Search

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.

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.

Raman spectroscopy is one of the important process analytical technology tools available for implementation in the continuous manufacturing of oral solid dosages. The aim of this study was to investigate several practical considerations in generating real-time measurements using Raman spectrometer at a tablet press feed frame, including the effects of fluorescence interference, photobleaching, feed-frame rpm, and material particle size. Fluorescence, in particular, is a significant drawback of Raman spectroscopy, compared to the use of near-infrared spectroscopy. Potential material sparing strategies were also investigated, including using stationary powders for calibration and isolation of feed-frame materials. Acetaminophen was used as the main active pharmaceutical ingredient (API), and microcrystalline cellulose (MCC) and lactose were used as excipients. The fluorescent behavior of MCC at 785 nm laser wavelength was reported and discussed. Raman spectra of a blend of MCC and acetaminophen and lactose and acetaminophen were collected at the feed frame of the tablet press. A series of preprocessing steps applied to remove the fluorescence interference was found to be effective, including the use of standard normal variate, subtraction of spectra of fluorescent material, baseline correction, and smoothing. Three different PLS models were prepared for different scenarios and their performances were compared. The models were able to predict the concentration of acetaminophen with root mean squared error prediction (RMSEP) of 0.29% w/w when there was no fluorescence interference and 0.57% w/w when there was fluorescence interference in background spectra. The study demonstrated the feasibility of using Raman spectroscopy for API concentration prediction even in the case of fluorescent interference and showed that Raman measurements were robust; that is, they were not much affected by feed-frame rpm and excipient particle size.