How many rare diseases are there?

"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.

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.

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.

Ultra-rare ultra-care: Assessing the impact of caring for children with ultra rare diseases

BACKGROUND

We sought to assesses the impact of caring for children with ultra rare diseases (URDs) on family carers and to analyse the way these experiences differ among the caregivers of children diagnosed through prenatal or newborn screening, and those with symptom-based diagnosis.

METHODS

A total of 200 caregivers of 219 URDs children completed an on-line survey regarding the challenges and experiences of caregivers of URDs children.

RESULTS

The majority of URD caregivers felt burdened by their children's health problems, emotional and behavioural changes. 46.5% reported feelings of care overload, 43% coped poorly with the stress, and many experienced a variety of feelings of distress towards the role of caregiver. While most caregivers struggled with the diagnostic odyssey and were dissatisfied with the healthcare services for URD children, caregivers of children diagnosed through prenatal or newborn screening were significantly less burdened than the parents of children with symptom-based diagnoses.

CONCLUSION

Although caregivers of URDs children experience physical and emotional strain, they are often neglected by the healthcare system. A bio-psychosocial approach to URDs should therefore also include family caregivers' physical and psychosocial needs. Apart from financial and emotional support, enhancing access to genetic testing and newborn screening should be prioritised.

Clustering rare diseases within an ontology-enriched knowledge graph

OBJECTIVE

Identifying sets of rare diseases with shared aspects of etiology and pathophysiology may enable drug repurposing. Toward that aim, we utilized an integrative knowledge graph to construct clusters of rare diseases.

MATERIALS AND METHODS

Data on 3242 rare diseases were extracted from the National Center for Advancing Translational Science Genetic and Rare Diseases Information center internal data resources. The rare disease data enriched with additional biomedical data, including gene and phenotype ontologies, biological pathway data, and small molecule-target activity data, to create a knowledge graph (KG). Node embeddings were trained and clustered. We validated the disease clusters through semantic similarity and feature enrichment analysis.

RESULTS

Thirty-seven disease clusters were created with a mean size of 87 diseases. We validate the clusters quantitatively via semantic similarity based on the Orphanet Rare Disease Ontology. In addition, the clusters were analyzed for enrichment of associated genes, revealing that the enriched genes within clusters are highly related.

DISCUSSION

We demonstrate that node embeddings are an effective method for clustering diseases within a heterogenous KG. Semantically similar diseases and relevant enriched genes have been uncovered within the clusters. Connections between disease clusters and drugs are enumerated for follow-up efforts.

CONCLUSION

We lay out a method for clustering rare diseases using graph node embeddings. We develop an easy-to-maintain pipeline that can be updated when new data on rare diseases emerges. The embeddings themselves can be paired with other representation learning methods for other data types, such as drugs, to address other predictive modeling problems.

Digital child health: opportunities and obstacles. A joint statement of European Academy of Paediatrics and European Confederation of Primary Care Paediatricians

The advancement of technology and the increasing digitisation of healthcare systems have opened new opportunities to transform the delivery of child health services. The importance of interoperable electronic health data in enhancing healthcare systems and improving child health care is evident. Interoperability ensures seamless data exchange and communication among healthcare entities, providers, institutions, household and systems. Using standardised data formats, coding systems, and terminologies is crucial in achieving interoperability and overcoming the barriers of different systems, formats, and locations. Paediatricians and other child health stakeholders can effectively address data structure, coding, and terminology inconsistencies by promoting interoperability and improving data quality and accuracy of children and youth, according to guidelines of the World Health Organisation. Thus, ensure comprehensive health assessments and screenings for children, including timely follow-up and communication of results. And implement effective vaccination schedules and strategies, ensuring timely administration of vaccines and prompt response to any concerns or adverse events. Developmental milestones can be continuously monitored. This can improve care coordination, enhance decision-making, and optimise health outcomes for children. In conclusion, using interoperable electronic child health data holds great promise in advancing international child healthcare systems and enhancing the child's care and well-being. By promoting standardised data exchange, interoperability enables timely health assessments, accurate vaccination schedules, continuous monitoring of developmental milestones, coordination of care, and collaboration among child healthcare professionals and the individual or their caregiver. Embracing interoperability is essential for creating a person-centric and data-driven healthcare ecosystem where the potential of digitalisation and innovation can be fully realized.

The Medical Action Ontology: A tool for annotating and analyzing treatments and clinical management of human disease

BACKGROUND

Navigating the clinical literature to determine the optimal clinical management for rare diseases presents significant challenges. We introduce the Medical Action Ontology (MAxO), an ontology specifically designed to organize medical procedures, therapies, and interventions.

METHODS

MAxO incorporates logical structures that link MAxO terms to numerous other ontologies within the OBO Foundry. Term development involves a blend of manual and semi-automated processes. Additionally, we have generated annotations detailing diagnostic modalities for specific phenotypic abnormalities defined by the Human Phenotype Ontology (HPO). We introduce a web application, POET, that facilitates MAxO annotations for specific medical actions for diseases using the Mondo Disease Ontology.

FINDINGS

MAxO encompasses 1,757 terms spanning a wide range of biomedical domains, from human anatomy and investigations to the chemical and protein entities involved in biological processes. These terms annotate phenotypic features associated with specific disease (using HPO and Mondo). Presently, there are over 16,000 MAxO diagnostic annotations that target HPO terms. Through POET, we have created 413 MAxO annotations specifying treatments for 189 rare diseases.

CONCLUSIONS

MAxO offers a computational representation of treatments and other actions taken for the clinical management of patients. Its development is closely coupled to Mondo and HPO, broadening the scope of our computational modeling of diseases and phenotypic features. We invite the community to contribute disease annotations using POET (https://poet.jax.org/). MAxO is available under the open-source CC-BY 4.0 license (https://github.com/monarch-initiative/MAxO).

FUNDING

NHGRI 1U24HG011449-01A1 and NHGRI 5RM1HG010860-04.

What can GPT-4 do for Diagnosing Rare Eye Diseases? A Pilot Study

INTRODUCTION

Generative pretrained transformer-4 (GPT-4) has gained widespread attention from society, and its potential has been extensively evaluated in many areas. However, investigation of GPT-4's use in medicine, especially in the ophthalmology field, is still limited. This study aims to evaluate GPT-4's capability to identify rare ophthalmic diseases in three simulated scenarios for different end-users, including patients, family physicians, and junior ophthalmologists.

METHODS

We selected ten treatable rare ophthalmic disease cases from the publicly available EyeRounds service. We gradually increased the amount of information fed into GPT-4 to simulate the scenarios of patient, family physician, and junior ophthalmologist using GPT-4. GPT-4's responses were evaluated from two aspects: suitability (appropriate or inappropriate) and accuracy (right or wrong) by senior ophthalmologists (> 10 years' experiences).

RESULTS

Among the 30 responses, 83.3% were considered "appropriate" by senior ophthalmologists. In the scenarios of simulated patient, family physician, and junior ophthalmologist, seven (70%), ten (100%), and eight (80%) responses were graded as "appropriate" by senior ophthalmologists. However, compared to the ground truth, GPT-4 could only output several possible diseases generally without "right" responses in the simulated patient scenarios. In contrast, in the simulated family physician scenario, 50% of GPT-4's responses were "right," and in the simulated junior ophthalmologist scenario, the model achieved a higher "right" rate of 90%.

CONCLUSION

To our knowledge, this is the first proof-of-concept study that evaluates GPT-4's capacity to identify rare eye diseases in simulated scenarios involving patients, family physicians, and junior ophthalmologists. The results indicate that GPT-4 has the potential to serve as a consultation assisting tool for patients and family physicians to receive referral suggestions and an assisting tool for junior ophthalmologists to diagnose rare eye diseases. However, it is important to approach GPT-4 with caution and acknowledge the need for verification and careful referrals in clinical settings.

Qualified placebo for trials of herbal medicine treatment in rare diseases? A cross-sectional analysis

BACKGROUND

While substantial placebos have been used in herbal medicine (HM) clinical trials for rare diseases, the use and quality of reporting of HM-placebo remain unclear. We aim to describe the use of HM-placebo in clinical trials for rare diseases and determine the quality of reporting in these trials.

METHODS

This is a cross-sectional study. We searched PubMed, Embase, Web of Science, SinoMed, China National Knowledge Infrastructure, WanFang database, China Science and Technology Journal Database, National Institute of Informatics Support Academic Information Services, ClinicalTrials.gov and Chinese Clinical Trials Registry from their inception date to 14 February 2023 to identify registered and published trials that use placebos as a comparator in rare diseases. We collected data on placebo use reporting and the efficacy and safety of placebo. Descriptive statistics, the Chi-square test, and Binary multivariable logistic regression analysis were used to determine the placebo characteristics of the HM trial and its effect on reporting.

RESULTS

Among the 55 studies, we included that with a median administration time of placebo of 84 days (IQR 42-180) and a median placebo sample size of 30 (IQR 24-54). About half of the trials (27, 49.1%) did not provide their ethical approvals, and one trial had details of informed consent. None of the studies were fully reported and more than half of the items reported less than 50%. A total of 10 trials (18.2%) of placebo has active ingredients even though none of them performed pharmacological inert tests. Of the 29 studies with available data on adverse events, 5 (17.2%) trials did not show a better safety profile in the placebo group. Under the context that a relatively high-quality report is defined as a report with more than 9 items, there was a statistically significant difference between the two groups in the rate of relatively high-quality reports of the administration time (p = 0.047, OR 0.10, 95% CI 0.01 to 0.90), but the results are not representative.

CONCLUSION

The overall situation of HM-placebo in the field of rare diseases was poor. In particular, the placebo is tied to the quality of trials, and poor placebo hinders the generation of high-quality evidence for herbal clinical trials in the field of rare diseases. We summarize the current methods of assessment involved in the use of placebos and propose various considerations for placebos in different contexts. Our study can greatly promote rare disease researchers to review the quality of their placebo and clinical trials. It is imperative to guarantee that meticulously conducted research generates clinical evidence of the highest caliber. We also expect that in the future, more rigorous relevant standards about the reporting and design of HM-placebo will be developed. High-quality clinical trials are the prerequisite for the wide clinical application of herbal medicines for rare diseases.

Genetic newborn screening and digital technologies: A project protocol based on a dual approach to shorten the rare diseases diagnostic path in Europe

Since 72% of rare diseases are genetic in origin and mostly paediatrics, genetic newborn screening represents a diagnostic "window of opportunity". Therefore, many gNBS initiatives started in different European countries. Screen4Care is a research project, which resulted of a joint effort between the European Union Commission and the European Federation of Pharmaceutical Industries and Associations. It focuses on genetic newborn screening and artificial intelligence-based tools which will be applied to a large European population of about 25.000 infants. The neonatal screening strategy will be based on targeted sequencing, while whole genome sequencing will be offered to all enrolled infants who may show early symptoms but have resulted negative at the targeted sequencing-based newborn screening. We will leverage artificial intelligence-based algorithms to identify patients using Electronic Health Records (EHR) and to build a repository "symptom checkers" for patients and healthcare providers. S4C will design an equitable, ethical, and sustainable framework for genetic newborn screening and new digital tools, corroborated by a large workout where legal, ethical, and social complexities will be addressed with the intent of making the framework highly and flexibly translatable into the diverse European health systems.

Role of pharmacists in the context of rare diseases: a scoping review protocol

INTRODUCTION

Rare diseases are chronic conditions, generally incurable, progressive and disabling, which may result in early death. Access to therapeutic products, both medicines and appropriate medical devices, is essential to prevent the progression of the disease and maintain the patients' quality of life. Pharmacists can be part of health teams, in charge of guiding patients' journey, monitoring pharmacotherapy and identifying risks. This scoping review aims to identify and summarise evidence on the role of pharmacists and its impact in the field of rare diseases.

METHODS AND ANALYSIS

The searches will be conducted according to the Preferred Reporting Items for Systematic Reviews and Meta-Analyses reporting guideline for protocols. Three electronic databases will be consulted. Studies reporting on qualitative and/or quantitative data from any world region will be considered. There will be no language or initial time limit for studies inclusion, until December 2022. To be eligible for inclusion, studies must focus on the role pharmacists in clinical services aimed at promote the access to medicines, prevention and resolution of problems related to pharmacotherapy. No assessments of items' quality will be made, as the purpose of this scoping review is to synthesise and describe the coverage of the evidence. Clinical, humanistic or economic outcomes from studies that meet the inclusion criteria will be included in the review. The analysis will synthesise the available evidence and may be able to push pharmaceutical practice forward, aiding professionals, educators and managers in the implementation of new approaches to better meet the needs of rare diseases and providing opportunities for future research.

ETHICS AND DISSEMINATION

Primary data will not be collected in this study and formal ethical approval is not required. The findings of this study will be disseminated through peer-reviewed publications and conference presentations.

A mouse model of ZTTK syndrome reveals indispensable SON functions in organ development and hematopoiesis

Rare diseases are underrepresented in biomedical research, leading to insufficient awareness. Zhu-Tokita-Takenouchi-Kim (ZTTK) syndrome is a rare disease caused by genetic alterations that result in heterozygous loss-of-function of SON. While ZTTK syndrome patients suffer from numerous symptoms, the lack of model organisms hamper our understanding of both SON and this complex syndrome. Here, we developed Son haploinsufficiency (Son+/-) mice as a model of ZTTK syndrome and identified the indispensable roles of Son in organ development and hematopoiesis. Son+/- mice recapitulated clinical symptoms of ZTTK syndrome, including growth retardation, cognitive impairment, skeletal abnormalities, and kidney agenesis. Furthermore, we identified hematopoietic abnormalities in Son+/- mice, similar to those observed in human patients. Surface marker analyses and single-cell transcriptome profiling of hematopoietic stem and progenitor cells revealed that Son haploinsufficiency inclines cell fate toward the myeloid lineage but compromises lymphoid lineage development by reducing key genes required for lymphoid and B cell lineage specification. Additionally, Son haploinsufficiency causes inappropriate activation of erythroid genes and impaired erythroid maturation. These findings highlight the importance of the full gene dosage of Son in organ development and hematopoiesis. Our model serves as an invaluable research tool for this rare disease and related disorders associated with SON dysfunction.

DDX3X Syndrome Behavioral Manifestations with Particular Emphasis on Psycho-Pathological Symptoms-A Review

(1) Background: Identification of typical behavioral manifestations in patients with DEAD-Box Helicase 3 X-linked gene (DDX3X) variants plays a crucial role in accurately diagnosing and managing the syndrome. The objective of this paper was to carry out a review of medical and public databases and assess the behavioral features of the DDX3X syndrome (DDX3X), with a particular focus on psycho-pathological symptoms. (2) Methods: An extensive computerized search was conducted in various databases, including PubMed, Medline Complete, Science Direct, Scopus, and Web of Science. Specific keywords and Medical Subject Headings were used to ensure the inclusion of relevant studies. The Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA) guidelines were applied to assess the methodological quality of the manuscripts. (3) Results: Only nine papers out of the 272 assessed met the inclusion criteria. These articles revealed various psycho-pathological manifestations in patients with the DDX3X syndrome. Intellectual disability (ID) or developmental disability (DD), speech delay, autism spectrum disorder (ASD), attention deficit hyperactivity disorder (ADHD), generalized anxiety disorder (GAD), self-injurious behaviors (SIBs), sensory symptoms and sleep disturbance were demonstrated to be the most common psycho-pathological behavior manifestations. (4) Conclusions: Patients with the DDX3X syndrome manifest a wide spectrum of psycho-pathological symptoms. A comprehensive investigation of these symptoms in patients is essential for early diagnosis and effective therapy.

Genomic newborn screening for rare diseases

Rare diseases are a leading cause of infant mortality and lifelong disability. To improve outcomes, timely diagnosis and effective treatments are needed. Genomic sequencing has transformed the traditional diagnostic process, providing rapid, accurate and cost-effective genetic diagnoses to many. Incorporating genomic sequencing into newborn screening programmes at the population scale holds the promise of substantially expanding the early detection of treatable rare diseases, with stored genomic data potentially benefitting health over a lifetime and supporting further research. As several large-scale newborn genomic screening projects launch internationally, we review the challenges and opportunities presented, particularly the need to generate evidence of benefit and to address the ethical, legal and psychosocial issues that genomic newborn screening raises.

BBLN triggers CAMK2D pathology in mice under cardiac pressure overload and potentially in unrepaired hearts with tetralogy of Fallot

Tetralogy of Fallot (TOF) is one of the most prevalent congenital heart defects, with adverse cardiac remodeling and long-term cardiac complications. Here, searching for pathomechanisms, we find upregulated bublin coiled-coil protein (BBLN) in heart specimens of TOF patients with cyanosis, which positively correlates with cardiac remodeling pathways. Human BBLN, a protein with largely unknown function, promoted heart failure features, with increased mortality when overexpressed in mice, in a protein dosage-dependent manner. BBLN enhanced cardiac inflammation, fibrosis and necroptosis by calcium/calmodulin-dependent protein kinase II delta (CAMK2D) activation, whereas a BBLN mutant with impaired CAMK2D binding was inert. Downregulation of CAMK2D by an interfering RNA retarded BBLN-induced symptoms of heart failure. Endogenous BBLN was induced by hypoxia as a major TOF feature in human patients and by chronic pressure overload in mice, and its downregulation decreased CAMK2D hyperactivity, necroptosis and cardiovascular dysfunction. Thus, BBLN promotes CAMK2D-induced pathways to pathological cardiac remodeling, which are triggered by hypoxia in TOF.