bims-tyki2d 2026-04-26 papers

Issue of 2026–04–26
four papers selected by
Zoya Panahloo, UCB

Int J Neonatal Screen. 2026 Mar 26. pii: 19. [Epub ahead of print]12(2):

A Multi-Stakeholder Perspective on Integrating Genomic Sequencing into Newborn Screening: An Interview Study.

Saskia G Smits, Suzanne M Onstwedder, Tessel Rigter, Wendy Rodenburg, Lidewij Henneman.

Interest in the genomic sequencing of healthy newborns has raised a discussion on whether this technology should be introduced into existing newborn screening (NBS) programs. This qualitative study explores a multi-stakeholder perspective on the future of genomic sequencing in NBS. Semi-structured interviews were conducted with 26 professionals involved in NBS or in clinical genome sequencing in the Netherlands. Participants highlighted opportunities such as the possibility to use one test for a wide range of genetic conditions, reducing diagnostic odyssey, expanding the scope of NBS, and increasing program efficiency. Challenges were raised regarding genetic variant interpretation, expected increased parental anxiety, data privacy issues, difficulties with information provision, and high costs. Three areas of tension between participants' perspectives were identified: screening strategy, screening performance, and roles and responsibilities. It was emphasized that implementing genomic sequencing should not risk reducing the current high NBS participation, and that enhancing knowledge, communication, and collaboration between all stakeholders is needed. Although most participants did not believe genomic sequencing as a first-tier test is currently desirable and feasible, they acknowledged it has a role to play in the future of NBS. Future decision-making should consider the potential impact on the participation rate, program quality, and balancing benefits and harms.

Keywords: genomic sequencing; interviews; neonatal; newborn screening; stakeholder perspectives

DOI: https://doi.org/10.3390/ijns12020019

J Pharm Bioallied Sci. 2026 Apr-Jun;18(2):18(2): 93-95

Orphan Drugs and Diseases: A Systematic Review.

Heena Dixit Tiwari, Manoj Kumar Sahoo, M R Murali, Rahul Tiwari, Kamatham Manjusha, M C Prashant.

Rare diseases, though individually infrequent, collectively affect over 300 million people worldwide. The development of orphan drugs to treat these conditions is hampered by regulatory inconsistencies, high costs, and limited clinical trial populations. To systematically review the current landscape of orphan drugs and rare diseases, focusing on definitions, therapeutic approaches, health economic evaluations, and stakeholder perspectives. A systematic review was conducted using 70 reference-screened articles, out of which 10 were selected based on relevance to rare diseases or orphan drug policy, economics, or clinical management. Study types included systematic and scoping reviews, observational studies, and health economic evaluations. Data extraction focused on definitions, healthcare roles, cost analyses, and clinical outcomes. Definitions of rare diseases varied globally, impacting drug approval processes. Economic evaluations revealed disparities in funding orphan drugs. Studies emphasized the growing role of pharmacists and stakeholders in therapeutic access. High-cost burdens, ethical considerations, and diagnostic advancements were recurring themes. Pediatric and syndromic rare conditions, like Duchenne muscular dystrophy and Turner syndrome, were notably covered. Effective rare disease care requires harmonized definitions, ethical pricing models, interdisciplinary healthcare roles, and evidence-based policies to ensure equitable access and sustainability.

Keywords: Healthcare policy; orphan drugs; pharmacoeconomics; rare diseases; systematic review

DOI: https://doi.org/10.4103/jpbs.jpbs_937_25

Genet Med Open. 2026 ;4 104394

Telethon Undiagnosed Disease Program: Structured approach to solving rare childhood-onset genetic diseases.

Telethon Undiagnosed Disease Study group

Purpose: Many children with severe genetic disorders remain undiagnosed despite advanced genomic technologies. Early diagnosis is vital for prognosis, genetic counseling, and targeted treatment development. This study aims to increase diagnostic rates in complex pediatric cases and foster research into disease mechanisms.
Methods: Launched in 2016, the Telethon Undiagnosed Diseases Program provides a structured, multicenter approach to rare disease diagnosis. Standardized case submission criteria ensured consistent clinical data collection. Children with severe, multisystemic disorders and prior negative genetic tests were eligible. After case approval, trio-based exome sequencing was performed, with regular reanalysis for unsolved cases until December 2024.
Results: Between June 2016 and December 2023, 1338 cases were submitted by 60 clinicians from 22 Italian centers; 1019 were accepted. A definitive genetic diagnosis was achieved in 49% of cases, implicating 330 genes. Most pathogenic variants (70.2%) were de novo, reflecting demographic trends, such as delayed parenthood. The remainder included autosomal recessive or X-linked variants, with homozygosity observed in 9% of patients.
Conclusion: The Telethon Undiagnosed Diseases Program significantly shortened the average diagnostic odyssey of ∼8 years. Children born after 2016 benefited from faster diagnoses. This initiative offers a scalable, cost-effective model for improving diagnosis, guiding treatment, and supporting therapeutic innovation in rare pediatric diseases.

Keywords: Exome sequencing; Genetic diagnosis; Pediatric genomics; Rare diseases; Undiagnosed Diseases Program

DOI: https://doi.org/10.1016/j.gimo.2026.104394

Int J Med Inform. 2026 Apr 16. pii: S1386-5056(26)00182-6. [Epub ahead of print]215 106442

Architectural and translational perspectives on clinical decision support systems for rare disease diagnosis: a scoping review.

Erdener Özçetin, Sümeyye Ebrar Baş, Furkan Özpay.

BACKGROUND: Rare diseases remain difficult to diagnose because of phenotypic heterogeneity, limited clinical familiarity, and fragmented health data infrastructures. Clinical decision support systems (CDSS) have emerged as promising tools to support earlier recognition and more consistent diagnostic reasoning. However, the literature spans diverse technological paradigms, making it difficult to understand how these systems collectively contribute to clinical decision-making and their translational implementation.
OBJECTIVE: This scoping review aimed to map diagnostic CDSS developed for rare disease diagnosis and to examine how data infrastructures, phenotype-driven reasoning frameworks, and artificial intelligence-based approaches contribute to clinical decision support and their translation into practice.
METHODS: A PRISMA-ScR-guided scoping review was conducted. Searches were performed primarily in PubMed (MEDLINE), with supplementary screening in Google Scholar; the final search was completed on 30 November 2025. Records were screened in two stages and eligible studies were charted according to CDSS type, data sources, analytical methods, validation strategies, explainability features, interoperability elements, and reported evidence of clinical integration. Findings were synthesized using a taxonomy-based thematic approach rather than through quantitative pooling.
RESULTS: The reviewed literature clustered into four main technological paradigms: information-retrieval systems, phenotype- and ontology-driven reasoning tools, data-driven predictive models based on EHRs and AI methods, and interoperable infrastructures such as federated learning and knowledge graphs. In addition, a separate group of studies addressed clinical evaluation and translation readiness across these paradigms. Across these areas, the field showed substantial methodological diversity, but evidence for external validation, workflow-level integration, and real-world clinical implementation remained limited. Interoperability, explainability, and governance were recurring challenges across paradigms.
CONCLUSIONS: Rare disease CDSS research is moving from isolated diagnostic tools toward broader, interconnected diagnostic ecosystems. Progress toward clinically actionable implementation will depend on standardized data representations, stronger cross-institutional validation, explainable outputs aligned with clinical workflows, and interoperable infrastructures supported by appropriate governance. This review provides a taxonomy and conceptual framework to support the translational development of rare disease diagnostic CDSS.

Keywords: Artificial intelligence; Clinical decision support systems; Diagnostic reasoning; Electronic health records; Phenotype ontology; Rare diseases

DOI: https://doi.org/10.1016/j.ijmedinf.2026.106442