Trial readiness: understanding the natural history of rare diseases
Inherited metabolic diseases (IMD) represent the largest and still growing group of treatable genetic disorders and are increasingly amenable to targeted interventions that achieve varying degrees of prognostic improvement. Innovative therapies are on the horizon and offer promising opportunities fo...
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| Main Authors: | , , , , , , |
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| Format: | Article (Journal) |
| Language: | English |
| Published: |
November 2025
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| In: |
Journal of inherited metabolic disease
Year: 2025, Volume: 48, Issue: 6, Pages: 1-10 |
| ISSN: | 1573-2665 |
| DOI: | 10.1002/jimd.70102 |
| Online Access: | Verlag, kostenfrei, Volltext: https://doi.org/10.1002/jimd.70102 Verlag, kostenfrei, Volltext: https://onlinelibrary.wiley.com/doi/abs/10.1002/jimd.70102 
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| Author Notes: | Thomas Opladen, Ulrike Mütze, Florian Gleich, Sven F. Garbade, Oya Kuseyri Hübschmann, Matthias Zielonka, Stefan Kölker |
MARC
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| 520 | |a Inherited metabolic diseases (IMD) represent the largest and still growing group of treatable genetic disorders and are increasingly amenable to targeted interventions that achieve varying degrees of prognostic improvement. Innovative therapies are on the horizon and offer promising opportunities for disease-changing treatment for a variety of IMDs. For the development of clinical trials specifically for IMDs and in the context of trial readiness, a thorough understanding of the natural history of the IMDs is indispensable for an objective evaluation of meaningful improvement of novel treatment options. Patient registries are key instruments in this regard, since they are recognized as powerful instruments for the collection of longitudinal real-world data, elucidating the phenotypic diversity of disease courses, understanding the impact of diagnosis and treatment on clinical outcomes, and investigating prognostic factors. At the same time, they enable the collection of patient-specific outcome parameters (PROMs) that improve the understanding of the natural phenotype in rare diseases by identifying clinically relevant endpoints, disease burden over time, unmet medical needs, and the impact of diseases and prescribed diets and medication on the quality of life of patients and caregivers. Meta-analysis and quantitative retrospective natural history modeling allow the evaluation of the disease course with the help of published aggregate data, where patient registries are not available. Finally, the various data sources provide the theoretical basis for practical applications such as the creation of consensus-based guidelines, pass studies, and mathematical modeling. This review describes the various options for evaluating and understanding the natural history of rare IMDs in detail, with the ultimate aim of achieving adequate trial readiness. | ||
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