The high cost and the long time required to bring drugs into commerce is driving efforts to repurpose FDA approved drugs—to find new uses for which they weren’t intended, and to thereby reduce the overall cost of commercialization, and shorten the lag between drug discovery and availability. In comparison to traditional drug repositioning, which relies on serendipitous clinical discoveries, computational methods can systemize the drug search and facilitate the drug development timeline even further. In this dissertation, I report on the development, testing and application of a promising new approach to drug repositioning.
This novel computational drug repositioning method is based on mining a human functional linkage network for inversely correlated modules of drug and disease gene targets. Functional linkage network is an evidence-weighted network that provides a quantitative measure of the degree of functional association among any set of human genes. The method takes account of multiple information sources, including gene mutation, gene expression, and functional connectivity and proximity of within module genes.
The method was used to identify candidates for treating breast and prostate cancer. We found that (i) the recall rate for FDA approved drugs for breast and (prostate) cancer is 20/20 (10/11), while the rates for drugs in clinical trials were 131/154 and (82/106); (ii) the Area Under the ROC Curve performance substantially exceeds that of two comparable previously published methods; (iii) preliminary in vitro studies indicate that 5/5 identified breast cancer candidates have therapeutic indices superior to that of Doxorubicin in Luminal-A (MCF7) and Triple-Negative (SUM149) breast cancer cell lines. I briefly discuss the biological plausibility of the candidates at a molecular level in the context of the biological processes that they mediate.
In conclusion, our method provides a unique way of prioritizing disease causal genes and identifying drug candidates for repositioning, based on innovative computational method. The method appears to offer promise for the identification of multi-targeted drug candidates that can correct aberrant cellular functions. In particular the computational performance exceeded that of existing computational methods. The approach has the potential to provide a more efficient drug discovery pipeline.
| Identifer | oai:union.ndltd.org:bu.edu/oai:open.bu.edu:2144/19066 |
| Date | 03 November 2016 |
| Creators | Chen, Hsiao-Rong |
| Source Sets | Boston University |
| Language | en_US |
| Detected Language | English |
| Type | Thesis/Dissertation |
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