Dr. Lain Discusses Predictive Modeling for PsO Biologic Selection
Edward (Ted) Lain, MD, MBA, reminded attendees at the Winter Clinical Hawaii 2025 meeting in Waikoloa Village, Hawaii, that a method exists for selecting the best class of biologic for each psoriasis patient.
“I just want to keep this in the public conscience here,” Dr. Lain said. “There is a way to determine personalization of medicine.”
The process involves using predictive models to help determine the right biologic through analysis of genes known to respond to different therapeutic targets: IL-17, IL-23, and TNF alpha.
It starts with capturing keratinocytes with a microneedling device and sending to a laboratory for genetic analysis. Genes were prospectively validated as response classifiers in the STAMP study, based on achieving a 75% improvement in PASI at Week 12. In patients with a Week 0 PASI of 8 or higher, the positive predictive value in the three response classifiers was 85.7% for TNF inhibitors, 92.3% for IL-17 inhibitors, and 93.1% for IL-23 inhibitors.1
“Again, a way for us to personalize and make sure we are choosing the right biologic from the get-go,” Dr. Lain said.
- Bagel J, Wang Y, et al. A Machine Learning-Based Test for Predicting Response to Psoriasis Biologics. SKIN. 2021;5(6):621–638. doi: 10.25251/skin.5.6.5.