Evidence map›Paper›PMID 41915424›Full record

ReviewCPT: pharmacometrics & systems pharmacology2026

The Potential of Digital Twins for Pediatric Rare Diseases.

Rahuman S Malik-Sheriff, Anurag Limdi, Evangelia Petsalaki, Henning Hermjakob, Ellen M McDonagh

Abstract readReview
In one paragraph

Review in CPT: pharmacometrics & systems pharmacology, 2026. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 1 paper.

0numbers the graph read from it
0cells of the map it votes in
1citing papers in PubMed
–field-weighted citation impact
1 · What the graph read from it

What it found

Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.

The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.

2 · The registry

The trial behind it

Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.

Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.

3 · Its place in the literature

Who cites it

1 citing paper in PubMed.

  1. Review
4 · The record

Corrections and comments

PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.

5 · Who and what money

Authors and funding

5 authors.

Rahuman S Malik-SheriffEuropean Molecular Biology Laboratory, European Bioinformatics Institute (EMBL-EBI), Cambridge, UK.ORCID https://orcid.org/0000-0003-0705-9809
Anurag LimdiEuropean Molecular Biology Laboratory, European Bioinformatics Institute (EMBL-EBI), Cambridge, UK.
Evangelia PetsalakiEuropean Molecular Biology Laboratory, European Bioinformatics Institute (EMBL-EBI), Cambridge, UK.ORCID https://orcid.org/0000-0002-8294-2995
Henning HermjakobEuropean Molecular Biology Laboratory, European Bioinformatics Institute (EMBL-EBI), Cambridge, UK.ORCID https://orcid.org/0000-0001-8479-0262
Ellen M McDonaghEuropean Molecular Biology Laboratory, European Bioinformatics Institute (EMBL-EBI), Cambridge, UK.ORCID https://orcid.org/0000-0001-5806-6174

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

Rare diseases affect over 300 million people globally, with approximately 75% manifesting in childhood. Their diagnosis is often delayed and approved treatments are lacking for most of the conditions. Pediatric rare diseases research is further complicated by ethical constraints and developmental diversity across childhood. Digital Twins, virtual representations of patients built from mechanistic and AI/ML models, offer a promising solution by enabling hypothesis testing, precision diagnostics, personalized therapies, and in silico trials for pediatric rare diseases. This article discusses the potential of DT applications in advancing precision medicine for pediatric rare diseases, alongside associated regulatory perspectives, modeling strategies, uncertainty analysis, as well as data, ethical and legal challenges.

Indexed as

Precision MedicineRare DiseasesChildDigital HealthHumans

Identifiers

PMID41915424
PMCPMC13140324

What Socratic holds

Textmetadata
LicenceCC BY-NC
Read underepoch 390

Registered trials

None linked

Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the Socratic graph.