Effects of tableting process parameters and powder lubrication levels on tablet surface temperature and moisture content.

Punch sticking is a recurrent problem during the pharmaceutical tableting process. Powder moisture content plays a key role in the buildup of sticking; it evaporates due to increased tablet temperature, accumulates at the punch-tablet interface, and causes sticking through capillary force. This study investigated the effects of compaction pressure (CP), compaction speed (CS), and lubrication level (magnesium stearate (MgSt) ratio) on tablet surface temperature (TST) and tablet surface moisture content (TSMC). TST and TSMC were measured with an infrared thermal camera and near-infrared sensor, respectively. Microcrystalline cellulose was used as the tableting powder and MgSt as the lubricant. The low range of CS values (16-32 mm/s) considered in this study did not have significant effects on TST and TSMC. MgSt ratio had a significant positive effect on TST; this may be explained by the increase in powder blend effusivity with the addition of MgSt. However, MgSt ratio did not have a significant effect on TSMC. CP had a significant positive effect on both TST and TSMC. Increased CP induced higher heat generation through particle deformation and friction during the compaction phase, leading to increased TST. Furthermore, the water vapor diffusion rate through the powder bed might have increased due to the rise in thermal energy and led to further moisture accumulation at the tablet-punch interface, causing the significant positive effect of CP on TSMC. This result may explain the occurrence of sticking regardless of the CP applied during the tableting process.

Wafer-scale detachable monocrystalline germanium nanomembranes for the growth of III-V materials and substrate reuse.

Germanium (Ge) is increasingly used as a substrate for high-performance optoelectronics, photovoltaics, and electronic devices. These devices are usually grown on thick and rigid Ge substrates manufactured by classical wafering techniques. Nanomembranes (NMs) provide an alternative to this approach while offering wafer-scale lateral dimensions, weight reduction, waste limitation, and cost effectiveness. Herein, we introduce the Porous germanium Efficient Epitaxial LayEr Release (PEELER) process, which consists of the fabrication of wafer-scale detachable Ge NMs on porous Ge (PGe) and substrate reuse. We demonstrate the growth of Ge NMs with monocrystalline quality as revealed by high-resolution transmission electron microscopy (HRTEM) characterization. Together with the surface roughness below 1 nm, it makes the Ge NMs suitable for growth of III-V materials. Additionally, the embedded nanoengineered weak layer enables the detachment of the Ge NMs. Finally, we demonstrate the wet-etch-reconditioning process of the Ge substrate, allowing its reuse, to produce multiple free-standing NMs from a single parent wafer. The PEELER process significantly reduces the consumption of Ge in the fabrication process, paving the way for a new generation of low-cost flexible optoelectronic devices.

Moisture Behavior of Pharmaceutical Powder during the Tableting Process.

The moisture content of pharmaceutical powder is a key parameter contributing to tablet sticking during the tableting process. This study investigates powder moisture behavior during the compaction phase of the tableting process. Finite element analysis software COMSOL Multiphysics® 5.6 was used to simulate the compaction microcrystalline cellulose (VIVAPUR PH101) powder and predict temperature and moisture content distributions, as well as their evolution over time, during a single compaction. To validate the simulation, a near-infrared sensor and a thermal infrared camera were used to measure tablet surface temperature and surface moisture, respectively, just after ejection. The partial least squares regression (PLS) method was used to predict the surface moisture content of the ejected tablet. Thermal infrared camera images of the ejected tablet showed powder bed temperature increasing during compaction and a gradual rise in tablet temperature along with tableting runs. Simulation results showed that moisture evaporate from the compacted powder bed to the surrounding environment. The predicted surface moisture content of ejected tablets after compaction was higher compared to that of loose powder and decreased gradually as tableting runs increased. These observations suggest that the moisture evaporating from the powder bed accumulates at the interface between the punch and tablet surface. Evaporated water molecules can be physiosorbed on the punch surface and cause a capillary condensation locally at the punch and tablet interface during dwell time. Locally formed capillary bridge may induce a capillary force between tablet surface particles and the punch surface and cause the sticking.

Insights into tablet sticking: a quantitative case study with an ibuprofen and methocarbamol-based formulation.

OBJECTIVES AND METHODS: Tablet sticking is a continuous accumulation of pharmaceutical powder onto tooling surfaces during compression. Its occurrence greatly impacts tablet productivity, quality attributes, and tooling age. In a previous study, the authors proposed a multivariate data analysis approach to gain insights into tablet sticking directly on the industrial stage. The objective was to determine the combination of factors that could help distinguish between batches affected and unaffected by sticking. The present study aims to generalize this approach by extending it to quantitative predictions of punch sticking intensity. A total of 345 variables was gathered on 28 industrial batches of an ibuprofen and methocarbamol-based formulation. RESULT AND CONCLUSION: Using PLS regression models, it was shown that the association of granulation duration and compression force allows to significantly explain ∼60% of sticking variations of studied formulation. In addition, unlike the classification models developed in the earlier work, the validation residues in the present study were found to be normally distributed (Shapiro-Wilks p value = 0.96) and independent from the target variable (R2 = 9.5%).

An Investigation into the Spatial Distribution of Moisture in Freeze-Dried Products Using NIR Spectroscopy and Chemical Imaging.

Near-infrared (NIR) spectroscopy (NIRS) is a widely accepted method of measuring moisture in pharmaceutical freeze-dried products, both during the process and in the finished products. Multiple NIR measurement approaches have been introduced to monitor product moisture in freeze-dried vials. However, the spatial moisture gradients within a vial have not been investigated in depth. Like any other point-focused process analytical technology (PAT) tool, a spectrum produced by NIRS represents an average over a given area of the product vial. Implementing a point-focused NIR on any random position without proper understanding of spatial moisture variations within the vial may severely impact the reliability of the results. The present work focuses on understanding the moisture distribution within freeze-dried vials. We performed an investigation using NIR tools, NIR chemical imaging (NIR-CI), and NIRS to understand the spatial variations in moisture on the outer surface (i.e., periphery) of the freeze-dried vials. To achieve this, the moisture distribution within individual vials was mapped using NIR images. Then, NIRS was used to determine the necessity of using multiple measurement points to produce robust models quantifying the moisture inside freeze-dried products. Overall, the results show a simplified version of the phenomenon in which non-homogenous distribution of moisture, as well as the non-uniform drying front, occur within the vials. The findings from the NIRS-based partial least squares (PLS) models indicate that to achieve reliable product/process information, measurements must be drawn from multiple measurement points on the surface of the freeze-dried products.

A multivariate data analysis approach to tablet sticking on an industrial scale: a qualitative case study of an ibuprofen-based formulation.

OBJECTIVES: Sticking is one of the most common and damaging issues that occur during tablet manufacturing. Sticking is the adhesion of powder onto tooling surfaces during compression. Because of the numerous factors involved in its occurrence, understanding tablet sticking requires the simultaneous investigation of these factors to clarify their possible interactions. However, conducting such a study experimentally can present a significant financial and technical burden. In this study, we aimed to leverage the large amount of data that is usually generated during industrial manufacturing to gain insights into sticking. METHODS: This was achieved by collecting and analyzing a total of 71 historical batches that used an ibuprofen-based formulation. We associate each batch with a hundred parameters, including a qualitative descriptor of sticking, and employ a predefined methodology based primarily on multivariate data analysis. RESULTS AND CONCLUSIONS: Our results highlight the role of lubrication, water content, and the low melting point of ibuprofen in its sticking tendency. Based on these findings, we propose and discuss an industrial manufacturing data analysis approach to sticking and its associated systematic methodology, consisting of collection, exploration, and data modeling.

Pharmaceutical tablet compression: measuring temporal and radial concentration profiles to better assess segregation.

Concentration monitoring inside a tablet press feed frame is important not only to assess the composition of the powder blend compressed into tablets but also to detect quality affecting phenomena such as powder segregation. Near infrared spectroscopy has been successfully used to monitor powder concentration inside the feed frame; however, so far, this methodology does not provide information on local spatial variability, since it probes a very small area of powder sample. Near infrared chemical imaging (NIR CI) has the potential to improve process monitoring because it can simultaneously acquire a plurality of spectra covering nearly the entire width of the feed frame, thereby making it possible to detect local variations in powder concentration. The present work uses both NIRS and NIR CI to monitor the concentration of Ibuprofen and Ascorbic acid in multi-component mock pharmaceutical blends flowing through the feed frame of an industrial tablet press. The concentrations of Ibuprofen and Ascorbic acid were successfully monitored in multi-component powder blends. NIR spectral wavelength ranges and pre-treatments were simultaneously optimized via a genetic algorithm. N-way PLS approach for concentration monitoring was found to be more suitable than regular PLS when analyzing spectral images and provided the ability to visualize spatial segregation.

Evaluating the impact of NIR pre-processing methods via multiblock partial least-squares.

Near-infrared (NIR) spectral data are used in many applications to predict physical and chemical properties. However, it can result in poor predictive models when untreated spectra are directly used to estimate these properties. Many pre-preprocessing techniques are available to reduce noise and variance unrelated to the studied property but choosing which one to apply can be tricky. Existing methods to select a pre-processing are time-consuming or do not allow for a meaningful comparison of the different techniques. Even though new methods focus on extracting complementary information from each pre-processing, an optimal combination is still required to obtain efficient predictive models and avoid extensive computational costs. Here, we propose an approach using multiblock partial least squares (MBPLS) to simultaneously compare the impact of the pre-processing techniques on spectral data and as a result on the regression models. Superloadings provide qualitative and quantitative information on pre-processed data. This tool helps compare and determine which pre-processing technique, or combinations thereof, that may be appropriate for a dataset, not just a single "best" one. Using this, the analyst is then better equipped to make a final choice when selecting which ones to include. This method is tested on artificial signals and NIR spectra from corn samples.

Wet granulation end point prediction using dimensionless numbers in a mixer torque rheometer: Relationship between capillary and Weber numbers and the optimal wet mass consistency.

The optimal wet mass consistency during wet granulation is often determined using the hand squeezing test. In this study, torque values recorded inside the wet mass were measured using a mixer torque rheometer (MTR) via multiple additions of liquid. The main objective of this work was to predict the optimal wet mass consistency of pharmaceutical powders using the modified capillary (Ca∗) and Weber (We∗) dimensionless numbers. The results show that the optimal wet mass consistency versus Ca∗ (or We∗) can be fitted with a power-law function, whereas the improved capillary number Ca' proposed in this work gives different relationships and behaviors depending on the spreadability and wettability of the blend. The wettability was obtained by measuring the contact angle between the liquids and the pharmaceutical powders. The surface free energy and the polar and dispersive parts of a liquid's surface energy were obtained from Young's equation and the Owens-Wendt-Rabel-Kaelble (OWRK) model. This study demonstrated the importance of the interfacial energy σb-s and the pore radius, Rpore in the establishment of a dimensionless number, Ca∗, that can satisfactorily predict with an R2 of 0.80, the optimal wet mass consistency of pharmaceutical powders measured by the MTR.

Non-invasive detection technologies of solid foreign matter and their applications to lyophilized pharmaceutical products: A review.

Good Manufacturing Practice Regulations, under the Food and Drug Administration (FDA), stipulate that all pharmaceutical products must be free of any contaminants, including, namely, any foreign solid objects. Lyophilization is a common manufacturing method that consists of several steps where foreign materials may enter the product. The presence of unintended particles in freeze drying, which will herein be referred to under the term 'Lyophilization', is of great concern to the authorities responsible for drug safety and effectiveness. In the pharmaceutical industry, presently, the inspection of lyophilized products for foreign matter particulates relies on visual inspection where only the outer surface of the lyophilized cake is visible. This review is motivated by the need for new control strategies for foreign matter (FM) detection in lyophilized products; more specifically, it assesses the reliability of non-destructive technologies for FM detection in dried samples. Emerging technologies applied in other industries, such as various types of spectroscopies and imaging (e.g. chemical, X-ray, ultrasound, thermal and terahertz), are evaluated based on compatibility with the intended application, with identification of the possible technical challenges.

Current RNA-seq methodology reporting limits reproducibility.

Ribonucleic acid sequencing (RNA-seq) identifies and quantifies RNA molecules from a biological sample. Transformation from raw sequencing data to meaningful gene or isoform counts requires an in silico bioinformatics pipeline. Such pipelines are modular in nature, built using selected software and biological references. Software is usually chosen and parameterized according to the sequencing protocol and biological question. However, while biological and technical noise is alleviated through replicates, biases due to the pipeline and choice of biological references are often overlooked. Here, we show that the current standard practice prevents reproducibility in RNA-seq studies by failing to specify required methodological information. Peer-reviewed articles are intended to apply currently accepted scientific and methodological standards. Inasmuch as the bias-less and optimal RNA-seq pipeline is not perfectly defined, methodological information holds a meaningful role in defining the results. This work illustrates the need for a standardized and explicit display of methodological information in RNA-seq experiments.

Factorial study of the RNA-seq computational workflow identifies biases as technical gene signatures.

RNA-seq is a modular experimental and computational approach aiming in identifying and quantifying RNA molecules. The modularity of the RNA-seq technology enables adaptation of the protocol to develop new ways to explore RNA biology, but this modularity also brings forth the importance of methodological thoroughness. Liberty of approach comes with the responsibility of choices, and such choices must be informed. Here, we present an approach that identifies gene group-specific quantification biases in current RNA-seq software and references by processing datasets using diverse RNA-seq computational pipelines, and by decomposing these expression datasets with an independent component analysis matrix factorization method. By exploring the RNA-seq pipeline using this systemic approach, we identify genome annotations as a design choice that affects to the same extent quantification results as does the choice of aligners and quantifiers. We also show that the different choices in RNA-seq methodology are not independent, identifying interactions between genome annotations and quantification software. Genes were mainly affected by differences in their sequence, by overlapping genes and genes with similar sequence. Our approach offers an explanation for the observed biases by identifying the common features used differently by the software and references, therefore providing leads for the betterment of RNA-seq methodology.

Unmixing noisy co-registered spectrum images of multicomponent nanostructures.

Analytical electron microscopy plays a key role in the development of novel nanomaterials. Electron energy-loss spectroscopy (EELS) and energy-dispersive X-ray spectroscopy (EDX) datasets are typically processed to isolate the background-subtracted elemental signal. Multivariate tools have emerged as powerful methods to blindly map the components, which addresses some of the shortcomings of the traditional methods. Here, we demonstrate the superior performance of a new multivariate optimization method using a challenging EELS and EDX dataset. The dataset was recorded from a spectrum image P-type metal-oxide-semiconductor stack with 7 components exhibiting heavy spectral overlap and a low signal-to-noise ratio. Compared to peak integration, independent component analysis, Baysian Linear Unmixing and Non-negative matrix factorization, the method proposed was the only one to identify the EELS spectra of all 7 components with the corresponding abundance profiles. Using the abundance of each component, it was possible to retrieve the EDX spectra of all the components, which were otherwise impossible to isolate, regardless of the method used. We expect that this robust method will bring a significant improvement for the chemical analysis of nanomaterials, especially for weak signals, dose-sensitive specimen or signals suffering heavy spectral overlap.

Monitoring microsphere coating processes using PAT tools in a bench scale fluid bed.

Among the factors that influence adherence to medication within the pediatric population, taste/irritation has been identified as a critical barrier to patient compliance. With the goal of improving compliance, microspheres (matrix systems within which the drug is dispersed) can be coated with a reverse enteric polymer that will prevent the release of the drug in the oral cavity while maintaining an immediate release once the drug product reaches the stomach, thereby achieving a taste neutral profile. In this work, the in-line performance of three process analytical technology (PAT) tools is evaluated in order to monitor the microsphere coating process. These tools are Raman spectroscopy, near-infrared spectroscopy and focused beam reflectance measurements, together with process data and raw material attributes. The ability of these different sources of information to predict the coating's barrier performance is evaluated by using a combined-data-approach: multiblock partial least squares (MBPLS). Results show that Raman spectroscopy has a superior predictive performance and that it has the potential to monitor the coating process of the microspheres as well as to detect process discrepancies (such as spray rate changes), demonstrating its usefulness for the monitoring of fluid bed coating processes. It was also demonstrated that Raman can be used to clearly differentiate batches with significantly difference in-vitro dissolution performance. This monitoring is considered critical to ensure consistent coating performance for this thin film barrier membrane that is essential to patient compliance.

Building Process Understanding of Fluid Bed Taste Mask Coating of Microspheres.

Taste is routinely cited as one of the major contributing factors that negatively influence pediatric patient compliance. A promising solution is coated microsphere systems, which provide doses of active pharmaceutical ingredients (API) subdivided into a plurality of small dosage units. In this work, the microspheres were coated with Kollicoat® Smartseal, a reverse enteric polymer, which acts to minimize or prevent the release of API in the neutral pH of the oral cavity, which results in a masking effect of the unpleasant taste of the API. A screening of seven key variables in a Wurster coating process was evaluated by D-optimal design and by analysis of variance. The percentage of API released at pH 6.2 was used as a surrogate method for the taste-masking performance evaluation of Kollicoat® Smartseal. The seven studied variables were: product bed temperature, inlet airflow, atomizing air pressure, spray rate (process parameters), coating level, plasticizer level, solids in coating suspension (material attributes), and curing. Results show that coating level, plasticizer level, product bed temperature, and spray rate are the critical process parameters and reinforce the importance of curing to reduce the overall variability within the batch by promoting complete film formation. The link between material attributes, process parameters, and quality attributes were demonstrated to allow a better understanding of the parameters that affect the API release profile at neutral pH (in vitro) while not injuring release at acidic pH (in vitro). It was demonstrated that not only thickness but also coating morphology have an impact on the dissolution in 50 mM potassium phosphate buffer, pH 6.2.

In-line monitoring of Ibuprofen during and after tablet compression using near-infrared spectroscopy.

Near infrared spectroscopy (NIRS) used as process analytical technology tool to monitor Active Pharmaceutical Ingredient concentrations during tablet manufacturing has been reported to enhance overall product quality assurance. NIRS applications in different manufacturing stages are facilitated by their ability to handle different sample presentations - be it solids, liquids, gels or powders. The present study evaluates NIRS suitability for monitoring Ibuprofen concentrations (coated pellets form) inside the feed frame of a tableting press as well as in output tablets. Process monitoring was undertaken with quantitative chemometric analysis. NIRS-based predictions of concentrations both inside the feed frame and in tablets were compared to ultraviolet (UV) spectroscopy assays of temporally stratified tablet samples. Process dynamics were also compared in terms of concurrent concentrations change kinetics in the feed frame and in output tablets. NIRS showed good sensitivity to Ibuprofen concentrations despite the use of coated pellets. Ibuprofen contents as low as 1.7% w/w were detected effectively. NIRS-based quantitative predictions in the feed frame and in tablets closely matched the UV assay values of sampled tablets. As anticipated from the 2-wheel feed frame geometry, upon the addition of each consecutive blend, results show that the predicted concentration inside the feed frame were delayed compared with that of the tablets exiting the tablet press. For these tests, the delay was estimated to be 1.25 min. This finding highlights the importance of NIRS probe position inside the feed frame as a function of its geometry. Successive feed frame and tablet monitoring by NIRS could prove beneficial for real time release testing of tablet formulations.

Specificity of process analytical tools in the monitoring of multicomponent pharmaceutical powders.

The application of Process Analytical Technologies in pharmaceutical manufacturing has been the subject of many studies. Active pharmaceutical ingredient monitoring in real time throughout the manufacturing process is commonly the target of many such implementations. The tools in place must be sensitive to, and selective of, the parameter(s) to be monitored, i.e. in the case of component quantification, they must respond to the component in question and be robust against all others. In this study, four different ingredients (riboflavin, ferrous fumarate, ginseng, and ascorbic acid) in a multi-component blend were monitored by three different tools (near infrared spectroscopy, laser-induced fluorescence and red-green-blue camera) using a full factorial design. The goal was to develop efficient and robust concentration-reading/prediction models able to assess and monitor component interference. Despite relatively high complexity of the blend studied, the three tools demonstrated reasonable specificity for the tracked ingredients (and showed advantages when combined), taking into account larger acceptance criteria typical of dietary products. In certain cases, some interference might lead to biased predictions, highlighting the importance of good calibration. The tools tested and the methodology proposed has divulged their potential in monitoring these components, despite the complexity of the 31-component blend.

A Hierarchical Multivariate Curve Resolution Methodology To Identify and Map Compounds in Spectral Images.

The use of spectroscopic methods, such as near-infrared or Raman, for quality control applications combined with the constant search for finer details leads to the acquisition of increasingly complex data sets. This should not prevent the user from characterizing a sample by identifying and mapping its chemical compounds. Multivariate data analysis methods make it possible to obtain qualitative and quantitative information from such data sets. However, samples containing a large (and/or unknown) number of species, segregated trace compounds (present in few pixels), low signal-to-noise ratios (SNR), and often insufficient spatial resolutions still represent significant hurdles for the analyst.

Developing a quality by design approach to model tablet dissolution testing: an industrial case study.

This study applied the concept of Quality by Design (QbD) to tablet dissolution. Its goal was to propose a quality control strategy to model dissolution testing of solid oral dose products according to International Conference on Harmonization guidelines. The methodology involved the following three steps: (1) a risk analysis to identify the material- and process-related parameters impacting the critical quality attributes of dissolution testing, (2) an experimental design to evaluate the influence of design factors (attributes and parameters selected by risk analysis) on dissolution testing, and (3) an investigation of the relationship between design factors and dissolution profiles. Results show that (a) in the case studied, the two parameters impacting dissolution kinetics are active pharmaceutical ingredient particle size distributions and tablet hardness and (b) these two parameters could be monitored with PAT tools to predict dissolution profiles. Moreover, based on the results obtained, modeling dissolution is possible. The practicality and effectiveness of the QbD approach were demonstrated through this industrial case study. Implementing such an approach systematically in industrial pharmaceutical production would reduce the need for tablet dissolution testing.

Using multiple Process Analytical Technology probes to monitor multivitamin blends in a tableting feed frame.

As Process Analytical Technology (PAT) implementation grows in the pharmaceutical industry, more studies are being performed to evaluate its suitability in new applications and processes within the manufacturing chain. As the last step in tablet production, the compression stage represents a critical phase that ensures product quality. In-line control put in place at this stage has the potential to detect powder blends that are out of specification limits and, thus, help to improve product quality. The objectives of the present project are to quantify the composition of a commercial 31-component multivitamin powder blend in real time on an industrial feed frame, using 3 different PAT tools: light-induced fluorescence spectroscopy, near infrared spectroscopy and red, green and blue color imaging. To do so, the concentrations of 5 components (Beta-Carotene, Riboflavin, Ferrous Fumarate, Ginseng and Ascorbic Acid) were alternately changed and monitored with one or many probes. Transition periods between batches served to quantify different powder flow dynamics with sequential composition step changes. The results showed that 4 out of 5 components, each present in commercially-relevant concentrations, could be monitored by one or more tools. Flow dynamics were measured and found to vary significantly in different powder blends.