This article describes the process characterization and development of models to inform a process control strategy to prepare (R,R)-epoxy ketone 2, an intermediate in the manufacture of carfilzomib. Model calibration for relevant unit operations and the development of a dynamic integrated flowsheet-level model in gPROMS FormulatedProducts software enabled investigation of the impact of process disturbances and model uncertainties on the critical quality attributes (CQAs) and identification of critical process disturbances and failure modes to guide a process control strategy. The model development was similar to that described in the previous parts of this series, but with the added complexity of comparing two distinct kinetic formulations for the epoxidation reaction. The main CQAs for this process were (1) the conversion of enone 1 (target ≥99.0 mol % conversion) and (2) the purity target for solids prior to cake wash (target ≥97.5% purity by weight). Conversion of enone was not always achieved with the expected disturbances: whereas 99.5% conversion was expected for normal operating conditions, 97.2% conversion was predicted for the worst-case combination of disturbances. The chiral purity of crystalline (R,R)-epoxy ketone 2 was not always achieved with the expected disturbances: 98.2% purity was expected for normal operating conditions, and 96.7% purity was expected for the worst-case combination of disturbances. These analyses allowed for rank ordering of critical process parameters that impact conversion and suitable manipulated variables to develop a robust process control strategy for the manufacturing scheme.

This article details efforts to characterize and develop a process control strategy for the manufacture of enone 2, a carfilzomib drug substance intermediate obtained through a Barbier-type Grignard reaction of morpholine amide 1. This includes the development of a novel mechanistic model for the heterogeneous Barbier-type Grignard reaction. After the model was characterized with laboratory-scale batch experiments, its performance was compared with experimental data collected under continuous operating conditions. Under nominal operating conditions, the experimentally measured conversion of morpholine amide varied from 94.3% to 96.7%, a range that was encompassed by the model. With a mechanistic model validated under continuous operating conditions, relationships between the magnesium charging interval and the variability in conversion of morpholine amide 1 to enone 2 were determined to further explore the experimental design space. The remaining unit operations were subsequently characterized, and the models developed for the individual operations were integrated into a flowsheet-level dynamic process model implemented in the gPROMS FormulatedProducts software. The impact of various process disturbances and model uncertainties on the critical quality attributes were then investigated, and critical process parameters, failure modes, and control strategies to address these disturbances were identified. The process was found to be most sensitive to operational disturbances in the supplied reactants: morpholine amide 1 and 2-bromopropene (2-BP). As 1 is manufactured upstream by the process described in Part 1 of this series, in silico analysis of potential process control strategies focused on manipulation of the 2-BP concentration and flow rate into the primary reactor. Overall, this work highlights the benefits of using mathematical modeling to deepen the understanding of pharmaceutical manufacturing processes and enable integrated unit operations in a continuous manufacturing setting.

This article details process characterization efforts and the development of corresponding process models to inform a process control strategy to produce a carfilzomib drug substance intermediate, morpholine amide 3. Model calibration for relevant unit operations and development of a dynamic integrated flowsheet-level model in gPROMS FormulatedProducts software allowed an investigation of the impact of process disturbances and model uncertainties on critical quality attributes (CQAs) and identification of critical process disturbances and failure modes to guide the process control strategy. The main CQA for this step was the conversion of Boc-D-leucine monohydrate (≥95%). The model was used to ensure that a state of control would be maintained in the presence of disturbances to target process parameters. The process was found to be robust against the analyzed disturbance scenarios, including worst-case combined disturbances. One case study highlights the dynamics of flow blockage for a key reagent, N,N′-carbonyldiimidazole (CDI), resulting from line clogging and the relationship between blockage duration and reaction conversion. The blockage was studied in silico, and the model demonstrated that the acceptance criteria for reaction conversion were met even with a flow rate reduction of 40%. The detrimental impact on the product concentration in the downstream process, however, required modification of the final distillation operation. The revised distillation column operation was demonstrated to address this concern and tolerated variable concentrations of morpholine amide while achieving the target water specification (<0.25 wt %). The results of this in silico analysis were verified with a production-scale demonstration of morpholine amide synthesis at a throughput of 12 kg/day to experimentally evaluate the impact of disturbances on the control strategy and overall performance of the system.

Ultrafiltration and diafiltration (UF/DF) unit operations are widely used for the manufacture of therapeutic antibodies to control drug substance protein concentration, pH, and excipient properties. During UF/DF, molecular interactions and volume

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