Gao Shengqiao, Han Lu, Luo Dan, Xiao Zhiyong, Liu Gang, Zhang Yongxiang, Zhou Wenxia
Beijing Institute of Pharmacology and Toxicology, State Key Laboratory of Toxicology and Medical Countermeasures, Beijing 100850, China.
Beijing Institute of Pharmacology and Toxicology, State Key Laboratory of Toxicology and Medical Countermeasures, Beijing 100850, China.
Pharmacol Res. 2022 Jun;180:106225. doi: 10.1016/j.phrs.2022.106225. Epub 2022 Apr 20.
Analysis of drug-induced expression profiles facilitated comprehensive understanding of drug properties. However, many compounds exhibit weak transcription responses though they mostly possess definite pharmacological effects. Actually, as a representative example, over 66.4% of 312,438 molecular signatures in the Library of Integrated Cellular Signatures (LINCS) database exhibit low-transcriptional activities (i.e. TAS-low signatures). When computing the association between TAS-low signatures with shared mechanism of actions (MOAs), commonly used algorithms showed inadequate performance with an average area under receiver operating characteristic curve (AUROC) of 0.55, but the computation accuracy of the same task can be improved by our developed tool Genetic profile activity relationship (GPAR) with an average AUROC of 0.68. Up to 36 out of 74 TAS-low MOAs were well trained with AUROC ≥ 0.7 by GPAR, higher than those by other approaches. Further studies showed that GPAR benefited from the size of training samples more significantly than other approaches. Lastly, in biological validation of the MOA prediction for a TAS-low drug Tropisetron, we found an unreported mechanism that Tropisetron can bind to the glucocorticoid receptor. This study indicated that GPAR can serve as an effective approach for the accurate identification of low-transcriptional activity drugs and their MOAs, thus providing a good tool for drug repurposing with both TAS-low and TAS-high signatures.
药物诱导表达谱分析有助于全面了解药物特性。然而，许多化合物虽然大多具有明确的药理作用，但转录反应较弱。实际上，作为一个代表性例子，综合细胞信号库（LINCS）数据库中312438个分子特征中超过66.4%表现出低转录活性（即TAS低特征）。在计算TAS低特征与共享作用机制（MOA）之间的关联时，常用算法表现不佳，受试者操作特征曲线下面积（AUROC）平均为0.55，但我们开发的工具基因谱活性关系（GPAR）可提高同一任务的计算准确性，平均AUROC为0.68。在74个TAS低MOA中，GPAR对多达36个进行了良好训练，AUROC≥0.7，高于其他方法。进一步研究表明，与其他方法相比，GPAR从训练样本大小中获益更显著。最后，在对一种TAS低药物曲哌立松的MOA预测的生物学验证中，我们发现了一种未报道的机制，即曲哌立松可与糖皮质激素受体结合。这项研究表明，GPAR可作为一种有效方法，用于准确识别低转录活性药物及其MOA，从而为利用TAS低和TAS高特征进行药物再利用提供一个良好工具。
