Development and Application of Laboratory Tools To Predict Particle Properties upon Scale-Up in Agitated Filter-Dryers

Properties of Packed Bed Structures Formed during Filtration: A Two and Three-Dimensional Model

Agglomeration is an issue that causes many problems during secondary processing for pharmaceutical companies, causing material to need further processing and costing additional time and resources to ensure a satisfactory outcome. A potential source of agglomeration arises from the particle contacts established during filtration that lead to robust agglomerates forming during drying, so that a necessary first step toward understanding agglomeration is to study the packing properties of filtration beds. Here, we present two and three-dimensional models simulating the formation of packed bed structures during filtration. The models use circular and spherical particles of different sizes, mimicking the bimodal particle size distributions sometimes encountered in industrial practice. The statistics of packing and void formation, along with the distribution of interparticle contacts and percolation structures, are presented and discussed in the context of filtration, drying, and agglomeration. The model paves the way for predictive capabilities that can lead to the rational design of processes to minimize the impact of agglomeration.

SeDeM as a Tool to Validate Drug Substance Manufacturing Processes and Assess Scalability and Suitability for Direct Compression: Supplier Screening

During the development of an oral solid form of a drug substance, a thorough understanding of the critical material attributes is necessary, as the physical properties of the active pharmaceutical ingredient (API) can profoundly influence the drug product's manufacturability, critical quality attributes, and bioavailability. The objective of this study was to validate the manufacturing process of the drug Linezolid from three different sources at both the pilot and industrial scale and to identify differences in critical material attributes between the API manufacturers. Furthermore, the scalability factor between the pilot and industrial scale and the suitability of a process for direct compression were also evaluated. In the present study, the different sources of API were characterized by SeDeM methodology, particle size distribution, and scanning electron microscopy determinations. The statistical analysis revealed that no statistically significant differences were found for any of the parameters under study for the same API source analyzed on both scales. On the other hand, for most of the parameters evaluated, statistical differences were observed between the different sources. It was concluded that SeDeM was able to successfully validate the API manufacturing process, assess scalability, and distinguish between sources. Therefore, it could be highly valuable in the formulation phase to select the best API source.

A Novel Computational Approach Coupled with Machine Learning to Predict the Extent of Agglomeration in Particulate Processes

Solid particle agglomeration is a prevalent phenomenon in various processes across the chemical, food, and pharmaceutical industries. In pharmaceutical manufacturing, agglomeration is both desired in unit operations like wet granulation and undesired in unit operations such as agitated filter drying of highly potent active pharmaceutical ingredients (API). Agglomeration needs to be controlled for optimal physical properties of the API powder. Even after decades of work in the field, there is still very limited understanding of how to quantify, predict, and control the extent of agglomeration, owing to the complex interaction between the solvent and the solid particles and stochasticity imparted by mixing. Furthermore, a large size of industrial scale particulate process systems makes it computationally intractable. To overcome these challenges, we present a novel theory and computational methodology to predict the agglomeration extent by coupling the experimental measurements of agglomeration risk zone or "sticky zone" with discrete element method. The proposed model shows good agreement with experiments. Further, a machine learning model was built to predict agglomeration extent as a function of input variables, such as material properties and processing conditions, in order to build a digital twin of the unit operation. While the focus of the present study is the agglomeration of particles during industrial drying processes, the proposed methodology can be readily applied to numerous other particulate processes where agglomeration is either desired or undesired.

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

The attrition of drug particles during the process of dry granulation, which may (or may not) be incorporated into granules, could be an important factor in determining the subsequent performance of that granulation, including key factors such as sticking to punches and bio-performance of the dosage form. It has previously been demonstrated that such attrition occurs in one common dry granulation process train; however, the fate of these comminuted particles in granules was not determined. An understanding of the phenomena of attrition and incorporation into granule will improve our ability to understand the performance of granulated systems, ultimately leading to an improvement in our ability to optimize and model the process. Unique feeding mechanisms, geometry, and milling systems of roller compaction equipment mean that attrition could be more or less substantial for any given equipment train. In this work, we examined attrition of API particles and their incorporation into granule in an equipment train from Gerteis, a commonly used equipment train for dry granulation. The results demonstrate that comminuted drug particles can exist free in post-milling blends of roller compaction equipment trains. This information can help better understand the performance of the granulations, and be incorporated into mechanistic models to optimize such processes.

Developing a Batch Isolation Procedure and Running It in an Automated Semicontinuous Unit: AWL CFD25 Case Study

A key challenge during the transition from laboratory/small batch to continuous manufacturing is the development of a process strategy that can easily be adopted for a larger batch/continuous process. Industrial practice is to develop the isolation strategy for a new drug/process in batch using the design of experiment (DoE) approach to determine the best isolation conditions and then transfer the isolation parameters selected to a large batch equipment/continuous isolation process. This stage requires a series of extra investigations to evaluate the effect of different equipment geometry or even the adaptation of the parameters selected to a different isolation mechanism (e.g., from dead end to cross flow filtration) with a consequent increase of R&D cost and time along with an increase in material consumption. The CFD25 is an isolation device used in the first instance to develop an isolation strategy in batch (optimization mode) using a screening DoE approach and to then verify the transferability of the strategy to a semicontinuous process (production mode). A d-optimal screening DoE was used to determine the effect of varying the input slurry. Properties such as solid loading, particle size distribution, and crystallization solvent were investigated to determine their impact on the filtration and washing performance and the characteristics of the dry isolated product. A series of crystallization (ethanol, isopropanol, and 3-methylbutan-1-ol) and wash solvents (n-heptane, isopropyl acetate and n-dodcane) were used for the process. To mimic a real isolation process, paracetamol-related impurities, acetanilide and metacetamol, were dissolved in the mother liquor. The selected batch isolation strategy was used for the semicontinuous isolation run. Throughput and filtration parameters, such as cake resistance and flow rate, cake residual liquid content and composition, cake purity, particle–particle aggregation, and extent and strength of agglomerates, were measured to evaluate the consistency of the isolated product produced during a continuous experiment and compared with the isolated product properties obtained during the batch process development. Overall, the CFD25 is a versatile tool which allows both new chemical entity process development in batch and the production of the active pharmaceutical ingredient in semicontinuous mode using the same process parameters without changing equipment. The isolated product properties gained during the semicontinuous run are overall comparable between samples. The residual solvent content and composition differs between some samples due to filter plate blockage. In general, the mean properties obtained during semicontinuous running are comparable with the product properties simulated using the DoE.