ArticleClinical and translational science2021
A Dynamic Quantitative Systems Pharmacology Model of Inflammatory Bowel Disease: Part 2 - Application to Current Therapies in Crohn's Disease.
Article in Clinical and translational science, 2021. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 17 papers, 2 of them syntheses that pooled it.
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Who cites it
17 citing papers in PubMed, 2 syntheses or guidelines pooled it.
- Mathematical modeling in autoimmune diseases: from theory to clinical application.Frontiers in immunology · 2024Pooled it
- Recent applications of quantitative systems pharmacology and machine learning models across diseases.Journal of pharmacokinetics and pharmacodynamics · 2022Pooled it
- Methotrexate's effect on cells and adalimumab immunogenicity in axial spondyloarthritis: A mathematical study.PLoS computational biology · 2026Article
- Advancing IBD Management: A Literature Review on the Role of Non-Invasive Blood-Based Biomarkers in Predicting and Assessing Pharmacodynamic Response to Treatment.Clinical and translational science · 2026Review
- A mathematical model to simulate the biological action of Infliximab on TNF-Bulletin of mathematical biology · 2026Article
- Research progress in molecular targeted therapy for inflammatory bowel disease.Journal of translational medicine · 2025Review
- A Step-by-Step Workflow for Performing In Silico Clinical Trials With Nonlinear Mixed Effects Models.CPT: pharmacometrics & systems pharmacology · 2025Article
- Virtual patients inspired by multiomics predict the efficacy of an anti-IFNα mAb in cutaneous lupus.iScience · 2025Article
- Systems immunology: When systems biology meets immunology.Frontiers in immunology · 2025Review
- Conceptual quantitative systems pharmacology framework for supporting clinical trial design in organ-specific autoimmune and rare diseases.Frontiers in immunology · 2025Review
- Combining mechanistic modeling with machine learning as a strategy to predict inflammatory bowel disease clinical scores.Frontiers in pharmacology · 2025Article
- The Role of Pharmacometrics in Advancing the Therapies for Autoimmune Diseases.Pharmaceutics · 2024Review
- Trends in Targeted Therapy Usage in Inflammatory Bowel Disease: TRENDY Study of ENEIDA.Pharmaceutics · 2024Article
- Cell-level systems biology model to study inflammatory bowel diseases and their treatment options.CPT: pharmacometrics & systems pharmacology · 2023Article
- A Computational Platform Integrating a Mechanistic Model of Crohn's Disease for Predicting Temporal Progression of Mucosal Damage and Healing.Advances in therapy · 2022Article
- Computational models in inflammatory bowel disease.Clinical and translational science · 2022Review
- Predictive approaches in inflammatory bowel disease.Clinical and translational science · 2022Article
Corrections and comments
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Authors and funding
5 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Inflammatory bowel disease (IBD) is a heterogeneic disease with a variety of treatments targeting different mechanisms. A multistate, mechanistic, mathematical model of IBD was developed in part 1 of this two-part article series. In this paper, application of the model to predict response of key clinical biomarkers following different treatment options for Crohn's disease was explored. Five therapies, representing four different mechanisms of action, were simulated in the model and longitudinal profiles of key clinical markers, C-reactive protein and fecal calprotectin were compared with clinical observations. Model simulations provided an accurate match with both central tendency and variability observed in biomarker profiles. We also applied the model to predict biomarker and clinical response in an experimental, combination therapy of existing therapeutic options and provide possible mechanistic basis for the increased response. Overall, we present a validated, modular, mechanistic model construct, which can be applied to explore key biomarkers and clinical outcomes in IBD.
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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 OpenQuestion graph.