Integrated continuous melt granulation-based powder-to-tablet line: process investigation and scale-up on the same equipment

Material-sparing degradation-kinetics model for thermolabile drug stability assessment during twin-screw melt granulation - Insights with gabapentin

Twin-Screw Melt Granulation (TSMG) has emerged as a viable manufacturing process for continuous granulation due to short residence time (<1 min) while maintaining drug crystallinity. However, TSMG relies on heat and shear, making it challenging to process thermolabile drugs like gabapentin (GABA). The absence of a material-sparing model to evaluate TSMG feasibility for such drugs has limited adoption. This study aims to develop an empirical model based on the degradation kinetics of thermolabile drugs. In this work, GABA-HPC (Hydroxypropylcellulose) model system with gabalactam (GABA-L) as degradant was selected. In-process sampling using screw pullout indicated GABA degradation followed different rates in pre-granulation (before kneading zone) and granulation sections(kneading zone to discharge) due to the granule growth and attrition downstream of kneading zone. Leveraging DSC and ball-milling to simulate the stresses encountered during TSMG, a model was developed linking the GABA degradation kinetics of milled and unmilled blends to granule temperature and residence time, providing a comprehensive approach for calculating GABA-L. One key finding was that only 10-30 % GABA-L degraded in pre-granulation while majority of degradation occurred in the granulation section despite its shorter residence time (30-35 % of total) highlighting the importance of a deeper mechanistic understanding of TSMG. The combined model encapsulating both sections showed promise as predicted %GABA-L correlated well with actual %GABA-L after TSMG (R2 = 0.927) for various granulation runs conducted with 30° and 60° kneading block configurations. This work represents one of the first attempts at developing a material-sparing model for feasibility evaluation studies for TSMG of thermolabile drugs.

Optimizing twin-screw melt granulation: The role of overflight clearance on granulation behavior

Twin-screw melt granulation (TSMG) relies on the dispersive and distributive mixing at the kneading zone for granule growth to happen highlighting the critical role played by the kneading elements in TSMG. Despite extensive research conducted on the impact of screw geometry in melt compounding, there is not enough literature for TSMG. Disc width for the kneading elements was 2 mm, contrary to the standard 5 mm. The objective of this study was to evaluate if varying overflight clearance (OC) can alter the paradigm for TSMG. The new elements reduce the peak shear at kneading zone however a higher barrel temperature and degree of fill (DoF) is required to compensate to attain similar granule attributes. The change in DoF was achieved through a combination of modified screw configuration to pre-densify powders before kneading and processing at a lower screw speed. Despite the higher barrel temperature, process optimization of thermally unstable gabapentin was carried out. Using the new elements, compressible granules (Tensile strength > 2 MPa) with low % GABA-L content were manufactured despite increasing OC to 0.4 mm. Granule stability at 40 °C, ambient humidity for 6 months indicated gabapentin was stable (% GABA-L ≪0.4 %) despite a high barrel temperature of 120 °C.

Development of a PAT platform for the prediction of granule tableting properties

In this work, the feasibility of implementing a process analytical technology (PAT) platform consisting of Near Infrared Spectroscopy (NIR) and particle size distribution (PSD) analysis was evaluated for the prediction of granule downstream processability. A Design of Experiments-based calibration set was prepared using a fluid bed melt granulation process by varying the binder content, granulation time, and granulation temperature. The granule samples were characterized using PAT tools and a compaction simulator in the 100-500 kg load range. Comparing the systematic variability in NIR and PSD data, their complementarity was demonstrated by identifying joint and unique sources of variation. These particularities of the data explained some differences in the performance of individual models. Regarding the fusion of data sources, the input data structure for partial least squares (PLS) based models did not significantly impact the predictive performance, as the root mean squared error of prediction (RMSEP) values were similar. Comparing PLS and artificial neural network (ANN) models, it was observed that the ANNs systematically provided superior model performance. For example, the best tensile strength, ejection stress, and detachment stress prediction with ANN resulted in an RMSEP of 0.119, 0.256, and 0.293 as opposed to the 0.180, 0.395, and 0.430 RMSEPs of the PLS models, respectively. Finally, the robustness of the developed models was assessed.