Conceptual quantitative systems pharmacology framework for supporting clinical trial design in organ-specific autoimmune and rare diseases

Organ-specific autoimmune and rare inflammatory diseases present significant challenges for clinical trial design due to profound patient heterogeneity, small population sizes, and complex tissue-specific pathophysiology. To address these hurdles, this study proposes a conceptual Quantitative Systems Pharmacology (QSP) framework tailored to support end-to-end clinical trial design in this high-need area. The framework integrates multi-source data, including preclinical, omics, and real-world evidence, via Bayesian methods to inform prior distributions for model parameters. A core multiscale mechanistic model links intracellular signaling, cellular dynamics, and tissue-level pathology to simulate disease progression and drug effects. Virtual patient populations are generated by sampling from Bayesian posteriors, capturing real-world biological heterogeneity. These cohorts then undergo in silico clinical trial simulations to evaluate and optimize key design elements, such as dosing regimens, endpoint selection, patient stratification, and adaptive strategies, prior to real-world implementation. By providing a structured, disease-agnostic workflow, the framework enables rational decision-making for dose optimization, biomarker identification, and patient enrichment. It addresses critical bottlenecks in drug development for these complex diseases, offering a powerful tool to de-risk trials and improve the efficiency and success rate of clinical development programs.