Process knowledge for drug substance production via kinetic modeling, parameter estimability analysis and reaction optimization†

Abstract

A mechanistic model is developed to study the formation of 2,6-difluoropurine-9-THP from starting material 2,6-dichloropurine-9-THP. The 2,6-difluoropurine-9-THP product is an intermediate used in the synthesis of islatravir (MK-8591), a therapy for treatment of HIV. Kinetic parameters are estimated from 26 batch reactor experiments. An error-in-variables-model (EVM) approach is used for parameter estimation to address uncertainty in initial concentrations of trimethylamine (TMA), a gaseous reagent. A parameter subset selection method is used to determine that 33 out of 39 model parameters should be estimated along with 26 uncertain initial concentrations. The remaining six parameters are kept at their initial values to prevent overfitting of available data. EVM parameter estimates are compared with estimates obtained using a traditional weighted-least-squares approach that neglects input uncertainties. The EVM estimates provide a better fit to the data and, as shown using cross-validation, improved accuracy for model predictions. The resulting model and EVM parameter values are used to find reactor conditions that maximize product yield while obeying constraints on temperature, the initial ratio of TMA to starting material, batch time, and the volume of solvent. An optimal yield of 92.04% is predicted, which is higher than the yield of 90.26% at the best experimental conditions in the data set. Contour plots are used to highlight the insensitivity of the optimal yield to batch time and solvent volume, indicating that a yield of 91.83% could be obtained using a 50% lower batch time and 33% less solvent.