Biomaterials by design: Harnessing data for future development

Gold-Hydrogel Nanocomposites for High-Resolution Laser-Based 3D Printing of Scaffolds with SERS-Sensing Properties

Although visible light-based stereolithography (SLA) represents an affordable technology for the rapid prototyping of 3D scaffolds for in vitro support of cells, its potential could be limited by the lack of functional photocurable biomaterials that can be SLA-structured at micrometric resolution. Even if innovative photocomposites showing biomimetic, bioactive, or biosensing properties have been engineered by loading inorganic particles into photopolymer matrices, main examples rely on UV-assisted extrusion-based low-resolution processes. Here, SLA-printable composites were obtained by mixing a polyethylene glycol diacrylate (PEGDA) hydrogel with multibranched gold nanoparticles (NPs). NPs were engineered to copolymerize with the PEGDA matrix by implementing a functionalization protocol involving covalent grafting of allylamine molecules that have C═C pendant moieties. The formulations of gold nanocomposites were tailored to achieve high-resolution fast prototyping of composite scaffolds via visible light-based SLA. Furthermore, it was demonstrated that, after mixing with a polymer and after laser structuring, gold NPs still retained their unique plasmonic properties and could be exploited for optical detection of analytes through surface-enhanced Raman spectroscopy (SERS). As a proof of concept, SERS-sensing performances of 3D printed plasmonic scaffolds were successfully demonstrated with a Raman probe molecule (e.g., 4-mercaptobenzoic acid) from the perspective of future extensions to real-time sensing of cell-specific markers released within cultures. Finally, biocompatibility tests preliminarily demonstrated that embedded NPs also played a key role by inducing physiological cell-cytoskeleton rearrangements, further confirming the potentialities of such hybrid nanocomposites as groundbreaking materials in laser-based bioprinting.

Tailor-Made Polysaccharides for Biomedical Applications

Polysaccharides (PSAs) are carbohydrate-based macromolecules widely used in the biomedical field, either in their pure form or in blends/nanocomposites with other materials. The relationship between structure, properties, and functions has inspired scientists to design multifunctional PSAs for various biomedical applications by incorporating unique molecular structures and targeted bulk properties. Multiple strategies, such as conjugation, grafting, cross-linking, and functionalization, have been explored to control their mechanical properties, electrical conductivity, hydrophilicity, degradability, rheological features, and stimuli-responsiveness. For instance, custom-made PSAs are known for their worldwide biomedical applications in tissue engineering, drug/gene delivery, and regenerative medicine. Furthermore, the remarkable advancements in supramolecular engineering and chemistry have paved the way for mission-oriented biomaterial synthesis and the fabrication of customized biomaterials. These materials can synergistically combine the benefits of biology and chemistry to tackle important biomedical questions. Herein, we categorize and summarize PSAs based on their synthesis methods, and explore the main strategies used to customize their chemical structures. We then highlight various properties of PSAs using practical examples. Lastly, we thoroughly describe the biomedical applications of tailor-made PSAs, along with their current existing challenges and potential future directions.

Recent Advances in 3D Printing of Smart Scaffolds for Bone Tissue Engineering and Regeneration

The repair and functional reconstruction of bone defects resulting from severe trauma, surgical resection, degenerative disease, and congenital malformation pose significant clinical challenges. Bone tissue engineering (BTE) holds immense potential in treating these severe bone defects, without incurring prevalent complications associated with conventional autologous or allogeneic bone grafts. 3D printing technology enables control over architectural structures at multiple length scales and has been extensively employed to process biomimetic scaffolds for BTE. In contrast to inert and functional bone grafts, next-generation smart scaffolds possess a remarkable ability to mimic the dynamic nature of native extracellular matrix (ECM), thereby facilitating bone repair and regeneration. Additionally, they can generate tailored and controllable therapeutic effects, such as antibacterial or antitumor properties, in response to exogenous and/or endogenous stimuli. This review provides a comprehensive assessment of the progress of 3D-printed smart scaffolds for BTE applications. It begins with an introduction to bone physiology, followed by an overview of 3D printing technologies utilized for smart scaffolds. Notable advances in various stimuli-responsive strategies, therapeutic efficacy, and applications of 3D-printed smart scaffolds are discussed. Finally, the review highlights the existing challenges in the development and clinical implementation of smart scaffolds, as well as emerging technologies in this field.

AI energized hydrogel design, optimization and application in biomedicine

Traditional hydrogel design and optimization methods usually rely on repeated experiments, which is time-consuming and expensive, resulting in a slow-moving of advanced hydrogel development. With the rapid development of artificial intelligence (AI) technology and increasing material data, AI-energized design and optimization of hydrogels for biomedical applications has emerged as a revolutionary breakthrough in materials science. This review begins by outlining the history of AI and the potential advantages of using AI in the design and optimization of hydrogels, such as prediction and optimization of properties, multi-attribute optimization, high-throughput screening, automated material discovery, optimizing experimental design, and etc. Then, we focus on the various applications of hydrogels supported by AI technology in biomedicine, including drug delivery, bio-inks for advanced manufacturing, tissue repair, and biosensors, so as to provide a clear and comprehensive understanding of researchers in this field. Finally, we discuss the future directions and prospects, and provide a new perspective for the research and development of novel hydrogel materials for biomedical applications.

Unleashing the Power of Artificial Intelligence in Materials Design

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.

MLATE: Machine learning for predicting cell behavior on cardiac tissue engineering scaffolds

Cardiovascular disease is one of the leading causes of mortality worldwide and is responsible for millions of deaths annually. One of the most promising approaches to deal with this problem, which has spread recently, is cardiac tissue engineering (CTE). Many researchers have tried developing scaffolds with different materials, cell lines, and fabrication methods to help regenerate heart tissue. Machine learning (ML) is one of the hottest topics in science and technology, revolutionizing many fields and changing our perspective on solving problems. As a result of using ML, some scientific issues have been resolved, including protein-folding, a challenging problem in biology that remained unsolved for 50 years. However, it is not well addressed in tissue engineering. An AI-based software was developed by our group called MLATE (Machine Learning Applications in Tissue Engineering) to tackle tissue engineering challenges, which highly depend on conducting costly and time-consuming experiments. For the first time, to the best of our knowledge, a CTE scaffold dataset was created by collecting specifications from the literature, including different materials, cell lines, and fabrication methods commonly used in CTE scaffold development. These specifications were used as variables in the study. Then, the CTE scaffolds were rated based on cell behaviors such as cell viability, growth, proliferation, and differentiation on the scaffold on a scale of 0-3. These ratings were considered a function of the variables in the gathered dataset. It should be stated that this study was merely based on information available in the literature. Then, twenty-eight ML algorithms were applied to determine the most effective one for predicting cell behavior on CTE scaffolds fabricated by different materials, compositions, and methods. The results indicated the high performance of XGBoost with an accuracy of 87%. Also, by implementing ensemble learning algorithms and using five algorithms with the best performance, an accuracy of 93% with the AdaBoost Classifier and Voting Classifier was achieved. Finally, the open-source software developed in this study was made available for everyone by publishing the best model along with a step-by-step guide to using it online at: https://github.com/saeedrafieyan/MLATE.

A User's Guide to Machine Learning for Polymeric Biomaterials

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.

How can machine learning and multiscale modeling benefit ocular drug development?

The eyes possess sophisticated physiological structures, diverse disease targets, limited drug delivery space, distinctive barriers, and complicated biomechanical processes, requiring a more in-depth understanding of the interactions between drug delivery systems and biological systems for ocular formulation development. However, the tiny size of the eyes makes sampling difficult and invasive studies costly and ethically constrained. Developing ocular formulations following conventional trial-and-error formulation and manufacturing process screening procedures is inefficient. Along with the popularity of computational pharmaceutics, non-invasive in silico modeling & simulation offer new opportunities for the paradigm shift of ocular formulation development. The current work first systematically reviews the theoretical underpinnings, advanced applications, and unique advantages of data-driven machine learning and multiscale simulation approaches represented by molecular simulation, mathematical modeling, and pharmacokinetic (PK)/pharmacodynamic (PD) modeling for ocular drug development. Following this, a new computer-driven framework for rational pharmaceutical formulation design is proposed, inspired by the potential of in silico explorations in understanding drug delivery details and facilitating drug formulation design. Lastly, to promote the paradigm shift, integrated in silico methodologies were highlighted, and discussions on data challenges, model practicality, personalized modeling, regulatory science, interdisciplinary collaboration, and talent training were conducted in detail with a view to achieving more efficient objective-oriented pharmaceutical formulation design.

Perspectives on development of biomedical polymer materials in artificial intelligence age

Polymer materials are widely used in biomedicine, chemistry and material science, whose traditional preparations are mainly based on experience, intuition and conceptual insight, having been applied to the development of many new materials, but facing great challenges due to the vast design space for biomedical polymers. So far, the best way to solve these problems is to accelerate material design through artificial intelligence, especially machine learning. Herein, this paper will introduce several successful cases, and analyze the latest progress of machine learning in the field of biomedical polymers, then discuss the opportunities of this novel method. In particular, this paper summarizes the material database, open-source determination tools, molecular generation methods and machine learning models that have been used for biopolymer synthesis and property prediction. Overall, machine learning could be more effectively deployed on the material design of biomedical polymers, and it is expected to become an extensive driving force to meet the huge demand for customized designs.

Machine Learning in Tissue Engineering

Machine learning (ML) and artificial intelligence have accelerated scientific discovery, augmented clinical practice, and deepened fundamental understanding of many biological phenomena. ML technologies have now been applied to diverse areas of tissue engineering research, including biomaterial design, scaffold fabrication, and cell/tissue modeling. Emerging ML-empowered strategies include machine-optimized polymer synthesis, predictive modeling of scaffold fabrication processes, complex analyses of structure-function relationships, and deep learning of spatialized cell phenotypes and tissue composition. The emergence of ML in tissue engineering, while relatively recent, has already enabled increasingly complex and multivariate analyses of the relationships between biological, chemical, and physical factors in driving tissue regenerative outcomes. This review highlights the novel methodologies, emerging strategies, and areas of potential growth within this rapidly evolving area of research. Impact statement Machine learning (ML) has accelerated scientific discovery and augmented clinical practice across multiple fields. Now, ML has driven exciting new paradigms in tissue engineering research, including machine-optimized biomaterial design, predictive modeling of scaffold fabrication, and spatiotemporal analysis of cell and tissue systems. The emergence of ML in tissue engineering, while relatively recent, has already enabled increasingly complex analyses of the relationships between biological, chemical, and physical factors in driving tissue regenerative outcomes. This review highlights the novel methodologies, emerging strategies, and areas of potential growth within this rapidly evolving area of research.

Bottom-up design of hydrogels for programmable drug release

Hydrogels are a promising drug delivery system for biomedical applications due to their biocompatibility and similarity to native tissue. Programming the release rate from hydrogels is critical to ensure release of desired dosage over specified durations, particularly with the advent of more complicated medical regimens such as combinatorial drug therapy. While it is known how hydrogel structure affects release, the parameters that can be explicitly controlled to modulate release ab initio could be useful for hydrogel design. In this review, we first survey common physical models of hydrogel release. We then extensively go through the various input parameters that we can exercise direct control over, at the levels of synthesis, formulation, fabrication and environment. We also illustrate some examples where hydrogels can be programmed with the input parameters for temporally and spatially defined release. Finally, we discuss the exciting potential and challenges for programming release, and potential implications with the advent of machine learning.

Potential of Recycled Silicon and Silicon-Based Thermoelectrics for Power Generation

Thermoelectrics can convert waste heat to electricity and vice versa. The energy conversion efficiency depends on materials figure of merit, zT, and Carnot efficiency. Due to the higher Carnot efficiency at a higher temperature gradient, high-temperature thermoelectrics are attractive for waste heat recycling. Among high-temperature thermoelectrics, silicon-based compounds are attractive due to the confluence of light weight, high abundance, and low cost. Adding to their attractiveness is the generally defect-tolerant nature of thermoelectrics. This makes them a suitable target application for recycled silicon waste from electronic (e-waste) and solar cell waste. In this review, we summarize the usage of high-temperature thermoelectric generators (TEGs) in applications such as commercial aviation and space voyages. Special emphasis is placed on silicon-based compounds, which include some recent works on recycled silicon and their thermoelectric properties. Besides materials design, device designing considerations to further maximize the energy conversion efficiencies are also discussed. The insights derived from this review can be used to guide sustainable recycling of e-waste into thermoelectrics for power harvesting.