Robot-scientists will lead tomorrow's biomaterials discovery

Intelligent Biomaterialomics: Molecular Design, Manufacturing, and Biomedical Applications

Materialomics integrates experiment, theory, and computation in a high-throughput manner, and has changed the paradigm for the research and development of new functional materials. Recently, with the rapid development of high-throughput characterization and machine-learning technologies, the establishment of biomaterialomics that tackles complex physiological behaviors has become accessible. Breakthroughs in the clinical translation of nanoparticle-based therapeutics and vaccines have been observed. Herein, recent advances in biomaterials, including polymers, lipid-like materials, and peptides/proteins, discovered through high-throughput screening or machine learning-assisted methods, are summarized. The molecular design of structure-diversified libraries; high-throughput characterization, screening, and preparation; and, their applications in drug delivery and clinical translation are discussed in detail. Furthermore, the prospects and main challenges in future biomaterialomics and high-throughput screening development are highlighted.

Linear Binary Classifier to Predict Bacterial Biofilm Formation on Polyacrylates

Bacterial infections are increasingly problematic due to the rise of antimicrobial resistance. Consequently, the rational design of materials naturally resistant to biofilm formation is an important strategy for preventing medical device-associated infections. Machine learning (ML) is a powerful method to find useful patterns in complex data from a wide range of fields. Recent reports showed how ML can reveal strong relationships between bacterial adhesion and the physicochemical properties of polyacrylate libraries. These studies used robust and predictive nonlinear regression methods that had better quantitative prediction power than linear models. However, as nonlinear models' feature importance is a local rather than global property, these models were hard to interpret and provided limited insight into the molecular details of material-bacteria interactions. Here, we show that the use of interpretable mass spectral molecular ions and chemoinformatic descriptors and a linear binary classification model of attachment of three common nosocomial pathogens to a library of polyacrylates can provide improved guidance for the design of more effective pathogen-resistant coatings. Relevant features from each model were analyzed and correlated with easily interpretable chemoinformatic descriptors to derive a small set of rules that give model features tangible meaning that elucidate relationships between the structure and function. The results show that the attachment of Pseudomonas aeruginosa and Staphylococcus aureus can be robustly predicted by chemoinformatic descriptors, suggesting that the obtained models can predict the attachment response to polyacrylates to identify anti-attachment materials to synthesize and test in the future.

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.

Automated Fabrication of Streptavidin-Based Self-assembled Materials for High-Content Analysis of Cellular Response to Growth Factors

The automation of liquid-handling routines offers great potential for fast, reproducible, and labor-reduced biomaterial fabrication but also requires the development of special protocols. Competitive systems demand for a high degree in miniaturization and parallelization while maintaining flexibility regarding the experimental design. Today, there are only a few possibilities for automated fabrication of biomaterials inside multiwell plates. We have previously demonstrated that streptavidin-based biomimetic platforms can be employed to study cellular behaviors on biomimetic surfaces. So far, these self-assembled materials were made by stepwise assembly of the components using manual pipetting. In this work, we introduce for the first time a fully automated and adaptable workflow to functionalize glass-bottom multiwell plates with customized biomimetic platforms deposited in single wells using a liquid-handling robot. We then characterize the cell response using automated image acquisition and subsequent analysis. Furthermore, the molecular surface density of the biomimetic platforms was characterized in situ using fluorescence-based image correlation spectroscopy. These measurements were in agreement with standard ex situ spectroscopic ellipsometry measurements. Due to automation, we could do a proof of concept to study the effect of heparan sulfate on the bioactivity of bone morphogenetic proteins on myoblast cells, using four different bone morphogenetic proteins (BMPs) (2, 4, 6, and 7) in parallel, at five increasing concentrations. Using such an automated self-assembly of biomimetic materials, it may be envisioned to further investigate the role of a large variety of extracellular matrix (ECM) components and growth factors on cell signaling.

Bacterial Cellulose-A Remarkable Polymer as a Source for Biomaterials Tailoring

Nowadays, the development of new eco-friendly and biocompatible materials using ‘green’ technologies represents a significant challenge for the biomedical and pharmaceutical fields to reduce the destructive actions of scientific research on the human body and the environment. Thus, bacterial cellulose (BC) has a central place among these novel tailored biomaterials. BC is a non-pathogenic bacteria-produced polysaccharide with a 3D nanofibrous structure, chemically identical to plant cellulose, but exhibiting greater purity and crystallinity. Bacterial cellulose possesses excellent physicochemical and mechanical properties, adequate capacity to absorb a large quantity of water, non-toxicity, chemical inertness, biocompatibility, biodegradability, proper capacity to form films and to stabilize emulsions, high porosity, and a large surface area. Due to its suitable characteristics, this ecological material can combine with multiple polymers and diverse bioactive agents to develop new materials and composites. Bacterial cellulose alone, and with its mixtures, exhibits numerous applications, including in the food and electronic industries and in the biotechnological and biomedical areas (such as in wound dressing, tissue engineering, dental implants, drug delivery systems, and cell culture). This review presents an overview of the main properties and uses of bacterial cellulose and the latest promising future applications, such as in biological diagnosis, biosensors, personalized regenerative medicine, and nerve and ocular tissue engineering.

Expanding Biomaterial Surface Topographical Design Space through Natural Surface Reproduction

Surface topography is a tool to endow biomaterials with bioactive properties. However, the large number of possible designs makes it challenging to find the optimal surface structure to induce a specific cell response. The TopoChip platform is currently the largest collection of topographies with 2176 in silico designed microtopographies. Still, it is exploring only a small part of the design space due to design algorithm limitations and the surface engineering strategy. Inspired by the diversity of natural surfaces, it is assessed as to what extent the topographical design space and consequently the resulting cellular responses can be expanded using natural surfaces. To this end, 26 plant and insect surfaces are replicated in polystyrene and their surface properties are quantified using white light interferometry. Through machine-learning algorithms, it is demonstrated that natural surfaces extend the design space of the TopoChip, which coincides with distinct morphological and focal adhesion profiles in mesenchymal stem cells (MSCs) and Pseudomonas aeruginosa colonization. Furthermore, differentiation experiments reveal the strong potential of the holy lotus to improve osteogenesis in MSCs. In the future, the design algorithms will be trained with the results obtained by natural surface imprint experiments to explore the bioactive properties of novel surface topographies.

Machine learning and big data provide crucial insight for future biomaterials discovery and research

Machine learning have been widely adopted in a variety of fields including engineering, science, and medicine revolutionizing how data is collected, used, and stored. Their implementation has led to a drastic increase in the number of computational models for the prediction of various numerical, categorical, or association events given input variables. We aim to examine recent advances in the use of machine learning when applied to the biomaterial field. Specifically, quantitative structure properties relationships offer the unique ability to correlate microscale molecular descriptors to larger macroscale material properties. These new models can be broken down further into four categories: regression, classification, association, and clustering. We examine recent approaches and new uses of machine learning in the three major categories of biomaterials: metals, polymers, and ceramics for rapid property prediction and trend identification. While current research is promising, limitations in the form of lack of standardized reporting and available databases complicates the implementation of described models. Herein, we hope to provide a snapshot of the current state of the field and a beginner's guide to navigating the intersection of biomaterials research and machine learning. STATEMENT OF SIGNIFICANCE: Machine learning and its methods have found a variety of uses beyond the field of computer science but have largely been neglected by those in realm of biomaterials. Through the use of more computational methods, biomaterials development can be expediated while reducing the need for standard trial and error methods. Within, we introduce four basic models that readers can potentially apply to their current research as well as current applications within the field. Furthermore, we hope that this article may act as a "call to action" for readers to realize and address the current lack of implementation within the biomaterials field.

Chips for Biomaterials and Biomaterials for Chips: Recent Advances at the Interface between Microfabrication and Biomaterials Research

In recent years, the use of microfabrication techniques has allowed biomaterials studies which were originally carried out at larger length scales to be miniaturized as so-called "on-chip" experiments. These miniaturized experiments have a range of advantages which have led to an increase in their popularity. A range of biomaterial shapes and compositions are synthesized or manufactured on chip. Moreover, chips are developed to investigate specific aspects of interactions between biomaterials and biological systems. Finally, biomaterials are used in microfabricated devices to replicate the physiological microenvironment in studies using so-called "organ-on-chip," "tissue-on-chip" or "disease-on-chip" models, which can reduce the use of animal models with their inherent high cost and ethical issues, and due to the possible use of human cells can increase the translation of research from lab to clinic. This review gives an overview of recent developments at the interface between microfabrication and biomaterials science, and indicates potential future directions that the field may take. In particular, a trend toward increased scale and automation is apparent, allowing both industrial production of micron-scale biomaterials and high-throughput screening of the interaction of diverse materials libraries with cells and bioengineered tissues and organs.

High-Throughput Methods in the Discovery and Study of Biomaterials and Materiobiology

The complex interaction of cells with biomaterials (i.e., materiobiology) plays an increasingly pivotal role in the development of novel implants, biomedical devices, and tissue engineering scaffolds to treat diseases, aid in the restoration of bodily functions, construct healthy tissues, or regenerate diseased ones. However, the conventional approaches are incapable of screening the huge amount of potential material parameter combinations to identify the optimal cell responses and involve a combination of serendipity and many series of trial-and-error experiments. For advanced tissue engineering and regenerative medicine, highly efficient and complex bioanalysis platforms are expected to explore the complex interaction of cells with biomaterials using combinatorial approaches that offer desired complex microenvironments during healing, development, and homeostasis. In this review, we first introduce materiobiology and its high-throughput screening (HTS). Then we present an in-depth of the recent progress of 2D/3D HTS platforms (i.e., gradient and microarray) in the principle, preparation, screening for materiobiology, and combination with other advanced technologies. The Compendium for Biomaterial Transcriptomics and high content imaging, computational simulations, and their translation toward commercial and clinical uses are highlighted. In the final section, current challenges and future perspectives are discussed. High-throughput experimentation within the field of materiobiology enables the elucidation of the relationships between biomaterial properties and biological behavior and thereby serves as a potential tool for accelerating the development of high-performance biomaterials.

Automation and data-driven design of polymer therapeutics

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.

Shifting Gears in Biomaterials Discovery

High-throughput screening of cell-biomaterial interactions combined with machine learning algorithms leads the way toward the future of medical device manufacturing: in silico modeling of cell and tissue response. Bio-compatible medical implants will have a huge clinical impact.

Screening as a strategy to drive regenerative medicine research

In the field of regenerative medicine, optimization of the parameters leading to a desirable outcome remains a huge challenge. Examples include protocols for the guided differentiation of pluripotent cells towards specialized and functional cell types, phenotypic maintenance of primary cells in cell culture, or engineering of materials for improved tissue interaction with medical implants. This challenge originates from the enormous design space for biomaterials, chemical and biochemical compounds, and incomplete knowledge of the guiding biological principles. To tackle this challenge, high-throughput platforms allow screening of multiple perturbations in one experimental setup. In this review, we provide an overview of screening platforms that are used in regenerative medicine. We discuss their fabrication techniques, and in silico tools to analyze the extensive data sets typically generated by these platforms.

Emerging Trends in Information-Driven Engineering of Complex Biological Systems

Synthetic biological systems are used for a myriad of applications, including tissue engineered constructs for in vivo use and microengineered devices for in vitro testing. Recent advances in engineering complex biological systems have been fueled by opportunities arising from the combination of bioinspired materials with biological and computational tools. Driven by the availability of large datasets in the "omics" era of biology, the design of the next generation of tissue equivalents will have to integrate information from single-cell behavior to whole organ architecture. Herein, recent trends in combining multiscale processes to enable the design of the next generation of biomaterials are discussed. Any successful microprocessing pipeline must be able to integrate hierarchical sets of information to capture key aspects of functional tissue equivalents. Micro- and biofabrication techniques that facilitate hierarchical control as well as emerging polymer candidates used in these technologies are also reviewed.