Predictive Modeling of Patient Response to JAK/STAT Inhibitors and Dynamic Patient-Matching
TLDR Machine learning can predict how well patients with alopecia areata will respond to certain treatments.
The study focused on alopecia areata (AA), an autoimmune disease causing hair loss, and investigated patient responses to JAK/STAT inhibitors. Researchers used machine learning and ARACNe networks to identify molecular predictors of drug response for four compounds: tofacitinib, ruxolitinib, abatacept, and intralesional triamcinolone. They defined non-responder status based on SALT and ALADIN scores and identified ten candidate master regulators whose activity produced distinct gene signatures. These signatures helped predict treatment efficacy, aiming to create a tool for assessing patient response before treatment.
