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As pharmaceutical manufacturing is transitioning from traditional batch to continuous manufacturing (CM), there is a lack of tools for CM design and development, which can integrate drug substance and drug product unit operations for overall evaluation. Recently, a Python-based PharmaPy framework was proposed to advance the design, simulation, and analysis of continuous pharmaceutical processes. However, the initial library of models only addressed upstream drug substance processing. In this work, new capabilities, including drug product unit operations such as feeder, blender, and tablet press, have been added to the PharmaPy framework, enabling end-to-end study and optimizing the effects of material properties and process conditions on solid oral dosage products. The platform supports computational efficiency and model accuracy by allowing the development of different mechanistic and semi-mechanistic models. Sensitivity analysis is performed on the integrated end-to-end simulator to identify critical input variables influencing product quality and control strategies. The analysis lowers the complexity of the model by ranking significant input variables. Finally, feasibility studies are conducted on extracted influential input variables to characterize the process design space and achieve desirable output. The enhanced PharmaPy package can now support decision-making from early research and development stages through manufacturing. 

We present a systematic and automatic approach for integrating tableting reduced-order models with upstream unit operations. The approach not only identifies the upstream critical material attributes and process parameters that describe the coupling to the first order and, possibly, the second order, but it also selects the mathematical form of such coupling and estimates its parameters. Specifically, we propose that the coupling can be generally described by normalized bivariate rational functions. We demonstrate this approach for dry granulation, a unit operation commonly used to enhance the flowability of pharmaceutical powders by increasing granule size distribution, which, inevitably, negatively impacts tabletability by reducing the particle porosity and imparting plastic work. Granules of different densities and size distributions are made with a 10% w/w acetaminophen and 90% w/w microcrystalline cellulose formulation, and tablets with a wide range of relative densities are fabricated. This approach is based on product and process understanding, and, in turn, it is not only essential to enabling the end-to-end integration, control, and optimization of dry granulation and tableting processes, but it also offers insight into the granule properties that have a dominant effect on each of the four stages of powder compaction, namely die filling, compaction, unloading, and ejection. 

The determination of the design space (DS) in a pharmaceutical process is a crucial aspect of the quality-by-design (QbD) initiative which promotes quality built into the desired product. This is achieved through a deep understanding of how the critical quality attributes (CQAs) and process parameters (CPPs) interact that have been demonstrated to provide quality assurance. For computational inexpensive models, the original process model can be directly deployed to identify the design space. One such crucial process is the Tablet Press (TP), which directly compresses the powder blend into individual units of the final product or adds dry or wet granulation to meet specific formulation needs. In this work, we identify the design space of input variables in a TP such that there is a (probabilistic) guarantee that the tablets meet the quality constraints under a set of operating conditions. A reduced-order model of TP is assigned for this purpose where the effects of lubricants and glidants are used to characterize the design space to achieve the desired tablet CQAs. The probabilistic design space, which takes into account interactions between crucial process parameters and important quality characteristics including model uncertainty, is also approximated because of the high cost associated with the comprehensive experiments. 

Implementing a condition-based maintenance strategy requires an effective condition monitoring (CM) system that can be complicated to develop and even harder to maintain. In this paper, we review the main complexities of developing condition monitoring systems and introduce a four-stage framework that can address some of these difficulties. The framework achieves this by first using process knowledge to create a representation of the process condition. This representation can be broken down into simpler modules, allowing existing monitoring systems to be mapped to their corresponding module. Data-driven models such as machine learning models could then be used to train the modules that do not have existing CM systems. Even though data-driven models tend to not perform well with limited data, which is commonly the case in the early stages of pharmaceutical process development, application of this framework to a pharmaceutical roller compaction unit shows that the machine learning models trained on the simpler modules can make accurate predictions with novel fault detection capabilities. This is attributed to the incorporation of process knowledge to distill the process signals to the most important ones vis-à-vis the faults under consideration. Furthermore, the framework allows the holistic integration of these modular CM systems, which further extend their individual capabilities by maintaining process visibility during sensor maintenance. 

The pharmaceutical industry is looking for new and innovative ways of manufacturing to improve product quality and reduce process complexity. In manufacturing oral solid dosage products, blending is a crucial step in ensuring the homogeneity of active pharmaceutical ingredients (APIs) in the final product. Currently, batch and continuous blending are the two commonly used modes for blending in the industry. However, these methods have limitations in terms of blending time, manual intervention, and flexibility in handling multiple ingredients. To address these limitations, this study aims to explore the feasibility and benefits of using a semicontinuous blending mode in the pharmaceutical industry. A case study is conducted using a binary blend of microcrystalline cellulose and acetaminophen to compare the performance of the semicontinuous mode of blending with the batch and continuous blending modes. The results show that the semicontinuous blending setup can produce blends with good blend uniformity and homogeneity and that the output can be used for both batch and continuous downstream operations. The effect of variation in the three most important process parameters, impeller rotation per minute, blending time, and fill level on the blend uniformity, is also investigated. The semicontinuous blending mode had a higher line rate of 12.5 kg/hour than a similarly sized batch blender at 3.6 kg/hour and less than that of a continuous blender. The benefits of the new blending mode include reduced blending time, minimal manual intervention, flexibility in blending multiple ingredients, easier scale-up, and a smaller footprint. Overall, this study highlights the relative advantages of using this new semicontinuous blending mode in pharmaceutical manufacturing and its potential as a good alternative to the existing blending modes. The semicontinuous mode is well placed between the batch blending and continuous blending mode, with many benefits over the former mode and performance comparable to the latter continuous mode.