How many rare diseases are there?

Leaving no patient behind! Expert recommendation in the use of innovative technologies for diagnosing rare diseases

Genetic diagnosis plays a crucial role in rare diseases, particularly with the increasing availability of emerging and accessible treatments. The International Rare Diseases Research Consortium (IRDiRC) has set its primary goal as: "Ensuring that all patients who present with a suspected rare disease receive a diagnosis within one year if their disorder is documented in the medical literature". Despite significant advances in genomic sequencing technologies, more than half of the patients with suspected Mendelian disorders remain undiagnosed. In response, IRDiRC proposes the establishment of "a globally coordinated diagnostic and research pipeline". To help facilitate this, IRDiRC formed the Task Force on Integrating New Technologies for Rare Disease Diagnosis. This multi-stakeholder Task Force aims to provide an overview of the current state of innovative diagnostic technologies for clinicians and researchers, focusing on the patient's diagnostic journey. Herein, we provide an overview of a broad spectrum of emerging diagnostic technologies involving genomics, epigenomics and multi-omics, functional testing and model systems, data sharing, bioinformatics, and Artificial Intelligence (AI), highlighting their advantages, limitations, and the current state of clinical adaption. We provide expert recommendations outlining the stepwise application of these innovative technologies in the diagnostic pathways while considering global differences in accessibility. The importance of FAIR (Findability, Accessibility, Interoperability, and Reusability) and CARE (Collective benefit, Authority to control, Responsibility, and Ethics) data management is emphasized, along with the need for enhanced and continuing education in medical genomics. We provide a perspective on future technological developments in genome diagnostics and their integration into clinical practice. Lastly, we summarize the challenges related to genomic diversity and accessibility, highlighting the significance of innovative diagnostic technologies, global collaboration, and equitable access to diagnosis and treatment for people living with rare disease.

Operational description of rare diseases: a reference to improve the recognition and visibility of rare diseases

Improving health and social equity for persons living with a rare disease (PLWRD) is increasingly recognized as a global policy priority. However, there is currently no international alignment on how to define and describe rare diseases. A global reference is needed to establish a mutual understanding to inform a wide range of stakeholders for actions. A multi-stakeholder, global panel of rare disease experts, came together and developed an Operational Description of Rare Diseases. This reference describes which diseases are considered rare, how many persons are affected and why the rare disease population demands specific attention. The operational description of rare diseases is framed in two parts: a core definition of rare diseases, complemented by a descriptive framework of rare diseases. The core definition includes parameters that permit the identification of which diseases are considered rare, and how many persons are affected. The descriptive framework elaborates on the impact and burden of rare diseases on patients, their caregivers and families, healthcare systems, and society overall. The Operational Description of Rare Diseases establishes a common point of reference for decision-makers across the world who strive to understand and address the unmet needs of persons living with a rare disease. Adoption of this reference is essential to improving the visibility of rare conditions in health systems across the world. Greater recognition of the burden of rare diseases will motivate new actions and policies to address the unmet needs of the rare disease community.

A systematic review of studies that estimated the burden of chronic non-communicable rare diseases using disability-adjusted life years

BACKGROUND

Initiatives aiming to assess the impact of rare diseases on population health might be hampered due to the complexity of disability-adjusted life years (DALYs) estimation. This study aimed to give insight into the epidemiological data sources and methodological approaches used in studies that estimated DALYs for chronic non-communicable rare diseases (CNCRD), and compare its results.

METHODS

A literature strategy was developed for peer-review search in Embase and Medline, and also performed on grey literature databases and population health and/or rare disease-focused websites. We included studies that determined the burden of CNCRD listed on the Orphanet's and/or the Genetic and Rare Diseases information center (GARD) websites. We excluded communicable and occupational diseases, rare cancers, and cost-effectiveness/benefit studies. Two researchers independently screened the identified records and extracted data from the final included studies. We used the Guidelines for Accurate and Transparent Health Estimates Reporting (GATHER) statement to assess the quality of reporting of the included studies. The data synthesis depicted the studies' characteristics, their distribution by geographic coverage and the group of disease(s) they focused on, the methods and data input sources used and estimated DALY per case.

RESULTS

In total, 533 titles were screened, and 18 studies were included. These studies covered 19 different CNCRDs, of which most fell in the disease category "Diseases of the nervous system". Diverse methodological approaches and data input sources were observed among burden of CNCRD studies. A wide range of DALY per case was observed across the different studies and diseases included.

CONCLUSIONS

A low number of burden of CNCRD studies was observed and most estimates resulted from multi-country studies, underlining the importance of international cooperation to further CNCRD research. This study revealed a lack of epidemiological data and harmonization of methods which hampers comparisons across burden of CNCRD studies.

VUS next in rare diseases? Deciphering genetic determinants of biomolecular condensation

The diagnostic odysseys for rare disease patients are getting shorter as next-generation sequencing becomes more widespread. However, the complex genetic diversity and factors influencing expressivity continue to challenge accurate diagnosis, leaving more than 50% of genetic variants categorized as variants of uncertain significance.Genomic expression intricately hinges on localized interactions among its products. Conventional variant prioritization, biased towards known disease genes and the structure-function paradigm, overlooks the potential impact of variants shaping the composition, location, size, and properties of biomolecular condensates, genuine membraneless organelles swiftly sensing and responding to environmental changes, and modulating expressivity.To address this complexity, we propose to focus on the nexus of genetic variants within biomolecular condensates determinants. Scrutinizing variant effects in these membraneless organelles could refine prioritization, enhance diagnostics, and unveil the molecular underpinnings of rare diseases. Integrating comprehensive genome sequencing, transcriptomics, and computational models can unravel variant pathogenicity and disease mechanisms, enabling precision medicine. This paper presents the rationale driving our proposal and describes a protocol to implement this approach. By fusing state-of-the-art knowledge and methodologies into the clinical practice, we aim to redefine rare diseases diagnosis, leveraging the power of scientific advancement for more informed medical decisions.

A scoping review of health literacy in rare disorders: key issues and research directions

BACKGROUND

The ability to find, understand, appraise and utilise health information is crucial among individuals living with rare disorders. The aim of this study was to give a comprehensive overview of the literature on health literacy in adult persons with rare disorders.

METHODS

We applied a scoping review methodology and performed a systematic search in 2021 in bibliographic databases. Searches were conducted in Medline (Ovid), Embase (Ovid), PsycInfo (Ovid), CINAHL (ebsco), and ERIC (Ovid). References were sorted and evaluated for inclusion using EndNote and Covidence. This review was guided by the question "What are the characteristics of research on health literacy in rare disorders?"

RESULTS

The database searches yielded 75 eligible reports. A total of 6223 individuals with rare disorders were represented alongside 1707 caregivers. The reports in this review have included study participants representing a total of 80 different rare disorders with unique ORPHA and ICD-10 codes. The results revealed that persons with rare disorders often exhibit gaps in health literacy through a lack of knowledge and access to information related to self-management, their own diagnosis and health, as well as daily coping and social rights. In addition, the importance of aid and information from healthcare personnel and the significance of getting social support from others in the same situation were accentuated.

CONCLUSION

This review emphasizes the importance of reinforcing health literacy among persons with rare disorders through peer support and education. This is the first review to give a comprehensive and state-of-the-art overview of literature investigating health literacy among persons with rare disorders and offers a basis for further research.

Rare disease diagnosis using knowledge guided retrieval augmentation for ChatGPT

Although rare diseases individually have a low prevalence, they collectively affect nearly 400 million individuals around the world. On average, it takes five years for an accurate rare disease diagnosis, but many patients remain undiagnosed or misdiagnosed. As machine learning technologies have been used to aid diagnostics in the past, this study aims to test ChatGPT's suitability for rare disease diagnostic support with the enhancement provided by Retrieval Augmented Generation (RAG). RareDxGPT, our enhanced ChatGPT model, supplies ChatGPT with information about 717 rare diseases from an external knowledge resource, the RareDis Corpus, through RAG. In RareDxGPT, when a query is entered, the three documents most relevant to the query in the RareDis Corpus are retrieved. Along with the query, they are returned to ChatGPT to provide a diagnosis. Additionally, phenotypes for thirty different diseases were extracted from free text from PubMed's Case Reports. They were each entered with three different prompt types: "prompt", "prompt + explanation" and "prompt + role play." The accuracy of ChatGPT and RareDxGPT with each prompt was then measured. With "Prompt", RareDxGPT had a 40 % accuracy, while ChatGPT 3.5 got 37 % of the cases correct. With "Prompt + Explanation", RareDxGPT had a 43 % accuracy, while ChatGPT 3.5 got 23 % of the cases correct. With "Prompt + Role Play", RareDxGPT had a 40 % accuracy, while ChatGPT 3.5 got 23 % of the cases correct. To conclude, ChatGPT, especially when supplying extra domain specific knowledge, demonstrates early potential for rare disease diagnosis with adjustments.

Experiences of quality of life and access to health services among rare disease caregivers: a scoping review

BACKGROUND

Research on rare diseases focuses less on caregivers, who play an important role in meeting the medical and social needs of the people they care for. Caregivers of people with rare diseases face negative outcomes due to problems with diagnosis, caring for complex conditions and expensive treatments. However, the factors that affect their quality of life are poorly understood. Poor mental and physical health of caregivers has a direct impact on the person they are caring for.

METHODS

To explore the literature on this topic, we conducted a scoping review in which we identified and analysed relevant studies to find out how extensively this topic has been researched. The articles were retrieved from the bibliographic databases PubMed, Ovid Medline and Ebsco Cinahl.

RESULTS

We initially identified 299 references and then included thirty-four articles. The included articles address three main topics, namely caregiver quality of life, health care accessibility, and the impact of health care accessibility on caregiver QOL.

CONCLUSION

This study provides information that is important to multiple providers of services as it can help to better understand caregivers and people with rare diseases and improve the quality of services offered. It highlights areas with the greatest need for change and offers insight into the complexity of caring for people with rare diseases, assisting policymakers in developing policies to support informal caregivers.

A Humanized Yeast Model for Studying TRAPP Complex Mutations; Proof-of-Concept Using Variants from an Individual with a TRAPPC1-Associated Neurodevelopmental Syndrome

Variants in membrane trafficking proteins are known to cause rare disorders with severe symptoms. The highly conserved transport protein particle (TRAPP) complexes are key membrane trafficking regulators that are also involved in autophagy. Pathogenic genetic variants in specific TRAPP subunits are linked to neurological disorders, muscular dystrophies, and skeletal dysplasias. Characterizing these variants and their phenotypes is important for understanding the general and specialized roles of TRAPP subunits as well as for patient diagnosis. Patient-derived cells are not always available, which poses a limitation for the study of these diseases. Therefore, other systems, like the yeast Saccharomyces cerevisiae, can be used to dissect the mechanisms at the intracellular level underlying these disorders. The development of CRISPR/Cas9 technology in yeast has enabled a scar-less editing method that creates an efficient humanized yeast model. In this study, core yeast subunits were humanized by replacing them with their human orthologs, and TRAPPC1, TRAPPC2, TRAPPC2L, TRAPPC6A, and TRAPPC6B were found to successfully replace their yeast counterparts. This system was used for studying the first reported individual with an autosomal recessive disorder caused by biallelic TRAPPC1 variants, a girl with a severe neurodevelopmental disorder and myopathy. We show that the maternal variant (TRAPPC1 p.(Val121Alafs*3)) is non-functional while the paternal variant (TRAPPC1 p.(His22_Lys24del)) is conditional-lethal and affects secretion and non-selective autophagy in yeast. This parallels defects seen in fibroblasts derived from this individual which also showed membrane trafficking defects and altered Golgi morphology, all of which were rescued in the human system by wild-type TRAPPC1. This study suggests that humanized yeast can be an efficient means to study TRAPP subunit variants in the absence of human cells and can assign significance to variants of unknown significance (VUS). This study lays the foundation for characterizing further TRAPP variants through this system, rapidly contributing to disease diagnosis.

Feeding and Nutritional Key Features of Crisponi/Cold-Induced Sweating Syndrome

Feeding difficulties are constantly present in patients with Crisponi/cold-induced sweating syndrome type 1 (CS/CISS1). The aim of our study was to describe their prevalence and evolution from birth to adult age. We performed an observational study at the Department of Life Sciences and Public Health, Rome. Fourteen patients were included in this study (six M; mean age: 18 years; SD: 10.62 years; median age: 15 years; age range: 6-44 years); six were adults (43%). Data on oral motor abilities from birth were collected. Meal duration, presence of swallowing reflex, dysphagia symptoms, difficulty chewing, and drooling management were assessed. At birth, all patients needed enteral feeding. Introduction of solid food was postponed beyond the age of 18 months in 43% of patients. During childhood and adolescence, mealtime was characterized by increased duration (43%) accompanied by fatigue during chewing (43%), food spillage from the nasal cavities (21%), sialorrhea (86%), and poor/reduced appetite (57%). A mature rotatory chewing skill was never achieved. This report expands the phenotype description of CS/CISS1 and also improves the overall management and prevention of complications in this ultra-rare disease.

Leveraging Generative AI to Accelerate Biocuration of Medical Actions for Rare Disease

BACKGROUND

Structured representations of clinical data can support computational analysis of individuals and cohorts, and ontologies representing disease entities and phenotypic abnormalities are now commonly used for translational research. The Medical Action Ontology (MAxO) provides a computational representation of treatments and other actions taken for the clinical management of patients. Currently, manual biocuration is used to assign MAxO terms to rare diseases, enabling clinical management of rare diseases to be described computationally for use in clinical decision support and mechanism discovery. However, it is challenging to scale manual curation to comprehensively capture information about medical actions for the more than 10,000 rare diseases.

METHODS

We present AutoMAxO, a semi-automated workflow that leverages Large Language Models (LLMs) to streamline MAxO biocuration for rare diseases. AutoMAxO first uses LLMs to retrieve candidate curations from abstracts of relevant publications. Next, the candidate curations are matched to ontology terms from MAxO, Human Phenotype Ontology (HPO), and MONDO disease ontology via a combination of LLMs and post-processing techniques. Finally, the matched terms are presented in a structured form to a human curator for approval.

RESULTS

We used this approach to process 4,918 unique medical abstracts and identified annotations for 21 rare genetic diseases, we extracted 18,631 candidate disease-treatment curations, 538 of which were confirmed and transferred to the MAxO annotation dataset.

CONCLUSION

The results of this project underscore the potential of generative AI to accelerate precision medicine by enabling a robust and comprehensive curation of the primary literature to represent information about diseases and procedures in a structured fashion. Although we focused on MAxO in this project, similar approaches could be taken for other biomedical curation tasks.

Getting the Dose Right in Drug Development for Rare Diseases: Barriers and Enablers

In the relentless pursuit of optimizing drug development, the intricate process of determining the ideal dosage unfolds. This involves "dose-finding" studies, crucial for providing insights into subsequent registration trials. However, the challenges intensify when tackling rare diseases. The complexity arises from poorly understood pathophysiologies, scarcity of appropriate animal models, and limited natural history understanding. The inherent heterogeneity, coupled with challenges in defining clinical end points, poses substantial challenges, hindering the utility of available data. The small affected population, low disease awareness, and restricted healthcare access compound the difficulty in conducting dose-finding studies. This white paper delves into critical dose selection aspects, focusing on key therapeutic areas, such as oncology, neurology, hepatology, metabolic rare diseases. It also explores dose selection challenges posed by pediatric rare diseases as well as novel modalities, including enzyme replacement therapies, cell and gene therapies, and oligonucleotides. Several examples emphasize the pivotal role of clinical pharmacology in navigating the complexities associated with these diseases and emerging treatment modalities.

Time to diagnosis for a rare disease: managing medical uncertainty. A qualitative study

BACKGROUND

People with a rare disease commonly experience long delays from the onset of symptoms to diagnosis. Rare diseases are challenging to diagnose because they are clinically heterogeneous, and many present with non-specific symptoms common to many diseases. We aimed to explore the experiences of people with myositis, primary immunodeficiency (PID), and sarcoidosis from symptom onset to diagnosis to identify factors that might impact receipt of a timely diagnosis.

METHODS

This was a qualitative study using semi-structured interviews. Our approach was informed by Interpretive Phenomenological Analysis (IPA). We applied the lens of uncertainty management theory to tease out how patients experience, assess, manage and cope with puzzling and complex health-related issues while seeking a diagnosis in the cases of rare diseases.

RESULTS

We conducted interviews with 26 people with a rare disease. Ten participants had been diagnosed with a form of myositis, 8 with a primary immunodeficiency, and 8 with sarcoidosis. Time to diagnosis ranged from 6 months to 12 years (myositis), immediate to over 20 years (PID), and 6 months to 15 years (sarcoidosis). We identified four themes that described the experiences of participants with a rare disease as they sought a diagnosis for their condition: (1) normalising and/or misattributing symptoms; (2) particularising by clinicians; (3) asserting patients' self-knowledge; and (4) working together through the diagnosable moment.

CONCLUSIONS

Managing medical uncertainty in the time before diagnosis of a rare disease can be complicated by patients discounting their own symptoms and/or clinicians discounting the scale and impact of those symptoms. Persistence on the part of both clinician and patient is necessary to reach a diagnosis of a rare disease. Strategies such as recognising pattern failure and accommodating self-labelling are key to diagnosis.

"In God We Trust": An Exploratory Study of the Associations Between Religiosity and the Caregiving Experiences of Parents of Children with Rare Diseases in Poland

Most children with a rare disease are cared for by their family members but parenting such a child is extremely demanding due to the complexity and severity of symptoms, with serious physical, emotional, social, and financial consequences for caregivers. Although religion may serve as a positive coping strategy, little is known about its role in helping caregivers manage the stress related to the burden of caregiving in Poland. Therefore, we surveyed 925 Polish family caregivers of children with rare diseases to understand the association between caregivers' religiosity and their caring experiences. The findings suggest that parents' religiosity is associated with a more positive caregiving experience, perceived quality of life, and experienced caregiving burden. While religious caregivers reported experiencing less distressing emotions and stressed the encouraging impact of their child's disease on their life more often, non-religious caregivers experienced role captivity and role overload more frequently. Since religion may serve as a source of strength and a protecting factor against mental health problems and the burden of caregiving, healthcare professionals should be aware of the importance of religious and spiritual care, and caregivers' religiosity should be considered an integral part of a holistic approach.

Evaluation of the Diagnostic Accuracy of GPT-4 in Five Thousand Rare Disease Cases

Large language models (LLM) have shown great promise in supporting differential diagnosis, but 23 available published studies on the diagnostic accuracy evaluated small cohorts (number of cases, 30-422, mean 104) and have evaluated LLM responses subjectively by manual curation (23/23 studies). The performance of LLMs for rare disease diagnosis has not been evaluated systematically. Here, we perform a rigorous and large-scale analysis of the performance of a GPT-4 in prioritizing candidate diagnoses, using the largest-ever cohort of rare disease patients. Our computational study used 5267 computational case reports from previously published data. Each case was formatted as a Global Alliance for Genomics and Health (GA4GH) phenopacket, in which clinical anomalies were represented as Human Phenotype Ontology (HPO) terms. We developed software to generate prompts from each phenopacket. Prompts were sent to Generative Pre-trained Transformer 4 (GPT-4), and the rank of the correct diagnosis, if present in the response, was recorded. The mean reciprocal rank of the correct diagnosis was 0.24 (with the reciprocal of the MRR corresponding to a rank of 4.2), and the correct diagnosis was placed in rank 1 in 19.2% of the cases, in the first 3 ranks in 28.6%, and in the first 10 ranks in 32.5%. Our study is the largest to be reported to date and provides a realistic estimate of the performance of GPT-4 in rare disease medicine.

ASACO: Automatic and Serial Analysis of CO-expression to discover gene modifiers with potential use in drug repurposing

Massive gene expression analyses are widely used to find differentially expressed genes under specific conditions. The results of these experiments are often available in public databases that are undergoing a growth similar to that of molecular sequence databases in the past. This now allows novel secondary computational tools to emerge that use such information to gain new knowledge. If several genes have a similar expression profile across heterogeneous transcriptomics experiments, they could be functionally related. These associations are usually useful for the annotation of uncharacterized genes. In addition, the search for genes with opposite expression profiles is useful for finding negative regulators and proposing inhibitory compounds in drug repurposing projects. Here we present a new web application, Automatic and Serial Analysis of CO-expression (ASACO), which has the potential to discover positive and negative correlator genes to a given query gene, based on thousands of public transcriptomics experiments. In addition, examples of use are presented, comparing with previous contrasted knowledge. The results obtained propose ASACO as a useful tool to improve knowledge about genes associated with human diseases and noncoding genes. ASACO is available at http://www.bioinfocabd.upo.es/asaco/.

Survival prediction landscape: an in-depth systematic literature review on activities, methods, tools, diseases, and databases

Survival prediction integrates patient-specific molecular information and clinical signatures to forecast the anticipated time of an event, such as recurrence, death, or disease progression. Survival prediction proves valuable in guiding treatment decisions, optimizing resource allocation, and interventions of precision medicine. The wide range of diseases, the existence of various variants within the same disease, and the reliance on available data necessitate disease-specific computational survival predictors. The widespread adoption of artificial intelligence (AI) methods in crafting survival predictors has undoubtedly revolutionized this field. However, the ever-increasing demand for more sophisticated and effective prediction models necessitates the continued creation of innovative advancements. To catalyze these advancements, it is crucial to bring existing survival predictors knowledge and insights into a centralized platform. The paper in hand thoroughly examines 23 existing review studies and provides a concise overview of their scope and limitations. Focusing on a comprehensive set of 90 most recent survival predictors across 44 diverse diseases, it delves into insights of diverse types of methods that are used in the development of disease-specific predictors. This exhaustive analysis encompasses the utilized data modalities along with a detailed analysis of subsets of clinical features, feature engineering methods, and the specific statistical, machine or deep learning approaches that have been employed. It also provides insights about survival prediction data sources, open-source predictors, and survival prediction frameworks.

Health Care Costs After Genome-Wide Sequencing for Children With Rare Diseases in England and Canada

Importance

Etiologic diagnoses for rare diseases can involve a diagnostic odyssey, with repeated health care interactions and inconclusive diagnostics. Prior studies reported cost savings associated with genome-wide sequencing (GWS) compared with cytogenetic or molecular testing through rapid genetic diagnosis, but there is limited evidence on whether diagnosis from GWS is associated with reduced health care costs.

Objective

To measure changes in health care costs after diagnosis from GWS for Canadian and English children with suspected rare diseases.

Design, Setting, and Participants

This cohort study was a quasiexperimental retrospective analysis across 3 distinct English and Canadian cohorts, completed in 2023. Mixed-effects generalized linear regression was used to estimate associations between GWS and costs in the 2 years before and after GWS. Difference-in-differences regression was used to estimate associations of genetic diagnosis and costs. Costs are in 2019 US dollars. GWS was conducted in a research setting (Genomics England 100 000 Genomes Project [100KGP] and Clinical Assessment of the Utility of Sequencing and Evaluation as a Service [CAUSES] Research Clinic) or clinical outpatient setting (publicly reimbursed GWS in British Columbia [BC], Canada). Participants were children with developmental disorders, seizure disorders, or both undergoing GWS between 2014 and 2019. Data were analyzed from April 2021 to September 2023.

Exposures

GWS and genetic diagnosis.

Main Outcomes and Measures

Annual health care costs and diagnostic costs per child.

Results

Study cohorts included 7775 patients in 100KGP, among whom 788 children had epilepsy (mean [SD] age at GWS, 11.6 [11.1] years; 400 female [50.8%]) and 6987 children had an intellectual disability (mean [SD] age at GWS, 8.2 [8.4] years; 2750 female [39.4%]); 77 patients in CAUSES (mean [SD] age at GWS, 8.5 [4.4] years; 33 female [42.9%]); and 118 publicly reimbursed GWS recipients from BC (mean [SD] age at GWS, 5.5 [5.2] years; 58 female [49.2%]). GWS diagnostic yield was 143 children (18.1%) for those with epilepsy and 1323 children (18.9%) for those with an intellectual disability in 100KGP, 47 children (39.8%) in the BC publicly reimbursed setting, and 42 children (54.5%) in CAUSES. Mean annual per-patient spending over the study period was $5283 (95% CI, $5121-$5427) for epilepsy and $3373 (95% CI, $3322-$3424) for intellectual disability in the 100KGP, $724 (95% CI, $563-$886) in CAUSES, and $1573 (95% CI, $1372-$1773) in the BC reimbursed setting. Receiving a genetic diagnosis from GWS was not associated with changed costs in any cohort.

Conclusions and Relevance

In this study, receiving a genetic diagnosis was not associated with cost savings. This finding suggests that patient benefit and cost-effectiveness should instead drive GWS implementation.

Global health for rare diseases through primary care

Rare diseases affect over 300 million people worldwide and are gaining recognition as a global health priority. Their inclusion in the UN Sustainable Development Goals, the UN Resolution on Addressing the Challenges of Persons Living with a Rare Disease, and the anticipated WHO Global Network for Rare Diseases and WHO Resolution on Rare Diseases, which is yet to be announced, emphasise their significance. People with rare diseases often face unmet health needs, including access to screening, diagnosis, therapy, and comprehensive health care. These challenges highlight the need for awareness and targeted interventions, including comprehensive education, especially in primary care. The majority of rare disease research, clinical services, and health systems are addressed with specialist care. WHO Member States have committed to focusing on primary health care in both universal health coverage and health-related Sustainable Development Goals. Recognising this opportunity, the International Rare Diseases Research Consortium (IRDiRC) assembled a global, multistakeholder task force to identify key barriers and opportunities for empowering primary health-care providers in addressing rare disease challenges.

Increasing Diversity, Equity, Inclusion, and Accessibility in Rare Disease Clinical Trials

Diversity, equity, inclusion, and accessibility (DEIA) are foundational principles for clinical trials and medical research. In rare diseases clinical research, where numbers of participants are already challenged by rarity itself, maximizing inclusion is of particular importance to clinical trial success, as well as ensuring the generalizability and relevance of the trial results to the people affected by these diseases. In this article, we review the medical and gray literature and cite case examples to provide insights into how DEIA can be proactively integrated into rare diseases clinical research. Here, we particularly focus on genetic diversity. While the rare diseases DEIA literature is nascent, it is accelerating as many patient advocacy groups, professional societies, training and educational organizations, researcher groups, and funders are setting intentional strategies to attain DEIA goals moving forward, and to establish metrics to ensure continued improvement. Successful examples in underserved and underrepresented populations are available that can serve as case studies upon which rare diseases clinical research programs can be built. Rare diseases have historically been innovation drivers in basic, translational, and clinical research, and ultimately, all populations benefit from data diversity in rare diseases populations that deliver novel insights and approaches to how clinical research can be performed.

NeoGx: Machine-Recommended Rapid Genome Sequencing for Neonates

Background

Genetic disease is common in the Level IV Neonatal Intensive Care Unit (NICU), but neonatology providers are not always able to identify the need for genetic evaluation. We trained a machine learning (ML) algorithm to predict the need for genetic testing within the first 18 months of life using health record phenotypes.

Methods

For a decade of NICU patients, we extracted Human Phenotype Ontology (HPO) terms from clinical text with Natural Language Processing tools. Considering multiple feature sets, classifier architectures, and hyperparameters, we selected a classifier and made predictions on a validation cohort of 2,241 Level IV NICU admits born 2020-2021.

Results

Our classifier had ROC AUC of 0.87 and PR AUC of 0.73 when making predictions during the first week in the Level IV NICU. We simulated testing policies under which subjects begin testing at the time of first ML prediction, estimating diagnostic odyssey length both with and without the additional benefit of pursuing rGS at this time. Just by using ML to accelerate initial genetic testing (without changing the tests ordered), the median time to first genetic test dropped from 10 days to 1 day, and the number of diagnostic odysseys resolved within 14 days of NICU admission increased by a factor of 1.8. By additionally requiring rGS at the time of positive ML prediction, the number of diagnostic odysseys resolved within 14 days was 3.8 times higher than the baseline.

Conclusions

ML predictions of genetic testing need, together with the application of the right rapid testing modality, can help providers accelerate genetics evaluation and bring about earlier and better outcomes for patients.

Comparative policy analysis of national rare disease funding policies in Australia, Singapore, South Korea, the United Kingdom and the United States: a scoping review

BACKGROUND

Rare diseases pose immense challenges for healthcare systems due to their low prevalence, associated disabilities, and attendant treatment costs. Advancements in gene therapy, such as treatments for Spinal Muscular Atrophy (SMA), have introduced novel therapeutic options, but the high costs, exemplified by Zolgensma® at US$2.1 million, present significant financial barriers. This scoping review aimed to compare the funding approaches for rare disease treatments across high-performing health systems in Australia, Singapore, South Korea, the United Kingdom (UK), and the United States (US), aiming to identify best practices and areas for future research.

METHODS

In accordance with the PRISMA-ScR guidelines and the methodological framework by Arksey and O'Malley and ensuing recommendations, a comprehensive search of electronic databases (Medline, EMBASE, and Cochrane) and grey literature from health department websites and leading national organizations dedicated to rare diseases in these countries was conducted. Countries selected for comparison were high-income countries with advanced economies and high-performing health systems: Australia, Singapore, South Korea, the UK, and the US. The inclusion criteria focused on studies detailing drug approval processes, reimbursement decisions and funding mechanisms, and published from 2010 to 2024.

RESULTS

Based on a thorough review of 18 published papers and grey literature, various strategies are employed by countries to balance budgetary constraints and access to rare disease treatments. Australia utilizes the Life Saving Drugs Program and risk-sharing agreements. Singapore depends on the Rare Disease Fund, which matches public donations. South Korea's National Health Insurance Service covers specific orphan drugs through risk-sharing agreements. The UK relies on the National Institute for Health and Care Excellence (NICE) to evaluate treatments for cost-effectiveness, supported by the Innovative Medicines Fund. In the US, a combination of federal and state programs, private insurance and non-profit support is used.

CONCLUSION

Outcome-based risk-sharing agreements present a practical solution for managing the financial strain of costly treatments. These agreements tie payment to actual treatment efficacy, thereby distributing financial risk and promoting ongoing data collection. Countries should consider adopting and expanding these agreements to balance immediate expenses with long-term benefits, ultimately ensuring equitable access to crucial treatments for patients afflicted by rare diseases.

Invisible patients in rare diseases: parental experiences with the healthcare and social services for children with rare diseases. A mixed method study

This study explores the experiences of Polish caregivers of children with rare disease (CRD) with health care and social services for CRD. A mixed-methods approach was employed, using an open-ended questionnaire with a convenience sample. Quantitative data presented through descriptive statistics, were complemented by thematic analysis applied to qualitative responses. Responses from 925 caregivers of 1002 children with CRD revealed that the duration of the diagnostic journey varied, spanning from 0 to 18 years, with an average time of 1.7 years. Similarly, the average number of physicians consulted before receiving the correct diagnosis was 4.8. The Internet was basic source of information about children's disease. Although caregivers were to some extent satisfied with the quality of health care for CRD, they complained at the accessibility of health care and social services, physicians' ignorance regarding RDs, the lack of co-ordinated care and financial and psychological support. To break the cycle of the diagnostic and therapeutic odyssey that may aggravate the condition of CRD, cause parental stress and financial burden there is a need to change our view on CRD from cure to family-oriented care. Multifaceted challenges and needs of CRD families should be prioritized.

Advance Care Planning for Children With Rare Diseases: A Pilot RCT

BACKGROUND AND OBJECTIVE

Pediatric rare diseases are often life-limiting conditions and/or require constant caregiving. Investigators assessed the initial efficacy of the FAmily CEntered (FACE) pediatric advance care planning (pACP), FACE-Rare, intervention on families' quality of life.

METHODS

A pilot-phase, single-blinded, intent-to-treat, randomized controlled clinical trial enrolled families from 1 pediatric quaternary hospital between 2021 and 2023. Intervention families received 3 weekly 60-minute (FACE-Rare pACP) sessions: (1) Carer Support Needs Assessment Tool or Action Plan, (2) Carer Support Needs Assessment Tol Action Plan Review, and (3) Pediatric Next Steps: Respecting Choices pACP. Controls received treatment as usual (TAU). Outcome measures were Beck Anxiety Inventory, Family Appraisal of Caregiving, Functional Assessment of Chronic Illness Therapy (FACIT)-Spirituality, and health care utilization. Generalized mixed effect models with γ response assessed the intervention effect at 3-month follow-up.

RESULTS

Children (n = 21) were aged 1 to 10 years, 48% male, 24% Black; and 100% technology dependent. Primary family caregivers (n = 21) were aged 30 to 43 years, 19% male, 19% Black; and 27% household income below the Federal poverty level. Dyads underwent 1:1 randomization: 9 to FACE-Rare and 12 to TAU. TAU caregivers reported statistically lower meaning and peace than FACE-Rare caregivers (0.9, P = .03, confidence interval [CI]: 0.75-0.99). Black caregivers reported significantly less caregiver distress (0.7, P = .04, CI: 0.47-0.98) than non-Black caregivers. Poor families reported more anxiety (3.5, P = .002, CI: 1.62-7.94), more caregiver strain (1.2, P = .006, CI: 1.07-1.42); and less family well-being (0.8, P = .02, CI: 0.64-0.95).

CONCLUSIONS

FACE®-Rare was feasible, acceptable, safe, and demonstrated initial efficacy, providing greater feelings of meaning and peace to caregivers. Poverty impacted well-being. A multisite trial is needed to determine generalizability.

Exome and genome sequencing in a heterogeneous population of patients with rare disease: Identifying predictors of a diagnosis

PURPOSE

Exome (ES) and genome sequencing (GS) are increasingly being utilized for individuals with rare and undiagnosed diseases; however, guidelines on their use remain limited. This study aimed to identify factors associated with diagnosis by ES and/or GS in a heterogeneous population of patients with rare and undiagnosed diseases.

METHODS

In this case control study, we reviewed data from 400 diagnosed and 400 undiagnosed randomly selected participants in the Undiagnosed Diseases Network, all of whom had undergone ES and/or GS. We analyzed factors associated with receiving a diagnosis by ES and/or GS.

RESULTS

Factors associated with a decreased odds of being diagnosed included adult symptom onset, singleton sequencing, and having undergone ES and/or GS before acceptance to the Undiagnosed Diseases Network (48%, 51%, and 32% lower odds, respectively). Factors that increased the odds of being diagnosed by ES and/or GS included having primarily neurological symptoms and having undergone prior chromosomal microarray testing (44% and 59% higher odds, respectively).

CONCLUSION

We identified several factors that were associated with receiving a diagnosis by ES and/or GS. This will ideally inform the utilization of ES and/or GS and help manage expectations of individuals and families undergoing these tests.

Single-cell metabolomics in rare disease: From technology to disease

With the development of clinical experience and technology, rare diseases (RDs) are gradually coming into the limelight. As they often lead to poor prognosis, it is urgent to promote the accuracy and rapidity of diagnosis and promote the development of therapeutic drugs. In recent years, with the rapid improvement of single-cell sequencing technology, the advantages of multi-omics combined application in diseases have been continuously explored. Single-cell metabolomics represents a powerful tool for advancing our understanding of rare diseases, particularly metabolic RDs, and transforming clinical practice. By unraveling the intricacies of cellular metabolism at a single-cell resolution, this innovative approach holds the potential to revolutionize diagnosis, treatment, and management strategies, ultimately improving outcomes for RDs patients. Continued research and technological advancements in single-cell metabolomics are essential for realizing its full potential in the field of RDs diagnosis and therapeutics. It is expected that single-cell metabolomics can be better applied to RDs research in the future, for the benefit of patients and society.

A corpus of GA4GH Phenopackets: case-level phenotyping for genomic diagnostics and discovery

The Global Alliance for Genomics and Health (GA4GH) Phenopacket Schema was released in 2022 and approved by ISO as a standard for sharing clinical and genomic information about an individual, including phenotypic descriptions, numerical measurements, genetic information, diagnoses, and treatments. A phenopacket can be used as an input file for software that supports phenotype-driven genomic diagnostics and for algorithms that facilitate patient classification and stratification for identifying new diseases and treatments. There has been a great need for a collection of phenopackets to test software pipelines and algorithms. Here, we present phenopacket-store. Version 0.1.12 of phenopacket-store includes 4916 phenopackets representing 277 Mendelian and chromosomal diseases associated with 236 genes, and 2872 unique pathogenic alleles curated from 605 different publications. This represents the first large-scale collection of case-level, standardized phenotypic information derived from case reports in the literature with detailed descriptions of the clinical data and will be useful for many purposes, including the development and testing of software for prioritizing genes and diseases in diagnostic genomics, machine learning analysis of clinical phenotype data, patient stratification, and genotype-phenotype correlations. This corpus also provides best-practice examples for curating literature-derived data using the GA4GH Phenopacket Schema.

Artificial intelligence in drug repurposing for rare diseases: a mini-review

Drug repurposing, the process of identifying new uses for existing drugs beyond their original indications, offers significant advantages in terms of reduced development time and costs, particularly in addressing unmet medical needs in rare diseases. Artificial intelligence (AI) has emerged as a transformative force in healthcare, and by leveraging AI technologies, researchers aim to overcome some of the challenges associated with rare diseases. This review presents concrete case studies, as well as pre-existing platforms, initiatives, and companies that demonstrate the application of AI for drug repurposing in rare diseases. Despite representing a modest part of the literature compared to other diseases such as COVID-19 or cancer, the growing interest, and investment in AI for drug repurposing in rare diseases underscore its potential to accelerate treatment availability for patients with unmet medical needs.

Drug repurposing for rare diseases

Repurposing drugs for rare diseases is a creative and cost-efficient method for creating new treatment options for certain conditions. This technique entails repurposing existing pharmaceuticals for new uses by utilizing established information regarding pharmacological characteristics, modes of operation, safety profiles, and interactions with biological systems. Creating new treatments for uncommon diseases is frequently difficult because of factors including small patient groups, disease intricacy, and insufficient knowledge of disease pathobiology. Drug repurposing is a more efficient and cost-effective approach compared to developing new drugs from scratch. It typically requires collaboration among academia, pharmaceutical firms, and patient advocacy groups.

Trends from two decades of orphan designations in paediatric rare neuromuscular diseases

Rare diseases are characterized by substantial unmet need mostly because the majority have limited, or no treatment options and a large number also affect children. Since the inception of EU orphan regulation in 2000 the European Medicines Agency Committee for Orphan Medicinal Products has received several applications for paediatric rare neuromuscular diseases (PERAN) however treatment options remain limited. Here we discuss the results form an observational, retrospective, cross-sectional study to characterize the currently authorised orphan medicinal products (OMP) and orphan designations (OD) given to products for PERAN in the last two decades. In the EU about half of PERAN diseases have at least one active OD approved since 2000, and about half of these are for Duchenne muscular dystrophy (DMD). The large majority of PERAN diseases do not have an authorised medicine with only 6 OMP currently authorised for Spinal muscular atrophy (3); DMD (1) and Myasthenia gravis (2). One in five products have inactive or discontinued regulatory development but clinical trials are ongoing for the vast majority of PERAN diseases, and more than half are in the final stage of clinical research with significantly more products with medical plausibility based in clinical data reaching advanced stages in clinical development.

Patient experiences of interprofessional collaboration and intersectoral communication in rare disease healthcare in Germany - a mixed-methods study

BACKGROUND

Rare diseases are often complex, chronic and many of them life-shortening. In Germany, healthcare for rare diseases is organized in expert centers for rare diseases. Most patients additionally have regional general practicioners and specialists for basic medical care. Thus, collaboration and information exchange between sectors is highly relevant. Our study focuses on the patient and caregiver perspective on intersectoral and interdisciplinary care between local healthcare professionals (HCPs) and centers for rare diseases in Germany. The aims were (1) to investigate patients' and caregivers' general experience of healthcare, (2) to analyse patients' and caregivers' perception of collaboration and cooperation between local healthcare professionals and expert centers for rare diseases and (3) to investigate patients' and caregivers' satisfaction with healthcare in the expert centers for rare diseases.

RESULTS

In total 299 individuals of whom 176 were patients and 123 were caregivers to pediatric patients participated in a survey using a questionnaire comprising several instruments and constructs. Fifty participants were additionally interviewed using a semistructured guideline. Most patients reported to receive written information about their care, have a contact person for medical issues and experienced interdisciplinary exchange within the centers for rare diseases. Patients and caregivers in our sample were mainly satisfied with the healthcare in the centers for rare diseases. The qualitative interviews showed a rather mixed picture including experiences of uncoordinated care, low engagement and communication difficulties between professionals of different sectors. Patients reported several factors that influenced the organization and quality of healthcare e.g. engagement and health literacy in patients or engagement of HCPs.

CONCLUSIONS

Our findings indicate the high relevance of transferring affected patients to specialized care as fast as possible to provide best medical treatment and increase patient satisfaction. Intersectoral collaboration should exceed written information exchange and should unburden patients of being and feeling responsible for communication between sectors and specialists. Results indicate a lack of inclusion of psychosocial aspects in routine care, which suggests opportunities for necessary improvements.

Health-related quality of life and associated risk factors in patients with Multiple Osteochondromas: a cross-sectional study

PURPOSE

To evaluate the health-related quality of life and associated risk factors for Multiple Osteochondromas patients.

METHODS

A cross-sectional, observational study was conducted from May to December 2022 during the routine visit to the referral center for rare skeletal disorders. All patients with Multiple Osteochondromas aged ≥ 3 years were included. EuroQol 5-dimension questionnaires, and demographic, clinical, and surgical history data were collected. Descriptive statistics, Fisher's exact test, One-sample t-test, Spearman's correlation, and multiple linear and logistic regression were performed to analyze the data. Results are reported following STROBE guidelines.

RESULTS

A total of 128 patients were included in the study, with a mean age of 14 [SD, 10] years. The mean EQ-5D Index Value was 0.863 [SD, 0.200] and the EQ-VAS was 84 [SD, 19] with a positive correlation between two scores [r = 0.541, p < 0.001]. Patients frequently referred problems in pain/discomfort [78.8%], anxiety/depression [50%], and usual activities [38.8%] dimensions. Increasing age was the common risk factor for health-related quality of life [p < 0.000], as well as Index Value and VAS scores were significantly lower in surgical patients [p = 0.001 and p < 0.001, respectively].

CONCLUSION

Increasing age and surgical procedures were found highly associated with reduced health-related quality of life in Multiple Osteochondromas patients. Our findings provide relevant information to support the establishment of patient-centered healthcare pathways and pave the way for further research into medical and non-medical therapeutic strategies for these patients.

Are Current Educational and Therapeutic Programs, Directed at Rare Disease Transplant Candidates and Recipients, Sufficient to Support Them on the Path From Diagnosis to Life After Allogenic Transplantation?-Recommendations for Member State Policymakers

For many rare disease (RD) patients, allogenic transplantation represents an effective therapy, improving overall survival rates and quality of life (QoL). Globally, ∼1% of liver transplants are performed for RDs and rare indications. However, patients and carers report unmet needs on their pathway toward treatment-in education and therapeutic measures, oftentimes shouldering expertise-building responsibility themselves. These issues are exacerbated in child patients. Estimates indicate that 6% to 8% of Poland's population (2.3-3 million persons) are burdened by RDs and potentially face such issues. This work aims to identify shortcomings of Polish policy in the field of educational and therapeutic measures for RD transplant candidates and recipients. Based on solutions introduced by pioneers, recommendations are formulated regarding priority actions. An analysis of national, transnational, and individual-center programs, directed at patients during their path from diagnosis to life post-transplant, was conducted. The investigation uncovered measure gaps not addressed by the National Plan for Rare Diseases-in fields of patient and stakeholder education (pre- and post-transplant), psychological care provision, specialized center creation, integration of data scattered among registries with the national insurer's database, and artificial intelligence (AI) tool implementation to support both early diagnostic efforts and tailoring of patient treatment. Programs directed at RD transplant candidates and recipients must aim to ensure that a satisfactory psychosomatic condition of the patient is maintained before and following the procedure, therefore lending credence to success. This necessitates early diagnosis schemes, and personalized medicine, multidisciplinary approaches to the individual, achievable only through big data system creation and AI introduction.

Longitudinal natural history studies based on real-world data in rare diseases: Opportunity and a novel approach

Growing interest in therapeutic development for rare diseases necessitate a systematic approach to the collection and curation of natural history data that can be applied consistently across this group of heterogenous rare diseases. In this study, we discuss the challenges facing natural history studies for leukodystrophies and detail a novel standardized approach to creating a longitudinal natural history study using existing medical records. Prospective studies are uniquely challenging for rare diseases. Delays in diagnosis and overall rarity limit the timely collection of natural history data. When feasible, prospective studies are often cross-sectional rather than longitudinal and are unlikely to capture pre- or early- symptomatic disease trajectories, limiting their utility in characterizing the full natural history of the disease. Therapeutic development in leukodystrophies is subject to these same obstacles. The Global Leukodystrophy Initiative Clinical Trials Network (GLIA-CTN) comprises of a network of research institutions across the United States, supported by a multi-center biorepository protocol, to map the longitudinal clinical course of disease across leukodystrophies. As part of GLIA-CTN, we developed Standard Operating Procedures (SOPs) that delineated all study processes related to staff training, source documentation, and data sharing. Additionally, the SOP detailed the standardized approach to data extraction including diagnosis, clinical presentation, and medical events, such as age at gastrostomy tube placement. The key variables for extraction were selected through face validity, and common electronic case report forms (eCRF) across leukodystrophies were created to collect analyzable data. To enhance the depth of the data, clinical notes are extracted into "original" and "imputed" encounters, with imputed encounter referring to a historic event (e.g., loss of ambulation 3 months prior). Retrospective Functional Assessments were assigned by child neurologists, using a blinded dual-rater approach and score discrepancies were adjudicated by a third rater. Upon completion of extraction, data source verification is performed. Data missingness was evaluated using statistics. The proposed methodology will enable us to leverage existing medical records to address the persistent gap in natural history data within this unique disease group, allow for assessment of clinical trajectory both pre- and post-formal diagnosis, and promote recruitment of larger cohorts.

Between hope and reality: treatment of genetic diseases through nucleic acid-based drugs

Rare diseases (RD) affect a small number of people compared to the general population and are mostly genetic in origin. The first clinical signs often appear at birth or in childhood, and patients endure high levels of pain and progressive loss of autonomy frequently associated with short life expectancy. Until recently, the low prevalence of RD and the gatekeeping delay in their diagnosis have long hampered research. The era of nucleic acid (NA)-based therapies has revolutionized the landscape of RD treatment and new hopes arise with the perspectives of disease-modifying drugs development as some NA-based therapies are now entering the clinical stage. Herein, we review NA-based drugs that were approved and are currently under investigation for the treatment of RD. We also discuss the recent structural improvements of NA-based therapeutics and delivery system, which overcome the main limitations in their market expansion and the current approaches that are developed to address the endosomal escape issue. We finally open the discussion on the ethical and societal issues that raise this new technology in terms of regulatory approval and sustainability of production.

'Integrating Ethics and Equity with Economics and Effectiveness for newborn screening in the genomic age: A qualitative study protocol of stakeholder perspectives

BACKGROUND

Newborn bloodspot screening is a well-established population health initiative that detects serious, childhood-onset, treatable conditions to improve health outcomes. With genomic technologies advancing rapidly, many countries are actively discussing the introduction of genomic assays into newborn screening programs. While adding genomic testing to Australia's newborn screening program could improve outcomes for infants and families, it must be considered against potential harms, ethical, legal, equity and social implications, and economic and health system impacts. We must ask not only 'can' we use genomics to screen newborns?' but 'should we'?' and 'how much should health systems invest in genomic newborn screening?'.

METHODS

This study will use qualitative methods to explore understanding, priorities, concerns and expectations of genomic newborn screening among parents/carers, health professionals/scientists, and health policy makers across Australia. In-depth, semi-structured interviews will be held with 30-40 parents/carers recruited via hospital and community settings, 15-20 health professionals/scientists, and 10-15 health policy makers. Data will be analysed using inductive content analysis. The Sydney Children's Hospital Network Human Research Ethics Committee approved this study protocol [2023/ETH02371]. The Standards for Reporting Qualitative Research will guide study planning, conduct and reporting.

DISCUSSION

Few studies have engaged a diverse range of stakeholders to explore the implications of genomics in newborn screening in a culturally and genetically diverse population, nor in a health system underpinned by universal health care. As the first study within a multi-part research program, findings will be used to generate new knowledge on the risks and benefits and importance of ethical, legal, social and equity implications of genomic newborn screening from the perspective of key stakeholders. As such it will be the foundation on which child and family centered criteria can be developed to inform health technology assessments and drive efficient and effective policy decision-making on the implementation of genomics in newborn screening.

Explicable prioritization of genetic variants by integration of rule-based and machine learning algorithms for diagnosis of rare Mendelian disorders

BACKGROUND

In the process of finding the causative variant of rare diseases, accurate assessment and prioritization of genetic variants is essential. Previous variant prioritization tools mainly depend on the in-silico prediction of the pathogenicity of variants, which results in low sensitivity and difficulty in interpreting the prioritization result. In this study, we propose an explainable algorithm for variant prioritization, named 3ASC, with higher sensitivity and ability to annotate evidence used for prioritization. 3ASC annotates each variant with the 28 criteria defined by the ACMG/AMP genome interpretation guidelines and features related to the clinical interpretation of the variants. The system can explain the result based on annotated evidence and feature contributions.

RESULTS

We trained various machine learning algorithms using in-house patient data. The performance of variant ranking was assessed using the recall rate of identifying causative variants in the top-ranked variants. The best practice model was a random forest classifier that showed top 1 recall of 85.6% and top 3 recall of 94.4%. The 3ASC annotates the ACMG/AMP criteria for each genetic variant of a patient so that clinical geneticists can interpret the result as in the CAGI6 SickKids challenge. In the challenge, 3ASC identified causal genes for 10 out of 14 patient cases, with evidence of decreased gene expression for 6 cases. Among them, two genes (HDAC8 and CASK) had decreased gene expression profiles confirmed by transcriptome data.

CONCLUSIONS

3ASC can prioritize genetic variants with higher sensitivity compared to previous methods by integrating various features related to clinical interpretation, including features related to false positive risk such as quality control and disease inheritance pattern. The system allows interpretation of each variant based on the ACMG/AMP criteria and feature contribution assessed using explainable AI techniques.

Oral conditions of children with microcephaly associated with congenital Zika syndrome: a cross-sectional study

The aim of the present study was to compare the oral conditions of children with congenital Zika syndrome (CZS)-associated microcephaly, non-CZS-associated microcephaly, and normotypical children, as well as to characterize their sociodemographic aspects and medical history. A paired cross-sectional study was carried out on 14 children with CZS-associated microcephaly and 24 age-matched controls, in Belo Horizonte, in southeastern Brazil. Children's oral conditions were assessed: dental caries experience (dmft/DMFT indices); developmental defects of enamel (DDE) index; dental anomalies; mucosal changes; lip sealing, and malocclusion (overjet, overbite, and/or posterior crossbite alterations). The quality of oral hygiene was analyzed by the simplified oral hygiene index. The children's mothers also answered a questionnaire about sociodemographic and medical history data. The variables were analyzed descriptively. Female participants were more prevalent (60.5%), and the mean age of the participants was 4.9 years (±1.4) (range: 2-8 years) and 92.1% of their exhibited some oral condition. All participants with CZS-associated microcephaly showed absence of lip sealing and had malocclusion (100.0%). When compared to the other groups, children with CZS had a higher percentage of dental anomalies (35.7%), mucosal changes (71.4%), and unsatisfactory oral hygiene (64.3%). In a sample composed mainly of female participants aged less than 5 years, the prevalence of oral conditions and unsatisfactory oral hygiene was higher in the group with CZS-associated microcephaly, followed by the group with non-CZS-associated microcephaly. Normotypical children had the highest percentage of dental caries experience.

Mowat-Wilson Syndrome: Case Report and Review of ZEB2 Gene Variant Types, Protein Defects and Molecular Interactions

Mowat-Wilson syndrome (MWS) is a rare genetic neurodevelopmental congenital disorder associated with various defects of the zinc finger E-box binding homeobox 2 (ZEB2) gene. The ZEB2 gene is autosomal dominant and encodes six protein domains including the SMAD-binding protein, which functions as a transcriptional corepressor involved in the conversion of neuroepithelial cells in early brain development and as a mediator of trophoblast differentiation. This review summarizes reported ZEB2 gene variants, their types, and frequencies among the 10 exons of ZEB2. Additionally, we summarized their corresponding encoded protein defects including the most common variant, c.2083 C>T in exon 8, which directly impacts the homeodomain (HD) protein domain. This single defect was found in 11% of the 298 reported patients with MWS. This review demonstrates that exon 8 encodes at least three of the six protein domains and accounts for 66% (198/298) of the variants identified. More than 90% of the defects were due to nonsense or frameshift changes. We show examples of protein modeling changes that occurred as a result of ZEB2 gene defects. We also report a novel pathogenic variant in exon 8 in a 5-year-old female proband with MWS. This review further explores other genes predicted to be interacting with the ZEB2 gene and their predicted gene-gene molecular interactions with protein binding effects on embryonic multi-system development such as craniofacial, spine, brain, kidney, cardiovascular, and hematopoiesis.

Drug Repurposing and Lysosomal Storage Disorders: A Trick to Treat

Rare diseases, or orphan diseases, are defined as diseases affecting a small number of people compared to the general population. Among these, we find lysosomal storage disorders (LSDs), a cluster of rare metabolic diseases characterized by enzyme mutations causing abnormal glycolipid storage. Drug repositioning involves repurposing existing approved drugs for new therapeutic applications, offering advantages in cost, time savings, and a lower risk of failure. We present a comprehensive analysis of existing drugs, their repurposing potential, and their clinical implications in the context of LSDs, highlighting the necessity of mutation-specific approaches. Our review systematically explores the landscape of drug repositioning as a means to enhance LSDs therapies. The findings advocate for the strategic repositioning of drugs, accentuating its role in expediting the discovery of effective treatments. We conclude that drug repurposing represents a viable pathway for accelerating therapeutic discovery for LSDs, emphasizing the need for the careful evaluation of drug efficacy and toxicity in disease-specific contexts.

Automatically pre-screening patients for the rare disease aromatic l-amino acid decarboxylase deficiency using knowledge engineering, natural language processing, and machine learning on a large EHR population

OBJECTIVES

Electronic health record (EHR) data may facilitate the identification of rare diseases in patients, such as aromatic l-amino acid decarboxylase deficiency (AADCd), an autosomal recessive disease caused by pathogenic variants in the dopa decarboxylase gene. Deficiency of the AADC enzyme results in combined severe reductions in monoamine neurotransmitters: dopamine, serotonin, epinephrine, and norepinephrine. This leads to widespread neurological complications affecting motor, behavioral, and autonomic function. The goal of this study was to use EHR data to identify previously undiagnosed patients who may have AADCd without available training cases for the disease.

MATERIALS AND METHODS

A multiple symptom and related disease annotated dataset was created and used to train individual concept classifiers on annotated sentence data. A multistep algorithm was then used to combine concept predictions into a single patient rank value.

RESULTS

Using an 8000-patient dataset that the algorithms had not seen before ranking, the top and bottom 200 ranked patients were manually reviewed for clinical indications of performing an AADCd diagnostic screening test. The top-ranked patients were 22.5% positively assessed for diagnostic screening, with 0% for the bottom-ranked patients. This result is statistically significant at P < .0001.

CONCLUSION

This work validates the approach that large-scale rare-disease screening can be accomplished by combining predictions for relevant individual symptoms and related conditions which are much more common and for which training data is easier to create.

Impact of the COVID-19 Pandemic on People Living With Rare Diseases and Their Families: Results of a National Survey

BACKGROUND

With more than 103 million cases and 1.1 million deaths, the COVID-19 pandemic has had devastating consequences for the health system and the well-being of the entire US population. The Rare Diseases Clinical Research Network funded by the National Institutes of Health was strategically positioned to study the impact of the pandemic on the large, vulnerable population of people living with rare diseases (RDs).

OBJECTIVE

This study was designed to describe the characteristics of COVID-19 in the RD population, determine whether patient subgroups experienced increased occurrence or severity of infection and whether the pandemic changed RD symptoms and treatment, and understand the broader impact on respondents and their families.

METHODS

US residents who had an RD and were <90 years old completed a web-based survey investigating self-reported COVID-19 infection, pandemic-related changes in RD symptoms and medications, access to care, and psychological impact on self and family. We estimated the incidence of self-reported COVID-19 and compared it with that in the US population; evaluated the frequency of COVID-19 symptoms according to self-reported infection; assessed infection duration, complications and need for hospitalization; assessed the influence of the COVID-19 pandemic on RD symptoms and treatment, and whether the pandemic influenced access to care, special food and nutrition, or demand for professional psychological assistance.

RESULTS

Between May 2, 2020, and December 15, 2020, in total, 3413 individuals completed the survey. Most were female (2212/3413, 64.81%), White (3038/3413, 89.01%), and aged ≥25 years (2646/3413, 77.53%). Overall, 80.6% (2751/3413) did not acquire COVID-19, 2.08% (71/3413) acquired it, and 16.58% (566/3413) did not know. Self-reported cases represented an annual incidence rate of 2.2% (95% CI 1.7%-2.8%). COVID-19 cases were more than twice the expected (71 vs 30.3; P<.001). COVID-19 was associated with specific symptoms (loss of taste: odds ratio [OR] 38.9, 95% CI 22.4-67.6, loss of smell: OR 30.6, 95% CI 17.7-53.1) and multiple symptoms (>9 symptoms vs none: OR 82.5, 95% CI 29-234 and 5-9: OR 44.8, 95% CI 18.7-107). Median symptom duration was 16 (IQR 9-30) days. Hospitalization (7/71, 10%) and ventilator support (4/71, 6%) were uncommon. Respondents who acquired COVID-19 reported increased occurrence and severity of RD symptoms and use or dosage of select medications; those who did not acquire COVID-19 reported decreased occurrence and severity of RD symptoms and use of medications; those who did not know had an intermediate pattern. The pandemic made it difficult to access care, receive treatment, get hospitalized, and caused mood changes for respondents and their families.

CONCLUSIONS

Self-reported COVID-19 was more frequent than expected and was associated with increased prevalence and severity of RD symptoms and greater use of medications. The pandemic negatively affected access to care and caused mood changes in the respondents and family members. Continued surveillance is necessary.

Enabling personalised disease diagnosis by combining a patient's time-specific gene expression profile with a biomedical knowledge base

BACKGROUND

Recent developments in the domain of biomedical knowledge bases (KBs) open up new ways to exploit biomedical knowledge that is available in the form of KBs. Significant work has been done in the direction of biomedical KB creation and KB completion, specifically, those having gene-disease associations and other related entities. However, the use of such biomedical KBs in combination with patients' temporal clinical data still largely remains unexplored, but has the potential to immensely benefit medical diagnostic decision support systems.

RESULTS

We propose two new algorithms, LOADDx and SCADDx, to combine a patient's gene expression data with gene-disease association and other related information available in the form of a KB, to assist personalized disease diagnosis. We have tested both of the algorithms on two KBs and on four real-world gene expression datasets of respiratory viral infection caused by Influenza-like viruses of 19 subtypes. We also compare the performance of proposed algorithms with that of five existing state-of-the-art machine learning algorithms (k-NN, Random Forest, XGBoost, Linear SVM, and SVM with RBF Kernel) using two validation approaches: LOOCV and a single internal validation set. Both SCADDx and LOADDx outperform the existing algorithms when evaluated with both validation approaches. SCADDx is able to detect infections with up to 100% accuracy in the cases of Datasets 2 and 3. Overall, SCADDx and LOADDx are able to detect an infection within 72 h of infection with 91.38% and 92.66% average accuracy respectively considering all four datasets, whereas XGBoost, which performed best among the existing machine learning algorithms, can detect the infection with only 86.43% accuracy on an average.

CONCLUSIONS

We demonstrate how our novel idea of using the most and least differentially expressed genes in combination with a KB can enable identification of the diseases that a patient is most likely to have at a particular time, from a KB with thousands of diseases. Moreover, the proposed algorithms can provide a short ranked list of the most likely diseases for each patient along with their most affected genes, and other entities linked with them in the KB, which can support health care professionals in their decision-making.

Defining priorities in the transition from paediatric to adult healthcare for rare bone disease patients: a dialogic approach

The Italian patient association for Multiple Osteochondromas, Ollier Disease, and Maffucci Syndrome, Associazione Conto Alla Rovescia-ACAR Aps, conducted a mixed-methods study at its 2023 annual conference. The study included the Open Dialogue Approach and a feedback survey to identify the main priorities in the transitioning process from paediatric to adult healthcare for patients with Multiple Osteochondromas, Ollier Disease, and Maffucci Syndrome. The common needs identified by patients, families, caregivers, and healthcare professionals were coordination and continuity of care, patient empowerment and communication, social and practical support, and transition planning and support. This experience fostered a sense of collaboration and cooperation among stakeholders, helping to build trust and create a shared vision for improving the quality of care for these patients. Furthermore, it could be considered a starting point for other patient associations interested in using different approaches to identify the needs of their members and actively involve all stakeholders.

Artificial Intelligence for Drug Discovery: Are We There Yet?

Drug discovery is adapting to novel technologies such as data science, informatics, and artificial intelligence (AI) to accelerate effective treatment development while reducing costs and animal experiments. AI is transforming drug discovery, as indicated by increasing interest from investors, industrial and academic scientists, and legislators. Successful drug discovery requires optimizing properties related to pharmacodynamics, pharmacokinetics, and clinical outcomes. This review discusses the use of AI in the three pillars of drug discovery: diseases, targets, and therapeutic modalities, with a focus on small-molecule drugs. AI technologies, such as generative chemistry, machine learning, and multiproperty optimization, have enabled several compounds to enter clinical trials. The scientific community must carefully vet known information to address the reproducibility crisis. The full potential of AI in drug discovery can only be realized with sufficient ground truth and appropriate human intervention at later pipeline stages.

Drug repurposing for rare: progress and opportunities for the rare disease community

Repurposing is one of the key opportunities to address the unmet rare diseases therapeutic need. Based on cases of drug repurposing in small population conditions, and previous work in drug repurposing, we analyzed the most important lessons learned, such as the sharing of clinical observations, reaching out to regulatory scientific advice at an early stage, and public-private collaboration. In addition, current upcoming trends in the field of drug repurposing in rare diseases were analyzed, including the role these trends could play in the rare diseases' ecosystem. Specifically, we cover the opportunities of innovation platforms, the use of real-world data, the use of artificial intelligence, regulatory initiatives in repurposing, and patient engagement throughout the repurposing project. The outcomes from these emerging activities will help progress the field of drug repurposing for the benefit of patients, public health and medicines development.

Perry Disease: Bench to Bedside Circulation and a Team Approach

With technological applications, especially in genetic testing, new diseases have been discovered and new disease concepts have been proposed in recent years; however, the pathogenesis and treatment of these rare diseases are not as well established as those of common diseases. To demonstrate the importance of rare disease research, in this paper we focus on our research topic, Perry disease (Perry syndrome). Perry disease is a rare autosomal dominant neurodegenerative disorder clinically characterized by parkinsonism, depression/apathy, weight loss, and respiratory symptoms including central hypoventilation and central sleep apnea. The pathological classification of Perry disease falls under TAR DNA-binding protein 43 (TDP-43) proteinopathies. Patients with Perry disease exhibit DCTN1 mutations, which is the causative gene for the disease; they also show relatively uniform pathological and clinical features. This review summarizes recent findings regarding Perry disease from both basic and clinical perspectives. In addition, we describe technological innovations and outline future challenges and treatment prospects. We discuss the expansion of research from rare diseases to common diseases and the importance of collaboration between clinicians and researchers. Here, we highlight the importance of researching rare diseases as it contributes to a deeper understanding of more common diseases, thereby opening up new avenues for scientific exploration.

Attaining Diagnostic Excellence: How the Structure and Function of a Rare Disease Service Contribute to Ending the Diagnostic Odyssey

Patients with rare or otherwise undiagnosed disorders frequently find themselves on a diagnostic odyssey, the often-prolonged journey toward diagnosis that can be characterized by significant physical, emotional, and financial hardship, as well as by diagnostic errors and delays. The wider availability of clinical exome sequencing has helped end many diagnostic odysseys, though diagnostic success rates of around 35% for exome sequencing leave many patients undiagnosed. Diagnostic yields can be improved via the implementation of advanced genetic testing modalities, though both these modalities and exome sequencing perform significantly better when paired with high-quality phenotypic data. Diagnostic centers of excellence can improve outcomes for patients on a diagnostic odyssey by providing a process and environment that address shortfalls in diagnostic access while providing high-quality phenotyping. Features of successful undiagnosed and rare disease evaluation teams are discussed and an illustrative case is provided.

Determining Commonalities in the Experiences of Patients with Rare Diseases: A Qualitative Analysis of US Food and Drug Administration Patient Engagement Sessions

BACKGROUND

Rare diseases are estimated to affect more than one in ten Americans. However, most patients with a rare disease face significant emotional, physical, and social challenges. To better understand the burden of disease and unmet needs, the US Food and Drug Administration (FDA) conducts and supports multiple patient engagement platforms. We analyzed summaries from these discussions to identify commonalities among patients with disparate rare diseases, the results of which could inform priorities for cross-disease policies and medical product development.

METHODS

We conducted a qualitative analysis of patient engagement session summaries to investigate shared experiences across rare diseases. Cross-disease similarities were identified within four dimensions: product development/regulatory, clinical/physical, social/psychological, and economic/financial. Summaries from 29 rare diseases were included in our analyses.

RESULTS

Within the product development/regulatory dimension, we observed that patients and caregivers across rare diseases shared the desire for development of medical products that cured their disease or improved their overall quality of life. In the clinical/physical dimension, we found that patients had numerous common symptoms, including pain and fatigue. In the social/psychological dimension, we observed significant negative impact on mental health. Within the economic/financial dimension, patients and caregivers shared that disease burden caused significant financial hardships.

CONCLUSION

We found remarkable similarities among patients with rare diseases across all four dimensions. Our results indicate that, even among rare diseases with diverse etiologies, patients share numerous commonalties due to their diseases: a lack of effective treatment options, certain physical symptoms, mental health challenges, and financial concerns.