Metal-free synthesis of an estetrol key intermediate under intensified continuous flow conditions

Bianchi, Pauline; Dubart, Amaury; Moors, Maurane; Cornut, Damien; Duhirwe, Gilbert; Ampurdanés, Jordi; Monbaliu, Jean‐Christophe M.

doi:10.1039/d3re00051f

Cited by 6 publications

(7 citation statements)

References 60 publications

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“…A similar lab-scale SS setup was used for preparing a key enone intermediate for estetrol production, a new-generation pharmaceutical for birth control (Figure 7). 3 As we were commissioned to develop a metal-free synthesis, the syn- sulfoxide elimination appeared as an opportunity. 25 Confronted with highly active hormonal compounds, we opted for a QM approach to select the most suitable sulfoxide derivative.…”

Section: Superheated Flow Chemistrymentioning

confidence: 99%

“…We rely on an interdisciplinary approach which merges quantum mechanics (QM), physical organic chemistry, and process engineering. While computational approaches typically come a posteriori to rationalize experimental selectivities or guide corrective actions for suppressing byproducts, we have recently begun to increasingly rely on a priori computational intelligence. ,, Our work has converged toward predictive power tailored specifically to flow reactions that combines machine learning (ML) with quantum mechanics (QM). It provided nongeneric, specifically tailored conditions for each couple of reagents, thus drastically facilitating the exploration of the SH space.…”

Section: Introductionmentioning

confidence: 99%

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New Opportunities for Organic Synthesis with Superheated Flow Chemistry

Bianchi,

Monbaliu

2024

Acc. Chem. Res.

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Metrics & MoreArticle Recommendations CONSPECTUS: Flow chemistry has brought a fresh breeze with great promises for chemical manufacturing, yet critical deterrents persist. To remain economically viable at production scales, flow processes demand quick reactions, which are actually not that common. Superheated flow technology stands out as a promising alternative poised to confront modern chemistry challenges. While continuous micro-and mesofluidic reactors offer uniform heating and rapid cooling across different scales, operating above solvent boiling points (i.e., operating under superheated conditions) significantly enhances reaction rates. Despite the energy costs associated with high temperatures, superheated flow chemistry aligns with sustainability goals by improving productivity (process intensification), offering solvent flexibility, and enhancing safety. However, navigating the unconventional chemical space of superheated flow chemistry can be cumbersome, particularly for neophytes. Expanding the temperature/pressure process window beyond the conventional boiling point under the atmospheric pressure limit vastly increases the optimization space. When associated with conventional trial-and-error approaches, this can become exceedingly wasteful, resource-intensive, and discouraging. Over the years, flow chemists have developed various tools to mitigate these challenges, with an increased reliance on statistical models, artificial intelligence, and experimental (kinetics, preliminary test reactions under microwave irradiation) or theoretical (quantum mechanics) a priori knowledge. Yet, the rationale for using superheated conditions has been slow to emerge, despite the growing emphasis on predictive methodologies. To fill this gap, this Account provides a concise yet comprehensive overview of superheated flow chemistry. Key concepts are illustrated with examples from our laboratory's research, as well as other relevant examples from the literature. These examples have been thoroughly studied to answer the main questions Why? At what cost? How? For what? The answers we provide will encourage educated and widespread adoption. The discussion begins with a demonstration of the various advantages arising from superheated flow chemistry. Different reactor alternatives suitable for high temperatures and pressures are then presented. Next, a clear workflow toward strategic adoption of superheated conditions is resorted either using Design of Experiments (DoE), microwave test chemistry, kinetics data, or Quantum Mechanics (QM). We provide rationalization for chemistries that are well suited for superheated conditions (e.g., additions to carbonyl functions, aromatic substitutions, as well as C−Y [Y = N, O, S, C, Br, Cl] heterolytic cleavages). Lastly, we bring the reader to a rational decision analysis toward superheated flow conditions. We believe this Account will become a reference guide for exploring extended chemical spaces, accelerating organic synthesis, and advancing molecular sciences.

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Section: Superheated Flow Chemistrymentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

New Opportunities for Organic Synthesis with Superheated Flow Chemistry

Bianchi,

Monbaliu

2024

Acc. Chem. Res.

Self Cite

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“…These observations led us to develop a quantum chemistryguided assistant, a prototype of which was successfully applied to the development on a large scale of an intensified flow synthesis of a contraceptive pharmaceutical. [16] This prototype used DFT to provide an ideal set of reagents, residence times, and temperatures to reach the target conversion, therefore minimizing waste, spending, and exposure to highly active hormonal substances. Despite its effectiveness, the method had two main drawbacks: suboptimal accuracy of the estimated conditions and taxing computational requirements to access high quality ∆G ‡ information.…”

Section: Angewandte Chemie International Editionmentioning

confidence: 99%

Revisiting the Paradigm of Reaction Optimization in Flow with a Priori Computational Reaction Intelligence

Bianchi,

Monbaliu

2023

Angewandte Chemie

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The use of micro/meso‐fluidic reactors has resulted in both new scenarios for chemistry and new requirements for chemists. Through flow chemistry, large‐scale reactions can be performed in drastically reduced reactor sizes and reaction times. This obvious advantage comes with the concomitant challenge of re‐designing long‐established batch processes to fit these new conditions. The reliance on experimental trial‐and‐error to perform this translation frequently makes flow chemistry unaffordable, thwarting initial aspirations to revolutionize chemistry. By combining computational chemistry and machine learning, we have developed a model that provides predictive power tailored specifically to flow reactions. We show its applications to translate batch to flow, provide mechanistic insight, contribute reagent descriptors, and to synthesize a library of novel compounds in excellent yields after executing a single set of conditions.

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“…These observations led us to develop a quantum chemistry-guided assistant, a prototype of which was successfully applied to the development on a large scale of an intensified flow synthesis of a contraceptive pharmaceutical. [16] This prototype used DFT to provide an ideal set of reagents, residence times, and temperatures to reach the target conversion, therefore minimizing waste, spending, and exposure to highly active hormonal substances. Despite its effectiveness, the method had two main drawbacks: suboptimal accuracy of the estimated conditions and taxing computational requirements to access high quality ΔG ‡ information.…”

Section: Introductionmentioning

confidence: 99%

Revisiting the Paradigm of Reaction Optimization in Flow with a Priori Computational Reaction Intelligence

Bianchi,

Monbaliu

2023

Angew Chem Int Ed

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Metal-free synthesis of an estetrol key intermediate under intensified continuous flow conditions

Abstract: Estetrol is a natural biogenic estrogen with a promising forecast in hormonal-dependent treatments and growing established markets for oral contraception. Approved in 2021 by the European Medicines Agency (EMA) and...

Cited by 6 publications

References 60 publications

New Opportunities for Organic Synthesis with Superheated Flow Chemistry

New Opportunities for Organic Synthesis with Superheated Flow Chemistry

Revisiting the Paradigm of Reaction Optimization in Flow with a Priori Computational Reaction Intelligence

Revisiting the Paradigm of Reaction Optimization in Flow with a Priori Computational Reaction Intelligence

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