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Feedback in Flow for Accelerated Reaction Development.

Brandon J Reizman1, Klavs F Jensen1

  • 1Department of Chemical Engineering, Novartis Center for Continuous Manufacturing, Massachusetts Institute of Technology , Room 66-542A, 77 Massachusetts Avenue, Cambridge, Massachusetts 02139, United States.

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Summary
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Automated feedback in flow microfluidics optimizes chemical reactions by simultaneously adjusting discrete and continuous variables, minimizing experiments and accelerating process development. This approach enhances efficiency and reduces costs in pharmaceutical research.

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Area of Science:

  • Chemical Engineering
  • Process Chemistry
  • Automation

Background:

  • Continuous flow and high-throughput experimentation accelerate pharmaceutical process development and scale-up.
  • Automated feedback in flow offers efficient reaction characterization and optimization, minimizing experimental screening.
  • Current flow feedback systems primarily optimize continuous variables, neglecting discrete parameters like catalysts and solvents, leading to incomplete process knowledge.

Purpose of the Study:

  • To develop and present a novel system and strategy for simultaneously optimizing both discrete and continuous variables in chemical reactions.
  • To demonstrate the application of this automated platform for efficient reaction optimization and knowledge generation.
  • To reduce the time and resources required for chemical process development and scale-up.

Main Methods:

  • Coupling automated feedback with high-throughput reaction screening in droplet flow microfluidics.
  • Utilizing on-demand creation of sub-20 μL droplets with interchangeable reagents and catalysts.
  • Employing a design of experiments (DoE)-based adaptive response surface algorithm for optimization and analysis via LC/MS.

Main Results:

  • Successful optimization of discrete variables (solvent, catalyst, ligand) and continuous variables (temperature, concentration) simultaneously.
  • Identification of optimal reaction conditions for solvent selection in alkylation and catalyst-ligand selection in Suzuki-Miyaura cross-couplings.
  • Demonstration of rapid convergence and reduced experimental count through deductive reagent removal.

Conclusions:

  • Automated feedback in droplet flow microfluidics enables efficient, simultaneous optimization of discrete and continuous reaction variables.
  • This technology accelerates chemical process development, reduces experimental costs, and provides deeper mechanistic insights.
  • Future integration with chemoinformatics and machine learning promises further advancements in automated synthesis and drug discovery.