Effects of omitting titanium dioxide from the film coating of a pharmaceutical tablet - An industrial case study of attempting to comply with EU regulation 2022/63.

Recently, concerns have been raised about the safety of titanium dioxide (TiO2), a commonly used component of pharmaceutical film coatings. The European Union has recently prohibited the application of this material in the food industry, and it is anticipated that the same will happen in the pharmaceutical industry. For this reason, pharmaceutical manufacturers have to consider the possible impact of removing TiO2 from the film coating of tablets. In this paper, we present a case study of a commercially produced tablet where the film coating containing TiO2 was replaced with a coating using calcium carbonate (CaCO3) or with a transparent coating. The performance of the coatings was compared by measuring the moisture absorption rate and the dissolution profile of the tablets. In these regards, there were negligible differences between the coating types. The tablets contained a highly photosensitive drug, the ability of the coatings to protect the drug was evaluated through environmental stability and photostability measurements. The HPLC results showed that the inclusion of TiO2 does not provide additional benefits, when humidity and thermal stress is applied, however its role was vital in protecting the drug from external light. There were several decomposition products which appeared in large quantities when TiO2 was missing from the coating. These results imply that photosensitivity is an issue, replacing TiO2 will be challenging, though its absence can be tolerated when the drug does not need to be protected from light.

Development of a PAT platform for the prediction of granule tableting properties.

In this work, the feasibility of implementing a process analytical technology (PAT) platform consisting of Near Infrared Spectroscopy (NIR) and particle size distribution (PSD) analysis was evaluated for the prediction of granule downstream processability. A Design of Experiments-based calibration set was prepared using a fluid bed melt granulation process by varying the binder content, granulation time, and granulation temperature. The granule samples were characterized using PAT tools and a compaction simulator in the 100-500 kg load range. Comparing the systematic variability in NIR and PSD data, their complementarity was demonstrated by identifying joint and unique sources of variation. These particularities of the data explained some differences in the performance of individual models. Regarding the fusion of data sources, the input data structure for partial least squares (PLS) based models did not significantly impact the predictive performance, as the root mean squared error of prediction (RMSEP) values were similar. Comparing PLS and artificial neural network (ANN) models, it was observed that the ANNs systematically provided superior model performance. For example, the best tensile strength, ejection stress, and detachment stress prediction with ANN resulted in an RMSEP of 0.119, 0.256, and 0.293 as opposed to the 0.180, 0.395, and 0.430 RMSEPs of the PLS models, respectively. Finally, the robustness of the developed models was assessed.

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.

Improvement of drug processability in a connected continuous crystallizer system using formulation additive.

Continuous crystallization in the presence of polymer additives is a promising method to omit some drug formulation steps by improving the technological and also pharmacological properties of crystalline active ingredients. Accordingly, this study focuses on developing an additive-assisted continuous crystallization process using polyvinylpyrrolidone in a connected ultrasonicated plug flow crystallizer and an overflow mixed suspension mixed product removal (MSMPR) crystallizer system. We aimed to improve the flowability characteristics of small, columnar primary plug flow crystallizer-produced acetylsalicylic acid crystals as a model drug by promoting their agglomeration in MSMPR crystallizer with polyvinylpyrrolidone. The impact of the cooling antisolvent crystallization process parameters (temperature, polymer amount, total flow rate) on product quality and quantity was investigated. Finally, a spatially segmented antisolvent dosing method was also evaluated. The developed technology enabled the manufacture of purified, constant quality products in a short startup period, even with an 85% yield. We found that a higher polymer amount (7.5-14%) could facilitate agglomeration resulting in "good" flowability without altering the favorable dissolution characteristics of the primary particles.

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.

Challenges and Opportunities of Implementing Data Fusion in Process Analytical Technology-A Review.

The release of the FDA's guidance on Process Analytical Technology has motivated and supported the pharmaceutical industry to deliver consistent quality medicine by acquiring a deeper understanding of the product performance and process interplay. The technical opportunities to reach this high-level control have considerably evolved since 2004 due to the development of advanced analytical sensors and chemometric tools. However, their transfer to the highly regulated pharmaceutical sector has been limited. To this respect, data fusion strategies have been extensively applied in different sectors, such as food or chemical, to provide a more robust performance of the analytical platforms. This survey evaluates the challenges and opportunities of implementing data fusion within the PAT concept by identifying transfer opportunities from other sectors. Special attention is given to the data types available from pharmaceutical manufacturing and their compatibility with data fusion strategies. Furthermore, the integration into Pharma 4.0 is discussed.

Application of Artificial Neural Networks in the Process Analytical Technology of Pharmaceutical Manufacturing-a Review.

Industry 4.0 has started to transform the manufacturing industries by embracing digitalization, automation, and big data, aiming for interconnected systems, autonomous decisions, and smart factories. Machine learning techniques, such as artificial neural networks (ANN), have emerged as potent tools to address the related computational tasks. These advancements have also reached the pharmaceutical industry, where the Process Analytical Technology (PAT) initiative has already paved the way for the real-time analysis of the processes and the science- and risk-based flexible production. This paper aims to assess the potential of ANNs within the PAT concept to aid the modernization of pharmaceutical manufacturing. The current state of ANNs is systematically reviewed for the most common manufacturing steps of solid pharmaceutical products, and possible research gaps and future directions are identified. In this way, this review could aid the further development of machine learning techniques for pharmaceutical production and eventually contribute to the implementation of intelligent manufacturing lines with automated quality assurance.

Soft sensor for content prediction in an integrated continuous pharmaceutical formulation line based on the residence time distribution of unit operations.

In this study, a concentration predicting soft sensor was achieved based on the Residence Time Distribution (RTD) of an integrated, three-step pharmaceutical formulation line. The RTD was investigated with color-based tracer experiments using image analysis. Twin-screw wet granulation (TSWG) was directly coupled with a horizontal fluid bed dryer and an oscillating mill. Based on integrated measurement, we proved that it is also possible to couple the unit operations in silico. Three surrogate tracers were produced with a coloring agent to investigate the separated unit operations and the solid and liquid inputs of the TSWG. The soft sensor's prediction was compared to validating experiments of a 0.05 mg/g (15% of the nominal) concentration change with High-Performance Liquid Chromatography (HPLC) reference measurements of the active ingredient proving the adequacy of the soft sensor (RMSE < 4%).

UV/VIS imaging-based PAT tool for drug particle size inspection in intact tablets supported by pattern recognition neural networks.

The potential of machine vision systems has not currently been exploited for pharmaceutical applications, although expected to provide revolutionary solutions for in-process and final product testing. The presented paper aimed to analyze the particle size of meloxicam, a yellow model active pharmaceutical ingredient, in intact tablets by a digital UV/VIS imaging-based machine vision system. Two image processing algorithms were developed and coupled with pattern recognition neural networks for UV and VIS images for particle size-based classification of the prepared tablets. The developed method can identify tablets containing finer or larger particles than the target with more than 97% accuracy. Two algorithms were developed for UV and VIS images for particle size analysis of the prepared tablets. According to the applied statistical tests, the obtained particle size distributions were similar to the results of the laser diffraction-based reference method. Digital UV/VIS imaging combined with multivariate data analysis can provide a new non-destructive, rapid, in-line tool for particle size analysis in tablets.

Raman-based real-time dissolution prediction using a deterministic permeation model.

The purpose of this study was to develop a deterministic permeation model (DPM) that predicts the in vitro release profile of an active ingredient (API) embedded in hydroxypropyl-methylcellulose (HPMC) matrix tablets based on Raman spectra. So far in the literature, such mechanistic models were utilized only for formulation optimization (off-line dissolution prediction), while the real-time prediction of dissolution profiles based on Process Analytical Technology (PAT) data was performed by empirical methods such as Partial Least Squares (PLS) regression. Our work represents a novel conceptual approach that utilizes a mechanistic model to predict dissolution profiles based on data yielded by PAT tools. Tablets containing various API- and HPMC-amounts were produced using different compression pressures according to a 33 full factorial design, their Raman spectra were recorded before dissolution testing. The DPM was constructed using one-third of the measured dissolution profiles and is presented as a system of differential equations together with its analytical solution. The parameters of DPM were estimated by the training data set containing the spectroscopically determined API- and HPMC- amounts and the tableting pressures used, then the release profiles of the remaining two-thirds of the tablets were predicted. The Raman spectra-based predictions of DPM were compared with predictions of an Artificial Neural Network (ANN). It was found that the two methods yield similar results, however, the mechanistic approach has the benefit of requiring a lower amount of training samples. Although the model is based on a remarkable simplification of reality, it facilitates a deeper understanding of the behavior of the formulation. The DPM could improve our understanding of the effect of HPMC and tableting pressures on the release kinetics of the HPMC matrix tablets and participate in the development of PAT-based new surrogate dissolution methods for Real-Time Release testing (RTRt).

Raman mapping-based non-destructive dissolution prediction of sustained-release tablets.

In this paper, the applicability of Raman chemical imaging for the non-destructive prediction of the in vitro dissolution profile of sustained-release tablets is demonstrated for the first time. Raman chemical maps contain a plethora of information about the spatial distribution and the particle size of the components, compression force and even polymorphism. With proper data analysis techniques, this can be converted into simple numerical information which can be used as input in a machine learning model. In our work, sustained-release tablets using hydroxypropyl methylcellulose (HPMC) as matrix polymer are prepared, the concentration and particle size of this component varied between samples. Chemical maps of HPMC are converted into histograms with two different methods, an approach based on discretizing concentration values and a wavelet analysis technique. These histograms are then subjected to Principal Component Analysis, the score value of the first two principal components was found to represent HPMC content and particle size. These values are used as input in Artificial Neural Networks which are trained to predict the dissolution profile of the tablets. As a result, accurate predictions were obtained for the test tablets (the average f2 similarity value is higher than 59 with both methods). The presented methodology lays the foundations of the analysis of far more extensive datasets acquired with the emerging fast Raman imaging technology.

Flux-Based Formulation Development-A Proof of Concept Study.

The work aimed to develop the Absorption Driven Drug Formulation (ADDF) concept, which is a new approach in formulation development to ensure that the drug product meets the expected absorption rate. The concept is built on the solubility-permeability interplay and the rate of supersaturation as the driving force of absorption. This paper presents the first case study using the ADDF concept where not only dissolution and solubility but also permeation of the drug is considered in every step of the formulation development. For that reason, parallel artificial membrane permeability assay (PAMPA) was used for excipient selection, small volume dissolution-permeation apparatus was used for testing amorphous solid dispersions (ASDs), and large volume dissolution-permeation tests were carried out to characterize the final dosage forms. The API-excipient interaction studies on PAMPA indicated differences when different fillers or surfactants were studied. These differences were then confirmed with small volume dissolution-permeation assays where the addition of Tween 80 to the ASDs decreased the flux dramatically. Also, the early indication of sorbitol's advantage over mannitol by PAMPA has been confirmed in the investigation of the final dosage forms by large-scale dissolution-permeation tests. This difference between the fillers was observed in vivo as well. The presented case study demonstrated that the ADDF concept opens a new perspective in generic formulation development using fast and cost-effective flux-based screening methods in order to meet the bioequivalence criteria. Graphical Abstract.

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.

Real-time release testing of dissolution based on surrogate models developed by machine learning algorithms using NIR spectra, compression force and particle size distribution as input data.

In this work spectroscopic measurements, process data and Critical Material Attributes (CMAs) are used to predict the in vitro dissolution profile of sustained-release tablets with three machine learning methods, Artificial Neural Networks (ANN), Support Vector Machines (SVM) and Ensemble of Regression Trees (ERT). Beside the effect of matrix polymer content and compression force, the influence of active pharmaceutical ingredient (API) and matrix polymer particle size distribution (PSD) on the drug release rate of sustained tablets is studied. The matrix polymer PSD was found to be a significant factor, thus this factor was included in the dissolution prediction experiments. In order to evaluate the importance of the inclusion of PSD data, models without PSD data were also prepared and the results were compared. In the developed models, the API and hydroxypropyl-methylcellulose (HPMC) content is predicted from near-infrared (NIR) spectra, the compression force is measured by the tablet press and HPMC particle size is measured off-line. The predictions of ANN, SVM and ERT were compared to the measured dissolution profiles of the validation tablets, ANN yielded the most accurate results. In the presented work, data provided by Process Analytical Technology (PAT) sensors is combined with CMAs for the first time to realize the Real-Time Release Testing (RTRT) of tablet dissolution.

Continuous blending monitored and feedback controlled by machine vision-based PAT tool.

In a continuous powder blending process machine vision is utilized as a Process Analytical Technology (PAT) tool. While near-infrared (NIR) and Raman spectroscopy are reliable methods in this field, measurements become challenging when concentrations below 2 w/w% are quantified. However, an active pharmaceutical ingredient (API) with an intense color might be quantified in even lower quantities by images recorded with a digital camera. Riboflavin (RI) was used as a model API with orange color, its Limit of Detection was found to be 0.015 w/w% and the Limit of Quantification was 0.046 w/w% using a calibration based on the pixel value of images. A calibration for in-line measurement of RI concentration was prepared in the range of 0.2-0.45 w/w%, validation with UV/VIS spectrometry showed great accuracy with a relative error of 2.53 %. The developed method was then utilized for a residence time distribution (RTD) measurement in order to characterize the dynamics of the blending process. Lastly, the technique was applied in real-time feedback control of a continuous powder blending process. Machine vision based direct or indirect API concentration determination is a promising and fast method with a great potential for monitoring and control of continuous pharmaceutical processes.

Process Design of Continuous Powder Blending Using Residence Time Distribution and Feeding Models.

The present paper reports a thorough continuous powder blending process design of acetylsalicylic acid (ASA) and microcrystalline cellulose (MCC) based on the Process Analytical Technology (PAT) guideline. A NIR-based method was applied using multivariate data analysis to achieve in-line process monitoring. The process dynamics were described with residence time distribution (RTD) models to achieve deep process understanding. The RTD was determined using the active pharmaceutical ingredient (API) as a tracer with multiple designs of experiment (DoE) studies to determine the effect of critical process parameters (CPPs) on the process dynamics. To achieve quality control through material diversion from feeding data, soft sensor-based process control tools were designed using the RTD model. The operation block model of the system was designed to select feasible experimental setups using the RTD model, and feeder characterizations as digital twins, therefore visualizing the output of theoretical setups. The concept significantly reduces the material and instrumental costs of process design and implementation.

End-to-end continuous manufacturing of conventional compressed tablets: From flow synthesis to tableting through integrated crystallization and filtration.

An end-to-end continuous pharmaceutical manufacturing process was developed for the production of conventional direct compressed tablets on a proof-of-concept level for the first time. The output reaction mixture of the flow synthesis of acetylsalicylic acid was crystallized continuously in a mixed suspension mixed product removal crystallizer. The crystallizer was directly connected to a continuous filtration carousel device, thus the crystallization, filtration and drying of acetylsalicylic acid (ASA) was carried out in an integrated 2-step process. Steady state was reached during longer operations and the interaction of process parameters was evaluated in a series of experiments. The filtered crystals were ready for further processing in a following continuous blending and tableting experiment due to the good flowability of the material. The ASA collected during the crystallization-filtration experiments was fed into a continuous twin-screw blender along with microcrystalline cellulose as tableting excipient. After continuous blending Near-Infrared spectroscopy was applied to in-line analyze the drug content of the powder mixture. A belt conveyor carried the mixture towards an eccentric lab-scale tablet press, which continuously produced 500 mg ASA-loaded compressed tablets of 100 mg dose strength. Thus, starting from raw materials, the final drug product was obtained by continuous manufacturing steps with appropriate quality.

Digital UV/VIS imaging: A rapid PAT tool for crushing strength, drug content and particle size distribution determination in tablets.

The Process Analytical Technology (PAT) and the Quality-by-Design (QbD) approaches can efficiently facilitate the shift to the desired continuous manufacturing and real time release testing (RTRT). By this, it is vital to develop new, in-line analytical methods which fulfil the pharmaceutical requirements. The fast-developing digital imaging-based machine vision systems can provide revolutionary solutions not just in the automotive industry but in the pharmaceutical technology, as well. This study aimed to explore the capabilities of UV/VIS-based machine vision in tablet inspection as a PAT tool for the determination of compression force and crushing strength, drug content and drug distribution in tablets using meloxicam a yellow model drug. In the case of determining the compression force and crushing strength, the application of multivariate wavelet texture analysis (MWTA) based models provided relatively low prediction errors. To predict the drug content of meloxicam tablets CIELAB or RGB colorspace based algorithms were successfully developed and validated. UV/VIS imaging was also used to map the particle size distribution and spatial distribution of meloxicam, the results were compared to chemical maps obtained by Raman microscopy. Digital imaging combined with multivariate data analysis might be a valuable, high throughput, in-line PAT tool for automated inspection of pharmaceutical tablets.

Fast, Spectroscopy-Based Prediction of In Vitro Dissolution Profile of Extended Release Tablets Using Artificial Neural Networks.

The pharmaceutical industry has never seen such a vast development in process analytical methods as in the last decade. The application of near-infrared (NIR) and Raman spectroscopy in monitoring production lines has also become widespread. This work aims to utilize the large amount of information collected by these methods by building an artificial neural network (ANN) model that can predict the dissolution profile of the scanned tablets. An extended release formulation containing drotaverine (DR) as a model drug was developed and tablets were produced with 37 different settings, with the variables being the DR content, the hydroxypropyl methylcellulose (HPMC) content and compression force. NIR and Raman spectra of the tablets were recorded in both the transmission and reflection method. The spectra were used to build a partial least squares prediction model for the DR and HPMC content. The ANN model used these predicted values, along with the measured compression force, as input data. It was found that models based on both NIR and Raman spectra were capable of predicting the dissolution profile of the test tablets within the acceptance limit of the f2 difference factor. The performance of these ANN models was compared to PLS models using the same data as input, and the prediction of the ANN models was found to be more accurate. The proposed method accomplishes the prediction of the dissolution profile of extended release tablets using either NIR or Raman spectra.

Application of artificial neural networks for Process Analytical Technology-based dissolution testing.

This work proposes the application of artificial neural networks (ANN) to non-destructively predict the in vitro dissolution of pharmaceutical tablets from Process Analytical Technology (PAT) data. An extended release tablet formulation was studied, where the dissolution was influenced by the composition of the tablets and the tableting compression force. NIR and Raman spectra of the intact tablets were measured, and the dissolution of the tablets was modeled directly from the spectral data. Partial Least Square (PLS) regression and ANN models were developed for the different spectroscopic measurements individually as well as by combining them together. ANN provided up to 3% lower root mean square error for prediction (RMSEP) than the PLS models, due to its capability of modeling non-linearity between the process parameters and dissolution curves. The ANN model using reflection NIR spectra provided the most accurate predictions with 6.5 and 63 mean f1 and f2 values between the computed and measured dissolution curves, respectively. Furthermore, ANN served as a straightforward data fusion method without the need for additional preprocessing steps. The method could significantly advance data processing in the PAT environment, contribute to an enhanced real-time release testing procedure and hence the increased efficacy of dissolution testing.