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. Overall design: For the lupus mouse model, 7-week-old female MRL/lpr mice were treated with vehicle or cenerimod (0.2 mg/g food) until the end of the study, which was predefined as the time point when at least 20% morbidity/mortality was reached in one group (end of treatment week 10). RNA from blood and kidney from cenerimod- and vehicle-treated mice was isolated and prepared for sequencing using NUGEN Universal Plus mRNA kit with PolyA select (and globin depletion for the blood samples) according to protocol and sequenced on Illumina NextSeq 500 mRNA-seq platform. Age matched w C57BL/6 mice were used as healthy reference.