Towards the international interoperability of clinical research networks for rare diseases: recommendations from the IRDiRC Task Force.

BACKGROUND: Many patients with rare diseases are still lacking a timely diagnosis and approved therapies for their condition despite the tremendous efforts of the research community, biopharmaceutical, medical device industries, and patient support groups. The development of clinical research networks for rare diseases offers a tremendous opportunity for patients and multi-disciplinary teams to collaborate, share expertise, gain better understanding on specific rare diseases, and accelerate clinical research and innovation. Clinical Research Networks have been developed at a national or continental level, but global collaborative efforts to connect them are still lacking. The International Rare Diseases Research Consortium set a Task Force on Clinical Research Networks for Rare Diseases with the objective to analyse the structure and attributes of these networks and to identify the barriers and needs preventing their international collaboration. The Task Force created a survey and sent it to pre-identified clinical research networks located worldwide. RESULTS: A total of 34 responses were received. The survey analysis demonstrated that clinical research networks are diverse in their membership composition and emphasize community partnerships including patient groups, health care providers and researchers. The sustainability of the networks is mostly supported by public funding. Activities and research carried out at the networks span the research continuum from basic to clinical to translational research studies. Key elements and infrastructures conducive to collaboration are well adopted by the networks, but barriers to international interoperability are clearly identified. These hurdles can be grouped into five categories: funding limitation; lack of harmonization in regulatory and contracting process; need for common tools and data standards; need for a governance framework and coordination structures; and lack of awareness and robust interactions between networks. CONCLUSIONS: Through this analysis, the Task Force identified key elements that should support both developing and established clinical research networks for rare diseases in implementing the appropriate structures to achieve international interoperability worldwide. A global roadmap of actions and a specific research agenda, as suggested by this group, provides a platform to identify common goals between these networks.

Successfully Navigating Food and Drug Administration Orphan Drug and Rare Pediatric Disease Designations for AAV9-hPCCA Gene Therapy: The National Institutes of Health Platform Vector Gene Therapy Experience.

Orphan drug designation (ODD) is an important program intended to facilitate the development of orphan drugs in the United States. An orphan drug benefiting pediatric patients can qualify as a drug for a Rare Pediatric Disease Designation (RPDD) as well. The ODD and RPDD programs provide financial incentives for development of diagnostic drugs, preventive measures, and treatment of diseases affecting small patient populations (adult and pediatric) for which commercial development would otherwise be very challenging. In 2019, a multidisciplinary group of collaborators at National Institutes of Health (NIH) embarked upon a gene therapy platform program called Platform Vector Gene Therapy (PaVe-GT) intended to develop gene therapies for four such rare disorders. An important part of PaVe-GT is to publicly share scientific and regulatory experience gained at different stages during the implementation of the PaVe-GT platform utilizing illustrative examples. The PaVe-GT team recently obtained ODD and RPDD for an adeno-associated virus gene therapy to treat propionic acidemia. Given an increasing interest in obtaining ODD for gene therapy, especially by small companies, research investigators, and patient groups, we overview the submission process and subsequently provide examples of our ODD and RPDD applications. Our ODD and RPDD applications and templates can also be found on the PaVe-GT website. Shared reference documents will have great utility to assist parties who may have limited experience with the preparation of similar applications for their orphan product.

Global Regulatory and Public Health Initiatives to Advance Pediatric Drug Development for Rare Diseases.

The literature thoroughly describes the challenges of pediatric drug development for rare diseases. This includes (1) generating interest from sponsors, (2) small numbers of children affected by a particular disease, (3) difficulties with study design, (4) lack of definitive outcome measures and assessment tools, (5) the need for additional safeguards for children as a vulnerable population, and (6) logistical hurdles to completing trials, especially with the need for longer term follow-up to establish safety and efficacy. There has also been an increasing awareness of the need to engage patients and their families in drug development processes and to address inequities in access to pediatric clinical trials. The year 2020 ushered in yet another challenge-the COVID-19 pandemic. The pediatric drug development ecosystem continues to evolve to meet these challenges. This article will focus on several key factors including recent regulatory approaches and public health policies to facilitate pediatric rare disease drug development, emerging trends in product development (biologics, molecularly targeted therapies), innovations in trial design/endpoints and data collection, and current efforts to increase patient engagement and promote equity. Finally, lessons learned from COVID-19 about building adaptable pediatric rare disease drug development processes will be discussed.

The national economic burden of rare disease in the United States in 2019.

BACKGROUND: To provide a comprehensive assessment of the total economic burden of rare diseases (RD) in the United States (U.S.) in 2019. We followed a prevalence-based approach that combined the prevalence of 379 RDs with the per-person direct medical and indirect costs, to derive the national economic burden by patient age and type of RD. To estimate the prevalence and the direct medical cost of RD, we used claims data from three sources: Medicare 5% Standard Analytical File, Transformed Medicaid Statistical Information System, and Optum claims data for the privately insured. To estimate indirect and non-medical cost components, we worked with the rare disease community to design and implement a primary survey. RESULTS: There were an estimated 15.5 million U.S. children (N = 1,322,886) and adults (N = 14,222,299) with any of the 379 RDs in 2019 with a total economic burden of $997 billion, including a direct medical cost of $449 billion (45%), $437 billion (44%) in indirect costs, $73 billion in non-medical costs (7%), and $38 billion (4%) in healthcare costs not covered by insurance. The top drivers for excess medical costs associated with RD are hospital inpatient care and prescription medication; the top indirect cost categories are labor market productivity losses due to absenteeism, presenteeism, and early retirement. CONCLUSIONS: Our findings highlight the scale of the RD economic burden and call for immediate attention from the scientific communities, policy leaders, and other key stakeholders such as health care providers and employers, to think innovatively and collectively, to identify new ways to help improve the care, management, and treatment of these often-devastating diseases.

Artificial Intelligence in Medical Imaging and its Impact on the Rare Disease Community: Threats, Challenges and Opportunities.

Almost 1 in 10 individuals can suffer from one of many rare diseases (RDs). The average time to diagnosis for an RD patient is as high as 7 years. Artificial intelligence (AI)-based positron emission tomography (PET), if implemented appropriately, has tremendous potential to advance the diagnosis of RDs. Patient advocacy groups must be active stakeholders in the AI ecosystem if we are to avoid potential issues related to the implementation of AI into health care. AI medical devices must not only be RD-aware at each stage of their conceptualization and life cycle but also should be trained on diverse and augmented datasets representative of the end-user population including RDs. Inability to do so leads to potential harm and unsustainable deployment of AI-based medical devices (AIMDs) into clinical practice.

Scientific evidence based rare disease research discovery with research funding data in knowledge graph.

BACKGROUND: Limited knowledge and unclear underlying biology of many rare diseases pose significant challenges to patients, clinicians, and scientists. To address these challenges, there is an urgent need to inspire and encourage scientists to propose and pursue innovative research studies that aim to uncover the genetic and molecular causes of more rare diseases and ultimately to identify effective therapeutic solutions. A clear understanding of current research efforts, knowledge/research gaps, and funding patterns as scientific evidence is crucial to systematically accelerate the pace of research discovery in rare diseases, which is an overarching goal of this study. METHODS: To semantically represent NIH funding data for rare diseases and advance its use of effectively promoting rare disease research, we identified NIH funded projects for rare diseases by mapping GARD diseases to the project based on project titles; subsequently we presented and managed those identified projects in a knowledge graph using Neo4j software, hosted at NCATS, based on a pre-defined data model that captures semantics among the data. With this developed knowledge graph, we were able to perform several case studies to demonstrate scientific evidence generation for supporting rare disease research discovery. RESULTS: Of 5001 rare diseases belonging to 32 distinct disease categories, we identified 1294 diseases that are mapped to 45,647 distinct, NIH-funded projects obtained from the NIH ExPORTER by implementing semantic annotation of project titles. To capture semantic relationships presenting amongst mapped research funding data, we defined a data model comprised of seven primary classes and corresponding object and data properties. A Neo4j knowledge graph based on this predefined data model has been developed, and we performed multiple case studies over this knowledge graph to demonstrate its use in directing and promoting rare disease research. CONCLUSION: We developed an integrative knowledge graph with rare disease funding data and demonstrated its use as a source from where we can effectively identify and generate scientific evidence to support rare disease research. With the success of this preliminary study, we plan to implement advanced computational approaches for analyzing more funding related data, e.g., project abstracts and PubMed article abstracts, and linking to other types of biomedical data to perform more sophisticated research gap analysis and identify opportunities for future research in rare diseases.

The IDeaS initiative: pilot study to assess the impact of rare diseases on patients and healthcare systems.

BACKGROUND: Rare diseases (RD) are a diverse collection of more than 7-10,000 different disorders, most of which affect a small number of people per disease. Because of their rarity and fragmentation of patients across thousands of different disorders, the medical needs of RD patients are not well recognized or quantified in healthcare systems (HCS). METHODOLOGY: We performed a pilot IDeaS study, where we attempted to quantify the number of RD patients and the direct medical costs of 14 representative RD within 4 different HCS databases and performed a preliminary analysis of the diagnostic journey for selected RD patients. RESULTS: The overall findings were notable for: (1) RD patients are difficult to quantify in HCS using ICD coding search criteria, which likely results in under-counting and under-estimation of their true impact to HCS; (2) per patient direct medical costs of RD are high, estimated to be around three-fivefold higher than age-matched controls; and (3) preliminary evidence shows that diagnostic journeys are likely prolonged in many patients, and may result in progressive, irreversible, and costly complications of their disease CONCLUSIONS: The results of this small pilot suggest that RD have high medical burdens to patients and HCS, and collectively represent a major impact to the public health. Machine-learning strategies applied to HCS databases and medical records using sentinel disease and patient characteristics may hold promise for faster and more accurate diagnosis for many RD patients and should be explored to help address the high unmet medical needs of RD patients.

An integrative knowledge graph for rare diseases, derived from the Genetic and Rare Diseases Information Center (GARD).

BACKGROUND: The Genetic and Rare Diseases (GARD) Information Center was established by the National Institutes of Health (NIH) to provide freely accessible consumer health information on over 6500 genetic and rare diseases. As the cumulative scientific understanding and underlying evidence for these diseases have expanded over time, existing practices to generate knowledge from these publications and resources have not been able to keep pace. Through determining the applicability of computational approaches to enhance or replace manual curation tasks, we aim to both improve the sustainability and relevance of consumer health information, but also to develop a foundational database, from which translational science researchers may start to unravel disease characteristics that are vital to the research process. RESULTS: We developed a meta-ontology based integrative knowledge graph for rare diseases in Neo4j. This integrative knowledge graph includes a total of 3,819,623 nodes and 84,223,681 relations from 34 different biomedical data resources, including curated drug and rare disease associations. Semi-automatic mappings were generated for 2154 unique FDA orphan designations to 776 unique GARD diseases, and 3322 unique FDA designated drugs to UNII, as well as 180,363 associations between drug and indication from Inxight Drugs, which were integrated into the knowledge graph. We conducted four case studies to demonstrate the capabilities of this integrative knowledge graph in accelerating the curation of scientific understanding on rare diseases through the generation of disease mappings/profiles and pathogenesis associations. CONCLUSIONS: By integrating well-established database resources, we developed an integrative knowledge graph containing a large volume of biomedical and research data. Demonstration of several immediate use cases and limitations of this process reveal both the potential feasibility and barriers of utilizing graph-based resources and approaches to support their use by providers of consumer health information, such as GARD, that may struggle with the needs of maintaining knowledge reliant on an evolving and growing evidence-base. Finally, the successful integration of these datasets into a freely accessible knowledge graph highlights an opportunity to take a translational science view on the field of rare diseases by enabling researchers to identify disease characteristics, which may play a role in the translation of discover across different research domains.

Leveraging the UMLS As a Data Standard for Rare Disease Data Normalization and Harmonization.

OBJECTIVE: In this study, we aimed to evaluate the capability of the Unified Medical Language System (UMLS) as one data standard to support data normalization and harmonization of datasets that have been developed for rare diseases. Through analysis of data mappings between multiple rare disease resources and the UMLS, we propose suggested extensions of the UMLS that will enable its adoption as a global standard in rare disease. METHODS: We analyzed data mappings between the UMLS and existing datasets on over 7,000 rare diseases that were retrieved from four publicly accessible resources: Genetic And Rare Diseases Information Center (GARD), Orphanet, Online Mendelian Inheritance in Men (OMIM), and the Monarch Disease Ontology (MONDO). Two types of disease mappings were assessed, (1) curated mappings extracted from those four resources; and (2) established mappings generated by querying the rare disease-based integrative knowledge graph developed in the previous study. RESULTS: We found that 100% of OMIM concepts, and over 50% of concepts from GARD, MONDO, and Orphanet were normalized by the UMLS and accurately categorized into the appropriate UMLS semantic groups. We analyzed 58,636 UMLS mappings, which resulted in 3,876 UMLS concepts across these resources. Manual evaluation of a random set of 500 UMLS mappings demonstrated a high level of accuracy (99%) of developing those mappings, which consisted of 414 mappings of synonyms (82.8%), 76 are subtypes (15.2%), and five are siblings (1%). CONCLUSION: The mapping results illustrated in this study that the UMLS was able to accurately represent rare disease concepts, and their associated information, such as genes and phenotypes, and can effectively be used to support data harmonization across existing resources developed on collecting rare disease data. We recommend the adoption of the UMLS as a data standard for rare disease to enable the existing rare disease datasets to support future applications in a clinical and community settings.

Phenotypically Similar Rare Disease Identification from an Integrative Knowledge Graph for Data Harmonization: Preliminary Study.

BACKGROUND: Although many efforts have been made to develop comprehensive disease resources that capture rare disease information for the purpose of clinical decision making and education, there is no standardized protocol for defining and harmonizing rare diseases across multiple resources. This introduces data redundancy and inconsistency that may ultimately increase confusion and difficulty for the wide use of these resources. To overcome such encumbrances, we report our preliminary study to identify phenotypical similarity among genetic and rare diseases (GARD) that are presenting similar clinical manifestations, and support further data harmonization. OBJECTIVE: To support rare disease data harmonization, we aim to systematically identify phenotypically similar GARD diseases from a disease-oriented integrative knowledge graph and determine their similarity types. METHODS: We identified phenotypically similar GARD diseases programmatically with 2 methods: (1) We measured disease similarity by comparing disease mappings between GARD and other rare disease resources, incorporating manual assessment; 2) we derived clinical manifestations presenting among sibling diseases from disease classifications and prioritized the identified similar diseases based on their phenotypes and genotypes. RESULTS: For disease similarity comparison, approximately 87% (341/392) identified, phenotypically similar disease pairs were validated; 80% (271/392) of these disease pairs were accurately identified as phenotypically similar based on similarity score. The evaluation result shows a high precision (94%) and a satisfactory quality (86% F measure). By deriving phenotypical similarity from Monarch Disease Ontology (MONDO) and Orphanet disease classification trees, we identified a total of 360 disease pairs with at least 1 shared clinical phenotype and gene, which were applied for prioritizing clinical relevance. A total of 662 phenotypically similar disease pairs were identified and will be applied for GARD data harmonization. CONCLUSIONS: We successfully identified phenotypically similar rare diseases among the GARD diseases via 2 approaches, disease mapping comparison and phenotypical similarity derivation from disease classification systems. The results will not only direct GARD data harmonization in expanding translational science research but will also accelerate data transparency and consistency across different disease resources and terminologies, helping to build a robust and up-to-date knowledge resource on rare diseases.

Recent Advances in Systems and Network Medicine: Meeting Report from the First International Conference in Systems and Network Medicine.

The First International Conference in Systems and Network Medicine gathered together 200 global thought leaders, scientists, clinicians, academicians, industry and government experts, medical and graduate students, postdoctoral scholars and policymakers. Held at Georgetown University Conference Center in Washington D.C. on September 11-13, 2019, the event featured a day of pre-conference lectures and hands-on bioinformatic computational workshops followed by two days of deep and diverse scientific talks, panel discussions with eminent thought leaders, and scientific poster presentations. Topics ranged from: Systems and Network Medicine in Clinical Practice; the role of -omics technologies in Health Care; the role of Education and Ethics in Clinical Practice, Systems Thinking, and Rare Diseases; and the role of Artificial Intelligence in Medicine. The conference served as a unique nexus for interdisciplinary discovery and dialogue and fostered formation of new insights and possibilities for health care systems advances.

A framework for the investigation of rare genetic disorders in neuropsychiatry.

De novo and inherited rare genetic disorders (RGDs) are a major cause of human morbidity, frequently involving neuropsychiatric symptoms. Recent advances in genomic technologies and data sharing have revolutionized the identification and diagnosis of RGDs, presenting an opportunity to elucidate the mechanisms underlying neuropsychiatric disorders by investigating the pathophysiology of high-penetrance genetic risk factors. Here we seek out the best path forward for achieving these goals. We think future research will require consistent approaches across multiple RGDs and developmental stages, involving both the characterization of shared neuropsychiatric dimensions in humans and the identification of neurobiological commonalities in model systems. A coordinated and concerted effort across patients, families, researchers, clinicians and institutions, including rapid and broad sharing of data, is now needed to translate these discoveries into urgently needed therapies.

Understanding the evolving phenotype of vascular complications in telomere biology disorders.

Vascular complications such as bleeding due to gastrointestinal telangiectatic anomalies, pulmonary arteriovenous malformations, hepatopulmonary syndrome, and retinal vessel abnormalities are being reported in patients with telomere biology disorders (TBDs) more frequently than previously described. The international clinical care consortium of telomere-associated ailments and family support group Dyskeratosis Congenita Outreach, Inc. held a workshop on vascular abnormalities in the TBDs at the National Cancer Institute in October 2017. Clinicians and basic scientists reviewed current data on vascular complications, hypotheses for the underlying biology and developed new collaborations to address the etiology and clinical management of vascular complications in TBDs.

From scientific discovery to treatments for rare diseases - the view from the National Center for Advancing Translational Sciences - Office of Rare Diseases Research.

We now live in a time of unprecedented opportunities to turn scientific discoveries into better treatments for the estimated 30 million people in the US living with rare diseases. Despite these scientific advances, more than 90% of rare diseases still lack an effective treatment. New data and genetics technologies have resulted in the first transformational new treatments for a handful of rare diseases. This challenges us as a society to accelerate progress so that no disease and no patient is, ultimately, left behind in getting access to safe and effective therapeutics. This article reviews initiatives of the National Center for Advancing Translational Sciences (NCATS) Office of Rare Diseases Research (ORDR) that are aimed at catalyzing rare diseases research. These initiatives fall into two groups: Promoting information sharing; and building multi-disciplinary multi-stakeholder collaborations. Among ORDR's information sharing initiatives are GARD (The Genetics and Rare Diseases Information Center), RaDaR (The Rare Diseases Registries Program) and the NCATS Toolkit for Patient-Focused Therapy Development (Toolkit). Among the collaboration initiatives are the RDCRN (Rare Diseases Clinical Research Network), and the NCATS ORDR support for conferences and workshops. Despite the success of these programs, there remains substantial work to be done to build enhanced collaborations, clinical harmonization and interoperability, and stakeholder engagement so that the recent scientific advances can benefit all patients on the long list of rare diseases waiting for help.

Representation of Women and Minorities in Clinical Trials for New Molecular Entities and Original Therapeutic Biologics Approved by FDA CDER from 2013 to 2015.

BACKGROUND: The U.S. Food and Drug Administration (FDA) has made efforts to encourage adequate assessment of women, racial/ethnic minorities, and geriatric participants in clinical trials through regulations and guidance documents. This study surveyed the demographics of clinical trial participants and the presence of efficacy and safety analyses by sex for new drugs approved between 2013 and 2015 by the FDA Center for Drug Evaluation and Research. METHODS: New drug marketing applications submitted to FDA were surveyed for demographic data (sex, race, ethnicity, and age) and the presence of sex-based analyses for efficacy and safety. The Ratio of the Proportion of women in clinical trials for the indicated disease population relative to the estimated Proportion of women in the disease population (PPR) was calculated for new drug indications. RESULTS: Of the 102 new drugs in this cohort (defined as new molecular entity drugs and original therapeutic biologics), sex was reported for >99.9% of trial participants, and women accounted for 40.4% of these participants. An estimated 77.2% of participants were White, 6.4% were Black/African American, and 29.1% were aged ≥65 years. Sex-based analyses for both efficacy and safety were conducted for 93.1% of applications. PPR was calculated for 82 new drugs for a total of 60 indications, of which 50 indications (83.3%) had a PPR ≥0.80. CONCLUSIONS: Sex data are now collected for almost all study participants, and this study shows appropriate sex participation for most new drugs when estimated disease prevalence by sex (PPR) is considered. Therapeutic area and disease indication are important considerations when assessing the sex of participants because variation occurs depending on the disease under study. Some racial minorities, especially Blacks/African Americans, are still not well represented in most drug development programs and remain an area where improvement is needed.

Investigational New Drug applications: a 1-year pilot study on rates and reasons for clinical hold.

BACKGROUND: The Food and Drug Administration (FDA)'s Center for Drug Evaluation and Research (CDER) receives about 1500 initial Investigational New Drug applications (INDs) per year. In the first 30 days after initial IND submission, FDA conducts a review to determine whether the proposed investigation is safe to proceed, and if not, the IND may be placed on clinical hold. METHODS: A retrospective study of rates and reasons for clinical hold for all initial INDs submitted to CDER in fiscal year (FY) 2013 was performed. INDs were assessed for reasons that led to clinical hold, included chemistry, manufacturing and controls (CMC), animal toxicology or clinical issues. INDs were further categorized by commercial versus research sponsorship, and rare versus common disease indications. All INDs placed on hold were reassessed by whether they remained on hold within the first year following hold imposition. RESULTS: CDER received 1410 initial INDs in FY 2013, of which 125 (8.9%) were placed on hold during the first 30 days after initial submission. Of the INDs placed on hold, more than half became active within the first year after first imposition of hold. CMC reasons were most commonly cited, followed by clinical, then toxicology reasons. There were no substantive differences in rates and reasons for hold between INDs for rare or common disease indications, or between commercial or research INDs. CONCLUSIONS: The vast majority of initial INDs moved forward within 30 days after submission, and for those applications placed on hold, most became active within 1 year. The findings also suggest that many holds for new drug product programs can be avoided by following the available guidelines for investigational product development.

Immune response to enzyme replacement therapies in lysosomal storage diseases and the role of immune tolerance induction.

The US Food and Drug Administration (FDA) and National Organization for Rare Disease (NORD) convened a public workshop titled "Immune Responses to Enzyme Replacement Therapies: Role of Immune Tolerance Induction" to discuss the impact of anti-drug antibodies (ADAs) on efficacy and safety of enzyme replacement therapies (ERTs) intended to treat patients with lysosomal storage diseases (LSDs). Participants in the workshop included FDA staff, clinicians, scientists, patients, industry, and advocacy group representatives. The risks and benefits of implementing prophylactic immune tolerance induction (ITI) to reduce the potential clinical impact of antibody development were considered. Complications due to immune responses to ERT are being recognized with increasing experience and lengths of exposure to ERTs to treat several LSDs. Strategies to mitigate immune responses and to optimize therapies are needed. Discussions during the workshop resulted in the identification of knowledge gaps and future areas of research, as well as the following proposals from the participants: (1) systematic collection of longitudinal data on immunogenicity to better understand the impact of ADAs on long-term clinical outcomes; (2) development of disease-specific biomarkers and outcome measures to assess the effect of ADAs and ITI on efficacy and safety; (3) development of consistent approaches to ADA assays to allow comparisons of immunogenicity data across different products and disease groups, and to expedite reporting of results; (4) establishment of a system to widely share data on antibody titers following treatment with ERTs; (5) identification of components of the protein that are immunogenic so that triggers and components of the immune responses can be targeted in ITI; and (6) consideration of early ITI in patients who are at risk of developing clinically relevant ADA that have been demonstrated to worsen treatment outcomes.

Pre-Investigational New Drug Meetings With the FDA: Evaluation of Meeting Content and Characteristics of Applications for New Drug and Biologic Products.

INTRODUCTION: Prior studies suggested that holding preinvestigational new drug application (PIND) meetings with FDA has a positive effect on clinical development time (CDT). METHODS: New product marketing applications submitted to FDA CDER during fiscal years 2008-2012 were assessed for whether a PIND meeting was held and, if so, a qualitative assessment of meeting content was performed. RESULTS: Discussions contained in the PIND meeting minutes tended to reflect topics appropriate to an early phase of drug development, including chemistry, manufacturing, and controls (CMC) and safety topics (eg, nonclinical and clinical domains). Additionally, FDA commonly provided additional advice most often in the clinical and CMC domains. Applications for which a PIND meeting was held during drug development had shorter CDTs than those that did not. CONCLUSIONS: This analysis showed the importance of early communication with FDA during development, and small companies with limited regulatory experience may gain the greatest benefit from early communication with FDA.

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. Clinical trials are now focusing on progressively smaller subgroups within both common and rare disease populations, often based on genetic tests or biomarkers. 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.