Virtual patients inspired by multiomics predict the efficacy of an anti-IFNα mAb in cutaneous lupus

Lupus erythematosus is a heterogeneous autoimmune disease that requires treatments tailored to specific patient subsets. To evaluate <i>in silico</i> the efficacy of the anti-IFNα S95021 monoclonal antibody, we created a quantitative systems pharmacology model of cutaneous lupus and a virtual patient population, with attributes matching the diversity of actual patients. To this aim, we performed a multiomics profiling analysis of 337 lupus patients from the PRECISESADS cohort, thereby identifying four patient clusters with distinct immune dysregulation patterns, including various levels of type I interferon (IFN) pathway upregulation. Simulation of S95021 treatment in the virtual patient cohort (<i>n</i> = 241) predicted distinct clinical responses in patient clusters, with machine learning analysis further revealing biomarkers that distinguish predicted responders from non-responders. Combining multiomics profiling of actual patients with mechanistic mathematical modeling supports precision medicine by predicting drug responses based upon patient characteristics in a complex heterogeneous disease.