Combination of a rotating tube sample divider and dynamic image analysis for continuous on-line determination of granule size distribution

High-Speed Imaging-Based Particle Attribute Analysis of Spray-Dried Amorphous Solid Dispersions Using a Convolution Neural Network

Spray drying is a well-established method for preparing amorphous solid dispersion (ASD) formulations to improve the oral bioavailability of poorly soluble drugs. In addition to the characterization of the amorphous phase, particle attributes of spray-dried intermediates (SDIs), including particle size, morphology, and microstructure, need to be carefully studied and controlled for optimizing drug product performance. Although recent developments in microscopy technology have enabled the analysis of morphological attributes for individual SDI particles, a high-throughput method is highly desirable. In this work, a fingerprinting method exploiting high-speed dynamic imaging, laser diffraction (LD), and a convolutional neural network (CNN) was developed to characterize and quantify size and morphological distributions of particles in batches of spray-dried ASDs. This imaging technology enables the generation of hundreds of thousands of single-particle images in a few minutes that are analyzed by both unsupervised and supervised CNN models. The unsupervised data mining analysis demonstrated that a batch of SDI is a mixture of diverse particle subpopulations with varying sizes and morphological attributes. Motivated by this observation, we developed a CNN model that enabled rapid computation of the volumetric composition of the distinct particle subpopulations in a SDI batch, thus generating a morphological fingerprint. We implemented this high-speed imaging-based particle attribute analysis method to investigate SDIs containing hypromellose acetate succinate as a model system. The CNN fingerprint results enabled quantification of the changes in the morphological distribution of SDI batches prepared with variations in the spray drying process parameters, and the results were in line with the LD and electron microscopy data. Our experiments and analysis demonstrate the robustness and throughput of this fingerprinting approach for quantifying particle size and morphological distributions of individual SDI batches, which can help guide spray drying process development and thereby enable the development of a drug product with more robust process and optimized performance.

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

Purpose

In continuous manufacturing of pharmaceuticals, dry granulation is of interest because of its large throughput capacity and energy efficiency. In order to manufacture solid oral dosage forms continuously, valid control strategies for critical quality attributes should be established. To this date, there are no published control strategies for granule size distribution in continuous dry granulation.

Methods

In-line laser diffraction was used to determine the size of granules in a continuous roll compaction/dry granulation line (QbCon® dry). Different process parameters were evaluated regarding their influences on granule size. The identified critical process parameters were then incorporated into control strategies. The uncontrolled and the controlled processes were compared based on the resulting granule size. In both processes, a process parameter was changed to induce a shift in median particle size and the controller had to counteract this shift.

Results

In principle, all process parameters that affect the median particle size could also be used to control the particle size in a dry granulation process. The sieve impeller speed was found to be well suited to control the median particle size as it reacts fast and can be controlled independently of the throughput or material.

Conclusion

The median particle size in continuous roll compaction can be controlled by adjusting process parameters depending on real-time granule size measurements. The method has to be validated and explored further to identify critical requirements to the material and environmental conditions.

Model-Based Scale-Up Methodologies for Pharmaceutical Granulation

In the pharmaceutical industry, it is a major challenge to maintain consistent quality of drug products when the batch scale of a process is changed from a laboratory scale to a pilot or commercial scale. Generally, a pharmaceutical manufacturing process involves various unit operations, such as blending, granulation, milling, tableting and coating and the process parameters of a unit operation have significant effects on the quality of the drug product. Depending on the change in batch scale, various process parameters should be strategically controlled to ensure consistent quality attributes of a drug product. In particular, the granulation may be significantly influenced by scale variation as a result of changes in various process parameters and equipment geometry. In this study, model-based scale-up methodologies for pharmaceutical granulation are presented, along with data from various related reports. The first is an engineering-based modeling method that uses dimensionless numbers based on process similarity. The second is a process analytical technology-based modeling method that maintains the desired quality attributes through flexible adjustment of process parameters by monitoring the quality attributes of process products in real time. The third is a physics-based modeling method that involves a process simulation that understands and predicts drug quality through calculation of the behavior of the process using physics related to the process. The applications of these three scale-up methods are summarized according to granulation mechanisms, such as wet granulation and dry granulation. This review shows that these model-based scale-up methodologies provide a systematic process strategy that can ensure the quality of drug products in the pharmaceutical industry.

Development and Evaluation of an In-line and On-line Monitoring System for Granule Size Distributions in Continuous Roll Compaction/Dry Granulation Based on Laser Diffraction

Purpose

Roll compaction/dry granulation is established in manufacturing of solid oral dosage forms and, within the context of continuous manufacturing, it has sparked interest as material is fed, processed, and ejected continuously while also providing large possible throughputs. However, this amount of material has to be adequately controlled in real time to assure quality.

Methods

This research aimed at monitoring the critical quality attribute granule size distribution in continuous roll compaction/dry granulation (QbCon®; L.B. Bohle, Ennigerloh, Germany) using in-line and on-line laser diffraction. The influence of varying process parameters and excipient formulations was studied and evaluated with the prospect of using this technique to develop control loops. For this purpose, residence time parameters were assessed. In- and on-line data was compared with off-line laser diffraction and dynamic image analysis data.

Results

The system successfully monitored the granule size distribution in a variety of process parameters and throughputs (up to 27.5 kg/h). It was sensitive to changes in process parameters and changes in material blends, which could pose a potential threat to the final drug products’ quality. Average event propagation time from the compaction zone to the laser diffraction system of 17.7 s demonstrates the systems’ fast reaction time.

Conclusion

Results highlight laser diffraction as a valuable method of in- and on-line size determination and allow for the development of a control strategy using this principle.