Scope

Given the demographic challenges of an ageing population combined with rising patient expectation and the growing emphasis placed on cost containment by healthcare providers, economic regenerative medicine approaches for regeneration of damaged and diseased organs and tissues are a major clinical and socio-economic need. The scope of this chapter is to use skeletal regeneration as the exemplar to discuss classical and high-throughput screening approaches to biomaterials development for regenerative medicine, including choice and design of materials based on clinical need, biological assessment and regulatory issues.

Basic principles: development of materials for regenerative medicine

The increase in an ageing population in developed countries is accompanied by a growing need for replacement and repair of damaged organs and tissues. Transplantation of the patient’s own tissue is still considered the gold standard in many applications, but limited availability, and complications associated with harvesting of the so-called autograft, are becoming an important drawback. Tissues and organs from human or animal donors present issues of disease transmission and functional failure. Alternative strategies, based on biological growth factors, cell therapy and tissue-engineered constructs, are being explored as alternatives to the patient’s own tissue, but their use is hampered by biological instability and high costs. These issues demonstrate the need for strategies based on biomaterials, which are often synthetic, and thus less prone to instability problems. In addition, the fact that (synthetic) biomaterials can often be produced in large quantities and thus be available off-the-shelf is an important advantage when coping with an increasing need for regenerative approaches.

Scope

To screen biomaterials in a materiomics approach, libraries of materials are produced. Different materials are used, varying from metals and cements, to covalent polymers that can be either premixed or polymerized in situ, to supramolecular systems that can be applied in a modular approach. This chapter describes the generation of such libraries using different kinds of materials and chemistries. Additionally, the advantages and limitations of the application of these different systems/biomaterials in a materiomics approach are discussed.

Introduction

Different synthetic biomaterials are used for many biomedical applications, varying from metals and ceramic cements, to polymers and supramolecular systems. To screen these biomaterials in a materiomics approach, as said above, libraries of materials are produced. Variations in biomaterials are screened as continuous gradients or in a discrete fashion. The properties that are varied and methods used to create variation within these libraries depend on the type of biomaterial. For the hard metal and ceramic-based biomaterials, the surface interaction with tissue is the property of most interest, and therefore properties such as surface roughness and topography are varied. Covalent polymers are diversified using combinatorial chemistry. The dynamic and self-assembling nature of supramolecular systems allows for the development of material libraries using a modular approach by mixing and matching of different compounds modified with supramolecular moieties.

It’s that sense of unease when you step out of the airport terminal building and onto the streets of Kathmandu. Or the moment when you open the door to your new office to see unfamiliar faces waiting for you. Step out of your comfort zone and discover how exciting, thrilling and liberating it can be: a new world is waiting for you. This book is about stepping out of the comfort zone of your own scientific discipline and about exposing yourself to something new. Embrace all the scientific disciplines that build modern-day biomaterials research, in the cultural hotpot of materiomics. Don’t let the jargon and three-letter abbreviations of cell biology hold you back, nor the abracadabra of statistical models, nor the Latin terms for body parts and diseases. Learn a new language and a whole new culture is waiting for you.

The compilation of this book was initiated after an exciting conference termed ‘High throughput screening of biomaterials: shaping a new research area’, held beside the Amsterdam canals in April 2011. The meeting was attended by 50 selected scientists from all over the globe and across all the disciplines of biomaterials research, and the format of the conference took away that sense of unease. Chemists talked to clinicians, biologists listened to information scientists, engineers brainstormed with policy makers. We decided to bring this open and inviting atmosphere to the public through this book. Therefore, each chapter contains a tutorial on the topic for non-experts, gives an overview of the current status of that field and discusses how this technology will further shape the future of materiomics. The result of this exciting journey is presented here and was made possible only with the help of all the authors and those who contributed to the organization of the conference (Anouk Mentink), the editing of the book (Ruben Burer) or the chapters (Kristen Johnson). We hope that this book will be a scientific passport which lets you travel across the border of your discipline and helps you to learn to appreciate that of others. You won’t be disappointed. Enjoy your journey!

Scope

High-content imaging (HCI) plays a pivotal role in high-throughput screening (HTS) of biological responses to biomaterials. This chapter will give a brief introduction to its basic principles by explaining the most commonly used imaging techniques, describing the general structure of an image analysis pipeline and providing a summary of available HCI software. Special emphasis will be devoted to the initial steps of image analysis such as image correction, segmentation and feature extraction. Additionally we include a brief overview of relevant literature and description of promising new tools for HCI. Finally, two classic experiments will be described, which use state-of-the-art HCI in biomaterial science by experts in the field.

Origins of high-content imaging

The first person to observe micro-organisms with a microscopic device was the Dutchman Antoni van Leeuwenhoek (1), who was able to make and polish tiny lenses of high curvature which were the forerunners of the modern microscope. Robert Hooke, the English father of microscopy, later improved on the design and confirmed van Leeuwenhoek’s findings. Since then, light microscopy has evolved into its modern incarnation. The first use of fluorescence microscopy in biology was in 1881 when the bacteriologist Paul Ehrlich used fluorescin to observe the aqueous humour in the eye. The first immuno-staining was performed in 1950 by Melvin Kaplan (2). Green fluorescent protein (GFP) was the first fluorescent protein used in biology. It was isolated from jellyfish in 1961 by Shimomura and co-workers, and it was cloned by Prisher in 1992. The ability either to use antibody conjugated fluorophores or to clone GFP into chimaeric proteins led to the implementation of the fluorescence microscope in modern biology. High-content imaging (HCI) is the automation of fluorescence microscopy whereby the manual interpretation of images is replaced by computer algorithms.

Scope

Materiomics approaches may be applied broadly in the design and characterization of a wide range of materials, going beyond the biomedical focus. This chapter presents essential concepts relevant to the implementation of materiomics to physical and chemical properties not usually associated with biomedical materials. Recent progress in this area is reviewed briefly with several examples given in high-throughput measurement of organic and inorganic materials properties. The applications that are covered range from speciality coatings to membranes for fuel cells and metal–organic framework materials for carbon capture. Properties treated here include mechanical, spectroscopic and transport characteristics.

Tutorial on basic principles

Materials complexity

While previous chapters have focused on the application of materiomics concepts to biomedical materials, this chapter will emphasize how and why materiomics is being employed in materials research and development beyond the biomaterials field. The central challenge inherent to materials research and development is that most products consist of multiple components whose final properties are usually a sensitive function of composition and processing conditions. The complex interactions make it difficult, if not impossible, to design products a priori without significant experimentation. Academicians are often interested in characterizing fundamental phenomena, such as phase transitions or magnetization, and discovering how these phenomena can be used to develop novel materials functionalities.

Scope

Computational and statistical tools play an important role in materiomics, to provide insights in the underlying processes that allow certain materials to outperform other materials. In this chapter, we discuss numerous methods that allow the analysis of materiomics data. Specifically, we describe the use of statistical tests, ranking and data mining approaches, model learning and testing, as well as experimental design and the exchange of experimental results. Also, we review some of the important publications in this field from the past 15 years, organizing them according to the type of material descriptors that were used.

Basic principles of data analysis

Computational methods play an ever more important role in the study of material function. Partly, this is due to the increased scale of the experiments being performed, with an accompanying need for automated analyses. But the move from low-throughput towards high-throughput experiments entails more than just testing more materials simultaneously. The extra information these experiments produce is slowly catalysing a transition to a more rational approach to material discovery, in which not just material screening plays a role but also material modelling. Materials and their environments are approached as systems that can be modelled and thus explored in silico. This ‘systems approach to material research’ has been termed materiomics. This transition is certainly needed given the size of the materiome that one wants to explore: many material parameters can be varied and combined into a practically infinite palette of combinations. This far surpasses even the reach of high-throughput screenings. The question that will be addressed in this chapter is: how can we efficiently make use of our capability to perform high-throughput experiments, to explore and characterize such a large search space?

Scope

High-throughput and combinatorial research on biomaterials aims at the rapid development of new materials and the establishment of structure–function relationships. Therefore, knowledge of the chemistry of each material and its impact on physical properties is essential to understand the effect on its function as a biomaterial. A tutorial on basic physical and chemical properties of (polymeric) materials highlights these features and can be found in the first part of this chapter. The second part gives an overview of relevant techniques that can be used to screen these material properties in high throughput. In addition, several examples are described in which these methods are used to develop structure–function relationships between material properties and biological performance.

Basic principles: physical and chemical properties of polymeric biomaterials

Chemistry is a constant factor from which the performance of most (polymeric) biomaterials can be predicted, but this extrapolation becomes less obvious when numerous materials are mixed in huge combinatorial libraries. Therefore, researchers are becoming increasingly involved in high-throughput material research when successful correlations between biological performance and physico-chemical material properties are to be made. This accelerating trend can be extracted from many studies where high-throughput technologies are successfully applied to measure physical properties and biological performance of many different polymeric biomaterials. Physical properties such as hardness, topography and hydrophilicity are known to be important parameters in the biological evaluation of materials, because they allow or block the adhesion of biological compounds which is required to allow cell-spreading, migration, proliferation and differentiation. These properties are naturally different for every material or combination of materials, and relate primarily to the variable properties on the chemical level (molecular structure, functional groups and degradation). Therefore, the chemistry of a biomaterial directly contributes to its interaction with biological environments.

Scope

High-throughput screening (HTS) is carried out on two- (2D) and three-dimensional (3D) materials, with hundreds to thousands of conditions at various size scales. When hits are successfully found in HTS systems, upscaling to clinically relevant surfaces needs to be performed to validate whether the identified material and functionality can be replicated on the macroscale. In doing so, parameters such as surface chemistry, topography and sample dispensing must be controlled to maintain reproducibility. Here, we discuss the methods harnessed to replicate chemical and topographical features from the nano- to the macroscale in 2D and 3D systems. Technologies to control cell adhesion and 3D scaffold fabrication are introduced and discussed in terms of their potential for HTS.

Basic upscaling principles

HTS is a highly automated process that tests small amounts of large numbers of compounds for a desired function. In the previous chapters of this book, the general principles behind material chemistry and resulting physico-chemical properties, combinatorial chemistry, microfabrication technologies and development of tools to perform biological assays on HTS platforms have been described. These elements partly return here, where basic principles of polymer chemistry and surface topographies are introduced in the context of facing the technological challenges to upscale selected candidates to larger surfaces or medical devices with complex curved shapes. In addition, the basic principles behind implant fabrication technologies and precise cell deposition are discussed to illustrate the steps required to assimilate HTS into clinically relevant 3D systems.

Scope

Combinatorial chemistry and high-throughput synthesis of novel materials warrant a paradigm shift in current methods to analyse biological responses. This chapter will provide an overview on bioassay development and how novel assays amenable to high-throughput screening platforms can be adapted to more complex systems. Special emphasis will be devoted to the development of assays that can be used in platforms that closely mimic the in vivo complexity of tissues and organs. In that respect, assays that can cope with co-culture systems as well as 3D environments will be discussed. Moreover, modifications or development of new assays and techniques will be described as well as their respective advantages and disadvantages.

Basic principles of assay development

The ability to measure the speed of light changed the field of physics and the world. Chemical reactions led to the Big Bang and the creation of the Universe, but the ability to measure and control those reactions changed the face of the Earth. We can surely say that the need to see more, and in more detail, led to the development of technologies that made that possible and ultimately contributed to the advance of science and society.

Introduction to materiomics

The ability to regenerate and repair tissues and organs – using science and engineering to supplement biology – continuously intrigues and inspires those hoping that the frailty of our bodies can be ultimately avoided. From ancient times, a surprising range of unnatural materials have been used to (partially) substitute human tissues for medicinal purposes. For example, in the era of the Incas (c. 1500), moulded materials such as gold and silver were used for the ‘surgical’ repair of cranial defects. In addition, archaeological findings reveal a wide range of materials, such as bronze, wood and leather, being used to replace and repair parts of the human body. Continuous refinement led to the first evidence of materials successfully implanted inside the body, reportedly used to repair a bone defect in the seventeenth century (see Further Reading).

Even earlier than this, the relationships between anatomy (i.e. structure) and function of living systems had been explored by Leonardo da Vinci and Galileo Galilei, who were among the first few to apply fundamental science to biological systems. In the current age of technology, new materials for biomedical and clinical application have undergone a modern Renaissance, resulting in a surge in design and successful application (1–5). The concepts of tissue repair and substitution are constantly improving and becoming more accessible, as proven for example by the widespread occurrence (and popular approval) of total hip and knee replacements. But rather than replacement with synthetic analogues, can biological tissue(s) be directly engineered?

In previous chapters on optimization, we saw that once a process model in the form of mass balance equations is developed, it can be optimized using, among other methods, Pontryagin's maximum principle (PMP), or when a statistical model such as a neural network model is available, it can be optimized using a method of optimization that is most appropriate for the model.

As discussed in Chapter 4, various physical and chemical phenomena favor fed-batch operations. For physical reasons, such as the need to use auxotrophic mutants, attainment of high cell and metabolite concentrations, and alleviation of high viscosity, the use of fed-batch is obvious and intuitive, whereas for chemical reasons, such as substrate inhibition, glucose effects, and catabolite repressions, the use of fed-batch operation requires recognition of nonmonotonic specific rates such as cell growth, substrate consumption, and product formation, μ, σ,and π. In other words, at least one of the specific rates must exhibit a maximum with respect to the substrate concentration.

In this chapter, we consider simple chemical reactions in idealized reactors. Simplicity of such systems helps us understand more complex microbial reactions. With the brief description of each type of idealized reactor operation presented in Chapter 2, we consider now the simplest case of maximizing the reaction rate of a single isothermal reaction in a single reactor. This will be followed by the problem of maximizing the reaction rate using two reactors. Finally, the problem of maximizing the reaction rate of a single reaction by a single reactor with an inlet stream and an outlet stream is considered. This procedure leads naturally to the concept of fed-batch operation, a form of semi-batch operation in which there is only an inlet stream. The simplest fermentation, growing of microbial cells on a single limiting substrate, is used to illustrate the concept of fed-batch culture.

The primary objective of optimization of chemical and biochemical reactions is to maximize the reaction rate and/or yield (selectivity). For single reactions, maximization of the product formation rate or substrate consumption rate leads to the minimum reactor size or maximum productivity for a given reactor size. The minimum reactor size leads to minimum investment costs, and maximum productivity results in minimum maintenance costs. For multiple reactions, maximization of the product yield results in a minimum raw material cost, and maximization of the reaction rate leads to a minimum reactor size or a maximum productivity for a given reactor size.

In previous chapters, we looked at the basic concept of fed-batch operation as a means of manipulating the feed rate or substrate concentration to maximize the cell growth and product formation rates and harvest only at the end of the operation, not during the operation. Let us first maximize rigorously the conversion of a single reaction of an arbitrary rate expression. Then, we consider the question of the timing of the withdrawal. In the conventional fed-batch operation, the withdrawal is made only at the end of operation and not during the course of operation. Should we withdraw the reaction mixture only at the end of the run? Should we withdraw a part of the culture intermittently or continuously throughout the course of the operation? If the product is harvested all at once only at the end of the run, then the operation is the traditional fed-batch or repeated fed-batch (if a portion of the final reactor content is retained for the next cycle). However, if the product stream is withdrawn during the course in some fashion, then the operation resembles dynamic (variable volume) operation of a continuous-stirred tank reactor (CSTR). By allowing impulse feeding and withdrawal, one can also theoretically mimic temporally the spatial operation of a plug-flow reactor (PFR). Impulse feeding or withdrawal refers to adding or withdrawing a fixed amount instantaneously in the form of an impulse function as one would dump a bucketful of feed in an infinitesimally small time interval. Intuitively, a withdrawal during the course of operation does not change the reaction composition, and therefore, it appears that a withdrawal during the course of operation would not help. To answer this important question, we consider in the following section a rigorous solution to this fundamental question of when to withdraw.

The cellular behavior in a bioreactor has a complexity unparalleled in chemical reactors and consequently is very difficult to predict from the external bioreactor conditions. Cell growth is highly complex so that a drastic simplification is required before a manageable model can be developed. In many cases, the objective of mathematical model development is explicitly aimed at providing the basis for predicting, controlling, and optimizing the performance of a bioreactor. Mathematical models are indispensable in determining the optimal operating conditions of bioreactors, in particular, fed-batch bioreactors. Nonequation models such as neural network models can be developed and used for optimization purposes when it is not possible to write mass balance equations with appropriate specific rate expressions. However, the results obtained are not as satisfactory as those obtained from equation-based models. Nevertheless, when it is not possible to write one or more mass balance equations and appropriate rate expressions are not possible for key variables, this statistical approach is indispensable.

In general, models can be divided into two classes: phenomenological (unstructured) models and mechanistic (structured) models., Phenomenological models ignore various cellular processes that are involved, including specific enzymes and cellular structural components. These unstructured models are used to describe the overall observed microbial response in terms of readily measurable variables only, and no provision is made for changes in the internal components of the cells. Conversely, structured models are based on the cellular mechanisms and take into account various cellular processes that depend on the activities of enzymes and the structural components such as ribosomes, mitochondria, macromolecular components such as proteins, DNA and RNA, and carbohydrates. An advantage of such highly structured models is the potential for modeling the interrelationship of the metabolic processes and predicting the effects of internal genetic modification and external disturbances on the overall cellular process. The disadvantages of the highly structured models are the difficulty in determining the kinetics and kinetic constants in individual reactions because many of the cellular components are not readily measurable and computational difficulty is posed by a large number of balance equations that result. Although the complex nature of biological systems may require complicated models of large dimension, oversophistication of models should be avoided because it tends to defy the very purpose of modeling by obscuring the essence of the model and makes the prediction of cellular behavior exceedingly difficult.

The equation-based models presented in Chapter 6 require specific rates of cell growth, substrate consumption, intermediate formation, and product formation. In this chapter, we will review the traditional methods of determining these specific rates and present a method of determining without taking time derivatives the net specific rates utilizing fed-batch cultures. Traditional methods of generating experimental data involve using primarily shake flasks (batch reactors) and, secondarily, continuous flow reactors. A new method utilizes fed-batch cultures with constant feed rates and does not require differentiation of experimental data.

It is best to generate experimental data using the type of operation that is being contemplated; that is, if a batch culture is the ultimate mode of operation, it is best to generate specific rate data using batch cultures, and if a fed-batch operation is to be used, then it is best to use fed-batch operation to generate specific rate data. Compared to chemical reactions, microbial kinetics are complex and time variant so that frequently, rate data obtained at steady state operations may not apply well to dynamic operations.

A number of items need to be considered before getting into a formal optimization. We must decide what to optimize. Here we shall call the objective function the performance index we wish to maximize, and we will maximize the performance index. When the objective is to minimize rather than maximize, such as the minimum time to achieve a desired concentration, one would change the sign of the performance index (from positive to negative) and maximize it instead. Next is the question of what would be the best initial conditions and whether the final conditions should be left unspecified or specified. There is the question of what input (control) variables we should manipulate to maximize the chosen performance index. Finally, there are questions regarding physical constraints on the manipulated variable as well as the state variables. We shall look at these questions.

One of the primary objectives of reaction engineering is to maximize the rate of formation of the product (productivity) and/or the yield (selectivity or relative yield). For an existing plant, a faster product formation rate implies a higher productivity or a corresponding reduction in plant operating time and operating cost. The increased rate implies a smaller reactor and therefore a lower capital investment cost for new plants to be built. Likewise, an improved yield implies lower raw material costs for existing and new plants. One may wish to maximize the total amount of product produced per cycle of fed-batch operation or maximize the productivity, the amount of product produced per cycle per unit operating time.