A network-driven computational framework for identifying FDA-approved drug repurposing across heterogeneous brain...

Brain cancers are notorious for their heterogeneity, which complicates therapeutic decisions because of recurrently dysregulated signaling pathways. Cancer system characterizations have allowed the identification of some key components involved in brain cancer, such as the epidermal growth factor receptor, BRAF, platelet-derived growth factor receptor alpha, TP53, O6-methylguanine-DNA methyltransferase, cyclin-dependent kinase 1/2/3/4, cyclooxygenase 1/2, vascular endothelial growth factor receptor 2, telomerase reverse transcriptase, and CYP2D6, along with the U87 cell line. These components are the core focus of protocol designs for rational drug selection. For drug repurposing, these designed protocols are generally hypothesized with Food and Drug Administration (FDA)-approved drugs.

In the present study, a protocol was designed to address this complexity using the identified pathway components. These components served as the basis for defining the signatures of small molecules. The set of molecular signatures was then used to develop a network-driven computational framework. Accordingly, two in-house applications were developed, namely, a molecular profile generator called "in-mac" and a network-based database called "ReBrain", derived from FDA-approved drug molecules. In-mac is a computational bioassay platform that generates the activity profiles of small molecules, while ReBrain is a database used for broad-spectrum drug-repurposing analytics. The performance of the profile set was evaluated and validated using five machine-learning models with three different classified datasets.

A total of 2,809 FDA-approved drug molecules (molecular weight ≤500 Da) were profiled using in-mac, and each molecular profile included fifteen-dimensional activity signatures. The profile set was then proven to have significant potential for drug repurposing. The molecular profiles were next used in a regression analysis, followed by the calculation of the intermolecular Euclidean distances and the development of an intermolecular network. The ReBrain platform also enabled in silico knockout or knock-in capabilities for specific pathway components. Finally, network refinement was achieved using the molecular weights and distance thresholds.

The proposed profile-network-based method achieved 70%-95% accuracy for drug repurposing across different disease categories related to the brain. In-mac and ReBrain were used for the repurposing of known drugs for the treatment of brain cancer. As a result, three repurposed drugs were identified as priorities: (i) mefloquine (reference drug: vorasidenib citrate), (ii) clofibric acid (reference drug: carmustine), and armillarisin A (reference drug: lomustine). These results also suggest repurposing candidates for synergistic combinations across different brain tumors. The two applications developed in this work are freely accessible and in the public domain at https://assay.smallmoles.com/escorwin.