Uncovering combination therapies for immune-mediated inflammatory diseases through systems biology analysis on longitudinal patient data.

While targeted therapies have reshaped the clinical management of immune-mediated inflammatory diseases (IMIDs), primary non-response remains a major obstacle for many patients. Combining two targeted therapies is an emerging strategy to overcome this therapeutic ceiling, but the number of possible drug pairs makes prioritization difficult. For this objective, we present mitigation of non-response signature (MNRS), a computational approach that uses longitudinal blood transcriptomic data from six IMIDs treated with different targeted therapies to identify the most promising drug combinations. The approach identifies complementary pairs of biologic agents and small molecules, as well as potentially incompatible pairs. In rheumatoid arthritis, anti-TNF and anti-interleukin 6 receptor therapy emerges as highly complementary; single-cell analysis localizes this effect to CD14+ monocytes, and a collagen-induced arthritis mouse model confirms that the combination outperforms monotherapy. These findings show that longitudinal patient data help prioritize drug combinations for clinical testing across immune-mediated diseases.