- Title Information
- Title
- Accelerating Chemical Synthesis via High-throughput Technologies and Automated Platforms and its Application to Drug Discovery and Molecular Informatics
- Name:
Personal
- Name Part
- Dombroski, Amanda
- Role
- Role Term:
Text
- creator
- Name:
Personal
- Name Part
- Sello, Jason
- Role
- Role Term:
Text
- Advisor
- Name:
Personal
- Name Part
- Bowen, Wayne
- Role
- Role Term:
Text
- Reader
- Name:
Personal
- Name Part
- Creton, Robbert
- Role
- Role Term:
Text
- Reader
- Name:
Personal
- Name Part
- Basu, Amit
- Role
- Role Term:
Text
- Reader
- Name:
Personal
- Name Part
- Peterson, Randall
- Role
- Role Term:
Text
- Reader
- Name:
Corporate
- Name Part
- Brown University. Department of Molecular Pharmacology, Physiology and Biotechnology
- Role
- Role Term:
Text
- sponsor
- Origin Information
- Copyright Date
- 2020
- Physical Description
- Extent
- xxxvii, 1055 p.
- digitalOrigin
- born digital
- Note:
thesis
- Thesis (Ph. D.)--Brown University, 2020
- Genre (aat)
- theses
- Abstract
- Automation has been rapidly changing the research performed in the scientific community. With more high-throughput technologies available to both academic and industrial scientists, improvements to areas such as drug discovery and molecular informatics have been achieved. In the first portion of this work, we use these technologies to design and generate large synthetic libraries for the purpose of discovering medicinal compounds and perform evaluation with high-throughput behavioral profiling using the zebrafish model system. Specifically, we aim to improve drug discovery in the area of neuropharmacology through automation. Many of the top-selling pharmaceuticals are neuroactive small molecules, such as the anxiolytics, antidepressants, and antipsychotics. These compounds, in addition to the general anesthetics, share the same molecular target, the GABA-A receptor. However, the pharmacology of this receptor is not well understood and complicates drug development for GABA-A-related disorders. We synthesize analogs of anesthetics, as well as other classes of promiscuous GABA-A ligands, such as the pyrazoloquinolinones, to use as small molecule probes to better understand the complexities of GABA-A pharmacology. With this approach, novel anesthetics were discovered with comparable potency to marketed anesthetics. Additionally, these analogs may have a more favorable pharmacologic profile which can lead to increased clinical applications for high risk individuals. The ability to serve a broader range of patients without complications can be especially impactful to medical practitioners. A large, diverse library of pyrazoloquinolinones was also synthesized and used to explore polypharmacology of the GABA-A receptor and promiscuity of GABA-A ligands. The findings in this work have allowed a better understanding of the range of pharmacological activity of these promiscuous GABA-A ligands and in turn, the complexities of GABA-A pharmacology. These discoveries could also aid in developing and refining GABA-A-related therapies. In the second portion of this work, chemical libraries were synthesized for applications such as data storage or to perform chemical computations when combined with data science techniques. Small molecules were used in multiple demonstrations to store data in the form of images, which were recovered using instrumental analysis. Various chemical reactions were also exploited to perform chemical computation with case studies in image classification and image processing. With this work, we hope to contribute to the development of molecular data storage systems as alternatives to silicon-based platforms and change the perception of information processing. However, most importantly, this research has shown that we are on the verge of many scientific breakthroughs in both drug discovery and molecular informatics that could change the way therapies are developed or how information processing and storage occurs.
- Subject
- Topic
- Zebrafish Behavior
- Subject
- Topic
- GABA-A receptor
- Subject
- Topic
- Molecular Memory
- Subject (fast)
(authorityURI="http://id.worldcat.org/fast", valueURI="http://id.worldcat.org/fast/00822786")
- Topic
- Automation
- Subject (fast)
(authorityURI="http://id.worldcat.org/fast", valueURI="http://id.worldcat.org/fast/00808702")
- Topic
- Anesthetics
- Subject
- Topic
- Chemical Library Synthesis
- Subject
- Topic
- Chemical Computation
- Subject (fast)
(authorityURI="http://id.worldcat.org/fast", valueURI="http://id.worldcat.org/fast/01036458")
- Topic
- Neuropharmacology
- Subject
- Topic
- High-Throughput Behavioral Analysis
- Subject
- Topic
- Molecular Informatics
- Subject
- Topic
- Polypharmacology
- Subject (fast)
(authorityURI="http://id.worldcat.org/fast", valueURI="http://id.worldcat.org/fast/01110923")
- Topic
- Sedatives
- Subject (fast)
(authorityURI="http://id.worldcat.org/fast", valueURI="http://id.worldcat.org/fast/01133448")
- Topic
- Stimulants
- Subject
- Topic
- Sedation Reversal Agents
- Language
- Language Term (ISO639-2B)
- English
- Record Information
- Record Content Source (marcorg)
- RPB
- Record Creation Date
(encoding="iso8601")
- 20210607
- Type of Resource (primo)
- dissertations