Commercial scale validation of a process scale-up model for lubricant blending of pharmaceutical powders

A modified Kushner-Moore approach to characterising small-scale blender performance impact on tablet compaction

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.

Using AI/ML to predict blending performance and process sensitivity for Continuous Direct Compression (CDC)

Utilising three artificial intelligence (AI)/machine learning (ML) tools, this study explores the prediction of fill level in inclined linear blenders at steady state by mapping a wide range of bulk powder characteristics to processing parameters. Predicting fill levels enables the calculation of blade passes (strain), known from existing literature to enhance content uniformity. We present and train three AI/ML models, each demonstrating unique predictive capabilities for fill level. These models collectively identify the following rank order of feature importance: RPM, Mixing Blade Region (MB) size, Wall Friction Angle (WFA), and Feed Rate (FR). Random Forest Regression, a machine learning algorithm that constructs a multitude of decision trees at training time and outputs the mode of the classes (classification) or mean prediction (regression) of the individual trees, develops a series of individually useful decision trees. but also allows the extraction of logic and breakpoints within the data. A novel tool which utilises smart optimisation and symbolic regression to model complex systems into simple, closed-form equations, is used to build an accurate reduced-order model. Finally, an Artificial Neural Network (ANN), though less interrogable emerges as the most accurate fill level predictor, with an r2 value of 0.97. Following training on single-component mixtures, the models are tested with a four-component powdered paracetamol formulation, mimicking an existing commercial drug product. The ANN predicts the fill level of this formulation at three RPMs (250, 350 and 450) with a mean absolute error of 1.4%. Ultimately, the modelling tools showcase a framework to better understand the interaction between process and formulation. The result of this allows for a first-time-right approach for formulation development whilst gaining process understanding from fewer experiments. Resulting in the ability to approach risk during product development whilst gaining a greater holistic understanding of the processing environment of the desired formulation.

Effect of particle size on the dispersion behavior of magnesium stearate blended with microcrystalline cellulose

The majority of tablets manufactured contain lubricants to reduce friction during ejection. However, especially for plastically deforming materials, e.g., microcrystalline cellulose (MCC), the internal addition of lubricants is known to reduce tablet tensile strength. This reduction is caused by the surface coverage by lubricant particles, the extent of which depends on both process and formulation parameters. Previously published models to predict the lubrication effect on mechanical strength do not account for changes in the excipient particle size. In this study, the impact of both lubricant concentration and mixing time on the tensile strength of tablets consisting of three different grades of MCC and four grades of magnesium stearate (MgSt) was evaluated. By taking into account the particle size of the applied excipients, a unifying relationship between the theoretically estimated surface coverage and compactibility reduction was identified. Evaluating the dispersion kinetics of MgSt as a function of time reveals a substantial impact of the initial surface coverage on the dispersion rate, while the minimal tensile strength was found to be comparable for the majority of formulations. In summary, the presented work extends the knowledge of lubricant dispersion and facilitates the reduction of necessary experiments during the development of new tablet formulations.

Understanding the role of magnesium stearate in lowering punch sticking propensity of drugs during compression

The sticking of active pharmaceutical ingredient (API) to the surfaces of compaction tooling, frequently referred to as punch sticking, causes costly downtime or product failures in commercial tablet manufacturing. Magnesium stearate (MgSt) is a common tablet lubricant known to ameliorate the sticking problem, even though there exist exceptions. The mechanism by which MgSt lowers punch sticking propensity (PSP) by covering API surface is sensible but not yet experimentally proven. This work was aimed at elucidating the link between PSP and surface area coverage (SAC) of tablets by MgSt, in relation to some key formulation properties and process parameters, namely MgSt concentration, API loading, API particle size, and mixing conditions. The study was conducted using two model APIs with known high PSPs, tafamidis (TAF) and ertugliflozin-pyroglutamic acid (ERT). Results showed that PSP decreases exponentially with increasing SAC by MgSt. The composition of material stuck to punch face was also explored to better understand the onset of punch sticking and the impact of possible MgSt-effected punch conditioning event.

Prediction of the impact of lubrication on tablet compactibility

The tableting of most pharmaceutical formulations requires the addition of lubricants to reduce ejection forces, prevent tooling damage and tablet defects. The internal addition of lubricants is known to reduce tablet tensile strength, especially of mainly plastically deforming materials. To date, available models show only limited quantitative predictive accuracy for the influence of lubricant concentration on the mechanical strength of tablets. This study aims to fill this gap and present a model based on the Ryshkewitch-Duckworth equation that can estimate the compactibility profiles of lubricated formulations. Binary mixtures of different diluents (microcrystalline cellulose and lactose) were prepared with common lubricants (magnesium stearate and sodium stearyl fumarate) and subsequently tableted. The resulting compactibility profiles were fitted using the Ryshkewitch-Duckworth equation and the derived fit parameters (k_b and σ₀) were correlated with the lubricant concentration. Subsequently, an empirical model was established which requires a minimum of experimental data and is able to predict the tensile strength of lubricated diluent tablets. Consequently, the developed empirical model is an interesting and valuable addition to the existing multi-component compacting models available and offers the opportunity to accelerate experimentation in the development of new tablet formulations.

Impact of Vertical Blender Unit Parameters on Subsequent Process Parameters and Tablet Properties in a Continuous Direct Compression Line

The continuous manufacturing of solid oral-dosage forms represents an emerging technology among the pharmaceutical industry, where several process steps are combined in one production line. As all mixture components, including the lubricant (magnesium stearate), are passing simultaneously through one blender, an impact on the subsequent process steps and critical product properties, such as content uniformity and tablet tensile strength, is to be expected. A design of experiment (DoE) was performed to investigate the impact of the blender variables hold-up mass (HUM), impeller speed (IMP) and throughput (THR) on the mixing step and the subsequent continuous manufacturing process steps. Significant impacts on the mixing parameters (exit valve opening width (EV), exit valve opening width standard deviation (EV SD), torque of lower impeller (T_L), torque of lower impeller SD (T_L SD), HUM SD and blend potency SD), material attributes of the blend (conditioned bulk density (CBD), flow rate index (FRI) and particle size (d₁₀ values)), tableting parameters (fill depth (FD), bottom main compression height (BCH) and ejection force (EF)) and tablet properties (tablet thickness (TT), tablet weight (TW) and tensile strength (TS)) could be found. Furthermore, relations between these process parameters were evaluated to define which process states were caused by which input variables. For example, the mixing parameters were mainly impacted by impeller speed, and material attributes, FD and TS were mainly influenced by variations in total blade passes (TBP). The current work presents a rational methodology to minimize process variability based on the main blender variables hold-up mass, impeller speed and throughput. Moreover, the results facilitated a knowledge-based optimization of the process parameters for optimum product properties.

Experimental investigation and modelling of tensile strength of pharmaceutical tablets based on shear force applied by feed frame paddles

The feed frame is an essential device used in a rotary tablet press and it improves the performance of the powder filling process into dies. However, the feed frame affects critical quality attributes such as a tensile strength and a dissolution negatively due to a shear applied to powders from feed frame paddles, leading to over-lubrication. This effects may be significant for shear sensitive materials. The work focuses on the effect of tablet press parameters (die disk speed and feed frame speed) and mixture composition (amount of magnesium stearate) on the tensile strength and the prediction of the tensile strength by considering the extent of shear. It is found that within the investigated range of tablet press parameters and the amount of magnesium stearate, the feed frame speed and the amount of magnesium stearate have an impact on the tensile strength. Furthermore, a lubrication model based on the extent of shear is presented to predict the decreasing trend of the tensile strength of tablets during tableting process and the results demonstrate that the prediction of tensile strength is in good agreement with experimental measurements.

Mixing Cell: a Device to Mimic Extent of Lubrication and Shear in Continuous Tubular Blenders

Continuous manufacturing (CM) has clear potential for manufacturing solid oral dosages. It provides several advantages that may aid the manufacturing and quality of drug products. However, one of the main challenges of this technology is the relatively large amount of knowledge required and the amounts of material needed to develop the process during the early stages of development. Early process development evaluations of continuous manufacturing equipment often require larger amounts of material compared with batch, which hinder CM prospect for drugs during the early stages of process development. In this work, a small-scale evaluation of the mixing process occurring in a continuous mixing system was performed. The evaluation involved the use of a small-scale "mixing cell" which was able to replicate the lubrication process of a continuous mixer. It is worth mentioning that we designed the mixing cell by reconfiguration of an existing continuous tubular blender. The extent of lubrication evaluation was performed for three example formulations and was done by mimicking the amount of shear provided to a formulation by means of matching the number of paddle-passes that a formulation experiences within a continuous blending process in the batch mixing cell. The evaluation showed that the small-scale mixing cell was able to replicate the extent of lubrication-evaluated by measuring the tensile strength of compacts being made with both the continuous and mixing cell experiments-occurring in the continuous mixer using a fraction of the amount of materials needed to perform the same evaluation in the continuous blending process.

Combination of Colloidal Silicon Dioxide with Spray-Dried Solid Dispersion to Facilitate Discharge from an Agitated Dryer

A feasibility evaluation of the addition of fumed silica (SiO₂) into an agitated dryer to aid spray-dried solid dispersion intermediate (SDSDi) flow during secondary drying and discharge is described. The quantity of SiO₂ to enhance the flow character of SDSDi was assessed by measuring particle size distribution, bulk density, and flow-through-an-orifice. Results indicate that the addition of the SiO₂ did not alter the drying kinetics and did not impact the bulk particle size distribution of the SDSDi. While bulk density of SDSDi increased with the addition of SiO₂, the flow, and thus the recovery of the SDSDi-SiO₂ batch from the secondary dryer, was significantly higher than that for the intermediate alone. Imaging indicated uniform distribution of SiO₂ in the bulk powder and coating on intermediate particles. Premixing and co-sieving of the SiO₂ with a portion of pre-dry SDSDi promotes the uniform distribution of SiO₂ within the bulk powder bed.

Scale-Up of Lubricant Mixing Process by Using V-Type Blender Based on Discrete Element Method

A method for scale-up of a lubricant mixing process in a V-type blender was proposed. Magnesium stearate was used for the lubricant, and the lubricant mixing experiment was conducted using three scales of V-type blenders (1.45, 21 and 130 L) under the same fill level and Froude (Fr) number. However, the properties of lubricated mixtures and tablets could not correspond with the mixing time or the total revolution number. To find the optimum scale-up factor, discrete element method (DEM) simulations of three scales of V-type blender mixing were conducted, and the total travel distance of particles under the different scales was calculated. The properties of the lubricated mixture and tablets obtained from the scale-up experiment were well correlated with the mixing time determined by the total travel distance. It was found that a scale-up simulation based on the travel distance of particles is valid for the lubricant mixing scale-up processes.