Development of Suitable Plant-Scale Drying Conditions That Prevent API Agglomeration and Dehydration

Process analytical technology in Downstream-Processing of Drug Substances- A review

Process Analytical Technology (PAT) has revolutionized pharmaceutical manufacturing by providing real-time monitoring and control capabilities throughout the production process. This review paper comprehensively examines the application of PAT methodologies specifically in the production of solid active pharmaceutical ingredients (APIs). Beginning with an overview of PAT principles and objectives, the paper explores the integration of advanced analytical techniques such as spectroscopy, imaging modalities and others into solid API substance production processes. Novel developments in in-line monitoring at academic level are also discussed. Emphasis is placed on the role of PAT in ensuring product quality, consistency, and compliance with regulatory requirements. Examples from existing literature illustrate the practical implementation of PAT in solid API substance production, including work-up, crystallization, filtration, and drying processes. The review addresses the quality and reliability of the measurement technologies, aspects of process implementation and handling, the integration of data treatment algorithms and current challenges. Overall, this review provides valuable insights into the transformative impact of PAT on enhancing pharmaceutical manufacturing processes for solid API substances.

Steady-state modeling of a new continuous API dryer: Reduced-order model and investigation of dryer performance

In the present study, a reduced-order model is proposed to analyze a novel continuous dryer with an application in the pharmaceutical industry. The model was validated using process data from ibuprofen drying test runs, and the results were in good agreement with the experimental data. The test substance was an ibuprofen paste with an initial LOD of up to 30 w%. The simulations showed that the contact heat transfer coefficient can be correlated with the degree of wetness. Furthermore, a set of simulations was performed to analyze the influence of input parameters on the dryer's performance: i) the inlet air flow rate and ii) the inlet air temperature. The simulation results demonstrated that a variation in the inlet air temperature significantly affects the air temperature profile, while the inlet air flow rate has a minor effect. Besides, it was also established that the inlet solid LoD has the most considerable effect on the product quality (e.g., final solid moisture content). The results showed a deviation of less than 10% for the product LoD and the product temperature in most cases.

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.

Impact of Critical Material Attributes (CMAs)-Particle Shape on Miniature Pharmaceutical Unit Operations

The U.S. Food and Drug Administration (FDA) emphasizes drug product development by Quality by Design (QbD). Critical material attributes (CMAs) are a QbD element that has an impact on pharmaceutical operations and product quality. Pharmaceutical drugs often crystallize as needle-shaped (a CMA) particles and affect the process due to poor flowability, low bulk density, and high compressibility, and eventually the product performance. In this study, the product obtained from crystallization was needle-shaped Ciprofloxacin HCl (CIPRO), formed lumps during drying, and compacted during processing through feeders. To delump small amounts of materials and break the needles, multiple available devices (mortar-pestle, Krups grinder) and custom-made grinder were assessed before formulation. The processed CIPRO powder was then used to make tablets in the miniature tablet manufacturing unit developed by the team at MIT. The critical quality attributes (CQA) of the tablets, set by the United States Pharmacopeia (USP), were then assessed for the drug powder processed with each of these devices. Powder properties comparable to commercial CIPRO were obtained when the custom MIT-designed grinder was used, leading to tablets that meet the USP criteria, with comparable dissolution profiles of those for marketed CIPRO tablets. This study demonstrates how needle-shaped crystals have an impact on pharmaceutical operations, even if it is on a miniature scale, and how proper shape and subsequent flow properties can be obtained by processing the particles through the MIT team-designed grinder.

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.