A novel translational in-silico indication discovery framework identifies indications and predictive biomarkers for cenerimod

To fully explore a drug candidate's therapeutic potential, assessing its effectiveness across all possible diseases is essential. While traditional approaches match drug mechanisms to disease pathophysiology, they are limited by the high costs and slow progress of preclinical and clinical trials. This study introduces a novel in silico framework to identify new indications for drug candidates or repurpose approved drugs by analyzing their effects on gene expression in patients or animal models compared to controls. The framework integrate data from 13,602 patient samples across 146 diseases with drug candidate tested in preclinical models and use a neural network to reduce noise and improve sensitivity. The framework was exemplified with cenerimod, a S1P1 receptor modulator, which predicted its efficacy in immune-related diseases such as SLE, Psoriasis, and Crohn’s disease and kidney transplantation complications. Additionally, it identified six genes predictive of maximal clinical response in SLE patients, validated using RNA-seq data from a phase 2b cenerimod trial.