Process analytical technology (PAT) approach to the formulation of thermosensitive protein-loaded pellets: Multi-point monitoring of temperature in a high-shear pelletization

Abstract

In the literature there are some publications about the effect of impeller and chopper speeds on product parameters. However, there is no information about the effect of temperature. Therefore our main aim was the investigation of elevated temperature and temperature distribution during pelletization in a high shear granulator according to process analytical technology. During our experimental work, pellets containing pepsin were formulated with a high-shear granulator. A specially designed chamber (Opulus Ltd.) was used for pelletization. This chamber contained four PyroButton-TH® sensors built in the wall and three PyroDiff® sensors 1, 2 and 3 cm from the wall. The sensors were located in three different heights. The impeller and chopper speeds were set on the basis of 3 2 factorial design. The temperature was measured continuously in 7 different points during pelletization and the results were compared with the temperature values measured by the thermal sensor of the high-shear granulator. The optimization parameters were enzyme activity, average size, breaking hardness, surface free energy and aspect ratio. One of the novelties was the application of the specially designed chamber (Opulus Ltd.) for monitoring the temperature continuously in 7 different points during high-shear granulation. The other novelty of this study was the evaluation of the effect of temperature on the properties of pellets containing protein during high-shear pelletization.

Development and evaluation of bioadhesive buccal films based on sodium alginate for allergy therapy

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Figure 1 Schematic picture of the self-developed texture analyser. A measures the breaking hardness of films, while B measures the mucoadhesion force of films [ 31 ].

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Development and Characterization of Lysozyme Pellets Prepared by Extrusion and Spheronization Method

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Study of fluid bed granulation processes with new technology approaches

“…The inputs and outputs for both granulation techniques representing the independent and dependent variables, respectively, are summarized in Table 4. The ANN model also provides values of input strengths (weights), which indicate the significance of the effect of each input on the output [39] and predicts the evolution of temperature profiles. The data was divided into three categories, where 70% was used for training, 15% as the test and 15% as the validation randomly selected from the available database.…”

“…ANNs are computer systems developed to mimic the operations of the human brain, by mathematically modelling its neurophysiological structure. In an ANN, the nerve cells are replaced by computational units called neurons, and the strengths of the interconnections are represented by weights [39][40][41]. Using the process control system, quality assurance results, or energy usage data, an ANN develops supervisory set points for the system.…”

“…500 granules of each sample were checked. The aspect ratio (AR) was calculated with the following equation [39]:…”