Applied genetic research has answered numerous questions concerning the factors that contribute to the inheritance, causation and severity of human diseases. While earlier phases of research have tended to concentrate on straightforward genetic inheritance and causation, advances in laboratory science and technology have led to a shift in focus within the field of genetic research to more complex systems. Recent research centers its attention on the interaction of genetic disposition and environmental factors in the causation of disease, and the use of biomarkers to identify and guide therapy for these diseases.
Biomarkers are substances that can be detected in biosamples, such as blood or urine, that facilitate diagnosis of diseases or enable personalized treatment planning, tailoring and monitoring of therapy regimens. Biomarkers, which can be genomic, epigenetic or proteomic in nature, are seen as an important resource for an emerging approach to medical treatment, personalized medicine. This approach has been hailed as an advancement that will bring about a new era in medicine [1
Despite the enthusiasm that has grown up around this field, personalized medicine, both as a research topic and as an approach to medical care, is not yet based on a set of specific and well-defined methods and concepts [3
]. The various approaches have in common the goal of developing new pathways and therapy strategies that are better adapted to each patient’s individual physiology, thus making medical care more effective and efficient. One dominant view of personalized medicine anticipates that medical treatment will be improved by using the knowledge about physiological risks and genetic predispositions to customize therapeutic strategies and diagnostic evaluation. Since the detection and measurement of biomarkers will be the key to this approach, much of the current research in the area of personalized medicine focuses on the identification and measurement of biomarkers that identify or determine risk.
One of the challenges for developing a scientific basis for personalized medicine is the statistical consideration that arises when a large number of variables need to be considered. Technically speaking, approaches to personalized medicine are not focused on tailored treatments that are optimal for an individual patient. Rather, personalized medicine is an approach to medical care that accounts for a larger set of variables than used in the past. Patients with a single disease are stratified according to sociodemographic groupings, biomarker results, genetic test results and potentially many other factors. For this reason, some prefer to speak of stratified medicine rather than personalized medicine [4
]. In order to detect any single factor responsible for a small effect among a large number of parameters, large samples sizes are required.
Over the past decade, very large biorepositories and clinical databases have cropped up around the world. These large collections of bio-samples and medical record information are the innovative answer to the problem of generating very large, but also very expensive, sample sets for the elucidation of complex systems. Methods for planning and designing these biorepositories and corresponding databases are not yet standardized, and different approaches have been taken throughout the world. In some countries, like the UK, a large-scale population-based repository has been developed [101
]. In other countries, like the USA and Germany, research and healthcare institutions have led the way.
These differences occur within different national contexts that constrain and shape decisions about how to proceed. Public regard for government-organized research is certainly an important factor in some countries [5
]. In addition, federal and local policies that regulate research and privacy standards determine which models for biorepositories and databases are permissible. These factors influence decisions about informed consent, sample collection, data management, governance and many others.
In this article, we will compare and contrast the regulatory frameworks that have shaped the development of biorepositories in the USA and Germany. This comparison is important for at least two reasons:
- The institutional model for the development of biomarker-related research is similar in these two countries.
- The different regulatory frameworks in the two countries render certain models permissible in one country, but not in the other. As a result, different strategies for obtaining informed consent have to be established.
In order to ground this discussion on real-life examples, we will compare the efforts that have been undertaken at our local institutions, Vanderbilt University (TN, USA) and Ernst-Moritz-Arndt University Greifswald (Germany), to establish and operate biorepositories and the corresponding databases.
Vanderbilt University Medical Center in Nashville, has developed a biorepository (BioVu) based on leftover blood from clinical samples. Samples are obtained without formal informed consent, although patients are given the opportunity to opt out of this research. The Greifswald Approach to Individualized Medicine project (GANI_MED) at the Ernst-Moritz-Arndt University in Greifswald focuses on enabling biomarker research by developing a biobank comprised of biological samples and medical record information collected through routine clinical care from special hospital wards, such as cardiology, neurology and internal medicine.