Rare Disease Research Research Articles

Evolving Research and Development Landscape for Rare Diseases: Growing Concerns Over Orphan Drug Lag in Japan.

Patients with rare diseases worldwide face substantial unmet therapeutic needs. In Japan, drug lag-delays in drug approval compared to other countries-has resurfaced as a pressing public health issue. This study analyzes orphan drugs (ODs) approved in the United States (US) from 2005 to 2021, examining OD lag trends, and research and development (R&D) models to streamline OD development in Japan. Despite increased OD approval in the United States since 2018, the number of unapproved ODs in Japan has substantially increased. Although OD lag decreased, it has resurged since 2017. This is largely due to changes in the R&D strategies of pharmaceutical companies, which are driven by the growing presence of US- and Europe-based small- to mid-sized enterprises (SMEs) and the evolving industry landscape. Large foreign pharmaceutical companies have shifted toward a global development strategy for Japan, aiming for more efficient development and competitive advantage. This has been propelled by a move toward in-licensing earlier-stage drug candidates with global exclusivity from SMEs. Japanese pharmaceutical companies have focused on in-licensing late-stage drug candidates for the Japanese market from SMEs without a business presence in Japan, which have not been developed locally, thereby employing a bridging strategy for Japan. With the increase in ODs developed in the United States by these SMEs, this practice has substantially exacerbated the OD lag. As these SMEs are unlikely to enter the Japanese market, it is crucial for Japanese pharmaceutical companies to proactively pursue earlier, more proactive global partnerships with SMEs.

Insights from the First Five Years of the Prospective Ocular Tumor Study

ABSTRACT Purpose Standardized data collection is needed to improve research for rare diseases. In this manuscript, we describe our experience establishing the Prospective Ocular Tumor Study (POTS). Methods The ongoing POTS captures all patients with an ocular tumor seen on the Ocular Oncology Service at Mayo Clinic Rochester and collects patient demographics, tumor features, treatment, and outcomes. This manuscript reports data collected from July 2019-July 2024. Results During a 5-year time period, 1,766 patients enrolled in the database, with 975 (55%) females, 1,732 (98%) white race, and mean age 61.5 years. The most frequent tumor types were choroidal melanoma (n = 610 [34%]), choroidal nevus (n = 575 [32%]), iris nevus (n = 95 [5.3%]), iris melanoma (n = 46 [2.6%]), and vitreoretinal lymphoma (n = 46 [2.6%]). Conclusion The POTS is a valuable source of detailed, longitudinal data on rare ocular tumors. Expanding standardized data collection across multiple centers will facilitate improved outcomes research in ocular oncology.

Management of rare and undiagnosed diseases: insights from researchers and healthcare professionals in Türkiye

IntroductionDiagnosis, treatment and management of rare diseases (RD) pose unique challenges due to their complex nature, significantly impacting the daily experiences of researchers and healthcare professionals working in this field. Despite increasing awareness and progress in the field of RD worldwide in recent years, a significant gap remains in our understanding of the specific barriers that these professionals face in their work with RD. This study provides a detailed survey analysis that sheds light on the challenges that researchers and healthcare professionals face in diagnosing, treating, managing and conducting research on RD.MethodsWe developed a national online survey with three RD stakeholder groups (Researchers, Healthcare professionals and researcher-healthcare professionals) to identify the main challenges and needs in Türkiye for the diagnosis, treatment and follow-up processes of rare and undiagnosed diseases.ResultsThe survey was completed by 363 participants, revealing that participants face key challenges such as the need to refer patients to specialized centers, financial burdens, limited access to necessary tests, inadequate support for rare disease research and a lack of interdisciplinary collaboration. Participants also noted that RD are inherently difficult to conduct research on with small cohorts. Survey results also suggest a number of policy improvements to accelerate research on RD: increased funding, establishment of robust surveillance systems, and development of comprehensive national action plans and guidelines on RD.DiscussionTo the best of our knowledge, this is the first study to be conducted in Türkiye. This study contributes to the understanding of the needs of professionals in rare disease research and highlights the urgent need for system improvements to support them.

Leveraging domain knowledge for synthetic ultrasound image generation: a novel approach to rare disease AI detection.

This study explores the use of deep generative models to create synthetic ultrasound images for the detection of hemarthrosis in hemophilia patients. Addressing the challenge of sparse datasets in rare disease diagnostics, the study aims to enhance AI model robustness and accuracy through the integration of domain knowledge into the synthetic image generation process. The study employed two ultrasound datasets: a base dataset (Db) of knee recess distension images from non-hemophiliac patients and a target dataset (Dt) of hemarthrosis images from hemophiliac patients. The synthetic generation framework included a content generator (Gc) trained on Db and a context generator (Gs) to adapt these images to match Dt's context. This approach generated a synthetic target dataset (Ds), primed for AI training in rare disease research. The assessment of synthetic image generation involved expert evaluations, statistical analysis, and the use of domain-invariant perceptual distance and Fréchet inception distance for quality measurement. Expert evaluation revealed that images produced by our synthetic generation framework were comparable to real ones, with no significant difference in overall quality or anatomical accuracy. Additionally, the use of synthetic data in training convolutional neural networks demonstrated robustness in detecting hemarthrosis, especially with limited sample sizes. This study presents a novel approach for generating synthetic ultrasound images for rare disease detection, such as hemarthrosis in hemophiliac knees. By leveraging deep generative models and integrating domain knowledge, the proposed framework successfully addresses the limitations of sparse datasets and enhances AI model training and robustness. The synthetic images produced are of high quality and contribute significantly to AI-driven diagnostics in rare diseases, highlighting the potential of synthetic data in medical imaging.

Development of the Dutch translational knowledge agenda for inherited metabolic diseases.

Inherited metabolic diseases (IMDs) may have considerable implications for patients and their families. Despite their individual rarity, covering a spectrum of over 1800 distinct diseases, the diseases collectively exert a significant impact, with often lifelong disabilities. The United for Metabolic Diseases consortium was established to catalyze research with translation into the best possible care. To generate a translational knowledge agenda, which identifies and prioritizes research questions, directly relevant to patient care or for IMD patients and their families. Following a process established by the Knowledge Institute of the Dutch Association of Medical Specialists, we generated a comprehensive translational knowledge agenda for IMDs. A multidisciplinary steering committee, composed of 12 diverse metabolic experts collected research questions through an online questionnaire using snowballing. The 462 proposed questions were categorized and prioritized during a meeting attended by 22 representatives of all stakeholder groups. The resulting top 10 research questions cover multiple themes, i.e. prediction of disease progression, development of novel tools, mechanistic insights, improved diagnostics, therapeutic integration of multi-omics techniques, assessment of impact on daily life, expanding treatment avenues, optimal study designs, effect of lifestyle interventions, and data utilization using FAIR principles. This collective endeavor reflects the collaborative spirit needed for rare disease research. This knowledge agenda will guide funding directions and applications but will also boost interdisciplinary collaboration to push the field of IMDs research forward in a renewed UMD consortium. Patient engagement, transparency, and a comprehensive approach make this knowledge agenda a pivotal step toward addressing the pressing research needs and priorities in this domain.

Multi-stakeholder sessions on major innovation topics in rare disease clinical trials

BackgroundThe European Joint Programme on Rare Diseases aims to enhance the rare diseases research ecosystem by bringing together stakeholders such as research funders, institutions and patient organizations. Work Package 20 focuses on the validation, use and development of innovative methodologies for rare disease clinical trials. This paper reports on the outcomes of a retreat held in April 2023, where areas for innovation and educational needs in rare disease clinical trials were discussed in multi-stakeholder sessions.MethodsMulti-stakeholder sessions covered the topics: Future Educational System, Randomization in Rare Disease Clinical Trials, Endpoints in Rare Disease Clinical Trials and Using History Course Data. The sessions began with expert presentations to set the scene, followed by guided discussions facilitated by questions on a collaborative digital whiteboard. Participants wrote responses, which were then discussed live with the experts.ResultsTraining is needed for diverse stakeholders in rare disease clinical trials to enhance understanding and drive innovation. Challenges include a lack of standardized terminology for multiple endpoints, inadequate understanding of randomization in small sample studies and various obstacles in effectively using natural history data.ConclusionCreating a comprehensive and sustainable educational program for rare diseases clinical trial methodology requires strategic collaboration and adherence to FAIR principles. The workshop highlighted the need for innovations for topics in areas such as handling missing data, optimizing the extraction of information from small samples, remote endpoint measurement and new randomized inference techniques. Additionally, integrating innovations into tailored training programs is crucial for advancing the field.

Efficient reinterpretation of rare disease cases using Exomiser

Whole genome sequencing has transformed rare disease research; however, 50–80% of rare disease patients remain undiagnosed after such testing. Regular reanalysis can identify new diagnoses, especially in newly discovered disease-gene associations, but efficient tools are required to support clinical interpretation. Exomiser, a phenotype-driven variant prioritisation tool, fulfils this role; within the 100,000 Genomes Project (100kGP), diagnoses were identified after reanalysis in 463 (2%) of 24,015 unsolved patients after previous analysis for variants in known disease genes. However, extensive manual interpretation was required. This led us to develop a reanalysis strategy to efficiently reveal candidates from recent disease gene discoveries or newly designated pathogenic/likely pathogenic variants. Optimal settings to highlight new candidates from Exomiser reanalysis were identified with high recall (82%) and precision (88%) when including Exomiser’s automated ACMG/AMP classifier, which correctly converted 92% of variants from unknown significance to pathogenic/likely pathogenic. In conclusion, Exomiser efficiently reinterprets previously unsolved cases.

Basic and translational research in rare diseases in low- and middle-income countries: challenges and solutions.

In this document, we discuss the main difficulties faced by investigators in low- and middle-income countries (LMICs) and propose potential solutions. Challenges include the scarcity of experts in rare disorders, higher costs of supplies, underfunding, and limited patient advocacy groups. Establishing collaborations among patient advocacy groups, governments, investigators, and other stakeholders to create action plans can address many of these issues and promote research into rare diseases.

Privacy-by-Design with Federated Learning will drive future Rare Disease Research

Up to 6% of the global population is estimated to be affected by one of about 10,000 distinct rare diseases (RDs). RDs are, to this day, often not understood, and thus, patients are heavily underserved. Most RD studies are chronically underfunded, and research faces inherent difficulties in analyzing scarce data. Furthermore, the creation and analysis of representative datasets are often constrained by stringent data protection regulations, such as the EU General Data Protection Regulation. This review examines the potential of federated learning (FL) as a privacy-by-design approach to training machine learning on distributed datasets while ensuring data privacy by maintaining the local patient data and only sharing model parameters, which is particularly beneficial in the context of sensitive data that cannot be collected in a centralized manner. FL enhances model accuracy by leveraging diverse datasets without compromising data privacy. This is particularly relevant in rare diseases, where heterogeneity and small sample sizes impede the development of robust models. FL further has the potential to enable the discovery of novel biomarkers, enhance patient stratification, and facilitate the development of personalized treatment plans. This review illustrates how FL can facilitate large-scale, cross-institutional collaboration, thereby enabling the development of more accurate and generalizable models for improved diagnosis and treatment of rare diseases. However, challenges such as non-independently distributed data and significant computational and bandwidth requirements still need to be addressed. Future research must focus on applying FL technology for rare disease datasets while exploring standardized protocols for cross-border collaborations that can ultimately pave the way for a new era of privacy-preserving and distributed data-driven rare disease research.

The triple code model for advancing research in rare and undiagnosed diseases beyond the base pairs.

Rare and undiagnosed diseases pose significant challenges for understanding their mechanisms, diagnosis, and treatment. The Triple Code Model, an integrative paradigm described here, considers the combined influence of the genetic code, epigenetic code, and nuclear structure (an emerging code), as fundamental biochemical mechanisms underlying many rare diseases. Studies demonstrate dysfunctional membrane and cytoplasmic signals instruct the epigenome to ultimately impact the 3D structure and dynamics of the nucleus, highlighting their close interrelationships. Consequently, this model offers a holistic perspective on rare and undiagnosed diseases by moving beyond a solely genetic view. We propose that this integrated framework will efficiently guide rare disease research by taking it 'Beyond the Base Pairs,' leading to improved diagnostics and personalized treatments.

Hospital-wide access to genomic data advanced pediatric rare disease research and clinical outcomes

Boston Children’s Hospital has established a genomic sequencing and analysis research initiative to improve clinical care for pediatric rare disease patients. Through the Children’s Rare Disease Collaborative (CRDC), the hospital offers CLIA-grade exome and genome sequencing, along with other sequencing types, to patients enrolled in specialized rare disease research studies. The data, consented for broad research use, are harmonized and analyzed with CRDC-supported variant interpretation tools. Since its launch, 66 investigators representing 26 divisions and 45 phenotype-based cohorts have joined the CRDC. These studies enrolled 4653 families, with 35% of analyzed cases having a finding either confirmed or under further investigation. This accessible and harmonized genomics platform also supports additional institutional data collections, research and clinical, and now encompasses 13,800+ patients and their families. This has fostered new research projects and collaborations, increased genetic diagnoses and accelerated innovative research via integration of genomics research with clinical care.

PCR252 Rare Disease Patient and Caregiver Perspectives on Opportunities to Try New Rare Disease Treatments or Participate in Rare Disease Research: Results From an International Survey

PCR252 Rare Disease Patient and Caregiver Perspectives on Opportunities to Try New Rare Disease Treatments or Participate in Rare Disease Research: Results From an International Survey

Fostering the international interoperability of clinical research networks to tackle undiagnosed and under-researched rare diseases.

Clinical research is an essential component to advance diagnosis and therapeutic development. In 2022, the International Rare Diseases Research Consortium (IRDiRC) and the European Joint Programme on Rare Diseases (EJP RD) brought together key stakeholders from across the globe to discuss common themes in clinical research networks (CRNs) for rare diseases. Various topics were raised during discussions including current state of CRNs, the need for new CRNs, multi-stakeholder perspectives on value of CRNs, and ways to collaborate on a global scale. Communication and coordination between various groups, taking advantage of existing experiences, can expedite establishment and execution of complex collaborations that will be necessary for CRNs. In this perspective, we discuss opportunities and highlight key considerations for developing successful collaborative CRNs across the globe.

EPID-24. OPTIMIZING PATIENT RECRUITMENT FOR BRAIN TUMOR STUDIES: LEVERAGING THE POWER OF GOOGLE ADS TO CONNECT WITH AFFECTED FAMILIES

Abstract OBJECTIVE We assessed the effectiveness of using Google Ads to recruit individuals with familial glioma into a genetic research study. METHODS We conducted a comparative analysis between two periods: March 1 to June 30, 2023 (with Google Ads) and March 1 to June 30, 2021 (without Google Ads). We targeted individuals searching for brain tumor information using Google Ads, directing them to a study landing page. We analyzed metrics such as advertisement clicks, impressions, click-through rate, and cost per click. Our eligibility criteria included families with at least two members having glioma, with one currently living with the condition, aged 18 or older, and residing in the United States. RESULTS The 2023 Google Ads campaign generated 7,228 clicks, 82,242 impressions, an 8.79% click-through rate, and a cost per click of $0.62. The number of interested individuals increased from 13 in 2021 (without Google Ads) to 35 in 2023 (with Google Ads), with 20 indicating they found the study through Google Ads. However, the increase lacked statistical significance (x2=1.48, p=0.22). CONCLUSION Google Ads enhanced our participant outreach and recruitment for the glioma study, although the results were not statistically significant. This approach shows promise for recruiting participants for rare disease research. We recommend that future studies consider longer durations and larger sample sizes to better assess its impact. Additionally, due to the prevalence of misinformation about familial glioma, we submitted and conducted a survey to the Society of NeuroOncology to evaluate the attitudes and beliefs of physicians and other healthcare professionals towards familial glioma. This survey aims to address misconceptions and improve understanding within the healthcare community.

Leveraging Federated Learning for Privacy-Preserving Analysis of Multi-Institutional Electronic Health Records in Rare Disease Research

This research announces that the fresh federated learning structure is designed to enhance the privacy-preserving analysis of electronic health records (EHRs), and multiple institutions in this framework permit secure collaboration among institutions, allowing them to train machine-learning replicas without directly sharing patient data. We implemented and evaluated numerous machine-learning models to forecast patient treatment needs, including Logistic Regression, Decision-Tree-Classifiers, Support-Vectors-Classifiers, Random-Forests, and Stacking-Classifiers. The Random Forest classifier achieved the best performance with an accuracy of 90% and an F1 score of 80%, demonstrating that it handled complex and imbalanced datasets. This FL-based approach not only complies with privacy regulations such as HIPAA and GDPR but also overcomes significant challenges in data sharing, making it ideal for rare disease research. By enabling secure data aggregation across institutions, the framework significantly enhances the ability to study rare diseases and accelerates the discovery of new treatments. Future directions include extending this framework to other areas of healthcare and incorporating advanced machine-learning techniques to enhance its capabilities further. This research sets the new standard for secure and collaborative healthcare data analysis and promotes innovation and ethical practices in rare disease research.

What Is the Recipe for Success in Science?

This article narrates the life journey of a woman in science, drawing parallels between her grandmother’s improvisational apple pie making and her own path to success. Unlike a recipe, her career was shaped by unique, personal ingredients: a fervent love for learning from her parents; the importance of financial independence instilled by her mother; and a supportive network that facilitated both her family and career decisions. Through anecdotes, she emphasizes key life lessons: success is personal and varied; plans can change; guilt over work-life balance is self-imposed; and one should not be confined by traditional career paths. Her story highlights the importance of resilience, adaptability, and the pursuit of meaningful causes over conventional career milestones. Her transformative work in rare disease research underscores her commitment to leveraging her career for the greater good, embodying a blend of scientific curiosity and business acumen. The article concludes with a call to embrace learning, face failures, and build a trustworthy support network.

Review of national rare disease strategies: innovation driving change

Abstract Background A rare disease affects fewer than 5 in 10,000 people. More than 6,000 known rare diseases affect up to 6% of the European population. A national rare disease strategy sets out a country’s plan to reduce disease burden and improve quality of care for people with rare diseases. This review, requested by the Department of Health in Ireland to inform a new national strategy, described national rare disease strategies in 13 selected countries. Methods Strategies published from 2013 to 2023 were identified via online searches for Austria, Australia, Denmark, England, Finland, France, Germany, Ireland, the Netherlands, Northern Ireland, Portugal, Scotland and Wales. National representatives were contacted to confirm the identified resources. Data were extracted for the domains of: aims, priorities, implementation, governance and funding models. Descriptive analysis and narrative synthesis was undertaken. Results Improving treatment and coordination of care was a common overall aim. Priorities noted in all strategies were: screening and diagnosis; access to healthcare and coordination of services; rare disease research; and patient representation and empowerment. Implementation details varied to reflect the national context and time of strategy development. More recently-developed strategies included more precise actions and greater emphasis on innovations in genomics than older strategies. Strategies were developed, implemented, monitored and evaluated via combinations of newly-established bodies and or existing health authorities. Six countries did not specify a funding model or dedicated budget for strategy implementation. Conclusions National rare disease strategy contents and implementation approaches varied between countries and reflected innovations over time. This review will inform the development of Ireland’s new national rare disease strategy. Key messages • This review provided insights into national rare disease strategies over a 10-year period. • The findings will inform strategy development in Ireland, and are relevant to other countries.

Advancing Equity in Rare Disease Research: Insights From the Undiagnosed Disease Network.

Rare diseases affect 6%-8% of the population and present diagnostic challenges, particularly for historically marginalized ethnic and racial groups. The Undiagnosed Diseases Network (UDN) aims to enhance diagnosis rates and research participation among such minoritized groups. Aretrospective review was conducted from 2015 to 2023, analyzing2235 UDN participants to evaluate its progress toward this objective. Data on demographics, disease phenotypes, diagnostic outcomes, and socioeconomic factors were collected and statistical analyses assessed differences among ethnic and racial groups. This demonstrated that Hispanic and Black non-Hispanic groups were underrepresented, while White non-Hispanic participants were overrepresented in the UDN compared to the US population. Individuals whose primary language was not English were also significantly underrepresented. Diagnosis rates varied, with the highest rates among Asian non-Hispanic (39.5%) and Hispanic (35.3%) groups and the lowest rate in the White non-Hispanic group (26.8%) (p < 0.001). Binomial logistic regression found, however, that only participant age and disease phenotype predicted the likelihood of receiving a diagnosis (p < 0.001). Persistent ethnic and racial disparities in UDN participation appear to be associated with major differences in application rates. Under-enrollment of historically marginalized ethnic and racial groups may be due to economic hardships and language barriers. No differences in the diagnostic yield among ethnic and racial groups were observed after controlling for other factors. This work highlights the value of comprehensive genetic evaluations for addressing healthcare disparities and suggests priorities for advancing inclusion in rare disease research.

Implications of mappings between International Classification of Diseases clinical diagnosis codes and Human Phenotype Ontology terms.

Integrating electronic health record (EHR) data with other resources is essential in rare disease research due to low disease prevalence. Such integration is dependent on the alignment of ontologies used for data annotation. The international classification of diseases (ICD) is used to annotate clinical diagnoses, while the human phenotype ontology (HPO) is used to annotate phenotypes. Although these ontologies overlap in the biomedical entities they describe, the extent to which they are interoperable is unknown. We investigate how well aligned these ontologies are and whether such alignments facilitate EHR data integration. We conducted an empirical analysis of the coverage of mappings between ICD and HPO. We interpret this mapping coverage as a proxy for how easily clinical data can be integrated with research ontologies such as HPO. We quantify how exhaustively ICD codes are mapped to HPO by analyzing mappings in the unified medical language system (UMLS) Metathesaurus. We analyze the proportion of ICD codes mapped to HPO within a real-world EHR dataset. Our analysis revealed that only 2.2% of ICD codes have direct mappings to HPO in UMLS. Within our EHR dataset, less than 50% of ICD codes have mappings to HPO terms. ICD codes that are used frequently in EHR data tend to have mappings to HPO; ICD codes that represent rarer medical conditions are seldom mapped. We find that interoperability between ICD and HPO via UMLS is limited. While other mapping sources could be incorporated, there are no established conventions for what resources should be used to complement UMLS.

Telehealth for rare disease care, research, and education across the globe: A review of the literature by the IRDiRC telehealth task force

The International Rare Diseases Research Consortium (IRDiRC) Telehealth (TH) Task Force explored the use of TH for improving diagnosis, care, research, and education for rare diseases (RDs). The Task Force reviewed related literature published from January 2017 to August 2023, and identified various models and implementation strategies of TH for RD. The Task Force highlighted the reported value and benefits of using TH for RDs, along with the limitations and opportunities. The number of publications sharply increased since 2021, coinciding with the onset of the COVID-19 pandemic, which forced the rapid adoption of TH in many healthcare settings. One of the major benefits of TH for RDs lies in its capacity to surmount geographical barriers, which helps in overcoming the constraints posed by limited numbers and geographical dispersion of specialists. This was evident during the pandemic when TH was used to maintain a level of continued medical care and research when face-to-face visits were severely restricted. TH, through which clinical research can be decentralized, can also facilitate and enhance RD research by decreasing burden, expanding access, and enhancing efficiency. This will be especially beneficial when coupled with the adoption of digital health technologies, such as mobile health (mHealth) and wearable devices for remote monitoring (i.e., surveillance of outpatient data transmitted through devices), along with big data solutions. TH has also been shown to be an effective means for RD education and peer mentoring, enabling local health care providers (HCPs) to care for RD patients, which indirectly ensures that RD patients get the expertise and multidisciplinary care they need. However, limitations and weaknesses associated with using TH for RD care and research were also identified, including the inability to perform physical examinations and build relationships with HCPs. Therefore, TH has been recommended as a complement to, rather than substitute for, face-to-face consultations. There is also a concern that TH may lead to an amplification of health disparities and inequities related to social determinants of health for those with RDs due to lack of access to TH technologies, inadequate digital literacy, and geographical, socio-cultural, and linguistic barriers. Finally, the Task Force also discussed evidence and knowledge gaps that will benefit from future research efforts to help advance and expand the use of TH for RD care, research, and education.