Aug 2026· Orphanet Journal of Rare Diseases· 0 citations
TL;DR
An extensive European database of non-oncologic rare diseases was established, enabling the identification of five distinct groups that reflect progressive stages of knowledge generation and drug development, highlighting a substantial knowledge and development gap across the rare disease landscape.
Abstract
With rare diseases affecting 350 million people worldwide, medical knowledge and new drug development remain inconsistently spread between diseases. The PLUTO mission, a pioneering International Rare Diseases Research Consortium initiative, seeks to advance understanding of the current level of knowledge through comprehensive data-driven analysis. Our goal is to uncover hidden complexities of rare diseases and factors that facilitate or impair the development of new treatments.
For non-oncological rare diseases, we integrated data from Orphadata, MEDLINE, OMIM, ClinicalTrials.gov, Trialtrove, HPO and DrugBank databases using ontology-based semantic mapping and automated natural language processing (NLP). Research activity and drug development were evaluated using publications, clinical trials, EU orphan drug designations and EU marketing authorisations as key metrics.
We established an extensive European database of non-oncologic rare diseases, enabling the identification of five distinct groups that reflect progressive stages of knowledge generation and drug development. Half of the diseases were represented only by publications without evidence of clinical development, whereas fewer than 10% had reached orphan drug designation or marketing authorisation, highlighting a substantial knowledge and development gap across the rare disease landscape.
This study offers new insights into how non-oncological rare diseases knowledge changes over time and outlines criteria that may be necessary to move from a non-research stage towards active drug development. The rare disease landscape emerges as a non-linear system where progression is influenced by knowledge generation, creating a positively reinforcing cycle.
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This work demonstrates how AI-driven informatics can enhance rare disease diagnosis and exemplifies the role of digital tools in transforming precision medicine and healthcare delivery.
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