bims-curels Biomed News
on Leigh syndrome
Issue of 2026–05–10
ten papers selected by
Cure Mito Foundation
  1. Current state of the art in the transmission of mitochondrial DNA pathogenic variants: reviewing preventive-therapeutic alternatives.
  2. Generation of an iPSC line IUFi004-A-13 with homozygous NDUFS1 mutation for the study of Leigh syndrome.
  3. Supply and Demand in the Mathematics of Rare Disease Drug Development: Why Choosing the Right Model Is Crucial.
  4. Special packaging enables effective mitochondrial delivery.
  5. Promises, Pitfalls, and Paths Forward for the Pediatric Research Equity Act.
  6. Human organoids: Fit for drug discovery?
  7. Genomics in Health and Biomedicine.
  8. Leveraging External Data for Testing Experimental Therapies with Biomarker Interactions in Randomized Clinical Trials.
  9. The Dilemma between Data Protection and Data Altruism within the Context of Rare Diseases in the European Health Data Space.
  10. Artificial intelligence language models for medical text analysis: A systematic review.
  1. J Assist Reprod Genet. 2026 May 05.
    Current state of the art in the transmission of mitochondrial DNA pathogenic variants: reviewing preventive-therapeutic alternatives.
    Marta Reguera-Cabezas, Felipe Martínez-Pastor, Cristina Soriano-Úbeda.
      This review analyzes strategies to prevent or reduce the transmission of diseases caused by pathogenic variants in mitochondrial DNA (mtDNA). Among these, we will focus on prenatal screening, preimplantation genetic testing, gene-editing techniques, other molecular tools, and selected heterologous mitochondrial replacement techniques (MRTs), explaining their status and the uncertainties surrounding their clinical application. After this analysis and review, we recognise the limitations of the efficacy of prenatal and preimplantation genetic testing for mitochondrial DNA pathogenic variants, the legal constraints on gene editing, and the status of mitochondrial replacement techniques. MRTs are a safe and possibly more effective alternative for preventing diseases caused by mitochondrial DNA pathogenic variants.
    Keywords:  Assisted reproduction; Genetics; Heteroplasmy; Mitochondrial diseases; Mitochondrial replacement
    DOI:  https://doi.org/10.1007/s10815-026-03888-0
  2. Stem Cell Res. 2026 Apr 28. pii: S1873-5061(26)00098-X. [Epub ahead of print]94 104002
    Generation of an iPSC line IUFi004-A-13 with homozygous NDUFS1 mutation for the study of Leigh syndrome.
    Caleb Jerred, Haribaskar Ramachandran, Barbara Hildebrandt, Annika Zink, Natascia Ventura, Andrea Rossi, Alessandro Prigione.
      NDUFS1 is a critical component of mitochondrial respiratory chain Complex I (CI). Pathogenic variants of NDUFS1 can cause Leigh syndrome (LS), a severe pediatric mitochondrial disorder. To model NDUFS1-linked LS, we generated an iPSC line with homozygous missense mutations in exon 8 using CRISPR/Cas9. The cell line demonstrated typical morphology, expression of iPSC markers, ability to differentiate into all three germ layers, and genomic integrity. This model will enable the study of LS caused by CI in an isogenic context.
    DOI:  https://doi.org/10.1016/j.scr.2026.104002
  3. Clin Transl Sci. 2026 May;19(5): e70551
    Supply and Demand in the Mathematics of Rare Disease Drug Development: Why Choosing the Right Model Is Crucial.
    Marshall L Summar, Janet Woodcock.
      Clinical trials for rare diseases face a fundamental mathematical challenge that conventional randomized controlled trial (RCT) designs cannot overcome. With approximately 95% of the estimated 10,000-16,000 rare diseases lacking approved therapies, and drug development programs failing at rates exceeding 75% in non-oncology indications, the field confronts a stark reality: Traditional trial designs demand patient numbers that simply do not exist. This perspective article examines the critical mismatch between the statistical requirements of different trial designs (the "demand") and the actual patient populations available for study (the "supply"). We demonstrate mathematically that alternative trial designs-particularly patient-as-own-control and natural history comparator models-can reduce required sample sizes by 5- to 20-fold while maintaining statistical rigor. We further point out that a substantial proportion of rare disease trial failures stem not from therapeutic inefficacy but from recruitment and retention challenges inherent to underpowered RCT designs-challenges that are directly addressable through appropriately matched trial design. Given that most rare disease development programs receive only one opportunity to demonstrate efficacy, the continued application of inappropriate statistical models represents both a scientific failure and an ethical and economic challenge to the rare disease community. We propose that regulatory agencies formalize acceptance of alternative trial designs for rare diseases, supported by explicit mathematical frameworks that transparently account for genetic heterogeneity, pediatric populations, and the statistical efficiency gains achieved through within-subject correlation.
    Keywords:  clinical trial design; natural history controls; patient‐as‐own‐control; rare diseases; sample size; statistical power
    DOI:  https://doi.org/10.1111/cts.70551
  4. Cell Metab. 2026 May 05. pii: S1550-4131(26)00143-9. [Epub ahead of print]38(5): 838-840
    Special packaging enables effective mitochondrial delivery.
    Caleb Jerred, Judith Hatzfeld, Alessandro Prigione.
      Mitochondrial transplantation has emerged as a promising, though still experimental, strategy for treating mitochondria-related diseases. In a recent study in Cell, Du et al. demonstrate that packaging mitochondria within erythrocyte-derived plasma membranes enhances delivery efficiency and integration, thereby advancing the translational potential of this approach toward clinical application.
    DOI:  https://doi.org/10.1016/j.cmet.2026.04.005
  5. JAMA Health Forum. 2026 May 01. 7(5): e260993
    Promises, Pitfalls, and Paths Forward for the Pediatric Research Equity Act.
    Alexa Pagano, Danielle J Green, Jennifer L Goldman, Anjali Deshmukh.
       Importance: Since its enactment in 2003, the Pediatric Research Equity Act (PREA) has significantly increased the number of pediatric drug studies performed and expanded pediatric drug labeling. Despite these advancements, many drugs used in children still lack pediatric-specific US Food and Drug Administration (FDA) labeling, even when pediatric studies are required by law. Recent legislative changes strengthened the FDA's enforcement authority over PREA. As these changes are implemented, persistent gaps in pediatric drug development warrant examination. Addressing these gaps may help ensure that children are systematically included in clinical research and that medications used in children are supported by rigorous evidence on safety, dosing, and efficacy.
    Observations: Legislation to ensure pediatric drug safety and efficacy has a long history, yet pediatric-specific labeling frequently lags behind initial drug approval, with many studies required under PREA remaining incomplete for years after initial FDA approval. Consequently, off-label prescribing can be necessary for pediatric health care, leaving children exposed to medications lacking adequate pediatric evidence. Barriers to the timely completion of clinical trials are often attributed to challenges inherent to smaller, disease-specific pediatric populations; however, the FDA's limited enforcement authority and insufficient resources to address delayed pediatric trial completion play an underappreciated role. In addition, limited ability to track required studies and insufficient public transparency undermine the regulatory goals intended to protect children. With new legislative authority strengthening enforcement of PREA, understanding these barriers is essential to ensure this authority is effectively deployed to improve children's health.
    Conclusions and Relevance: To ensure children have access to safe, effective, evidence-based medications, effective policy changes are necessary. The FDA should use its new authority to ensure the timely completion of required pediatric studies and enhance public transparency by improving mechanisms to track PREA-mandated research.
    DOI:  https://doi.org/10.1001/jamahealthforum.2026.0993
  6. Stem Cell Reports. 2026 May 07. pii: S2213-6711(26)00109-8. [Epub ahead of print] 102898
    Human organoids: Fit for drug discovery?
    Annika Wittich, Kim Krieg, Philip Gribbon, Jakob J Metzger, Alessandro Prigione, Ole Pless.
      Organoids are self-organizing three-dimensional (3D) in vitro tissues derived from pluripotent stem cells (PSCs) that recapitulate key structural and functional features of human organs. Their multicellular architecture and physiological relevance make them promising new approach methodologies (NAMs) for disease modeling, drug discovery, and toxicity testing. However, their reliability and scalability for compound screening remain under evaluation. This review summarizes current human PSC-derived organoid screening strategies, highlighting available readouts, related machine learning methods, and their potential advantages over traditional screening models. We also discuss major challenges, including assay robustness, throughput limitations, and the need for standardized protocols. Advancing validated and scalable approaches will be essential for integrating organoids into pharmaceutical development and improving the translational success of drug candidates.
    Keywords:  assay development; drug discovery; high-throughput screening; hit validation; human pluripotent stem cells; organoids; small molecules; three-dimensional
    DOI:  https://doi.org/10.1016/j.stemcr.2026.102898
  7. Adv Exp Med Biol. 2026 ;1504 329-356
    Genomics in Health and Biomedicine.
    Maria Luís Cardoso, Hugo Martiniano, Luisa Mota-Vieira, Astrid Moura Vicente.
      Genomic information is rapidly becoming a cornerstone of personalized medicine, offering transformative potential for clinical practice. This chapter explores the critical role of genomics in enabling earlier diagnosis, precise treatment, risk prediction, and preventive healthcare strategies. Advances in sequencing technologies, data integration, and bioinformatics allow for tailored healthcare solutions based on an individual's genetic profile, combined with clinical, lifestyle, and environmental data. Integration with electronic health records, mHealth technologies, and artificial intelligence further enhances clinical decision-making. The chapter highlights current applications of genomic medicine in oncology, rare diseases, and pharmacogenomics and the growing relevance of polygenic risk scores in managing common chronic diseases. It also discusses the need for harmonized data governance, infrastructure development, professional training, and public engagement to ensure equitable and effective implementation. These developments are situated within the broader landscape of national and international initiatives-including ICPerMed, 1Million Genomes, and the Genome of Europe project-that aim to foster collaboration, standardization, and equitable access to genomic healthcare across populations. Clinical areas where genomics has already demonstrated substantial value are discussed while identifying key challenges and priorities for advancing the future of personalized medicine.
    Keywords:  Cancer genomics; Data governance; Genomics; Personalized medicine; Pharmacogenomics; Polygenic risk scores; Rare diseases
    DOI:  https://doi.org/10.1007/978-3-032-18966-0_16
  8. Biometrika. 2026 ;pii: asaf047. [Epub ahead of print]113(1):
    Leveraging External Data for Testing Experimental Therapies with Biomarker Interactions in Randomized Clinical Trials.
    B Ren, F Ferrari, S Fortini, S Ventz, L Trippa.
      In oncology the efficacy of novel therapeutics often differs across patient subgroups, and these variations are difficult to predict during the initial phases of the drug development process. The relation between the power of randomized clinical trials and heterogeneous treatment effects has been discussed by several authors. In particular, false negative results are likely to occur when the treatment effects concentrate in a subpopulation but the study design did not account for potential heterogeneous treatment effects. The use of external data from completed clinical studies and electronic health records has the potential to improve decision-making throughout the development of new therapeutics, from early-stage trials to registration. Here we discuss the use of external data to evaluate experimental treatments with potential heterogeneous treatment effects. We introduce a permutation procedure to test, at the completion of a randomized clinical trial, the null hypothesis that the experimental therapy does not improve the primary outcomes in any subpopulation. The permutation test leverages the available external data to increase power. Also, the procedure controls the false positive rate at the desired α -level without restrictive assumptions on the external data, for example, in scenarios with unmeasured confounders, different pre-treatment patient profiles in the trial population compared to the external data, and other discrepancies between the trial and the external data. We illustrate that the permutation test is optimal according to an interpretable criteria and discuss examples based on asymptotic results and simulations, followed by a retrospective analysis of individual patient-level data from a collection of glioblastoma clinical trials.
    Keywords:  Bayesian statistics; Decision theory; Glioblastoma; Heterogeneous treatment effect; Permutation test
    DOI:  https://doi.org/10.1093/biomet/asaf047
  9. Eur J Health Law. 2026 Apr 17. 33(2): 137-165
    The Dilemma between Data Protection and Data Altruism within the Context of Rare Diseases in the European Health Data Space.
    Laura Centeno Casado.
      This article analyses how the notion of health data under the GDPR has evolved through the legal instruments and provisions on health data sharing in the Data Governance Act (DGA) and the European Health Data Space (EHDS), aiming both legal sources to facilitate data access and governance, including electronic health data for its primary and secondary use, by establishing harmonised rules. These regulations open opportunities to enhance cross-border data access, the promotion of data altruism, and the development of data governance models facilitating biomedical research. In the specific context of rare diseases, however, significant challenges remain emerging from variations between EU Member States implementation of the EHDS. In particular, the EHDS's secondary use framework, the genomic and biobank data exception, and the coexistence with the DGA's consent‑based data altruism model create a complex legal landscape for rare disease research. This contribution intends to clarify the legal bases for secondary use to improve the capacity to protect data subjects' right to data protection, while preserving data value and utility in biomedical research within the context of rare diseases.
    DOI:  https://doi.org/10.1163/15718093-bja10169
  10. Artif Intell Med. 2026 Apr 28. pii: S0933-3657(26)00093-X. [Epub ahead of print]178 103441
    Artificial intelligence language models for medical text analysis: A systematic review.
    Amir Sorayaie Azar, Jamshid Bagherzadeh Mohasefi, Uffe Kock Wiil, Amin Naemi, Ali Ebrahimi.
      Medical text records serve as essential repositories of patient information, providing a foundation for informed clinical decision-making, accurate diagnosis, reliable prognosis, and effective treatment planning. Recent advancements in Artificial Intelligence (AI), particularly in Natural Language Processing (NLP) and Machine Learning (ML), have positioned AI-driven language models as powerful tools for analyzing, classifying, and generating medical textual data. In this systematic literature review, an initial search retrieved 548 records published between 1 January 2000 and 1 July 2024. After rigorous screening based on predefined inclusion and exclusion criteria, 22 original research articles were included. The review highlights substantial progress in applying advanced architectures such as Bidirectional Encoder Representations from Transformers (BERT) and Generative Pre-trained Transformers (GPT) to medical text processing tasks. These models consistently outperform conventional NLP and ML approaches, achieving superior results in disease classification, automated clinical documentation, and predictive analytics. However, critical challenges persist, including the limited availability of clinically validated datasets, variability in data preprocessing protocols, insufficient external validation, and the lack of interpretable AI frameworks, all of which collectively hinder clinical trust and large-scale adoption. Future research should prioritize the development of hybrid AI systems that integrate multimodal data sources (text, imaging, and structured records), incorporate explainable AI mechanisms, and adhere to standardized reporting frameworks. Addressing these methodological gaps will be pivotal in enhancing the reliability, clinical applicability, and impact of AI language models, thereby advancing evidence-based medicine, personalized treatment strategies, and overall patient care.
    Keywords:  Artificial intelligence; Language models; Medical text; Natural language processing
    DOI:  https://doi.org/10.1016/j.artmed.2026.103441
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