Implementing Feedback Granule Size Control in a Continuous Dry Granulation Line Using Controlled Impeller Speed of the Granulation Unit, Compaction Force and Gap Width

A modified mechanistic approach for predicting ribbon solid fraction at different roller compaction speeds

This research investigates the modeling of the pharmaceutical roller compaction process, focusing on the application of the Johanson model and the impact of varying roll speeds from 1 to 15 RPM on predictive accuracy of ribbon solid fraction. The classical Johanson's model was integrated with a dwell time parameter leading to an expression of a floating correction factor as a function of roll speed. Through systematic analysis of the effect of different roll speeds on the solid fraction of ribbons composed of microcrystalline cellulose, lactose, and their blends, corrective adjustment to the Johanson model was found to depend on both roll speed and formulation composition. Interestingly, the correction factor demonstrated excellent correlation with the blend's mechanical properties, namely yield stress (Py) and elastic modulus (E0), representative of the deformability of the powder. Validated by a multicomponent drug formulation with ±0.4-1.3 % differences, the findings underscore the utility of this modified mechanistic approach for precise prediction of ribbon solid fraction when Py or E0 is known for a given blend. Hence, this work advances the field by offering early insights for more accurate and controllable roller compaction operations during late-stage pharmaceutical manufacturing.

Ten years of the manufacturing classification system: a review of literature applications and an extension of the framework to continuous manufacture

The MCS initiative was first introduced in 2013. Since then, two MCS papers have been published: the first proposing a structured approach to consider the impact of drug substance physical properties on manufacturability and the second outlining real world examples of MCS principles. By 2023, both publications had been extensively cited by over 240 publications. This article firstly reviews this citing work and considers how the MCS concepts have been received and are being applied. Secondly, we will extend the MCS framework to continuous manufacture. The review structure follows the flow of drug product development focussing first on optimisation of API properties. The exploitation of links between API particle properties and manufacturability using large datasets seems particularly promising. Subsequently, applications of the MCS for formulation design include a detailed look at the impact of percolation threshold, the role of excipients and how other classification systems can be of assistance. The final review section focusses on manufacturing process development, covering the impact of strain rate sensitivity and modelling applications. The second part of the paper focuses on continuous processing proposing a parallel MCS framework alongside the existing batch manufacturing guidance. Specifically, we propose that continuous direct compression can accommodate a wider range of API properties compared to its batch equivalent.

AI-based Analysis of In-line Process Endoscope images for Real-time Particle Size Measurement in a Continuous Pharmaceutical Milling Process

This paper presents a case study on the first in-line application of AI-based image analysis for real-time pharmaceutical particle size measurement in a continuous milling process. An AI-based imaging system, which utilises a rigid endoscope, was tested for the real-time particle size measurement of solid NaCl powder used as a model API in the range of 200-1000µm. After creating a dataset containing annotated images of NaCl particles, it was used to train an AI model for detecting particles and measuring their size. The developed system could analyse overlapping particles without dispersing air, thus broadening its applicability. The performance of the system was evaluated by measuring pre-sifted NaCl samples with the imaging tool, after which it was installed into a continuous mill for in-line particle size measurement of a milling process. By analysing ∼100 particles/s, the system was able to accurately measure the particle size of sifted NaCl samples and detect particle size reduction when applied in the milling process. The Dv50 values and PSDs measured real-time with the AI-based system correlated well with the reference laser diffraction measurements (<6% mean absolute difference over the measured samples). The AI-based imaging system shows great potential for in-line particle size analysis, which, in line with the latest pharmaceutical QC trends, can provide valuable information for process development and control.

In-line particle size measurement based on image analysis in a fully continuous granule manufacturing line for rapid process understanding and development

The present paper serves as a demonstration how an in-line PAT tool can be used for rapid and efficient process development in a fully continuous powder to granule line consisting of an interconnected twin-screw wet granulator, vibrational fluid bed dryer, and a regranulating mill. A new method was investigated for the periodic in-line particle size measurement of high mass flow materials to obtain real-time particle size data of the regranulated product. The system utilises a vibratory feeder with periodically altered feeding intensity in order to temporarily reduce the mass flow of the material passing in front of the camera. This results in the drastic reduction of particle overlapping in the images, making image analysis a viable tool for the in-line particle size measurement of high mass-flow materials. To evaluate the performance of the imaging system, the effect of several milling settings and the liquid-to-solid ratio was investigated on the product's particle size in the span of a few hours. The particle sizes measured with the in-line system were in accordance with the expected trends as well as with the results of the off-line reference particle size measurements. Based on the results, the in-line imaging system can serve as a PAT tool to obtain valuable real-time information for rapid process development or quality assurance.

Tracking raw material flow through a continuous direct compression line Part I of II: Residence time distribution modeling and sensitivity analysis enabling increased process yield

Continuous manufacturing (CM) offers advantages in quality and space-time yield compared to common batch manufacturing. However, higher yield losses due to the start-up procedure make a broad application uneconomical. This work discusses the possibility of reducing yield losses by adjusting the degree of back-mixing. Back-mixing of nonconforming material from disturbances or start-up will result in the contamination of subsequent material. Therefore, higher degrees of back-mixing cause the discharge of additional material. Choosing an advantageous setting of operational parameters may be a simple way to change the degree of back-mixing. Based on direct compression, this work demonstrates the identification of promising parameters. Therefore, step-change experiments using color-marked material in the feeder, blender, and tablet press quantify the impact of three operational parameters per device. Models for the devices and the entire process result from those measurements. Subsequently, a global variance-based sensitivity analysis identifies the most influential parameters. As a result, adjusting the minimal filling level of the feeder and the rotational feed frame speed of the tablet press reduces back-mixing by more than 30%. At high costs of the raw materials, the resulting savings can significantly improve the economic performance of CM compared to batch manufacturing.