A CFD Digital Twin to Understand Miscible Fluid Blending.

The mixing of stratified miscible fluids with widely different material properties is a common step in biopharmaceutical manufacturing processes. Differences between the fluid densities and viscosities, however, can lead to order-of-magnitude increase in blend times relative to the blending of single-fluid systems. Moreover, the mixing performance in two-fluid systems can be strongly dependent on the Richardson number defined as the ratio of fluid buoyancy to fluid inertia. In this work, we combine lattice Boltzmann transport algorithms with graphics card-based computing hardware to build accelerated digital twins of a physical mixing tanks. The digital twins are designed to predict real-time fluid mechanics with a fidelity that rivals experimental characterization at orders-of-magnitude less cost than physical testing. After validating the twins against measured single- and multi-fluid mixing data, we use them to explore the physics governing fluid blending in stratified two-fluid systems. We use output from the twins to provide general guidance on stratified two-fluid mixing processes, as well as guidance for building such models for other types of physical systems.

Sensitivity Study to Assess the Robustness of Primary Drying Process in Pharmaceutical Lyophilization.

The objective of this work is to apply a sensitivity study to assess the robustness of the primary drying step of pharmaceutical lyophilization with respect to deviations in process parameters. The sensitivity study can provide valuable information regarding the effect of process input parameters on the product quality that can aid in designing robust lyophilization processes. In this study, the output response is related to its inputs using Smolyak sparse grid generalized polynomial chaos method, and the sensitivity was calculated using elementary effects method. Sensitivity of chamber pressure and shelf temperature on product temperature of 2 sucrose-based and one mannitol-based formulation was studied, and the results were analyzed in terms of risk of adverse effects due to process deviations on the product quality. The study revealed that the sensitivity varies among formulations, and preliminary information regarding the possible impact of process deviations can be obtained from the process cycle diagram. The product temperature showed greater sensitivity toward the change in the shelf temperature than toward change in the chamber pressure for the greater part of the primary drying stage. An aggressive process-deviation scenario at the late stage of primary drying was also studied for different formulations, and the results were consistent with the sensitivity study.

Leveraging Lyophilization Modeling for Reliable Development, Scale-up and Technology Transfer.

Modeling of the lyophilization process, based on the steady-state heat and mass transfer, is a useful tool in understanding and optimizing of the process, developing an operating design space following the quality-by-design principle, and justifying occasional process deviations during routine manufacturing. The steady-state model relies on two critical parameters, namely, the vial heat transfer coefficient, Kv, and the cake resistance, Rp. The classical gravimetric method used to measure Kv is tedious, time- and resource-consuming, and can be challenging and costly for commercial scale dryers. This study proposes a new approach to extract both Kv and Rp directly from an experimental run (e.g., temperature and Pirani profiles). The new methodology is demonstrated using 5% w/v mannitol model system. The values of Kv obtained using this method are comparable to those measured using the classic gravimetric method. Application of the proposed approach to process scale-up and technology transfer is illustrated using a case study. The new approach makes the steady-state model a simple and reliable tool for model parameterization, thus maximizes its capability and is particularly beneficial for transfer products from lab/pilot to commercial manufacturing.

Predictive models of lyophilization process for development, scale-up/tech transfer and manufacturing.

Scale-up and technology transfer of lyophilization processes remains a challenge that requires thorough characterization of the laboratory and larger scale lyophilizers. In this study, computational fluid dynamics (CFD) was employed to develop computer-based models of both laboratory and manufacturing scale lyophilizers in order to understand the differences in equipment performance arising from distinct designs. CFD coupled with steady state heat and mass transfer modeling of the vial were then utilized to study and predict independent variables such as shelf temperature and chamber pressure, and response variables such as product resistance, product temperature and primary drying time for a given formulation. The models were then verified experimentally for the different lyophilizers. Additionally, the models were applied to create and evaluate a design space for a lyophilized product in order to provide justification for the flexibility to operate within a certain range of process parameters without the need for validation.