Advisory Committee Chair
Brittany N Lasseinge
Advisory Committee Members
Anita Hjelmeland
Lara Ianov
Marek Napierala
Akinyemi I Ojesina
Elizabeth A Worthey
Document Type
Dissertation
Date of Award
2023
Degree Name by School
Doctor of Philosophy (PhD) Heersink School of Medicine
Abstract
Only ten percent of drugs in clinical trials are ultimately approved by the Food and Drug Administration (FDA). With many diseases and disorders needing therapeutic options, computational drug repurposing methodologies are great alternatives to some drug discovery methods due to quicker FDA approval and lower cost. We sought to gain insights to improve the identification of drug repurposing candidates that will be safe and effective to increase the success of computational drug repurposing approaches. In regards to identifying safe drug candidates, pharmacovigilance studies of adverse event case reports identified that women are more likely to experience an adverse event, and men are more likely to experience severe adverse events. We investigated drugs associated with sex-biased adverse events (SBAEs) and identified if there are commonly utilized drug targets and metabolism enzymes. Then, we learned that these common drug targets and metabolism enzymes were more likely to have sex-biased genomic features (i.e., gene expression and gene-regulatory network properties) that could be used in the future to select drug repurposing candidates less likely to cause SBAEs.
Prioritizing effective drug candidates is especially critical for cancers, as it is estimated that only five percent of oncology drugs in clinical trials are FDA approved. To address this challenge, previous cancer studies applied and validated transcriptomic signature reversion, a computational drug repurposing approach. However, it was unclear if different disease-associated gene signature methods for signature reversion resulted in different drug candidates. Therefore, for four low-survival cancers (i.e., glioblastoma [GBM], liver hepatocellular carcinoma [LIHC], lung adenocarcinoma [LUAD], and pancreatic adenocarcinoma [PAAD]), we applied three disease-associated gene signature methods for signature reversion: differential gene expression analysis (DESeq2 & limma) and transfer learning. Across the cancers, we identified several drug candidates from all three approaches that decreased cell growth in the PRISM cancer drug screen and were previously in cancer clinical trials. Overall, we provide evidence of sex-biased genomic features of SBAE-associated drug targets and metabolism enzymes and the benefit of multiple disease-associated gene signatures for transcriptomic signature reversion drug repurposing. These findings will be important for future sex-aware and cancer transcriptomic signature reversion drug repurposing applications.
Recommended Citation
Fisher, Jennifer L., "Application of Machine Learning and Network Approaches to Prioritize Safe and Efficacious Drug Repurposing Candidates" (2023). All ETDs from UAB. 430.
https://digitalcommons.library.uab.edu/etd-collection/430
