Reductive Aminations and Amidations, a Self-Optimising Reactor

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Project background (identification of the problem and its importance and relevance to sustainability)In the pharmaceutical industry over 90% of products contain nitrogen based functional groups, many of these are in the form of amines and amides. Reductive amidations and direct amidations often require several additives in stoichiometric amounts that reduce overall atom economy. Reducing the waste involved is of significant interest to the chemical industry in the UK.In recent years there has been significant work in developing C-N bond forming reactions with reduced waste by exchanging the classical ketones and aldehydes for carboxylic acids. These acids have the advantage of being more abundant and can be sustainably sourced however are less reactive.Recent work in this area has made use of silanes as an efficient reagent in both direct amidations and reductive aminations. Recently Stoll et al. demonstrated a reductive amination process using phenyl silane and zinc acetate, and while it did show excellent results its performance is limited by an inability to be immobilised for use in flow.1 Morisset et al. developed a direct amidation reaction using only phenyl silane at room temperature which showed significant results and the potential for further development into a flow platform.2 1 E. L. Stoll, T. Tongue, K. G. Andrews, D. Valette, D. J. Hirst and R. M. Denton, Chem. Sci., 2020, 11, 9494-9500.2 E. Morisset, A. Chardon, J. Rouden and J. Blanchet, European J. Org. Chem., 2020, 2020, 388-392.Proposed solution and methodologyProposed solution We propose to develop reductive amination and direct amidation protocols that make use of the dual functionality of silanes as both a coupling agent in direct amidations, and as a reductant in reductive aminations. We intend to deploy these protocols for both as a batch process and in a self-optimising flow reactor. To develop the process for the flow platform we will explore more sustainable polymer-based scaffolds to support and immobilise the silanes, as well as looking at how to regenerate and reuse these supported silanes. We also wish to employ machine learning techniques in order to make predictions around the reaction conditions to gain further understanding and control of the reaction. In addition, we wish to employ our machine learning process to the flow reactor. Methodology We intend to realise our preposed solution by the following steps 1. Develop reductive amination and direct amidation protocols using silanes supported on a polymer2. Explore the scope and limitations of the reactions through empirical and machine learning techniques 3. Further develop polymer scaffold to optimise porosity and retention times4. Build a self-optimising flow platform to use reductive amination and direct amidation protocols5. Apply the reductive amination and direct amidation protocols to exemplar APIs