(189l) Filtration Studies Combined with Mechanistic Modelling to Reliable API Process Understanding and Scale-up
- Conference: AIChE Annual Meeting
- Year: 2019
- Proceeding: 2019 AIChE Annual Meeting
- Group: Pharmaceutical Discovery, Development and Manufacturing Forum
Monday, November 11, 2019 - 3:30pm-5:00pm
Unlike the empirical or statistical models, the mechanistic model of a filtration relies on data from specific experiments related with the mathematical description of the physical processes during filtration. Thus, the model provides accurate answers to important questions raised when developing and scaling-up a filtration, namely the cake resistance and the compressibility index, and allows a deeper knowledge of the process.
Our methodology produces experimental data that will support the filtration development and the mechanistic model using DynoChem. Filtration data gathered from previous large-scale runs was used to validate the mechanistic model by direct comparison of key process variables.
We demonstrate the application of this methodology by showing case studies where specific experiments were performed to estimate the filtration scale-independent parameters and scale-up of the process. The examples will show the evolution of filtration processes with high cake resistance during development and the consequent results for the large scale runs, and the using of the models to support the scale-up of filtration processes with accurate predictions and consulting on the best strategies to optimize cycle time and reduce process risk.