Calibration, Validation, and Responder Analysis in a Virtual Population of Rheumatoid Arthritis Patients

Abstract: Quantitative and Systems Pharmacology disease models allow one to explore the mechanistic linkages which connect observed biological variability at different scales to differences in clinical response to treatment. These linkages, when balanced by consideration of pre-clinical and clinical data, can generate testable biological and clinical hypotheses which can impact all stages of the drug development process, especially when minimal clinical data is available for a novel treatment modality.
Here, I will discuss a process to generate, calibrate, and validate a collection of virtual patients from a mechanistic model of Rheumatoid Arthritis. The virtual population is designed to represent the heterogeneity in disease state and clinical response, with the ultimate goal of simulation of theoretical clinical trials in which each virtual patient contributes information to the predicted clinical result. This process extends past work in the QSP field to enable (virtual) patient-level analysis of simulated trials with varied clinical response rates, and it shows how they can provide insight into mechanisms which underlie response in both established and novel therapeutic strategies.