Use this URL to cite or link to this record in EThOS: https://ethos.bl.uk/OrderDetails.do?uin=uk.bl.ethos.782862
Title: High-throughput methods for reaction development using the Mosquito® liquid handling robot
Author: Battersby, David James
ISNI:       0000 0004 7968 4635
Awarding Body: University of Cambridge
Current Institution: University of Cambridge
Date of Award: 2019
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Abstract:
High-throughput technologies have dramatically advanced processes such as drug screening and protein crystallisation. Application of related high-throughput experimentation practices have the potential to revolutionise chemical synthesis. This thesis describes the standardisation and implementation of high-throughput protocols in the Gaunt group using the Mosquito® liquid-handling robot. An automated high-throughput protocol was established for quantitative data generation. The Chan-Lam reaction of an amine and aryl boronates was used as an initial proof-of-concept reaction to standardise the high-throughput protocol. All parts of the protocol were optimised including the creation of an Excel spreadsheet and Mosquito® dosing protocols. Quantitative data was achieved using high-throughput LCMS analysis with internal standards and calibration curves. Data were tested by statistical methods integrated into the Excel Spreadsheet. Using the standardised high-throughput protocol, reaction conditions for the Chan-Lam were assessed using multi-parallel arrays. A total of 30 boronic esters were screened against eight bases, six copper catalysts and two ligands, totaling 5,888 reactions. All reaction components were parameterised using experimental and computational descriptors to generate a predictive model, in collaboration with computational chemists. To rapidly triage reaction conditions, high-throughput TLC was developed. Three transformations were investigated using this technique, reducing the total analysis time to less than 2 hours for 1536-reactions. This study demonstrates the potential of automated equipment in reaction optimisation and discovery.
Supervisor: Gaunt, Matthew Sponsor: EPSRC ; GSK
Qualification Name: Thesis (Ph.D.) Qualification Level: Doctoral
EThOS ID: uk.bl.ethos.782862  DOI:
Keywords: High-throughput ; Synthesis ; Automation
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