A Slug Flow Platform with Multiple Process Analytics Facilitates Flexible Reaction Optimization
Abstract Flow processing offers many opportunities to optimize reactions in a rapid and automated manner, yet often requires relatively large quantities of input materials. To combat this, the use of a flexible slug flow reactor, equipped with two analytical instruments, for low‐volume optimization...
| Main Authors: | , , , , , , , |
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| Format: | Article |
| Language: | English |
| Published: |
Wiley
2024-04-01
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| Series: | Advanced Science |
| Subjects: | |
| Online Access: | https://doi.org/10.1002/advs.202308034 |
| _version_ | 1827296636056895488 |
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| author | Florian Wagner Peter Sagmeister Clemens E. Jusner Thomas G. Tampone Vidhyadhar Manee Frederic G. Buono Jason D. Williams C. Oliver Kappe |
| author_facet | Florian Wagner Peter Sagmeister Clemens E. Jusner Thomas G. Tampone Vidhyadhar Manee Frederic G. Buono Jason D. Williams C. Oliver Kappe |
| author_sort | Florian Wagner |
| collection | DOAJ |
| description | Abstract Flow processing offers many opportunities to optimize reactions in a rapid and automated manner, yet often requires relatively large quantities of input materials. To combat this, the use of a flexible slug flow reactor, equipped with two analytical instruments, for low‐volume optimization experiments are reported. A Buchwald–Hartwig amination toward the drug olanzapine, with 6 independent optimizable variables, is optimized using three different automated approaches: self‐optimization, design of experiments, and kinetic modeling. These approaches are complementary and provide differing information on the reaction: pareto optimal operating points, response surface models, and mechanistic models, respectively. The results are achieved using <10% of the material that would be required for standard flow operation. Finally, a chemometric model is built utilizing automated data handling and three subsequent validation experiments demonstrate good agreement between the slug flow reactor and a standard (larger scale) flow reactor. |
| first_indexed | 2024-04-24T14:41:57Z |
| format | Article |
| id | doaj.art-b436edb239a447328f4da96937ddb58e |
| institution | Directory Open Access Journal |
| issn | 2198-3844 |
| language | English |
| last_indexed | 2024-04-24T14:41:57Z |
| publishDate | 2024-04-01 |
| publisher | Wiley |
| record_format | Article |
| series | Advanced Science |
| spelling | doaj.art-b436edb239a447328f4da96937ddb58e2024-04-02T20:51:56ZengWileyAdvanced Science2198-38442024-04-011113n/an/a10.1002/advs.202308034A Slug Flow Platform with Multiple Process Analytics Facilitates Flexible Reaction OptimizationFlorian Wagner0Peter Sagmeister1Clemens E. Jusner2Thomas G. Tampone3Vidhyadhar Manee4Frederic G. Buono5Jason D. Williams6C. Oliver Kappe7Center for Continuous Flow Synthesis and Processing (CC FLOW) Research Center Pharmaceutical Engineering GmbH (RCPE) Inffeldgasse 13 Graz 8010 AustriaCenter for Continuous Flow Synthesis and Processing (CC FLOW) Research Center Pharmaceutical Engineering GmbH (RCPE) Inffeldgasse 13 Graz 8010 AustriaCenter for Continuous Flow Synthesis and Processing (CC FLOW) Research Center Pharmaceutical Engineering GmbH (RCPE) Inffeldgasse 13 Graz 8010 AustriaBoehringer Ingelheim Pharmaceuticals, Inc 900 Ridgebury Road Ridgefield CT 06877 USABoehringer Ingelheim Pharmaceuticals, Inc 900 Ridgebury Road Ridgefield CT 06877 USABoehringer Ingelheim Pharmaceuticals, Inc 900 Ridgebury Road Ridgefield CT 06877 USACenter for Continuous Flow Synthesis and Processing (CC FLOW) Research Center Pharmaceutical Engineering GmbH (RCPE) Inffeldgasse 13 Graz 8010 AustriaCenter for Continuous Flow Synthesis and Processing (CC FLOW) Research Center Pharmaceutical Engineering GmbH (RCPE) Inffeldgasse 13 Graz 8010 AustriaAbstract Flow processing offers many opportunities to optimize reactions in a rapid and automated manner, yet often requires relatively large quantities of input materials. To combat this, the use of a flexible slug flow reactor, equipped with two analytical instruments, for low‐volume optimization experiments are reported. A Buchwald–Hartwig amination toward the drug olanzapine, with 6 independent optimizable variables, is optimized using three different automated approaches: self‐optimization, design of experiments, and kinetic modeling. These approaches are complementary and provide differing information on the reaction: pareto optimal operating points, response surface models, and mechanistic models, respectively. The results are achieved using <10% of the material that would be required for standard flow operation. Finally, a chemometric model is built utilizing automated data handling and three subsequent validation experiments demonstrate good agreement between the slug flow reactor and a standard (larger scale) flow reactor.https://doi.org/10.1002/advs.202308034Buchwald–Hartwig aminationdata‐rich experimentationflow chemistrykineticsself‐optimization |
| spellingShingle | Florian Wagner Peter Sagmeister Clemens E. Jusner Thomas G. Tampone Vidhyadhar Manee Frederic G. Buono Jason D. Williams C. Oliver Kappe A Slug Flow Platform with Multiple Process Analytics Facilitates Flexible Reaction Optimization Advanced Science Buchwald–Hartwig amination data‐rich experimentation flow chemistry kinetics self‐optimization |
| title | A Slug Flow Platform with Multiple Process Analytics Facilitates Flexible Reaction Optimization |
| title_full | A Slug Flow Platform with Multiple Process Analytics Facilitates Flexible Reaction Optimization |
| title_fullStr | A Slug Flow Platform with Multiple Process Analytics Facilitates Flexible Reaction Optimization |
| title_full_unstemmed | A Slug Flow Platform with Multiple Process Analytics Facilitates Flexible Reaction Optimization |
| title_short | A Slug Flow Platform with Multiple Process Analytics Facilitates Flexible Reaction Optimization |
| title_sort | slug flow platform with multiple process analytics facilitates flexible reaction optimization |
| topic | Buchwald–Hartwig amination data‐rich experimentation flow chemistry kinetics self‐optimization |
| url | https://doi.org/10.1002/advs.202308034 |
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