Continuous direct compression: Development of an empirical predictive model and challenges regarding PAT implementation.

In this study, an empirical predictive model was developed based on the quantitative relationships between blend properties, critical quality attributes (CQA) and critical process parameters (CPP) related to blending and tableting. The blend uniformity and API concentration in the tablets were used to elucidate challenges related to the processability as well as the implementation of PAT tools. Thirty divergent ternary blends were evaluated on a continuous direct compression line (ConsiGma™ CDC-50). The trials showed a significant impact of the impeller configuration and impeller speed on the blending performance, whereas a limited impact of blend properties was observed. In contrast, blend properties played a significant role during compression, where changes in blend composition significantly altered the tablet quality. The observed correlations allowed to develop an empirical predictive model for the selection of process configurations based on the blend properties, reducing the number of trial runs needed to optimize a process and thus reducing development time and costs of new drug products. Furthermore, the trials elucidated several challenges related to blend properties that had a significant impact on PAT implementation and performance of the CDC-platform, highlighting the importance of further process development and optimization in order to solve the remaining challenges.

In-depth analysis of the long-term processability of materials during continuous feeding.

This study determined the feasibility of long-term continuous powder feeding and its effect on the overall process performance. Additionally, quantitative relationships were established between material properties, process settings and screw feeding responses during gravimetric feeding. Twelve divergent raw materials were processed over longer periods using a GEA Compact Feeder integrated in a continuous direct compression line (ConsiGma™ CDC-50). The resulting gravimetric feeding responses were combined with the material properties and process settings into an overall PLS model. The model elucidated the impact of the material descriptors for density; powder flow; particle size; compressibility; permeability and wall friction angle on the feeding process. Furthermore, long-term processing of the materials exhibited challenges related to the processability and refill consistency where a significant impact of the compressibility and cohesive/adhesive properties of the materials was observed. Overall, this approach provided insights into the feasibility of long-term continuous feeding which is not possible through 'short-term' feeding trials. Additionally, throughout this study, the need for material-specific adjustments of the feeding and refill equipment was highlighted.

Impact of material properties and process variables on the residence time distribution in twin screw feeding equipment.

Screw feeders are integrated as dispensing units in most continuous manufacturing platforms. Hence, characterizing and modelling the residence time distribution (RTD) of materials in feeders is indispensable to understand the traceability of raw materials from the drum till tablet, enabling the separation of non-confirming material. The proposed methodology addressed this leap in knowledge by characterizing materials, performing RTD trials according to an experimental design, applying RTD models and establishing a partial least square (PLS) regression model that links the material properties and process variables with the RTD responses as outputs. Results showed that RTD in screw feeders can be represented by a combination of plug-flow and mixed-flow. Three variables were found to impact the residence time distribution in feeders: flow rate, hopper level and conditioned bulk density. Interestingly, the plug-flow fraction was not affected by variation in flow rate or material properties. Consequently, simple PLS models could be developed that use density and flow rate to predict RTD at a given hopper level. This approach is powerful for RTD prediction based on bulk density in the early phases of development when control strategies for clinical manufacturing need to be established and material availability is still limited.

Impact of blend properties on die filling during tableting.

Based on characterization of a wide range of fillers and APIs, thirty divergent blends were composed and subsequently compressed on a rotary tablet press, varying paddle speed and turret speed. The tablet weight variability was determined of 20 grab samples consisting of each 20 tablets. Additionally, the bulk residence time, ejection force, pre-compression displacement, main compression force, die fill fraction and feed frame fill fraction were determined during each run. Multivariate data analysis was applied to investigate the relation between the process parameters, blend characteristics, product and process responses. Blends with metoprolol tartrate as API showed high ejection forces. This behavior could be linked to the high wall friction value of metoprolol tartrate. The main responses related to the die filling could be predicted via a PLS model based on blend characteristics. Tablet weight variability was highly correlated with the variability on pre-compression displacement and main compression force. A good predictive model for tablet weight variability was obtained taking the porosity, wall friction angle, flowability, density, compressibility and permeability into account. Additionally, turret speed and paddle speed were included in the calibration of the model. The applied approach can save resources (material, time) during early drug product development.