Continuous Twin-Screw wet granulation process with In-Barrel drying and NIR setup for Real-Time Moisture Monitoring.

The purpose of this study was to evaluate if wet granule formation and drying could take place in a single operation by utilizing in-barrel drying. The drying kinetics of the formulation were studied in order to select appropriate processing parameters and assess feasibility with short residence times in the extruder. The 18-mm extruder was operated in a 40:1 L:D ratio with 8 zones. The first two zones were used for material feeding and wet granule formation and the remaining zones were used for drying at elevated temperature. The impact of screw configuration as well as screw speed, feed rate, and residence time were all studied to optimize the drying process. Due to limitations of temperature and residence time, vacuum was added to enable sufficient drying. In-line NIR spectroscopy was incorporated into the twin-screw wet granulation (TSWG) process to monitor the moisture content of wet granules in real-time. The set-up was optimized and a predictive model was developed for future experiments. This study demonstrated the success of this technique on a pilot-scale (18-mm) extruder for the first time. Granules were formed and dried to a target loss on drying (LOD) of less than 2 % at moderate temperatures (100 °C - 110 °C) with one single operation. Streamlining wet granulation and drying into one unit operation can have a profound impact on pharmaceutical manufacturing reducing time, footprint, and environmental exposure due to reduced product transfers.

How Does the Dissimilarity of Screw Geometry Impact Twin-screw Melt Granulation?

Using a model formulation of 80% gabapentin and 20% hydroxypropyl cellulose (KlucelTM), we investigate how differences in the geometry of mixing elements in the Leistritz Nano-16 and Micro-18 extruders affect granulation mechanisms and the properties of the resulting granules. Two extruders, Leistritz Nano-16 and Micro-18, commonly used in development and manufacturing, respectively, were used. The kneading blocks of the Nano-16 extruder are less efficient in dispersive mixing than the kneading blocks of the Micro-18 due to the thinner discs (2.5 mm wide) of the Nano-16. Therefore, our model formulation could be granulated only under a higher degree of fill (DF) by enhancing the axial compaction and heating of the barrel. In contrast, the thicker (5 mm wide) kneading blocks of the Micro-18 extruder provide efficient dispersive mixing that enables granulation without axial compaction and barrel heating. The higher specific mechanical energy (SME) achieved at higher screw speeds and lower feed rates led to more granulation. Because of the difference in granulation mechanisms between the two extruders, critical processing parameters also differed. Tabletability and degradant content of granules correlated positively with DF for the Nano-16 but with SME for the Micro-18 extruder.

Twin-screw melt granulation: Current progress and challenges.

Twin-screw melt granulation (TSMG) is a new alternative method for granulation that offers several advantages over wet and dry granulation methods. TSMG has rapidly gained interest over recent years in the pharmaceutical industry. Since it is an inherently continuous process with controlled temperature and shear history, TSMG produces products with more consistent quality than the batch process. Several studies have investigated how various formulation and processing parameters influence granulation behavior and granule properties; however, there are still challenges that require a better mechanistic understanding. This review summarizes the current progress of TSMG while highlighting how various formulation and process parameters affect the physicochemical properties of granules. The challenges related to the process-induced physicochemical changes of drug substances are also discussed.

Application of Deep Learning Convolutional Neural Networks for Internal Tablet Defect Detection: High Accuracy, Throughput, and Adaptability.

Tablet defects encountered during the manufacturing of oral formulations can result in quality concerns, timeline delays, and elevated financial costs. Internal tablet cracking is not typically measured in routine inspections but can lead to batch failures such as tablet fracturing. X-ray computed tomography (XRCT) has become well-established to analyze internal cracks of oral tablets. However, XRCT normally generates very large quantities of image data (thousands of 2D slices per data set) which require a trained professional to analyze. A user-guided manual analysis is laborious, time-consuming, and subjective, which may result in a poor statistical representation and inconsistent results. In this study, we have developed an analysis program that incorporates deep learning convolutional neural networks to fully automate the XRCT image analysis of oral tablets for internal crack detection. The computer program achieves robust quantification of internal tablet cracks with an average accuracy of 94%. In addition, the deep learning tool is fully automated and achieves a throughput capable of analyzing hundreds of tablets. We have also explored the adaptability of the deep learning analysis program toward different products (e.g., different types of bottles and tablets). Finally, the deep learning tool is effectively implemented into the industrial pharmaceutical workflow.

Effect of screw profile and processing conditions on physical transformation and chemical degradation of gabapentin during twin-screw melt granulation.

Twin-screw melt granulation (TSMG) was applied to process a powder blend consisting of 80% gabapentin (GABA) and 20% hydroxypropyl cellulose. The effect of screw profile and processing conditions on the process-induced transformation and chemical degradation of gabapentin was studied. When a neutral kneading block was used, gabapentin underwent polymorphic transformation. A forward kneading block in combination with processing under torque conditions was required to minimize chemical degradation and to inhibit polymorphic transformation of gabapentin. Both the size of the extruded granules and gabapentin degradant level correlated positively with the specific rate, the ratio between feed rate and screw speed. At higher specific rate, the barrel was filled to a greater extent. The material packing and compressive forces were enhanced, as proven by the increased rupturing of CAMES® sensor beads and GABA crystal size reduction. This resulted in more interaction between the powder particles and facilitated granule growth. Simultaneously, this also resulted in higher degradant level. To attain adequate tabletability, the specific rate must reach a threshold value. The development of an optimum TSMG process requires balancing processing parameters based on the physical and chemical stability of GABA as well as its tabletability.

Effects of thermal binders on chemical stabilities and tabletability of gabapentin granules prepared by twin-screw melt granulation.

The effect of thermal binders on the physicochemical properties of gabapentin, a thermally labile drug, in granules prepared using twin-screw melt granulation was investigated in this study. Hydroxypropyl cellulose (HPC), a thermoplastic high molecular-weight binder, was compared against conventional low molecular-weight semi-crystalline thermal binders PEG 8000 and Compritol. Both the chemical degradation and polymorph form change of gabapentin were analyzed. The effects of particle size and molecular weight of HPC on the properties of granules were also studied. To overcome the high melt viscosity of HPC, higher barrel temperatures and higher specific mechanical energy were required to attain suitable granules. As a result, higher levels of gabapentin degradant were observed in HPC-based formulations. However, gabapentin form change was not observed in all formulations. Smaller particle size and lower molecular weight of HPC led to faster granule growth. The tabletability of granules was insensitive to the variations in particle size and molecular weight of HPC. Gabapentin crystal size reduction, HPC size reduction, and HPC enrichment on granule surface were observed for HPC-based granules.