(84c) Computational Fluid Dynamics Study to Develop a Scale-up Strategy for Non-Newtonian Fluids in Pharmaceutical Industry

The generic pharmaceutical industry relies on the speed and rigor in the drug development process. Getting it right the first time requires detailed product and process understanding. Various unit operations like sterilization, milling, mixing, etc. govern the rapidity of the process. Mixing is one such operation, developing a proper technical understanding can yield highly productive results and can reduce experimental work thereby proving to be cost and time effective. This work provides a comprehensive approach for understanding mixing in different equipment for several parameters and scale-up strategy using computational fluid dynamics.

The first question to ask is what comprises good mixing. The judging parameters include the uniform distribution of energy with less dissipation and getting the right CQAs in the samples. The major challenges in mixing can be attributed to non-uniformity and unpredictability in scale-up operations. Computational fluid dynamics (CFD) is an approach that is coming into action in the pharmaceutical industry. CFD Software (ANSYS in this example) can be leveraged to understand mixing profiles and help in analyzing the technical aspects and provide a generalized methodology for the problem.

As the complexity of molecules has increased and with process intensification, the need to develop technical solutions faster and having a standardized approach will support the entire industry to reach its goal of serving people better. This study gives a comparison of equipment based on histograms, contour plots, and vector plots for shear stress, velocity, and molecular viscosity, along with the discrete phase model to draw inferences and conclusions for non-Newtonian fluid systems. Transient simulations are used to determine the particle distribution in the mixing vessel volume being dispersed from a specified location. This helps in finding time to achieve uniformity throughout the volume. Using CFD the problem at lab scale is resolved by determining the CPPs which in turn gives us the ability to scale-up the product in a plant to achieve desired targets.

The impact of this study can be huge, as it can contribute not only to reduce experimental work at lab scale but can also improve visibility in the scale-up plant operations. It further adds as a standardized method for such issues.