Uncovering the most kinetically influential reaction pathway driving the generation of HCN from oxyma/DIC adduct: a theoretical study

The combination of ethyl (hydroxyimino)cyanoacetate (Oxyma) and diisopropylcarbodiimide (DIC) has demonstrated superior performance in amino acid activation for peptide synthesis. However, it was recently reported that Oxyma and DIC could react to generate undesired hydrogen cyanide (HCN) at 20 °C, raising safety concerns for the practical use of this activation strategy. To help minimize the risks, there is a need for a comprehensive investigation of the mechanism and kinetics of the generation of HCN. Here we show the results of the first systematic computational study of the underpinning mechanism, including comparisons with experimental data. Two pathways for the decomposition of the Oxyma/DIC adduct are derived to account for the generation of HCN and its accompanying cyclic product. These two mechanisms differ in the electrophilic carbon atom attacked by the nucleophilic sp2-nitrogen in the cyclization step and in the cyclic product generated. On the basis of computed “observed” activation energies, ΔGobs⧧, the mechanism that proceeds via the attack of the sp2-nitrogen at the oxime carbon is identified as the most kinetically favorable one, a conclusion that is supported by closer agreement between predicted and experimental 13C NMR data. These results can provide a theoretical basis to develop a design strategy for suppressing HCN generation when using Oxyma/DIC for amino acid activation

Integrating model-based design of experiments and computer-aided solvent design

Computer-aided molecular design (CAMD) methods can be used to generate promising solvents with enhanced reaction kinetics, given a reliable model of solvent effects on reaction rates. Herein, we use a surrogate model parameterised from computer experiments, more specifically, quantum-mechanical (QM) data on rate constants. The choice of solvents in which these computer experiments are performed is critical, considering the cost and difficulty of these QM calculations. We investigate the use of model-based design of experiments (MBDoE) to identify an information-rich solvent set and integrate this within a QM-CAMD framework. We find it beneficial to consider a wide range of solvents in designing the solvent set, using group contribution techniques to predict missing solvent properties. We demonstrate, via three case studies, that the use of MBDoE yields surrogate models with good statistics and leads to the identification of solvents with enhanced predicted performance with few iterations and at low computational cost