Search

A scalable four-step synthesis of molnupiravir from cytidine is described herein. The attractiveness of this approach is its fully chemical nature involving inexpensive reagents and more environmentally friendly solvents such as water, isopropanol, acetonitrile, and acetone. Isolation and purification procedures are improved in comparison to our earlier study as all intermediates can be isolated via recrystallization. The key steps in the synthesis, namely, ester formation, hydroxyamination, and deprotection were carried out on a multigram scale to afford molnupiravir in 36-41% yield with an average purity of 98 wt % by qNMR and 99 area% by HPLC.

Continuous flow chemistry has the potential to greatly improve efficiency in the synthesis of active pharmaceutical ingredients (APIs); however, the optimization of these processes can be complicated by a large number of variables affecting reaction success. In this work, a screening design of experiments was used to compare computational fluid dynamics (CFD) simulations with experimental results. CFD simulations and experimental results both identified the reactor residence time and reactor temperature as the most significant factors affecting product yield for this reaction within the studied design space. A point-to-point comparison of the results showed absolute differences in product yield as low as 2.4% yield at low residence times and up to 19.1% yield at high residence times with strong correlation between predicted and experimental percent yields. CFD was found to underestimate the product yields at low residence times and overestimate at higher residence times. The correlation in predicted product yield and the agreement in identifying significant factors in reaction performance reveals the utility of CFD as a valuable tool in the design of continuous flow tube reactors with significantly reduced experimentation.

Continuous flow chemistry has the potential to greatly improve efficiency in the synthesis of active pharmaceutical ingredients (APIs); however, the optimization of these processes can be complicated by a large number of variables affecting reaction success. In this work, a screening design of experiments was used to compare computational fluid dynamics (CFD) simulations with experimental results. CFD simulations and experimental results both identified the reactor residence time and reactor temperature as the most significant factors affecting product yield for this reaction within the studied design space. A point-to-point comparison of the results showed absolute differences in product yield as low as 2.4% yield at low residence times and up to 19.1% yield at high residence times with strong correlation between predicted and experimental percent yields. CFD was found to underestimate the product yields at low residence times and overestimate at higher residence times. The correlation in predicted product yield and the agreement in identifying significant factors in reaction performance reveals the utility of CFD as a valuable tool in the design of continuous flow tube reactors with significantly reduced experimentation.

In this work, a strategy for grouping compounds into sample mixtures containing solutes with distinct spectral and, optionally, with distinct chromatographic properties, in order to ensure successful solute resolution either chromatographically or with curve resolution methods is proposed. We name this strategy rational design of mixtures (RDM)., Rational design of mixtures for chromatographic peak tracking applications via multivariate selectivity Daniel W. Cook a, Kelson G. Oram b, Sarah C. Rutan a, *, Dwight R. Stoll b a Department of Chemistry, Virginia Commonwealth University, Richmond, VA, 23284, USA b Department of Chemistry, Gustavus Adolphus College, St. Peter, MN, 56082, USA a r t i c l e i n f o Article history: Received 18 September 2018 Received in revised form 24 January 2019 Accepted 27 February 2019 Available online 5 March 2019 Keywords: Multivariate curve resolution Rational design of mixtures Net analyte signal a b s t r a c t Chromatographic characterization and parameterization studies targeting many solutes require the judicious choice of operating conditions to minimize analysis time without compromising the accuracy of the results. To minimize analysis time, solutes are often grouped into a small number of mixtures; however, this increases the risk of peak overlap. While multivariate curve resolution methods are often able to resolve analyte signals based on their spectral qualities, these methods require that the chro-matographically overlapped compounds have dissimilar spectra. In this work, a strategy for grouping compounds into sample mixtures containing solutes with distinct spectral and, optionally, with distinct chromatographic properties, in order to ensure successful solute resolution either chromatographically or with curve resolution methods is proposed. We name this strategy rational design of mixtures (RDM). RDM utilizes multivariate selectivity as a metric for making decisions regarding group membership (i.e., whether to add a particular solute to a particular sample). A group of 97 solutes was used to demonstrate this strategy. Utilizing both estimated chromatographic properties and measured spectra to group these 97 analytes, only 12 groups were required to avoid a situation where two or more solutes in the same group could not be resolved either chromatographically (i