Combining mechanistic modeling with machine learning as a strategy to predict inflammatory bowel disease clinical scores.

ABSTRACT: Disease activity scores are efficacy endpoints in clinical trials of inflammatory bowel disease (IBD) therapies. Crohn's disease activity index (CDAI), Mayo endoscopic score (MES) and Mayo score are frequently used in clinical trials. They rely on either the physician's observation of the inflammatory state of the patient's gastrointestinal tissue alone or combined with the patient's subjective evaluation of general wellbeing. Given the importance of these scores in evaluating the efficacy of drug treatment and disease severity, there has been interest in developing a computational approach to reliably predict these scores. A promising approach is using mechanistic models such as quantitative systems pharmacology (QSP) which simulate the mechanisms of the disease and its modulation by the

SUBMITTER: Shim JV 

PROVIDER: S-EPMC11893853 | biostudies-literature | 2025

REPOSITORIES: biostudies-literature