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Related Experiment Video

Updated: Jun 17, 2025

Real-time Monitoring of Reactions Performed Using Continuous-flow Processing: The Preparation of 3-Acetylcoumarin as an Example

Real-time Monitoring of Reactions Performed Using Continuous-flow Processing: The Preparation of 3-Acetylcoumarin as an Example

Published on: November 18, 2015

Simultaneous reaction- and analytical model building using dynamic flow experiments to accelerate process

Peter Sagmeister^1,2, Lukas Melnizky^1,2, Jason D Williams^1,2

¹Institute of Chemistry, University of Graz, NAWI Graz Heinrichstrasse 28 8010 Graz Austria jason.williams@rcpe.at oliver.kappe@uni-graz.at.

Chemical Science

|August 9, 2024

Summary

This summary is machine-generated.

This study introduces a novel dual modeling approach for rapid pharmaceutical process development. It integrates Process Analytical Technology (PAT) with reaction optimization, significantly saving time and resources in Active Pharmaceutical Ingredient (API) synthesis.

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Area of Science:

Pharmaceutical Sciences
Chemical Engineering
Process Chemistry

Background:

Traditional pharmaceutical development involves lengthy, separate processes for reaction and analytical model development.
This often leads to iterative optimization and increased timelines for Active Pharmaceutical Ingredient (API) synthesis.

Purpose of the Study:

To introduce an innovative dual modeling approach integrating Process Analytical Technology (PAT) strategy with reaction optimization.
To expedite the development of synthetic routes and analytical models for APIs.

Main Methods:

A highly automated platform combining PAT strategy and reaction optimization.
Utilizing a "standard addition" approach for PAT calibration.
Employing dynamic experiments for process space screening and kinetic parameter fitting.
Leveraging open-source software for rapid kinetic fitting and in silico optimization.

Main Results:

Successful demonstration in amidation reactions and benznidazole API synthesis.
Achieved PAT calibration with minimal operator involvement.
Rapid kinetic parameter fitting and in silico optimization within minutes.
Significant reduction in process development timelines.

Conclusions:

The dual modeling approach offers a paradigm shift in pharmaceutical process development.
This automated workflow significantly accelerates chemical process understanding and optimization.
The method promises substantial time and resource savings for the pharmaceutical industry.