Leadership Auditorium, 2501 Student Center
Drug discovery is a high-cost, time-consuming, and labor-intensive process. With the declined approval rate for new drugs by the FDA, developing drug repositioning frameworks becomes crucial for improving patient care. Drug repositioning, known as old drugs for new uses, is an effective strategy to find new indications for existing drugs and is highly efficient, low-cost, and less risk. The proposed work is a network-based computational approach for subgroup cohort drug repositioning. Precision medicine is getting more attention to be applied in today’s healthcare system toward a more patient-centered system rather than a disease-based one. The phenotypic and genotypic variation among patients with the same disease required the drug discovery process, including drug repositioning, to be directed more to address the need for precision medicine implementation. In our work, we develop an exploratory data mining approach for subgroup discovery in the context of drug repositioning. A heterogeneous drug knowledge base with TCGA data was used to apply our method. For each subgroup, our approach results in a network with heterogeneous biomedical components, including different drugs that are more related to a given subgroup. Colon adenocarcinoma (COAD) samples was used as a test study. Our method has resulted in 24 subgroups with 17 candidate drugs for repositioning. We found that most of these drugs is prescribed for colon cancer, repositioning to treat this disease, or have the potential to be repurposed for COAD based on literature.