Review of real-time release testing of pharmaceutical tablets: State-of-the art, challenges and future perspective

Development and Application of Control Concepts for Twin-Screw Wet Granulation in the ConsiGmaTM-25: Part 2 Granule Size

Traditional operation modes, such as running the production processes at constant process settings or within a narrow design space, do not fully exploit the advantages of continuous pharmaceutical manufacturing. Integrating Quality by Control (QbC) algorithms as a standard component of production processes can mitigate the effect of diverse process disturbances and enhance process efficiency, particularly in terms of production costs and environmental footprint. This paper explores the potential of QbC algorithms for optimizing twin-screw wet granulation in the ConsiGmaTM-25 manufacturing line, specifically addressing granule size. It represents the second part of a study (Celikovic et al. (2024)) focused on granule composition. The concepts proposed in this work rely on process analytical technology (PAT) equipment for real-time monitoring of the granulation CQAs and a dynamic process model linking the granulation process parameters and the monitored CQAs. The granule size model identified via the local-linear-model-tree (LoLiMoT) algorithm is used to develop both a model predictive controller (MPC) and a granule size soft sensor. The MPC employs this model as a core component for selecting optimal granulation parameters to ensure the production of granules with target size. A digital operator assistant is developed to address disturbances that cannot be mitigated via MPC but can be eliminated by the plant operators. This study systematically outlines a workflow, starting from conceptualization, moving through simulation development, and finally ending with real-world application on a production line. In this final step, all proposed concepts are transferred to the ConsiGmaTM-25 manufacturing line, where their performance is validated through selected disturbance scenarios.

Flexible modelling of the dissolution performance of directly compressed tablets

In this study, a compartmental disintegration and dissolution model is proposed for the prediction and evaluation of the dissolution performance of directly compressed tablets. This dissolution model uses three compartments (Bound, Disintegrated, and Dissolved) to describe the state of each particle of active pharmaceutical ingredient. The disintegration of the tablet is captured by three fitting parameters. Two disintegration parameters, β0 and βt,0, describe the initial disintegration rate and the change in disintegration rate, respectively. A third parameter, α, describes the effect of the volume of dissolved drug on the disintegration process. As the tablet disintegrates, particles become available for dissolution. The dissolution rate is determined by the Nernst-Brunner equation, whilst taking into account the hydrodynamic effects within the vessel of a USP II (paddle) apparatus. This model uses the raw material properties of the active pharmaceutical ingredient (solubility, particle size distribution, true density), lending it towards early development activities during which time the amount of drug substance available may be limited. Additionally, the strong correlations between the fitting parameters and the tablet porosity indicate the potential to isolate the manufacturing effects and thus implement the model as part of a real-time release testing strategy for a continuous direct compression line.

Machine learning modeling for ultrasonic quality attribute assessment of pharmaceutical tablets for continuous manufacturing and real-time release testing

In in-process quality monitoring for Continuous Manufacturing (CM) and Critical Quality Attributes (CQA) assessment for Real-time Release (RTR) testing, ultrasonic characterization is a critical technology for its direct, non-invasive, rapid, and cost-effective nature. In quality evaluation with ultrasound, relating a pharmaceutical tablet's ultrasonic response to its defect state and quality parameters is essential. However, ultrasonic CQA characterization requires a robust mathematical model, which cannot be obtained with traditional first principles-based modeling approaches. Machine Learning (ML) using experimental data is emerging as a critical analytical tool for overcoming such modeling challenges. In this work, a novel Deep Neural Network-based ML-driven Non-Destructive Evaluation (ML-NDE) modeling framework is developed, and its effectiveness for extracting and predicting three CQAs, namely defect states, compression force levels, and amounts of disintegrant, is demonstrated. Using a robotic tablet handling experimental rig, each attribute's distinct waveform dataset was acquired and utilized for training, validating, and testing the respective ML models. This study details an advanced algorithmic quality assessment framework for pharmaceutical CM in which automated RTR testing is expected to be critical in developing cost-effective in-process real-time monitoring systems. The presented ML-NDE approach has demonstrated its effectiveness through evaluations with separate (unused) test datasets.

Generative data augmentation and automated optimization of convolutional neural networks for process monitoring

Chemometric modeling for spectral data is considered a key technology in biopharmaceutical processing to realize real-time process control and release testing. Machine learning (ML) models have been shown to increase the accuracy of various spectral regression and classification tasks, remove challenging preprocessing steps for spectral data, and promise to improve the transferability of models when compared to commonly applied, linear methods. The training and optimization of ML models require large data sets which are not available in the context of biopharmaceutical processing. Generative methods to extend data sets with realistic in silico samples, so-called data augmentation, may provide the means to alleviate this challenge. In this study, we develop and implement a novel data augmentation method for generating in silico spectral data based on local estimation of pure component profiles for training convolutional neural network (CNN) models using four data sets. We simultaneously tune hyperparameters associated with data augmentation and the neural network architecture using Bayesian optimization. Finally, we compare the optimized CNN models with partial least-squares regression models (PLS) in terms of accuracy, robustness, and interpretability. The proposed data augmentation method is shown to produce highly realistic spectral data by adapting the estimates of the pure component profiles to the sampled concentration regimes. Augmenting CNNs with the in silico spectral data is shown to improve the prediction accuracy for the quantification of monoclonal antibody (mAb) size variants by up to 50% in comparison to single-response PLS models. Bayesian structure optimization suggests that multiple convolutional blocks are beneficial for model accuracy and enable transfer across different data sets. Model-agnostic feature importance methods and synthetic noise perturbation are used to directly compare the optimized CNNs with PLS models. This enables the identification of wavelength regions critical for model performance and suggests increased robustness against Gaussian white noise and wavelength shifts of the CNNs compared to the PLS models.

At-line porosity sensing for non-destructive disintegration testing in immediate release tablets

Fully automated at-line terahertz time-domain spectroscopy in transmission mode is used to measure tablet porosity for thousands of immediate release tablets. The measurements are rapid and non-destructive. Both laboratory prepared tablets and commercial samples are studied. Multiple measurements on individual tablets quantify the random errors in the terahertz results. These show that the measurements of refractive index are precise, with the standard deviation on a single tablet being about 0.002, with variation between measurements being due to small errors in thickness measurement and from the resolution of the instrument. Six batches of 1000 tablets each were directly compressed using a rotary press. The tabletting turret speed (10 and 30 rpm) and compaction pressure (50, 100 and 200 MPa) were varied between the batches. As expected, the tablets compacted at the highest pressure have far lower porosity than those compacted at the lowest pressure. The turret rotation speed also has a significant effect on porosity. This variation in process parameters resulted in batches of tablets with an average porosity between 5.5 and 26.5%. Within each batch, there is a distribution of porosity values, the standard deviation of which is in the range 1.1 to 1.9%. Destructive measurements of disintegration time were performed in order to develop a predictive model correlating disintegration time and tablet porosity. Testing of the model suggested it was reasonable though there may be some small systematic errors in disintegration time measurement. The terahertz measurements further showed that there are changes in tablet properties after storage for nine months in ambient conditions.

Solid-state analysis for pharmaceuticals: Pathways to feasible and meaningful analysis

The solid state of matter is the preferred starting point for designing a pharmaceutical product. This is driven by both patient preferences and the relative ease of supplying a solid pharmaceutical product with desired quality and performance. Solid form diversity is increasingly prevalent as a crucial element in designing these products, which underpins the importance of solid-state analytical methods. This paper provides a critical analysis of challenges related to solid-state analytics, as well as considerations and suggestions for feasible and meaningful pharmaceutical analysis.

Laser-cutting: A novel alternative approach for point-of-care manufacturing of bespoke tablets

A novel subtractive manufacturing method to produce bespoke tablets with immediate and extended drug release is presented. This is the first report on applying fusion laser cutting to produce bespoke furosemide solid dosage forms based on pharmaceutical-grade polymeric carriers. Cylindric tablets of different sizes were produced by controlling the two-dimensional design of circles of the corresponding diameter. Immediate and extended drug release patterns were achieved by modifying the composition of the polymeric matrix. Thermal analysis and XRD indicated that furosemide was present in an amorphous form. The laser-cut tablets demonstrated no significant drug degradation (<2%) nor the formation of impurities were identified. Multi-linear regression was used to quantify the influences of laser-cutting process parameters (laser energy levels, scan speeds, and the number of laser applications) on the depth of the laser cut. The utility of this approach was exemplified by manufacturing tablets of accurate doses of furosemide. Unlike additive or formative manufacturing, the reported approach of subtractive manufacturing avoids the modification of the structure, e.g., the physical form of the drug or matrix density of the tablet during the production process. Hence, fusion laser cutting is less likely to modify critical quality attributes such as release patterns or drug contents. In a point-of-care manufacturing scenario, laser cutting offers a significant advantage of simplifying quality control and a real-time release of laser-cut products such as solid dosage forms and implants.

Towards simultaneous determination of tablet porosity and height by terahertz time-domain reflection spectroscopy

The quality control of pharmaceutical tablets is still based on testing small sample numbers using at- and off-line testing methods. Traditional in-process controls, such as tablet mass, height, mechanical strength, and disintegration time are time- and resource-consuming and poorly suited to support an effective transition towards continuous manufacturing. Another suitable parameter to monitor during production would be tablet porosity. Porosity can be linked to mechanical strength and disintegration but typically requires knowledge of tablet dimensions and mass. Tablet porosity measurements based on terahertz time-domain spectroscopy (THz-TDS) offer a fast and non-destructive approach to in-process control testing for physical tablet properties. This study presents THz-TDS reflection measurements as an alternative to the previously reported transmission setup. It is shown that the proposed method can determine porosity based on the reflected amplitude from the tablet surface, but also allows for precise determination of tablet height in the same measurement. The tablet mass can be estimated by combining the height and porosity measurements. This opens up for the opportunity to determine the tablet's mechanical strength by using the possible correlation to the determined porosity.

Machine learning framework for extracting micro-viscoelastic and micro-structural properties of compressed oral solid dosage forms

A compressed pharmaceutical oral solid dosage (OSD) form is a strongly micro-viscoelastic material composite arranged as a network of agglomerated particles due to its constituent powders and their bonding and fractural mechanical properties. An OSD product's Critical Quality Attributes, such as disintegration, drug release (dissolution) profile, and structural strength ("hardness"), are influenced by its micro-scale properties. Ultrasonic evaluation is direct, non-destructive, rapid, and cost-effective. However, for practical process control applications, the simultaneous extraction of the micro-viscoelastic and scattering properties from a tablet's ultrasonic response requires a unique solution to a challenging inverse mathematical wave propagation problem. While the spatial progression of a pulse traveling in a composite medium with known micro-scale properties is a straightforward computational task when its dispersion relation is known, extracting such properties from the experimentally acquired waveforms is often non-trivial. In this work, a novel Machine Learning (ML)-based micro-property extraction technique directly from waveforms, based on Multi-Output Regression models and Neural Networks, is introduced and demonstrated. Synthetic waveforms with a given set of micro-properties of virtual tablets are computationally generated to train, validate, and test the developed ML models for their effectiveness in the inverse problem of recovering specified micro-scale properties. The effectiveness of these ML models is then tested and demonstrated for a set of physical OSD tablets. The micro-viscoelastic and micro-structural properties of physical tablets with known properties have been extracted through experimentally acquired waveforms to exhibit their consistency with the generated ML-based attenuation results.

Regulatory Experience with Continuous Manufacturing and Real Time Release Testing for Dissolution in New Drug Applications

Regulatory submissions involving the use of continuous manufacturing (CM)1 and/or real-time release testing for dissolution (RTRT-D) to the United States Food and Drug Administration (FDA) were identified spanning several years. The submissions were for orally administered IR tablets and they were examined from a biopharmaceutics perspective to highlight commonly occurring issues which the FDA's assessment teams identified with the proposed use of CM and/or RTRT-D. The objective of this study is to provide recommendations for best practices that will help advance the field by (i) generating greater opportunities for (drug) Applicants2 to benefit from the implementation of advanced manufacturing approaches, (ii) improving high quality regulatory submissions involving CM and RTRT-D, and thus (iii) lessening the regulatory review burden. This paper has identified several common deficiencies, such as inadequate strategies for stratified sampling of drug product (DP) units, inappropriate design of experiments (DoE), inability of the proposed RTRT-D model to account for dissolution variability and to predict the entire time course of dissolution, insufficient documentation, and unsuitable in vitro dissolution methods.

Impact of Raman mapping area and intra-tablet homogeneity on the accuracy of sustained-release tablet dissolution prediction

This exploratory study investigated the minimum required Raman mapping area for predicting sustained-release tablet dissolution profiles based on intra-tablet homogeneity. The aim was to minimize scanning time while achieving reliable dissolution profile predictions. To construct the sample set, we controlled the blending time to introduce variability in the homogeneity of the tablets. The dissolution prediction models were established using the partial least squares regression under different Raman mapping area. The accuracies of the prediction results were evaluated according to the difference factor f1 and Intersection-Union two one-sided t-tests (IU TOST) methods, and the implications conveyed by the results were discussed. The results showed that the homogeneity of sustained-release tablet affects the minimum required mapping area, and the tablets with higher homogeneity show higher prediction accuracy when using the same mapping area to model the dissolution profiles of tablets.

On the influence of raw material attributes on process behaviour and product quality in a continuous WET granulation tableting line

Continuous manufacturing of oral solids is a complex process in which critical material attributes (CMAs), formulation and critical process parameters (CPPs) play a fundamental role. However, assessing their effect on the intermediate and final product's critical quality attributes (CQAs) remains challenging. The aim of this study was to tackle this shortcoming by evaluating the influence of raw material properties and formulation composition on the processability and quality of granules and tablets on a continuous manufacturing line. Powder-to-tablet manufacturing was performed using four formulations in various process settings. Pre-blends of different drug loadings (2.5 % w/w and 25% w/w) and two BCS classes (Class I and II) were continuously processed on an integrated process line ConsiGmaTM 25, including twin screw wet granulation, fluid bed drying, milling, sieving, in-line lubrication and tableting. The liquid-to-solid ratio and the granule drying time were varied to process granules under nominal, dry and wet conditions. It was shown that the BCS class and the drug dosage influenced the processability. Intermediate quality attributes, such as the loss on drying and the particle size distribution, directly correlated with the raw material's properties and process parameters. Process settings had a profound impact on the tablet's hardness, disintegration time, wettability and porosity.

Convolutional neural network-based evaluation of chemical maps obtained by fast Raman imaging for prediction of tablet dissolution profiles

In this work, the capabilities of a state-of-the-art fast Raman imaging apparatus are exploited to gain information about the concentration and particle size of hydroxypropyl methylcellulose (HPMC) in sustained release tablets. The extracted information is utilized to predict the in vitro dissolution profile of the tablets. For the first time, convolutional neural networks (CNNs) are used for the processing of the chemical images of HPMC distribution and to directly predict the dissolution profile based on the image. This new method is compared to wavelet analysis, which gives a quantification of the texture of HPMC distribution, carrying information regarding both concentration and particle size. A total of 112 training and 32 validation tablets were used, when a CNN was used to characterize the particle size of HPMC, the dissolution profile of the validation tablets was predicted with an average f₂ similarity value of 62.95. Direct prediction based on the image had an f₂ value of 54.2, this demonstrates that the CNN is capable of recognizing the patterns in the data on its own. The presented methods can facilitate a better understanding of the manufacturing processes, as detailed information becomes available with fast measurements.

Control oriented modeling of twin-screw granulation in the ConsiGmaTM-25 production plant

ConsiGma^TM-25 is a continuous production plant integrating a twin-screw granulation, fluid bed drying, granule conditioning, and a tableting unit. The particle size distribution (PSD), active pharmaceutical ingredient (API) content, and liquid content of wet granules after twin-screw granulation affect the quality of intermediate and final products. This paper proposes methods for real-time monitoring of these quantities and control-oriented modeling of the granulator. The PSD of wet granules is monitored via an in-line process analytical technology (PAT) probe based on the spatial velocimetry principle. The algorithm for signal processing and evaluation of PSD characteristics is developed and applied to the acquired PSD data. A dynamic process model predicting PSD characteristics from granulation parameters is trained via the local linear model tree (LoLiMoT) approach. The experimental data required for the model training are collected via systematically designed excitation runs. Finally, the performance of the identified model is examined and verified by means of a new set of validation runs. Furthermore, an in-line PAT probe based on Raman spectroscopy is developed and integrated after the granulator. The API- and liquid content of produced wet granules are evaluated from the spectral data by means of chemometric modeling, and chemometric models are validated on a separate set of experimental data. The solutions proposed in this research can be used as a reliable (and necessary) basis for the development of advanced quality-by-design control concepts (e.g., PSD process control). Such concepts would ultimately improve the ConsiGma^TM-25 process performance in terms of robustness against disturbances and quality of intermediate and final products.

Advancing non-destructive analysis of 3D printed medicines

Pharmaceutical 3D printing (3DP) has attracted significant interest over the past decade for its ability to produce personalised medicines on demand. However, current quality control (QC) requirements for traditional large-scale pharmaceutical manufacturing are irreconcilable with the production offered by 3DP. The US Food and Drug Administration (FDA) and the UK Medicines and Healthcare Products Regulatory Agency (MHRA) have recently published documents supporting the implementation of 3DP for point-of-care (PoC) manufacturing along with regulatory hurdles. The importance of process analytical technology (PAT) and non-destructive analytical tools in translating pharmaceutical 3DP has experienced a surge in recognition. This review seeks to highlight the most recent research on non-destructive pharmaceutical 3DP analysis, while also proposing plausible QC systems that complement the pharmaceutical 3DP workflow. In closing, outstanding challenges in integrating these analytical tools into pharmaceutical 3DP workflows are discussed.

Interpretable artificial neural networks for retrospective QbD of pharmaceutical tablet manufacturing based on a pilot-scale developmental dataset

As the pharmaceutical industry increasingly adopts the Pharma 4.0. concept, there is a growing need to effectively predict the product quality based on manufacturing or in-process data. Although artificial neural networks (ANNs) have emerged as powerful tools in data-rich environments, their implementation in pharmaceutical manufacturing is hindered by their black-box nature. In this work, ANNs were developed and interpreted to demonstrate their applicability to increase process understanding by retrospective analysis of developmental or manufacturing data. The in vitro dissolution and hardness of extended-release, directly compressed tablets were predicted from manufacturing and spectroscopic data of pilot-scale development. The ANNs using material attributes and operational parameters provided better results than using NIR or Raman spectra as predictors. ANNs were interpreted by sensitivity analysis, helping to identify the root cause of the batch-to-batch variability, e.g., the variability in particle size, grade, or substitution of the hydroxypropyl methylcellulose excipient. An ANN-based control strategy was also successfully utilized to mitigate the batch-to-batch variability by flexibly operating the tableting process. The presented methodology can be adapted to arbitrary data-rich manufacturing steps from active substance synthesis to formulation to predict the quality from manufacturing or development data and gain process understanding and consistent product quality.

Semi-mechanistic reduced order model of pharmaceutical tablet dissolution for enabling Industry 4.0 manufacturing systems

We propose a generalization of the Weibull dissolution model, referred to as generalized Weibull dissolution model, that seamlessly captures all three fractional dissolution rates experimentally observed in pharmaceutical solid tablets, namely decreasing, increasing, and non-monotonic rates. This is in contrast to traditional reduced order models, which capture at most two fractional dissolution rates and, thus, are not suitable for a wide range of product formulations hindering, for example, the adoption of knowledge management in the context of Industry 4.0. We extend the generalized Weibull dissolution model further to capture the relationship between critical process parameters (CPPs), critical materials attributes (CMAs), and dissolution profile to, in turn, facilitate real-time release testing (RTRT) and quality-by-control (QbC) strategies. Specifically, we endow the model with multivariate rational polynomials that interpolate the mechanistic limiting behavior of tablet dissolution as CPPs and CMAs approach certain values of physical significance (such as the upper and lower bounds of tablet porosity or lubrication conditions), thus the semi-mechanistic nature of the reduced order model. Restricting attention to direct compaction and using various case studies from the literature, we demonstrate the versatility and the capability of the semi-mechanistic ROM to estimate changes in dissolution due to process disturbances in tablet weight, porosity, lubrication conditions (i.e., the total amount of shear strain imparted during blending), and moisture content in the powder blend. In all of the cases considered in this work, the estimations of the model are in remarkable agreement with experimental data.

State-of-the-art and emerging trends in analytical approaches to pharmaceutical-product commercialization

The biopharmaceutical landscape continues to evolve rapidly, and associated modality complexity and the need to improve molecular understanding require concomitant advances in analytical approaches used to characterize and release the product. The Product Quality Attribute Assessment (PQAA) and Quality Target Product Profile (QTPP) frameworks help catalog and translate molecular understanding to process and product-design targets, thereby enabling reliable manufacturing of high-quality product. The analytical target profile forms the basis of identifying best-fit analytical methods for attribute measurement and continues to be successfully used to develop robust analytical methods for detailed product characterization as well as release and stability testing. Despite maturity across multiple testing platforms, advances continue to be made, several with the potential to alter testing paradigms. There is an increasing role for mass spectrometry beyond product characterization and into routine release testing as seen by the progress in multi-attribute methods and technologies, applications to aggregate measurement, the development of capillary zone electrophoresis (CZE) coupled with mass spectrometry (MS) and capillary isoelectric focusing (CIEF) with MS for measurement of glycans and charged species, respectively, and increased application to host cell protein measurement. Multitarget engaging multispecific modalities will drive advances in bioassay platforms and recent advances both in 1- and 2-D NMR approaches could make it the method of choice for characterizing higher-order structures. Additionally, rigorous understanding of raw material and container attributes is necessary to complement product understanding, and these collectively can enable robust supply of high-quality product to patients.

Application of chemometrics using direct spectroscopic methods as a QC tool in pharmaceutical industry and their validation

This present review described the application of chemometrics using direct spectroscopic methods at the quality control (QC) laboratory of Pharmaceutical Industries. Using chemometrics methods, all QC assessments during the fabrication processes of the drug preparations can be well performed. Chemometrics methods have some advantages compared to the conventional methods, i.e., non-destructive, can be performed directly to intake samples without any extractions, unnecessary performing stability studies, and cost-effective. To achieve reliable results of analyses, all methods must be validated first prior to routine applications. According to the current Pharmacopeia, the validation parameters are specificity/selectivity, accuracy, repeatability, intermediate precision, range, detection limit, quantification limit and robustness. These validation data must meet the acceptance criteria, that have been described by the analytical target profile (ATP) of the drug preparations.

Big data collection in pharmaceutical manufacturing and its use forproduct quality predictions

Advances in data science and digitalization are transforming the world, and the pharmaceutical industry is no exception. Multiple sensor-equipped manufacturing processes and laboratory analysis are the main sources of primary data, which have been utilized for the presented dataset of 1005 actual production batches of selected medicine. This dataset includes incoming raw material quality results, compression process time series and final product quality results for the selected product. The data is highly valuable for it provides an insight into every 10 seconds of the process trajectory for 1005 actual production batches along with product quality collected over several years. It therefore offers an opportunity to develop advanced analysis models and procedures which would lead to the omission of current conventional and time consuming laboratory testing. Benefits for both the industry and patient are obvious: reducing product lead times and costs of manufacture.

Measurement(s)	Incoming raw material quality (particle size distribution, water content, impurities level, residual solvents, pH) • In process control measurements of tablet core and film coated tablets (weight, thickness, diameter, hardness, yield of a process) • Final medicine quality on a representative sample of film coated tablets (drug release in defined time, active ingredient content, impurities level, residual solven content) • Process time series of selected tablet compression parameters (every 10 s of the compression process)
Technology Type(s)	Laboratory based analysis (particle sizer using laser diffraction, loss on drying method, HPLC method, GC method) • Automatic IPC check machine (combining balance, hardness, thickness and diameter measurements) • HPLC (High performance liquid chromatography), GC (gas chromatography) • Tablet compression machine calibrated sensors for the following main parameters: main and pre-compression force, fill depth, cylindrycal height, ejection force, number of wasted tablets.
Factor Type(s)	batch • code • strength • size • start • api_code • api_batch • smcc_batch • lactose_batch • starch_batch • api_water • api_total_impurities • api_l_impurity • api_content • api_ps01 • api_ps05 • api_ps09 • lactose_water • lactose_sieve0045 • lactose_sieve015 • lactose_sieve025 • smcc_water • smcc_td • smcc_bd • smcc_ps01 • smcc_ps05 • smcc_ps09 • starch_ph • starch_water • tbl_min_hardness • tbl_max_hardness • tbl_av_hardness • tbl_min_thickness • tbl_max_thickness • fct_min_thickness • fct_max_thickness • tbl_min_weight • tbl_max_weight • tbl_rsd_weight • fct_rsd_weight • fct_min_hardness • fct_max_hardness • fct_av_hardness • tbl_tensile • fct_tensile • tbl_yield • batch_yield • time series: tbl_speed • time series: fom • time series: main_comp • time series: tbl_fill • time series: SREL • time series: pre_comp • time series: produced • time series: waste • time series: cyl_main • time series: cyl_pre • time series: stiffness • time series: ejection
Sample Characteristic - Organism	Selected medicine
Sample Characteristic - Environment	manufacturing process
Sample Characteristic - Location	Pharmaceutical industry

Ascertain a minimum coating thickness for acid protection of enteric coatings by means of optical coherence tomography

Enteric coatings are designed to protect active pharmaceutical ingredients (APIs) against untimely release in the stomach. Acid protection of such coatings depends on the coating layer thickness and integrity, which must be determined in an accurate and reliable way to ensure the final product's desired performance. Our work addresses the use of optical coherence tomography (OCT) for characterizing the coating thickness and variability of an enteric-coated drug product and linking them to resistance against gastric fluid. In this study, three batches of enteric-coated tablets drawn during the manufacturing process were investigated. An industrial OCT system was used to establish the coating thickness variability of single tablets (intra-tablet), all tablets in a batch (inter-tablet) and between the batches (inter-batch). Based on the large amount of OCT data, we calculated a critical coating thickness for the investigated film coating, which was found to be 27.4 µm. The corresponding distribution has a mean coating thickness of 44.3 µm ± 7.8 µm. The final coated product has a final mean coating thickness of 63.4 µm ± 8.7 µm, guaranteeing that all tablets meet the quality criterion (i.e., acid protection). Based on the measured thickness distributions, already known distribution functions were considered and an additional, new function was proposed for characterizing the coating thickness distributions in the early stages of industrial coating processes. The proposed approach can be transferred to in-line monitoring of the tablet coating processes, which could drastically improve the production efficiency by ultimately allowing real-time release testing (RTRT).

Modular design principle based on compartmental drug delivery systems

The current manufacturing solutions for oral solid dosage forms are fundamentally based on technologies from the 19th century. This approach is well suited for mass production of one-size-fits-all products; however, it does not allow for a straight-forward personalization and mass customization of the pharmaceutical end-product. In order to provide better therapies to the patients, a need for innovative manufacturing concepts and product design principles has been rising. Additive manufacturing opens up a possibility for compartmentalization of drug products, including design of spatially separated multidrug and functional excipient compartments. This compartmentalized solution can be further expanded to modular design thinking. Modular design is referring to combination of building blocks containing a given amount of drug compound(s) and related functional excipients into a larger final product. Implementation of modular design principles is paving the way for implementing the emerging personalization potential within health sciences by designing compartmental and reactive product structures that can be manufactured based on the individual needs of each patient. This review will introduce the existing compartmentalized product design principles and discuss the integration of these into edible electronics allowing for innovative control of drug release.

Additive manufacturing in drug delivery: Innovative drug product design and opportunities for industrial application

Additive manufacturing (AM) or 3D printing is enabling new directions in product design. The adoption of AM in various industrial sectors has led to major transformations. Similarly, AM presents new opportunities in the field of drug delivery, opening new avenues for improved patient care. In this review, we discuss AM as an innovative tool for drug product design. We provide a brief overview of the different AM processes and their respective impact on the design of drug delivery systems. We highlight several enabling features of AM, including unconventional release, customization, and miniaturization, and discuss several applications of AM for the fabrication of drug products. This includes products that have been approved or are in development. As the field matures, there are also several new challenges to broad implementation in the pharmaceutical landscape. We discuss several of these from the regulatory and industrial perspectives and provide an outlook for how these issues may be addressed. The introduction of AM into the field of drug delivery is an enabling technology and many new drug products can be created through productive collaboration of engineers, materials scientists, pharmaceutical scientists, and industrial partners.

Development of a Controlled Continuous Low-Dose Feeding Process

This paper proposes a feed rate control strategy for a novel volumetric micro-feeder, which can accomplish low-dose feeding of pharmaceutical raw materials with significantly different powder properties. The developed feed-forward control strategy enables a constant feed rate with a minimum deviation from the set-point, even for materials that are typically difficult to accurately feed (e.g., due to high cohesion or low density) using conventional continuous feeders. Density variations observed during the feeding process were characterized via a displacement feed factor profile for each powder. The characterized effective displacement density profile was applied in the micro-feeder system to proactively control the feed rate by manipulating the powder displacement rate (i.e., computing the feed rate from the powder displacement rate). Based on the displacement feed factor profile, the feed rate can be predicted during the feeding process and at any feed rate set-point. Three pharmaceutically relevant materials were used for the micro-feeder evaluation: di-calcium phosphate (large-particle system, high density), croscarmellose sodium (small-particle system, medium density), and barium sulfate (very small-particle <10 μm, high density). A significant improvement in the feeding performance was achieved for all investigated materials. The feed rate deviation from the set-point and its relative standard deviation were minimal compared to operations without the control strategy.

Continuous downstream processing of milled electrospun fibers to tablets monitored by near-infrared and Raman spectroscopy

Electrospinning is a technology for manufacture of nano- and micro-sized fibers, which can enhance the dissolution properties of poorly water-soluble drugs. Tableting of electrospun fibers have been demonstrated in several studies, however, continuous manufacturing of tablets have not been realized yet. This research presents the first integrated continuous processing of milled drug-loaded electrospun materials to tablet form supplemented by process analytical tools for monitoring the active pharmaceutical ingredient (API) content. Electrospun fibers of an amorphous solid dispersion (ASD) of itraconazole and poly(vinylpyrrolidone-co-vinyl acetate) were produced using high speed electrospinning and afterwards milled. The milled fibers with an average fiber diameter of 1.6 ± 0.9 µm were continuously fed with a vibratory feeder into a twin-screw blender, which was integrated with a tableting machine to prepare tablets with ~ 10 kN compression force. The blend of fibers and excipients leaving the continuous blender was characterized with a bulk density of 0.43 g/cm³ and proved to be suitable for direct tablet compression. The ASD content, and thus the API content was determined in-line before tableting and at-line after tableting using near-infrared and Raman spectroscopy. The prepared tablets fulfilled the USP <905> content uniformity requirement based on the API content of ten randomly selected tablets. This work highlights that combining the advantages of electrospinning (e.g. less solvent, fast and gentle drying, low energy consumption, and amorphous products with high specific surface area) and the continuous technologies opens a new and effective way in the field of manufacturing of the poorly water-soluble APIs.

Review: Continuous Manufacturing of Small Molecule Solid Oral Dosage Forms

Continuous manufacturing (CM) is defined as a process in which the input material(s) are continuously fed into and transformed, and the processed output materials are continuously removed from the system. CM can be considered as matching the FDA's so-called 'Desired State' of pharmaceutical manufacturing in the twenty-first century as discussed in their 2004 publication on 'Innovation and Continuous Improvement in Pharmaceutical Manufacturing'. Yet, focused attention on CM did not really start until 2014, and the first product manufactured by CM was only approved in 2015. This review describes some of the benefits and challenges of introducing a CM process with a particular focus on small molecule solid oral dosage forms. The review is a useful introduction for individuals wishing to learn more about CM.

Tableting model assessment of porosity and tensile strength using a continuous wet granulation route

This paper presents a comprehensive assessment of the most widely used tablet compaction models in a continuous wet granulation tableting process. The porosity models, tensile strength models and lubricant models are reviewed from the literature and classified based on their formulations i.e. empirical or theoretical and applications, i.e. batch or continuous. The majority of these models are empirical and were initially developed for batch tabletting process. To ascertain their effectiveness and serviceability in the continuous tableting process, a continuous powder processing line of Diamond Pilot Plant (DiPP) installed at The University of Sheffield was used to provide the quantitative data for tablet model assessment. Magnesium stearate (MgSt) is used as a lubricant to investigate its influence on the tensile strength. Whilst satisfactory predictions from the tablet models can be produced, a compromise between the model fidelity and model simplicity needs to be made for a suitable model selection. The Sonnergaard model outperforms amongst the porosity models whilst the Reynolds model produces the best goodness of fitting for two parameters fitting porosity models. An improved tensile strength model is proposed to consider the influence of powder size and porosity in the continuous tableting process.

Rapid Prototyping of Miniaturized Powder Mixing Geometries

Continuous manufacturing is an important element of future manufacturing solutions enabling for both high product quality and streamlined development process. The increasing possibilities with computer simulations allow for innovating novel mixing principles applicable for continuous manufacturing. However, these innovative ideas based on simulations need experimental validation. The use of rapid prototyping based on additive manufacturing opens a possibility to evaluate these ideas at a low cost. In this study, a novel powder mixing geometry was prototyped using additive manufacturing and further, interfaced with an in-line near-IR spectrometer allowing for investigating the residence time distribution (RTD) in this geometry.

A Fast and Non-destructive Terahertz Dissolution Assay for Immediate Release Tablets

There is a clear need for a robust process analytical technology tool that can be used for on-line/in-line prediction of dissolution and disintegration characteristics of pharmaceutical tablets during manufacture. Tablet porosity is a reliable and fundamental critical quality attribute which controls key mass transport mechanisms that govern disintegration and dissolution behavior. A measurement protocol was developed to measure the total porosity of a large number of tablets in transmission without the need for any sample preparation. By using this fast and non-destructive terahertz spectroscopy method it is possible to predict the disintegration and dissolution of drug from a tablet in less than a second per sample without the need of a chemometric model. The validity of the terahertz porosity method was established across a range of immediate release (IR) formulations of ibuprofen and indomethacin tablets of varying geometries as well as with and without debossing. Excellent correlation was observed between the measured terahertz porosity, dissolution characteristics (time to release 50% drug content) and disintegration time for all samples. These promising results and considering the robustness of the terahertz method pave the way for a fully automated at-line/on-line porosity sensor for real time release testing of IR tablets dissolution.