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Inflammatory Bowel Disease (IBD) QSP Model

Predict disease activity score to personalize treatment

QSP model to predict disease activity score

The Inflammatory Bowel Disease (IBD) QSP Model is based on a mechanistic multi-state mathematical model to simulate disease mechanisms and predict treatment outcomes. It provides insights into gut tissue inflammation, necroptosis, and clinically measurable biomarkers, enabling researchers to refine therapeutic strategies. By leveraging advanced modeling techniques, this platform provides clinical score predictions such as the Mayo score and Crohn’s Disease Activity Index (CDAI). The model addresses critical questions in treatment strategy, including target selection, dosage optimization, and actionable steps. Certara’s IBD model applies to a range of treatment approaches.

Simulates Crohn’s disease and ulcerative colitis using shared biological mechanisms
Predicts clinical scores through machine-learning integration with gut markers
Guides clinical trial design with data-driven insights

Features of the IBD platform

This figure captures the full list of key biological compartments, cell types, and cytokines in the blood, gut, and lumen. It also captures the key inflammatory dynamics in the gut mucosa including the effects of the cytokines responsible for activating macrophages, dendritic cells, and neutrophils.

IBD QSP platform model

Outcomes

  • Necroptosis
  • Gut Tissue Inflammation
  • Estimation of clinically measurable biomarkers
  • Clinical score prediction

Diseases

  • Crohn’s Disease (CD)
  • Ulcerative Colitis (UC)

Treatment Strategies

  • 小分子药物
  • Anti-cytokine mAb
  • Combination therapies

Speak to a QSP expert

Get in touch with our QSP experts to learn about Certara’s Inflammatory Bowel Disease (IBD) QSP Platform and how it can be applied to support treatments for CD and UC to predict disease activity score.

Predict disease activity scores with precision
Optimize treatment strategies for Crohn’s disease and ulcerative colitis
Enhance clinical trial design with data-driven insights

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