Development of Screening Methodology for the Assessment of the Agglomeration Potential of APIs

Evaluating Resonant Acoustic Mixing as a Wet Granulation Process

Control of powder properties is crucial for industrial processes across the food, pharmaceutical, agriculture, and mineral processing industries, and granulation is an important tool for providing agglomerated particles with controllable properties. However, existing granulation processes are not readily integrated with other processing steps and are not appropriate for some types of materials. Adding resonant acoustic-based granulation to the toolkit has the potential to widen the achievable parameter space and, importantly, integrate granulation into chemistry and blending operations that are already being performed on the RAM platform, resulting in process intensification. Here, we demonstrate the formation of granules with particle sizes of ca. 1-3 mm in LabRAM II and examine the formation mechanisms in the context of common wet granulation processes. The RAM granulation process followed here involves first forming a large "doughball" agglomerate and then driving its breakup by evaporating the solvent, while impacting the doughball against the container walls. We show that this process is similar to the destructive nucleation model for high-shear wet granulation with the solvent evaporation in our case leading to the decrease in the liquid saturation of the doughball, a corresponding decrease in its tensile strength, and the acceleration in the RAM establishing the impact pressure when the doughball contacts the walls. This work provides a foundation for granulation process design with a resonant acoustic mixer and, through its link to existing granulation mechanisms, provides a path to a deeper understanding of the process.

Digital Design of Filtration and Washing of Active Pharmaceutical Ingredients via Mechanistic Modeling

To facilitate integrated end-to-end pharmaceutical manufacturing using digital design, a model capable of transferring material property information between operations to predict product attributes in integrated purification processes has been developed. The focus of the work reported here combines filtration and washing operations used in active pharmaceutical ingredient (API) purification and isolation to predict isolation performance without the need of extensive experimental work. A fixed Carman-Kozeny filtration model is integrated with several washing mechanisms (displacement, dilution, and axial dispersion). Two limiting cases are considered: case 1 where there is no change in the solid phase during isolation (no particle dissolution and/or growth), and case 2 where the liquid and solid phases are equilibrated over the course of isolation. In reality, all actual manufacturing conditions would be bracketed by these two limiting cases, so consideration of these two scenarios provides rigorous theoretical bounds for assessing isolation performance. This modeling approach aims to facilitate the selection of most appropriate models suitable for different isolation scenarios, without the requirement to use overly complex models for straightforward isolation processes. Mefenamic acid and paracetamol were selected as representative model compounds to assess a range of isolation scenarios. In each case, the objective of the models was to identify the purity of the product reached with a fixed wash ratio and minimize the changes to the crystalline particle attributes that occur during the isolation process. This was undertaken with the aim of identifying suitable criteria for the selection of appropriate filtration and washing models corresponding to relevant processing conditions, and ultimately developing guidelines for the digital design of filtration and washing processes.

Investigation of Quantitative X-ray Microscopy for Assessment of API and Excipient Microstructure Evolution in Solid Dosage Processing

Assessment and understanding of changes in particle size of active pharmaceutical ingredients (API) and excipients as a function of solid dosage form processing is an important but under-investigated area that can impact drug product quality. In this study, X-ray microscopy (XRM) was investigated as a method for determining the in situ particle size distribution of API agglomerates and an excipient at different processing stages in tablet manufacturing. An artificial intelligence (AI)-facilitated XRM image analysis tool was applied for quantitative analysis of thousands of individual particles, both of the API and the major filler component of the formulation, microcrystalline cellulose (MCC). Domain size distributions for API and MCC were generated along with the calculation of the porosity of each respective component. The API domain size distributions correlated with laser diffraction measurements and sieve analysis of the API, formulation blend, and granulation. The XRM analysis demonstrated that attrition of the API agglomerates occurred secondary to the granulation stage. These results were corroborated by particle size distribution and sieve potency data which showed generation of an API fines fraction. Additionally, changes in the XRM-calculated size distribution of MCC particles in subsequent processing steps were rationalized based on the known plastic deformation mechanism of MCC. The XRM data indicated that size distribution of the primary MCC particles, which make up the larger functional MCC agglomerates, is conserved across the stages of processing. The results indicate that XRM can be successfully applied as a direct, non-invasive method to track API and excipient particle properties and microstructure for in-process control samples and in the final solid dosage form. The XRM and AI image analysis methodology provides a data-rich way to interrogate the impact of processing stresses on API and excipients for enhanced process understanding and utilization for Quality by Design (QbD).

Potential of Deep Learning Methods for Deep Level Particle Characterization in Crystallization

Crystalline particle properties, which are defined throughout the crystallization process chain, are strongly tied to the quality of the final product bringing along the need of detailed particle characterization. The most important characteristics are the size, shape and purity, which are influenced by agglomeration. Therefore, a pure size determination is often insufficient and a deep level evaluation regarding agglomerates and primary crystals bound in agglomerates is desirable as basis to increase the quality of crystalline products. We present a promising deep learning approach for particle characterization in crystallization. In an end-to-end fashion, the interactions and processing steps are minimized. Based on instance segmentation, all crystals containing single crystals, agglomerates and primary crystals in agglomerates are detected and classified with pixel-level accuracy. The deep learning approach shows superior performance to previous image analysis methods and reaches a new level of detail. In experimental studies, L-alanine is crystallized from aqueous solution. A detailed description of size and number of all particles including primary crystals is provided and characteristic measures for the level of agglomeration are given. This can lead to a better process understanding and has the potential to serve as cornerstone for kinetic studies.

Employing Constant Rate Filtration To Assess Active Pharmaceutical Ingredient Washing Efficiency

Washing is a key step in pharmaceutical isolation to remove unwanted crystallization solvents and dissolved impurities (mother liquor) from the active pharmaceutical ingredient (API) filter cake to ensure the purity of the product whilst maximizing yield. It is therefore essential to avoid both product dissolution and impurity precipitation during washing, especially precipitation of impurities caused by the wash solvent acting as an antisolvent, affecting purity and causing agglomerate formation. This work investigates the wash solvent flow through a saturated filter cake to optimize washing by displacement, taking account of diffusional mechanisms and manipulating the wash contact time. Constant rate filtration/washing is employed in this study using readily available laboratory equipment. One advantage of using constant rate filtration in this work is that it allows for the collection of separate aliquots during all stages of filtration, washing, and deliquoring of the API cake. This enables a wash profile to be obtained, as well as providing an overall picture on the mass of API lost during isolation and so can assist in optimizing the washing strategy. Particle size analysis of damp cake obtained straight after washing is also performed using laser diffraction. This allowed for agglomerate formation caused during washing to be distinguished from agglomeration that would be caused by subsequent drying of the wet filter cake. This work aims at improving pharmaceutical product quality, increasing sustainability, and reducing manufacturing cost.

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.

Understanding API Static Drying with Hot Gas Flow: Design and Test of a Drying Rig Prototype and Drying Modeling Development

Developing a continuous isolation process to produce a pure, dry, free-flowing active pharmaceutical ingredient (API) is the final barrier to the implementation of continuous end-to-end pharmaceutical manufacturing. Recent work has led to the development of continuous filtration and washing prototypes for pharmaceutical process development and small-scale manufacture. Here, we address the challenge of static drying of a solvent-wet crystalline API in a fixed bed to facilitate the design of a continuous filter dryer for pharmaceutical development, without excessive particle breakage or the formation of interparticle bridges leading to lump formation. We demonstrate the feasibility of drying small batches on a time scale suitable for continuous manufacturing, complemented by the development of a drying model that provides a design tool for process development. We also evaluate the impact of alternative washing and drying approaches on particle agglomeration. We conclude that our approach yields effective technology, with a performance that is amenable to predictive modeling.

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.