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Predicting subjective or complex clinical outcomes in QSP models: challenges and approaches

Vincent Hurez, DVM, PhD
Senior Scientist at Rosa & Co LLC

Many clinical trials use complex disease activity scores to assess patient response, and the connections between biological components and these scores are often unclear. We explore how QSP modeling supports elucidation of disease pathophysiology and better-informed extrapolation between biological components and disease scores to facilitate prediction of clinical outcomes. Disease scores can be modeled by (1) identifying the components of each disease activity score, (2) formulating a biological rationale for associating specific biomarkers with each score component, and (3) calibrating the proposed function using clinical data from existing therapies. QSP models are valuable tools to integrate existing mechanistic and clinical data. The ability to integrate and generate plausible predictions of standard clinical disease scores in response to novel interventions improves the clinical acceptance and usability of QSP models.