitations of self-assembling peptide-based biomaterials, and we discuss the most widely used classes of assembling peptides, describing recent and promising approaches in tissue engineering, drug delivery, and clinics. We also suggest design strategies and hurdles that still need to be overcome to fully exploit their therapeutic potential.
The field of supramolecular peptides self-assembly has undergone outstanding growth since the early 1990s after the serendipitously discovery by Shuguang Zhang of an ionic self-complementary peptide ...as a repeating segment in a yeast protein. From then on, the field expanded at an accelerating pace and these self-assembled materials have become an integral part of a broad plethora of designer supramolecular nanomaterials useful for different applications ranging from 3D tissue cell cultures, regenerative medicine, up to optoelectronics. However, the supramolecular peptide based-nanomaterials available thus far for regenerative medicine still lack the dynamic complexity found in the biological structures that mediate regeneration. Indeed, self-assembling peptide (SAPs) suffer from poor mechanical stability, losing mechanical properties at low strains. Just like the extracellular matrix (ECM) of living systems, the chemical structure of the SAP-biomaterials should concurrently contain non-covalent and covalent bonds, bringing, respectively, infinite and finite lifetimes of interactions to obtain a reversibly dynamic matrix. In this review, will be highlighted the major advantages and current limitations of SAP-based biomaterials, and it will be discussed the most widely used strategies for precisely tune their mechanical properties (stiffness, resilience, strain-failure, stress resistance), describing recent and promising approaches in tissue engineering, regenerative medicine, and beyond.
Self-assembling peptides (SAPs) show great promise in regenerative medicine and beyond. Yet, their low mechanical rigidity and limited processability hinder many of their potential applications. In ...this work we describe the reinforcement of a synthetic biomimetic and β-sheet rich FAQRVPP-LDLK12 SAP by its integration with a naturally occurring polysaccharide - guar gum - to produce a shapeable and thermo-responsive composite hydrogel. Extensive characterization was performed via rheological measurements, CD spectroscopy, FT-IR spectroscopy, and AFM analyses. Composite hydrogels show preserved nanostructured morphology and cross-β structure aggregation typical of SAPs, improved mechanical properties (peaking at 1:1 weight ratio with G′~60 kPa) with self-healing and stress-relaxation propensities: they also exhibit reversible repeatable gel-sol transitions in response to thermal stimuli. Thanks to the dynamic properties of guar gum, stretchability was extended far beyond 5% strain of standard SAPs and processability allowed for the production of knotted threads and long (>10 cm) flexible sheets. Composite hydrogels of guar gum and SAPs may be added to the growing library of biomimetic SAP hydrogels used in 3D-printing, wound healing, suturing and beyond.
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•A synthetic biomimetic self-assembling peptide was reinforced through its integration with a naturally occurring polysaccharide guar gum.•Composite hydrogel showed preserved nanostructured morphology and cross-β structure aggregation typical of SAPs.•Composite hydrogel showed improved mechanical properties (peaking at 1:1 weight ratio with G’~60 kPa) with self-healing propensity.•Composite hydrogel exhibited reversible gel-sol transitions in response to thermal stimuli.•Stretchability of composite hydrogel was extended well beyond 5% strain-limit, typical of standard SAPs.
In the last decades, 3D printing has played a crucial role as an innovative technology for tissue and organ fabrication, patient-specific orthoses, drug delivery, and surgical planning. However, ...biomedical materials used for 3D printing are usually static and unable to dynamically respond or transform within the internal environment of the body. These materials are fabricated ex situ, which involves first printing on a planar substrate and then deploying it to the target surface, thus resulting in a possible mismatch between the printed part and the target surfaces. The emergence of 4D printing addresses some of these drawbacks, opening an attractive path for the biomedical sector. By preprogramming smart materials, 4D printing is able to manufacture structures that dynamically respond to external stimuli. Despite these potentials, 4D printed dynamic materials are still in their infancy of development. The rise of artificial intelligence (AI) could push these technologies forward enlarging their applicability, boosting the design space of smart materials by selecting promising ones with desired architectures, properties, and functions, reducing the time to manufacturing, and allowing the in situ printing directly on target surfaces achieving high-fidelity of human body micro-structures. In this review, an overview of 4D printing as a fascinating tool for designing advanced smart materials is provided. Then will be discussed the recent progress in AI-empowered 3D and 4D printing with open-loop and closed-loop methods, in particular regarding shape-morphing 4D-responsive materials, printing on moving targets, and surgical robots for in situ printing. Lastly, an outlook on 5D printing is given as an advanced future technique, in which AI will assume the role of the fifth dimension to empower the effectiveness of 3D and 4D printing for developing intelligent systems in the biomedical sector and beyond.
Supramolecular nanostructures formed through peptide self-assembly can have a wide range of applications in the biomedical landscape. However, they often lose biomechanical properties at low ...mechanical stress due to the non-covalent interactions working in the self-assembling process. Herein, we report the design of cross-linked self-assembling peptide hydrogels using a one-pot in situ gelation system, based on 1-ethyl-3-3-dimethylaminopropyl carbodiimide/N-hydroxysulfosuccinimide (EDC/sulfo-NHS) coupling, to tune its biomechanics. EDC/sulfo-NHS coupling led to limited changes in storage modulus (from 0.9 to 2 kPa), but it significantly increased both the strain (from 6% to 60%) and failure stress (from 19 to 35 Pa) of peptide hydrogel without impairing the spontaneous formation of β-sheet-containing nano-filaments. Furthermore, EDC/sulfo-NHS cross-linking bestowed self-healing and thixotropic properties to the peptide hydrogel. Lastly, we demonstrated that this strategy can be used to incorporate bioactive functional motifs after self-assembly on pre-formed nanostructures by functionalizing an Ac-LDLKLDLKLDLK-CONH
(LDLK12) self-assembling peptide with the phage display-derived KLPGWSG peptide involved in the modulation of neural stem cell proliferation and differentiation. The incorporation of a functional motif did not alter the peptide's secondary structure and its mechanical properties. The work reported here offers new tools to both fine tune the mechanical properties of and tailor the biomimetic properties of self-assembling peptide hydrogels while retaining their nanostructures, which is useful for tissue engineering and regenerative medicine applications.
The design of biomimetic porous scaffolds has been gaining attention in the biomedical sector lately. Shells, marine sponges, shark teeth, cancellous bone, sea urchin spine, and the armadillo armor ...structure are examples of biological systems that have already been studied to drive the design of innovative, porous, and multifunctional structures. Among these, triply periodic minimal surfaces (TPMSs) have attracted the attention of scientists for the fabrication of biomimetic porous scaffolds. The interest stems from their outstanding properties, which include mathematical controllable geometry features, highly interconnected porous architectures, high surface area to volume ratio, less stress concentration, tunable mechanical properties, and increased permeability. All these distinguishing features enable better cell adhesion, optimal integration to the surrounding tissue avoiding stress shieldings, a good permeability of fluid media and oxygen, and the possibility of vascularization. However, the sophisticated geometry of these TPMS-based structures has proven challenging to fabricate by conventional methods. The emergence of additive manufacturing (AM) and the enhanced manufacturing freedoms and flexibility it guarantees could solve some of the bottlenecks, thus leading to a surge of interest in designing and fabricating such structures in this field. Also, the feasibility of using AM technologies allows for obtaining size programmable TPMS printable in various materials, from polymers to metal alloys. Here, a comprehensive overview of 3D-printed TPMS porous structures is provided from a design for additive manufacturing (DfAM) and application perspective. First, design strategies, geometry design algorithms, and related topological optimization are introduced according to diverse requirements. Based on that, the performance control of TPMS and the pros and cons of the different AM processes for fabricating TPMS scaffolds are summarized. Lastly, practical applications of 3D-printed biomimetic TPMS porous structures for the biomedical field are presented to clarify the advantages and potential of such structures.
Three-dimensional (3D) printing is becoming a booming technology to fabricate scaffolds, orthoses, and prosthetic devices for tissue engineering, regenerative medicine, and rehabilitation for ...patients with disabling neurological diseases (such as amyotrophic lateral sclerosis, traumatic brain injuries, and spinal cord injuries). This is due to the potential of 3D printing to provide patient-specific designs, high structural complexity, and rapid on-demand fabrication at a low-cost. However, one of the major bottlenecks that limits the widespread acceptance of 3D printing for biomedical manufacturing is the lack of polymers, biomaterials, hydrogels, and bioinks functional for 3D printing, biocompatible, and more performing from the biomechanical point of view to meet the different needs. As a matter of fact the field is still struggling with processing of such materials into self-supporting devices with tunable biomechanics, optimal structures, degradation, and bioactivity. Here, will be highlighted all recent advances that have been made in the field of 3D printing in biomedicine, analyzing the polymers, hydrogels, and bioinks, according to their printability, ease of processability, cost, and properties such as mechanics, biocompatibility, and degradation rate. Finally, future considerations for 3D bio-fabrication will be discussed.
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The integration of artificial intelligence (AI) algorithms in materials design is revolutionizing the field of materials engineering thanks to their power to predict material properties, design de ...novo materials with enhanced features, and discover new mechanisms beyond intuition. In addition, they can be used to infer complex design principles and identify high-quality candidates more rapidly than trial-and-error experimentation. From this perspective, herein we describe how these tools can enable the acceleration and enrichment of each stage of the discovery cycle of novel materials with optimized properties. We begin by outlining the state-of-the-art AI models in materials design, including machine learning (ML), deep learning, and materials informatics tools. These methodologies enable the extraction of meaningful information from vast amounts of data, enabling researchers to uncover complex correlations and patterns within material properties, structures, and compositions. Next, a comprehensive overview of AI-driven materials design is provided and its potential future prospects are highlighted. By leveraging such AI algorithms, researchers can efficiently search and analyze databases containing a wide range of material properties, enabling the identification of promising candidates for specific applications. This capability has profound implications across various industries, from drug development to energy storage, where materials performance is crucial. Ultimately, AI-based approaches are poised to revolutionize our understanding and design of materials, ushering in a new era of accelerated innovation and advancement.
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