Abstract
This work demonstrates the application of state-of-the-art modeling techniques in pharmaceutical manufacturing for fluid bed granulation at varying scales to successfully predict process conditions and ultimately replace experiments during a technology transfer of five products. We describe a mathematical model able to simulate the time-dependent moisture profile in a fluid bed granulation process. The applicability of this model is then demonstrated by calibrating and validating it over a range of operating conditions, manufacturing scales, and formulations. The inherent capability of the moisture profile to serve as a simple, scale-independent surrogate is shown by the large number of successful scale-ups and transfers. A methodology to use this ‘digital twin’ to systematically explore the effects of uncertainty inherent in the process input and model parameter spaces and their impact on the process outputs is described. Two case studies exemplifying the utilization of the model in industrial practice to assess process robustness are provided. Lastly, a pathway to leverage model results to establish proven acceptable ranges for individual parameters is outlined.
Highlights
Fluid bed granulation (FBG) is a frequently-used formulation step whose benefits typically include improved flowability and compressibility of powders as well as reducing the risk of segregation
We provide a brief overview of the most important equations related to the evaporation rate, ṁ evap, the very core of our model and the location where the model parameters that need to be estimated from experiments have the greatest impact
In order to generalize the applicability of our fluid bed granulation model as much as possible, all four inputs are considered to be of interest, that is, we investigate the effects of the inlet air temperature, the inlet air flow rate, the spray rate of liquid binder, and the inlet air humidity, resulting in a type of “risk fingerprint” for a given system that is indicative of its inherent robustness
Summary
Fluid bed granulation (FBG) is a frequently-used formulation step whose benefits typically include improved flowability and compressibility of powders as well as reducing the risk of segregation. Among the most common approaches are mass-, heat-, and population balance models (PBMs) (Heinrich et al, 2005; Chaudhury et al, 2013; Hu et al, 2008; Muddu et al, 2018; Gupta, 2017), discrete and finite element methods (DEM and FEM), and computational fluid dynamics (CFD) (Sen et al, 2014; Mortier et al, 2011). Modeling studies in literature are mostly concerned with the computation of the evolution of particle size distribution (PSD) as well as granule moisture content typically captured as ‘loss-on-drying’ (LOD), and use granulation time as a process performance metric. On the other hand, are useful to describe the granule moisture trajectories over time as function of process conditions, which include fluidization air flow, inlet air temperature and humidity, binder solution spray rate etc. On the other hand, are useful to describe the granule moisture trajectories over time as function of process conditions, which include fluidization air flow, inlet air temperature and humidity, binder solution spray rate etc. (Heinrich et al, 2005; Hu et al, 2008; Muddu et al, 2018; Djuris et al, 2017)
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