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. 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.

为充分挖掘药物候选分子的治疗潜力，全面评估其对所有潜在疾病的有效性至关重要。传统研究方法多将药物作用机制与疾病病理生理学相匹配，但受限于临床前与临床试验的高额成本与缓慢进展。本研究提出一种全新的虚拟计算（in silico）框架，通过对比药物候选物在患者或动物模型中与对照组的基因表达差异，识别药物候选物的新适应症，或对已上市药物进行重定位。该框架整合了覆盖146种疾病的13602份患者样本数据，以及临床前模型中测试的药物候选物数据，并借助神经网络降低噪声、提升检测灵敏度。本研究以S1P1受体调节剂（S1P1 receptor modulator）西尼莫德（cenerimod）为例开展验证，该框架预测其在系统性红斑狼疮（SLE）、银屑病（Psoriasis）、克罗恩病（Crohn’s disease）等免疫相关疾病以及肾移植并发症中具有治疗功效。此外，研究还识别出6个可预测SLE患者最大临床响应的基因，并通过西尼莫德2b期临床试验的RNA测序（RNA-seq）数据完成验证。针对狼疮小鼠模型，研究人员将7周龄雌性MRL/lpr小鼠分为两组，分别给予赋形剂或西尼莫德（给药剂量为0.2 mg/g饲料）干预，直至预设的研究终点——即任一组别出现至少20%的发病率或死亡率时——本研究的终点设定为给药第10周结束。采集西尼莫德与赋形剂处理组小鼠的血液与肾脏组织，提取总RNA，参照试剂盒说明书采用NUGEN Universal Plus mRNA试剂盒（结合PolyA富集及血液样本的珠蛋白去除步骤）制备测序文库，随后在Illumina NextSeq 500 mRNA测序平台上完成测序，研究同时选取年龄匹配的C57BL/6小鼠作为健康对照样本。