Inflammatory Bowel Disease (IBD) QSP Model
Inflammatory Bowel Disease (IBD) QSP Model
Predict Disease Activity Score for Individualized Treatment Responses
A QSP model to predict disease activity score
Certara’s Inflammatory Bowel Disease (IBD) QSP Platform Model is based on a mechanistic multi-state mathematical model recently published in Clinical and Translational Science, to predict disease activity score and treatment responses for individual patients. This model delivers clinical score prediction such as the Mayo score and Crohn’s Disease Activity Index (CDAI). The IBD QSP model helps answer three important questions:
- What is the right target?
- What is the right dose?
- What is the right action?
Certara has developed a machine-learning approach to predict clinical disease activity scores using simulated gut markers from the QSP model. This comprehensive IBD QSP model can explore biological mechanisms and treatment targets. Clinical scores for IBD are generated from subjective questionnaires, and there are no known mechanistic links between gut biology and these scores. Certara’s QSP team has connected gut inflammatory markers with the clinical scores based on an extensive literature review to build a successful predictor. This predictor has also been applied to actual trial data to guide clinical trial design. The IBD QSP Model simulates both Crohn’s disease and ulcerative colitis using the same underlying biological mechanisms.
Outcomes
- Necroptosis
- Gut Tissue Inflammation
- Estimation of clinically measurable biomarkers
- Clinical score prediction
Diseases
- Crohn’s Disease
- Ulcerative Colitis
Treatment Strategies
- Small Molecules
- Anti-cytokine mAb
- Combination therapies
Meet the Experts
Douglas W. Chung, BS, MS
Sr Director, ABS
Douglas W. Chung is a highly experienced scientist and consultant specialized in mechanistic modeling to support drug discovery and development. His background is in biomedical engineering and focus is in quantitative systems pharmacology with over 12 years of experience consulting in biotech and pharmaceuticals. His passion is to grow the field of quantitative pharmacology by expanding diversity in the people, fields of expertise, and clinical trial populations.
Jaehee Shim, PhD
Associate Principal Scientist
Jaehee Shim is an associate principal scientist at Certara. She has extensive experience in modeling across various disease areas, including IBD, MDS, HNSCC, sickle cell disease, lymphoma, neutropenia, thrombocytopenia, and cardiac disease. She trained at the Icahn School of Medicine at Mount Sinai, where she developed a computational approach to combine gene signatures of individual patients with QSP modeling to generate patient-specific predictions for cancer medicine-derived cardiotoxicity. Her interests include exploring computational strategies to extend QSP model predictions to different clinical metrics that may be hard to link mechanistically, by combining modeling with machine learning approaches.
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