Digital twins in gastroenterology: From computational modeling to precision medicine and clinical translation

Table 3 Five inflammatory bowel disease modeling approaches as rows

Modeling method	Representative works	Data inputs	Key targets	Potential clinical applications	Current limitation
Cytokine signaling models	Wendelsdorf et al[67]; systems-level colonic inflammation models	Cytokine profiles. Immune cell populations. Regulatory pathway data	TNF-α, IL-6, IL-12, IL-23, regulatory T-cell networks	In silico drug target validation; identification of dominant inflammatory pathways per patient	Simplified pathway representations. Limited clinical validation
Hybrid immune-clinical model	Shim et al[66]; mechanistic + data-driven framework	Biomarker profiles. Disease activity scores. Longitudinal clinical data	Patient-specific immune parameters; clinical disease activity indices	Individualized disease activity prediction; treatment response stratification prior to biologic initiation	Small derivation cohorts. Requires longitudinal data inputs
Single-cell transcriptomic twins	Karolinska “disease mechanism” twin; scRNA-seq immune network models	scRNA-seq mucosal biopsies. Gene expression networks. Immune cell subset profiles	Regulatory molecular nodes; immune cell subset activity; anti-TNF response predictors	Patient-specific therapy selection; identification of non-responders priorto biologic initiation; precision immune phenotyping	Early development stage. High data acquisition burden. Significant computational complexity
Predictive clinical model	Longitudinal ML models for Crohn’s disease trajectory prediction	CRP, fecal calprotectin. Endoscopic findings. Medication history. Patient-reported symptoms	Disease flare probability; progression to complications; secondary loss of response	Dynamic disease monitoring; early intervention triggering; virtual clinical trial design and patient enrichment	Not full digital twins. Population-level derivation. Limited prospective validation
PK/PD digital twins	Emerging pharmacokinetic-immune signaling coupling models	Drug concentration levels. Antidrug antibody titres. Immune signaling readouts	Biologic drug concentrations; dosing interval optimization; immunogenicity prediction	Precision biologic dosing; prevention of secondary loss of response; individualized therapeutic drug monitoring	Largely conceptual. Requires prospective. PK/PD data. Not yet clinically validated

Cytokine signaling models, hybrid immune-clinical models, single-cell transcriptomic twins, predictive clinical models, and PK/PD digital twins with representative work, data inputs, targets, applications, and limitations. TNF: Tumor necrosis factor; IL: Interleukin; CRP: C-reactive protein; ML: Machine learning.