Abstract. In this work, we explore the idea of using mathematical models to build design space for the primary drying portion of freeze-drying process. We start by defining design space for freeze-drying, followed by defining critical quality attributes and critical process parameters. Then using mathematical model, we build an insilico design space. Input parameters to the model (heat transfer coefficient and mass transfer resistance) were obtained from separate experimental runs. Two lyophilization runs are conducted to verify the model predictions. This confirmation of the model predictions with experimental results added to the confidence in the insilico design space. This simple step-by-step approach allowed us to minimize the number of experimental runs (preliminary runs to calculate heat transfer coefficient and mass transfer resistance plus two additional experimental runs to verify model predictions) required to define the design space. The established design space can then be used to understand the influence of critical process parameters on the critical quality attributes for all future cycles.
Abstract. Liquid mixing scale-up in pharmaceutical industry has often been based on empirical approach in spite of tremendous understanding of liquid mixing scale-up in engineering fields. In this work, we attempt to provide a model-based approach to scale-up dissolution process from a 2 l lab-scale vessel to a 4,000 l scale vessel used in manufacturing. Propylparaben was used as a model compound to verify the model predictions for operating conditions at commercial scale that would result in similar dissolution profile as observed in lab scale. Geometric similarity was maintained between both of the scales to ensure similar mixing characteristics. We utilized computational fluid dynamics (CFD) to ensure that the operating conditions at laboratory and commercial scale will result in similar power per unit volume (P/V). Utilizing this simple scale-up criterion of similar P/V across different scales, results obtained indicate fairly good reproducibility of the dissolution profiles between the two scales. Utilization of concepts of design of experiments enabled summarizing scale-up results in statistically meaningful parameters, for example −90% dissolution in lab scale at a given time under certain operating conditions will result in 75-88% at commercial scale with 95% confidence interval when P/V is maintained constant across the two scales. In this work, we have successfully demonstrated that scale-up of solid dissolution can be done using a systematic process of lab-scale experiments followed by simple CFD modeling to predict commercial-scale experimental conditions.
scite is a Brooklyn-based organization that helps researchers better discover and understand research articles through Smart Citations–citations that display the context of the citation and describe whether the article provides supporting or contrasting evidence. scite is used by students and researchers from around the world and is funded in part by the National Science Foundation and the National Institute on Drug Abuse of the National Institutes of Health.