Determining the Impact of Roller Compaction Processing Conditions on Granule and API Properties

Roller compaction: Measuring ribbon porosity by terahertz spectroscopy and machine learning

Roller compaction is a crucial unit operation in pharmaceutical manufacturing, with its ribbon porosity widely recognised as a critical quality attribute. Terahertz spectroscopy has emerged as a fast and non-destructive technique to measure porosity in pharmaceutical products. From a sensing perspective, the irregular shape and uneven surface of fragmented ribbon pieces can affect the accuracy and precision of the measurements, particularly for techniques that probe only a small sampling volume. It is known that the porosity is not uniform within the ribbon structure, with variations expected across the width of the ribbon and in the microstructure corresponding to its surface texture. However, typical pharmaceutical analysis methods, such as envelope density, only report an average bulk porosity, are slow to operate and limited in accuracy. To address this challenge, we developed and trained convolutional neural network models using THz spectra as input to classify four types of topography typically encountered in ribbons: ridge, valley, flat plane and edge points. The classifiers achieved 91% validation accuracy in both identifying outliers and differentiating between ribbons of smooth and knurled surfaces. For the more challenging task of distinguishing between the ridges and valleys of knurled surfaces, an 81% testing accuracy was achieved. Once each measurement is paired with its topography, resolving the density distribution within the sample is possible. This data can be combined to arrive at an average bulk porosity value compatible with conventional pharmaceutical analysis.

The impacts of roller compaction on the quality attributes of simultaneously compressed micro and minitablets

The challenges of developing good quality low dose minitablets was assessed by systematically studying the effects of ibuprofen (IBU, a model compound) particle sizes (6-58 µm D50) and concentrations (0.1-3 %w/w), roller compaction forces (3-7 kN/cm), and the minitablet sizes (1.2, 1.5 and 2 mm diameter). A novel compression approach, where all three minitablet sizes were simultaneously produced in a single compression run was used. Roller compacted ribbons, granules, minitablets were characterized for physico-mechanical properties and minitablets were also characterized for stratified content uniformity and weight uniformity. The results showed that roll force was the more dominant factor to ribbon solid fraction or tensile strength and granule size enlargement. Minitablets obtained from the granules had good weight uniformity; all but one batch met the criteria. The precise control of tooling lengths across the various sizes was found profoundly important for achieving expected weights, solid fraction, and tensile strength of the simultaneously produced minitablets. The roller compaction process considerably improved the CU variability of the minitablets as compared to the direct compression process. Smaller particle size and higher concentration of IBU, increased roller compaction force, and larger minitablet size improved the potency and content uniformity; however, only the minitablet size was a statistically significant factor in this study. As a product strategic design criterion, a threshold of 25 minitablets in a dosage unit would ensure robust downstream filling and weight verification operations as well as dose accuracy and uniformity (would pass stage 1 criteria). This study results demonstrated feasibility of the novel simultaneous compression approach and the roller compaction process in developing good quality minitablets.

Past, Current, and Future: Application of Image Analysis in Small Molecule Pharmaceutical Development

The often-perceived limitations of image analysis have for many years impeded the widespread application of such systems as first line characterisation tools. Image analysis has, however, undergone a notable resurgence in the pharmaceutical industry fuelled by developments system capabilities and the desire of scientists to characterize the morphological nature of their particles more adequately. The importance of particle shape as well as size is now widely acknowledged. With the increasing use of modelling and simulations, and ongoing developments though the integration of machine learning and artificial intelligence, the utility of image analysis is increasing significantly driven by the richness of the data obtained. Such datasets provide means to circumvent the requirement to rely on less informative descriptors and enable the move towards the use of whole distributions. Combining the improved particle size and shape measurement and description with advances in modelling and simulations is enabling improved means to elucidate the link between particle and bulk powder properties. In addition to improved capabilities to describe input materials, approaches to characterize single components within multicomponent systems are providing scientists means to understand how their material may change during manufacture thus providing a means to link the behaviour of final dosage forms with the particle properties at the point of action. The aim is to provide an overview of image analysis and update readers with innovations and capabilities to other methods in the small molecule arena. We will also describe the use of AI for the improved analysis using image analysis.

Modified Roller Compaction Model to Account for Roll Speed Effect on Powder Compaction in Dry Granulation Process

This study introduces a modification to the roller compaction model proposed by Sousa et al.1 to account for the effect of roll speed on powder compaction in the dry granulation process. The proposed model enhances the prediction accuracy, particularly at higher roll speeds, which are often overlooked in existing models. The modified model is validated using literature data, demonstrating improved performance compared to the original model. Additionally, the model is applied to a pharmaceutical formulation, showing its applicability in an industrial context. The integration of the model into gPROMS allows for global sensitivity analysis and design space exploration, providing valuable insights for process optimization and scale-up. The study contributes to the understanding of roller compaction dynamics and offers a practical tool for decision-making in pharmaceutical manufacturing.

Determining the Impact of Roller Compaction Processing Conditions on Granulate and API Properties: Impact of Formulation API Load

Previous work demonstrated that roller compaction of a 40%w/w theophylline-loaded formulation resulted in granulate consisting of un-compacted fractions which were shown to constitute between 34 and 48%v/v of the granulate dependent on processing conditions. The active pharmaceutical ingredient (API) primary particle size within the un-compacted fraction was also shown to have undergone notable size reduction. The aim of the current work was to test the hypothesis that the observations may be more indicative of the relative compactability of the API due to the formulation being above the percolation threshold. This was done by assessing the impact of varied API loads in the formulation on the non-granulated fraction of the final granulate and the extent of attrition of API particles within the non-granulated fraction. The influence of processing conditions for all formulations was also investigated. The results verify that the observations, both of this study and the previous work, are not a consequence of exceeding the percolation threshold. The volume of un-compacted material within the granulate samples was observed to range between 34.7 and 65.5% depending on the API load and roll pressure, whilst the API attrition was equivalent across all conditions.

A Multi-variate Mathematical Model for Simulating the Granule Size Distribution in Roller Compaction-Milling Process

Granule size distribution (GSD) is one of the critical quality attributes in the roller compaction (RC) process. Determination of GSD for newly developed pharmaceutical compounds with unknown ribbon breakage behaviors at the RC milling step requires a quantitative insight into process parameters and ribbon attributes. Despite its pivotal role in mapping the process operating conditions to achieve desired granule size, limited work has been presented in literature with a focus on RC-milling modeling. In this study, a multi-variate mathematical model is presented to simulate the full size-distribution of granulated ribbons as a function of ribbon mechanical properties. Experimental data with a lab-scale oscillating milling apparatus were generated using ribbons made of various powder compositions. Model parameters were determined by fitting it to experimental data sets. Parameters obtained from the first step were correlated to ribbon Young's modulus. The model was validated by predicting GSD of data that were excluded in model development step. Predictive capabilities of the developed model were further explored by simulating GSD profiles of a granulated pharmaceutical excipient obtained at three different conditions of a real-scale Gerteis RC system. While maintaining the milling operating conditions similar to the lab-scale apparatus (i.e., screen size and spacing, and low rotor speed), the proposed modeling approach successfully predicted the GSD of roller compacted MCC powder as the model compound. This model can be alternatively utilized in conjunction with an RC model in order to facilitate the process understanding to obtain granule attributes as part of Quality-by-Design paradigm.