Linking molecular feature space and disease terms for the immunosuppressive drug rapamycin

Next to development of novel drugs also drug repositioning appears promising for tackling unmet clinical needs. Here Omics provided the ground for novel analysis strategies for linking drug and disease by integrating profiles on the molecular as well as the clinical data level. We developed a workflow for linking drugs and diseases for identifying repositioning options, and exemplify the procedure for the immunosuppressive drug rapamycin. Our strategy rests on delineating a drug-specific molecular profile by combining Omics data reflecting the drug's impact on the cellular status as well as drug-associated molecular features extracted from the scientific literature. For rapamycin the respective profile held 905 unique molecular features reflecting defined molecular processes as identified by molecular pathway and process enrichment analysis. Literature mining identified 419 diseases significantly associated with this rapamycin molecular feature list, and transforming the significance of gene-disease associations into a continuous score allowed us to compute ROC and precision-recall for comparing this disease list with diseases already undergoing clinical trials utilizing rapamycin. The AUC of this assignment was computed as 0.84, indicating excellent recovery of relevant disease terms solely based on the drug molecular feature profile. We verified relevant indications by comparing molecular feature sets characteristic for the identified diseases to the drug molecular feature profile, demonstrating highly significant overlaps. The presented workflow allowed positive identification of diseases associated with rapamycin utilizing the drug-specific molecular feature profile, and may be well applicable to other drugs of interest.