ArticleAdvances in therapy2022
A Computational Platform Integrating a Mechanistic Model of Crohn's Disease for Predicting Temporal Progression of Mucosal Damage and Healing.
Article in Advances in therapy, 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. An erratum has been issued. Cited by 23 papers.
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Who cites it
23 citing papers in PubMed.
- A digital twin approach for transforming prescription drug labeling.Frontiers in pharmacology · 2026Article
- A scoping review of human digital twins in healthcare applications and usage patterns.NPJ digital medicine · 2025Article
- Artificial Intelligence in Advancing Inflammatory Bowel Disease Management: Setting New Standards.Cancers · 2025Review
- Predicting survival in prospective clinical trials using weakly-supervised QSP.NPJ precision oncology · 2025Article
- Digital Twins for Clinical and Operational Decision-Making: Scoping Review.Journal of medical Internet research · 2025Article
- Artificial intelligence use for precision medicine in inflammatory bowel disease: a systematic review.American journal of translational research · 2025Review
- Combining mechanistic modeling with machine learning as a strategy to predict inflammatory bowel disease clinical scores.Frontiers in pharmacology · 2025Article
- Artificial intelligence in inflammatory bowel disease: innovations in diagnosis, monitoring, and personalized care.Therapeutic advances in gastroenterology · 2025Review
- Digital biomarkers and artificial intelligence: a new frontier in personalized management of inflammatory bowel disease.Frontiers in immunology · 2025Review
- Future Directions for Quantitative Systems Pharmacology.Handbook of experimental pharmacology · 2025Review
- Augmented intelligence in precision medicine: Transforming clinical decision-making with AI/ML and/or quantitative systems pharmacology models.Clinical and translational science · 2024Article
- Definitions and Characteristics of Patient Digital Twins Being Developed for Clinical Use: Scoping Review.Journal of medical Internet research · 2024Article
- Digital twins are integral to personalizing medicine and improving public health.Nature reviews. Gastroenterology & hepatology · 2024Article
- Evolution of digital twins in precision health applications: a scoping review study.Research square · 2024Article
- Machine Learning for Predicting Biologic Agent Efficacy in Ulcerative Colitis: An Analysis for Generalizability and Combination with Computational Models.Diagnostics (Basel, Switzerland) · 2024Article
- "Digital twins elucidate critical role of TNPJ systems biology and applications · 2024Article
- Coupling quantitative systems pharmacology modelling to machine learning and artificial intelligence for drug development: itsFrontiers in systems biology · 2024Article
- Impact of artificial intelligence on prognosis, shared decision-making, and precision medicine for patients with inflammatory bowel disease: a perspective and expert opinion.Annals of medicine · 2023Review
- Pediatric Crohn's disease diagnosis aidFrontiers in pediatrics · 2023Article
- A framework for multi-scale intervention modeling: virtual cohorts, virtual clinical trials, and model-to-model comparisons.Frontiers in systems biology · 2023Article
Corrections and comments
- Erratum issued
Authors and funding
16 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
introductionPhysicians are often required to make treatment decisions for patients with Crohn's disease on the basis of limited objective information about the state of the patient's gastrointestinal tissue while aiming to achieve mucosal healing. Tools to predict changes in mucosal health with treatment are needed. We evaluated a computational approach integrating a mechanistic model of Crohn's disease with a responder classifier to predict temporal changes in mucosal health.
methodsA hybrid mechanistic-statistical platform was developed to predict biomarker and tissue health time courses in patients with Crohn's disease. Eligible patients from the VERSIFY study (n = 69) were classified into archetypical response cohorts using a decision tree based on early treatment data and baseline characteristics. A virtual patient matching algorithm assigned a digital twin to each patient from their corresponding response cohort. The digital twin was used to forecast response to treatment using the mechanistic model.
resultsThe responder classifier predicted endoscopic remission and mucosal healing for treatment with vedolizumab over 26 weeks, with overall sensitivities of 80% and 75% and overall specificities of 69% and 70%, respectively. Predictions for changes in tissue damage over time in the validation set (n = 31), a measure of the overall performance of the platform, were considered good (at least 70% of data points matched), fair (at least 50%), and poor (less than 50%) for 71%, 23%, and 6% of patients, respectively.
conclusionHybrid computational tools including mechanistic components represent a promising form of decision support that can predict outcomes and patient progress in Crohn's disease.
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