“Advancing the frontier of rare disease modeling: a critical appraisal of in silico technologies” is ERAMET’s newest publication appearing in Nature Digital Medicine.

This new article published on 17th November, was prepared by the ERAMET team at the University of Catania.

Rare diseases affect hundreds of millions of people, yet researchers often have too little data to answer big clinical questions. The article walks us through how mechanistic models, AI/ML, and digital twins are being used across the rare-disease journey: spotting signals that speed diagnosis, narrowing options in drug discovery, strengthening preclinical evidence, and designing smarter clinical trials when patient numbers are small. Along the way, the hard questions are raised such as what makes a model credible, what data is needed, and how regulators can judge evidence that comes from a computer rather than a clinic.

The conclusion of the article balances the view on the topic. In silico methods can scale insights when patient numbers are small, support smarter study designs, and make evidence more transparent. But progress depends on credible validation, better data curation and sharing, clearer reporting standards, and regulatory guidance. The review closes with a roadmap of opportunities and open questions to help put these tools to work responsibly.

Read the full article here.