ABSTRACT
Continuous Direct Compaction (CDC) has emerged as a promising route towards producing solid dosage forms while reducing material, development time and energy consumption. Understanding the response of powder processing unit operations, especially blenders, is crucial. There is a substantial body of work around how lubrication via batch blender operation affects tablet critical quality attributes such as hardness and tensile strength. But, aside from being batch operations, the design of these blenders is such that they operate with low-shear, low-intensity mixing at Froude number values significantly below 0.4 (Froude number Fr being the dimensionless ratio of inertial to gravitational forces). The present work explores the performance of a mini-blender which has a fundamentally different mode of operation (static vessel with rotating blades around a mixing shaft as opposed to rotating vessel with no mixing shaft). This difference allows a substantially wider operating range in terms of speed and shear (and Fr values). The present work evaluates how its performance compares to other blenders studied in the literature. Tablet compaction data from blends produced at various intensities and regimes of mixing in the mini-blender follow a common trajectory. Model equations from literature are suitably modified by inclusion of the Froude number Fr, but only for situations where the Froude number was sufficiently high (1 < Fr). The results suggest that although a similar lubrication extent plateau is eventually reached it is the intensity of mixing (i.e. captured using the Froude number as a surrogate) which is important for the lubrication dynamics in the mini-blender, next to the number of revolutions. The degree of fill or headspace, on the other hand, is only crucial to the performance of common batch blenders. Testing using alternative formulations shows the same common trend across mixing intensities, suggesting the validity of the approach to capture lubrication dynamics for this system.
ABSTRACT
Recent years have seen the advent of Quality-by-Design (QbD) as a philosophy to ensure the quality, safety, and efficiency of pharmaceutical production. The key pharmaceutical processing methodology of Direct Compression to produce tablets is also the focus of some research. The traditional Design-of-Experiments and purely experimental approach to achieve such quality and process development goals can have significant time and resource requirements. The present work evaluates potential for using combined modelling and experimental approach, which may reduce this burden by predicting the properties of multicomponent tablets from pure component compression and compaction model parameters. Additionally, it evaluates the use of extrapolation from binary tablet data to determine theoretical pure component model parameters for materials that cannot be compacted in the pure form. It was found that extrapolation using binary tablet data - where one known component can be compacted in pure form and the other is a challenging material which cannot be - is possible. Various mixing rules have been evaluated to assess which are suitable for multicomponent tablet property prediction, and in the present work linear averaging using pre-compression volume fractions has been found to be the most suitable for compression model parameters, while for compaction it has been found that averaging using a power law equation form produced the best agreement with experimental data. Different approaches for estimating component volume fractions have also been evaluated, and using estimations based on theoretical relative rates of compression of the pure components has been found to perform slightly better than using constant volume fractions (that assume a fully compressed mixture). The approach presented in this work (extrapolation of, where necessary, binary tablet data combined with mixing rules using volume fractions) provides a framework and path for predictions for multicomponent tablets without the need for any additional fitting based on the multicomponent formulation composition. It allows the knowledge space of the tablet to be rapidly evaluated, and key regions of interest to be identified for follow-up, targeted experiments that that could lead to an establishment of a design and control space and forgo a laborious initial Design-of-Experiments.
