ReviewFEBS open bio2026
Digital twins to accelerate target identification and drug development for immune-mediated disorders.
Review in FEBS open bio, 2026. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 1 paper.
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.
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.
Who cites it
1 citing paper in PubMed.
- Narrative Review of Digital Twins in the Health Domain: Development, Application, and Evidence Consolidation.Medical sciences (Basel, Switzerland) · 2026Review
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
2 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Immune-related/mediated disorders (IDs) comprise a very diverse group of diseases affecting millions worldwide. The complexity and heterogeneity of IDs, coupled with individual variability in immune system responses, create multiple challenges for developing targeted therapies. These challenges often result in prolonged diagnostic timelines, higher treatment costs, and frequent failures in clinical trials. Recent advances in artificial intelligence (AI) and digital twin (DT) technology offer promising solutions to support and accelerate drug discovery and development for these conditions, with anticipated substantial improvement in success rates. As virtual replicas of biological systems, DTs can be constructed using multimodal data sources, including multi-omics, molecular profiling, imaging and clinical records. These in silico tools can accelerate precision medicine by identifying relevant drug targets, designing personalised treatments and predicting individual immune responses to drug candidates. Here, we review the current landscape of DTs supporting drug development for IDs. We describe the concepts behind mixed reality approaches combining AI-based models, traditional mathematical and computational models based on low-throughput experiments and empirical studies. We highlight concrete examples of precision medicine strategies for IDs informed by computational modelling. We also address the benefits, limitations, and ethical considerations of these approaches, and outline future directions for research and clinical translation. Impact statement This manuscript addresses the use of digital twins (DT) to accelerate drug discovery and development for Immune-mediated Disorders. It provides a comprehensive overview of the field and helps clarify complex concepts. Furthermore, it provides concrete examples of DT applications on immune-mediated disorders, and discusses perspectives, and current challenges.
Indexed as
Identifiers
What Socratic holds
Registered trials
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.