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Utilising three artificial intelligence (AI)/machine learning (ML) tools, this study explores the prediction of fill level in inclined linear blenders at steady state by mapping a wide range of bulk powder characteristics to processing parameters. Predicting fill levels enables the calculation of blade passes (strain), known from existing literature to enhance content uniformity. We present and train three AI/ML models, each demonstrating unique predictive capabilities for fill level. These models collectively identify the following rank order of feature importance: RPM, Mixing Blade Region (MB) size, Wall Friction Angle (WFA), and Feed Rate (FR). Random Forest Regression, a machine learning algorithm that constructs a multitude of decision trees at training time and outputs the mode of the classes (classification) or mean prediction (regression) of the individual trees, develops a series of individually useful decision trees. but also allows the extraction of logic and breakpoints within the data. A novel tool which utilises smart optimisation and symbolic regression to model complex systems into simple, closed-form equations, is used to build an accurate reduced-order model. Finally, an Artificial Neural Network (ANN), though less interrogable emerges as the most accurate fill level predictor, with an r 2 value of 0.97. Following training on single-component mixtures, the models are tested with a four-component powdered paracetamol formulation, mimicking an existing commercial drug product. The ANN predicts the fill level of this formulation at three RPMs (250, 350 and 450) with a mean absolute error of 1.4%. Ultimately, the modelling tools showcase a framework to better understand the interaction between process and formulation. The result of this allows for a first-time-right approach for formulation development whilst gaining process understanding from fewer experiments. Resulting in the ability to approach risk during product development whilst gaining a greater holistic understanding of the processing environment of the desired formulation. [Display omitted] [ABSTRACT FROM AUTHOR]

The drying capacity of a continuous vibrated fluid bed dryer was studied using a DoE by varying microcrystalline cellulose content in the formulation, water amount in the twin-screw granulation, inlet air temperature, air flow rate and the acceleration of the horizontal fluid-bed. Temperature and humidity profiles were measured along the dryer using wireless sensors. For the parameter space explored in this study, acceleration was the most influential process parameter of the dryer regarding the resulting granule moisture content. An empirical model was developed that allowed for fast and accurate moisture content prediction that could be incorporated into an enhanced control strategy. In addition, a mechanistic model was formulated that allow for prediction of temperature and moisture profiles, and most importantly the moisture content of the granules inside the dryer. The mechanistic model can be integrated to other unit operation models to provide overall understanding of an integrated continuous process line. The mechanistic model also makes it possible to define the equipment design requirements (e.g., length of the dryer) to meet the specific needs in terms of drying capacity, temperature and moisture profile., Competing Interests: Declaration of Competing Interest The authors declare the following financial interests/personal relationships which may be considered as potential competing interests: [Robin Meier reports a relationship with L.B. Bohle Maschinen und Verfahren GmbH that includes: employment. Andreas Altmeyer reports a relationship with L.B. Bohle Maschinen und Verfahren GmbH that includes: employment. Robin Meier and Andreas Altmeyer has patent pending to Licensee]., (Copyright © 2023 Elsevier B.V. All rights reserved.)

Despite a well-established process understanding, quality issues for compressed oral solid dosage forms are frequently encountered during various drug product development and production stages. In the current work, a non-destructive contact ultrasonic experimental rig integrated with a collaborative robot arm and an advanced vision system is presented and employed to quantify the effect of the shape of a compressed tablet on its mechanical properties. It is observed that these properties are affected by the tablet geometric shapes and found to be linearly sensitive to the compaction pressures. It is demonstrated that the presented approach significantly improves the repeatability of the experimental waveform acquisition. In addition, with the increased confidence levels in waveform acquisition accuracy and corresponding pressure and shear wave speeds due to improved measurement repeatability, we conclude that pharmaceutical compact materials can indeed have a negative Poisson's ratio, therefore can be auxetic. The presented technique and instrument could find critical applications in continuous tablet manufacturing, and its real-time quality monitoring as measurement repeatability has been significantly improved, minimizing product quality variations., (Copyright © 2022 Elsevier B.V. All rights reserved.)