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Bayesian Self-Optimization for Telescoped Continuous Flow Synthesis.

Adam D Clayton1, Edward O Pyzer-Knapp2, Mark Purdie3

  • 1Institute of Process Research and Development, Schools of Chemistry & Chemical and Process Engineering, University of Leeds, Leeds, LS2 9JT, UK.

Angewandte Chemie (International Ed. in English)
|November 8, 2022
PubMed
Summary
This summary is machine-generated.

This study introduces an automated continuous flow platform for optimizing multistep chemical syntheses, improving pharmaceutical development. The system achieved an 81% yield in 14 hours for a key pharmaceutical precursor.

Keywords:
Bayesian OptimizationContinuous FlowMachine LearningMedicinal ChemistrySustainable Chemistry

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Area of Science:

  • Chemical Synthesis
  • Process Optimization
  • Pharmaceutical Development

Background:

  • Multistep chemical syntheses are crucial for pharmaceutical development.
  • Individually optimized reactions can be inefficient due to interdependencies.
  • Telescoped reactions require simultaneous optimization for efficiency.

Purpose of the Study:

  • To develop an automated continuous flow platform for simultaneous optimization of telescoped reactions.
  • To apply this platform to a Heck cyclization-deprotection sequence for pharmaceutical precursor synthesis.
  • To enable accurate quantification and understanding of reaction pathways.

Main Methods:

  • Development of an automated continuous flow platform.
  • Application to a Heck cyclization-deprotection reaction sequence.
  • Implementation of multipoint sampling with online High-Performance Liquid Chromatography (HPLC).
  • Integration of Bayesian optimization techniques.

Main Results:

  • Achieved an 81% overall yield for the targeted pharmaceutical precursor.
  • Optimization completed in just 14 hours.
  • Identified a favorable competing pathway for desired product formation.
  • Enabled accurate quantification and in-depth understanding of reaction pathways.

Conclusions:

  • The automated continuous flow platform enables efficient simultaneous optimization of telescoped reactions.
  • This approach significantly accelerates the development of pharmaceutical precursors.
  • Bayesian optimization is effective for complex reaction optimization, revealing new pathways.