Journal of general internal medicine
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Important role of translational science in rare disease innovation, discovery, and drug development.
Rare diseases play a leading role in innovation and the advancement of medical and pharmaceutical science. Most rare diseases are genetic disorders or atypical manifestations of infectious, immunologic, or oncologic diseases; they all provide opportunities to study extremes of human pathology and provide insight into both normal and aberrant physiology. Recently, drug development has become increasingly focused on classifying diseases largely on genetic grounds; this has allowed the identification of molecularly defined targets and the development of targeted therapies. ⋯ Drug developers, researchers, and regulatory agencies face a variety of challenges throughout the life cycle of drug research and development for rare diseases. These include the small numbers of patients available for study, lack of knowledge of the disease's natural history, incomplete understanding of the basic mechanisms causing the disorder, and variability in disease severity, expression, and course. Traditional approaches to rare disease clinical research have not kept pace with advances in basic science, and increased attention to translational science is needed to address these challenges, especially diagnostic testing, registries, and novel trial designs.
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Postmarket surveillance of the comparative safety and efficacy of orphan therapeutics is challenging, particularly when multiple therapeutics are licensed for the same orphan indication. To make best use of product-specific registry data collected to fulfill regulatory requirements, we propose the creation of a distributed electronic health data network among registries. Such a network could support sequential statistical analyses designed to detect early warnings of excess risks. We use a simulated example to explore the circumstances under which a distributed network may prove advantageous. ⋯ We illustrate a process to assess whether sequential statistical analyses of registry data performed via distributed networks may prove a worthwhile infrastructure investment for pharmacovigilance.