The Way Forward for the Three Rs and Systems Biology
Published: December 6, 2007
My expertise is in biotechnology more specifically biochemistry and molecular biology driven approaches to early drug development and biosensor design (Leeds 1990-2004). More recently, legal training afforded me the opportunity to investigate in detail the legal and ethical considerations of medical research on patients and volunteers.
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For centuries experiments on animals have been used to benefit human lives. Indeed, many medical advances would not exist had it not been for animal experimentation. Whether the same holds true in the current climate where information from genomes, new technologies and new sophisticated analytical techniques are being developed is contested, particularly given that biomarkers, biosimulations and systems allow a holistic view of human disease, responses and physiology – often without recourse to animal studies. Here a brief glance of how cross-discipline approaches are being used to develop systems biology-based non-animal models is given.
What is Systems Biology?
Systems biology exploits advances in genetics, molecular biology, cell and tissue engineering, biophysical and imaging technologies, and bioinformatics and computational or mathematical methodologies, to provide a holistic view of a disease, human physiology or the effects of exogenous agents. This view is not reliant on in vivo studies in vertebrate, sentient animals.
In essence, systems biology can either involve assembling what is akin to pieces of a jigsaw puzzle to gain a more complete picture of in vivo human events (the top-down approach) or starts with a simple picture provided by studies in cells or lower organisms to extract information relevant to humans (the bottom-up approach). The first approach is most commonly used. This involves taking genome-, proteome- and/or metabonome-wide measurements to identify data suited to building up a picture representing how a disease or substance affects human physiology or the function of specific biochemical pathways. The second approach exploits the fact that fundamental cellular function has been, by and large, preserved during evolution such that bacteria, yeast and invertebrates share significant commonality with humans and other vertebrates. Here, engineered changes in cell or biochemical pathway function can be used to derive information relevant to humans.
The cross-over point from the top-down and bottom-up approaches is cytomics – the study of subcellular or cellular function – in bacterial or eukaryotic cells, such as Saccharomyces cerivisiae or mammalian cells – using protein engineering, gene silencing or knock-outs, or two- or three-hybrid systems.
A number of enabling technologies exist which are already being applied to systems biology. These include: automated sample collection, processing and analysis, advanced cell imaging techniques, microformatting and microfluidic technologies, mathematical modeling, meta-analysis and bioinformatics approaches. Innovative methods that utilize nanodevices, such as the silver superlens that has the ability to resolve cell function (1) combined with the use of miniature chips that control and manipulate the delivery and flow of biofluids, provide a way of monitoring and altering cellular biochemical events in real-time. Protein and/or DNA interactions can be monitored by using quantum energy transfer as a measure of interactions. Indeed some of the earliest biosensors relied on reporter genes that encode either acceptors or donors of energy. Non-genetic fluorescent probes include fluorescent dyes and quantum dots that permit whole body imaging and which can be colour-emission coded to monitor multiple events within a cell, tissue or organism (2).
So-called lab-on-a-chip, animal-on-a-chip, or human-on-a-chip methods (3) that allow information exchange between cells, analytical systems, or sensors representative of the functions of different biological organs to be monitored, together with automated data collection, mathematical modeling, and analytical techniques, can be used instead of certain studies in animals and are, indeed, used during early risk assessment. These systems may soon be able to incorporate organotypic culture systems consisting of several cell types assembled into arrays that represent the in vivo tissue equivalents, such as neurospheroids cultures that model the blood brain barrier (4). The accompanying microseparation techniques such as liquid chromatography, capillary electrophoresis, laser-induced fluorescence, and mass spectrometry, permit automated, rapid and sensitive bio-analysis with translational applications to microdialysis and microdosing studies in humans that provide information of direct relevance to human health and reduce the need for animal experiments and the risks associated with extrapolating some types of information from the test species to humans.
A challenge to the traditional approach to systems biology?
One of the key reasons for the reluctance to abandon animal experiments is the fact that most of the in vitro and ex vivo models that are currently available are only suited to the replacement of specific clinical endpoints. However, as the complexity of illnesses and human physiology are beginning to be unravelled it is increasingly clear that animal models of human disease, toxicity and physiology can give a false sense of confidence that substances such as medicines are safe and effective in humans. Take, for instance, the limited success with developing vaccines and treatments for HIV infections and a series of high-profile drug withdrawals. Only last year, we saw how assurances given about the safety of a new medical treatment can lead to catastrophic outcomes in clinical trials, when six otherwise healthy young men developed a rapid and severe, yet unanticipated response to a new medical product TGN1412 (4). Questions have been raised about how one can test a substance that is not only extremely human-specific, but which triggers a complex chain of events that are almost certainly likely to be different in many ways to those seen in animal models – regardless of how extensively those models have been engineered. These questions will almost certainly arise time and time again as gene and stem cell therapeutics continue to infiltrate the developmental pipeline and prompt further interest in human-specific models and test systems.
Biomarkers and biochemical networks
The term ‘omics’ covers a plethora of technologies that focus on changes in the functions or structures of tissues and cells and patterns of gene expression or metabolite profiles. Although each of the technologies has its unique advantages and limitations, they are suffer from one common problem, namely how information from the measurement of a biomarker translates to humans. Deciding which biomarkers are suited for a particular type of study and how data from these experiments are used is the subject of several initiatives, including the FDA’s Critical Path to New Medicines (5). It is essential that biomarkers be identified that capture differences between individual cell types, individuals and species, as well as between normal and disease states. Biomarkers should also be easily and reproducibly detectable, stable, and demonstrate a tangible link to the physiological phenomena they are designed to represent. Another important consideration is whether the biomarker can be readily measured without invasive tissue sampling, or in the case of animal studies, with a view to humane experimentation and temporal studies in a small number of animals.
The relevance of any biomarkers must also be determined by reference to the biological phenomena they are designed to represent. In this context, information from a variety of sources must be filtered through and assembled according to the confidence in a particular piece of information. Information may range from biomarkers (most commonly changes in tissue or cell function or morphology, metabolite or protein expression profiles) derived from epidemiology studies to data from in vitro and controlled clinical trials as well as animal studies, each with their problems for extrapolation to in vivo human situations. Hence, complex mathematical and statistical approaches afford meaning to the biomarker data. A number of databases that contain pathway information already exist and others that contain information about human polymorphisms and diseases – these sources of information are the starting points for datamining.
The impact of systems biology on preclinical and clinical practices
In this the post-genomic, molecular and cell biology microtechnology, bioinformatics era, the attitudes of scientists, governments and regulators towards alternatives research is being continually challenged. Three Rs research is no longer a sideline to mainstream science but infiltrates every aspect of responsible and humane experimentation research and testing. The number of animals used in a particular type of research has been reduced by better standards of animal husbandry, use of least invasive practices, surrogate endpoints and a variety of imaging techniques. The wastage of animals because of unnecessary research can be addressed by statistical design methods, selection of the most appropriate strain and by reference to the rapidly expanding knowledge bases.
Perhaps, more encouraging are programmes of work such as that undertaken by the European Centre for Validation of Alternative Methods (ECVAM) which aims to incorporate omics-based information from cell-based studies into the prediction of longer-term organ toxicity in humans (6). Such biomarkers are already helping to identify susceptibilities based on individual differences in metabolism and to assist with the design of human studies. It is known, for instance, that polymorphisms in key metabiolic enzymes can affect toxicity in certain patients given drugs such as cyclosporine (7) and the anti-cancer drug, Camptosar (8) whereas polymorphisms in membrane transporters might increase the risk of fatal heart arrhythymias in other patients (9).
Analysis of biomarker data adds to knowledge of systems biology and as such, systems biology approaches that are based on in vitro cell-based systems should be designed with these differences in mind to get a more reliable estimation of risk. The US FDA has produced guidance on the submission of pharmacogenomic information (10) that might be used as a guide to drug development and ensure that clinical studies in humans are representative of the subpopulation the drug is aimed to treat, as was the case for the drug Bidil.
In the words of William Russell and Rex Burch, co-authors of The Principles of Humane Experimental Technique published in 1959: ‘Replacement is always a satisfactory answer’. In the past few years, we have seen the advent, development, and consideration of cell-based assays, screens based on studies in less sentient invertebrate species, imaging techniques applied to patient studies and very early studies in human volunteers. There are now a number of centers that focus on validating these methods to expedite the replacement of tests on vertebrate laboratory animals, including ECVAM, the Interagency Coordinating Committee on the Validation of Alternative Methods (ICCVAM), and the Japanese Center for the Validation of Alternative Methods (JaCVAM) that have been set up under the auspices of national or international laws and laws. There are also laws such as the EU Cosmetics Directive that are specifically aimed at phasing out animal experiments for certain types of testing.
However, as encouraging as these activities are, we are faced with new challenges, such that there is no room for complacency. Windeyer cautioned in 1970 that “if law does not keep pace with medicine, it will continue to march ‘in the rear’, limping along” (11).The same is true of regulations that guide research and testing, particularly whether these have the flexibility and foresight to adapt to and accommodate changes in science and technology that challenge the traditional paradigm of animal-based research and testing. While, to date, systems biology is used to derive useful information that reduces or refines animal experiments, the drive must be to replace animal experiments.
©2007 Nirmala Bhogal
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