Advances in combinatorial chemistry have facilitated the production of large chemical libraries that can be used as tools to discover biological probes and therapeutics. High-throughput screening (HTS) strategies have emerged as the standard method to assess the biological activity of small molecules. These screens involve the individual analysis of each small molecule in multi-well plates, often requiring expensive automated methods and development of robust assays that may not translate to physiologically relevant contexts.
This problem of evaluating large numbers of reagents in physiologically relevant cell and animal models has been addressed for genetic reagents such as RNAi, CRISPR, and cDNA by creating barcoded retroviral libraries that can be used to infect target cells in culture or in animal models. Using these tools, effective reagents can be selected and decoded using a rapid and inexpensive procedure compared to testing of individual reagents one at a time in an arrayed fashion. In order to more efficiently analyze small molecules, a pooled approach would similarly be useful.
This dissertation describes the studies towards developing a pooled screening strategy for small molecules in cellular contexts. Through an initial screen, we set to phenotypically profile small molecule biological activity in a pooled fashion, while simultaneously gain insight about an individual, active molecule’s mechanism of action. I first describe the design of the pooled screen and define the goals necessary for successful application. Next, I outline the steps taken and challenges encountered during the invention of each component of the technology. Finally, I discuss a computational, target-based approach to design small molecules appropriate for future applications of the new screening technology.
| Identifer | oai:union.ndltd.org:columbia.edu/oai:academiccommons.columbia.edu:10.7916/D8NZ8KW9 |
| Date | January 2017 |
| Creators | Yozwiak, Carrie Elizabeth |
| Source Sets | Columbia University |
| Language | English |
| Detected Language | English |
| Type | Theses |
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