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.

Machine vision-based non-destructive dissolution prediction of meloxicam-containing tablets

Machine vision systems have emerged for quality assessment of solid dosage forms in the pharmaceutical industry. These can offer a versatile tool for continuous manufacturing while supporting the framework of process analytical technology, quality-by-design, and real-time release testing. The aim of this work is to develop a digital UV/VIS imaging-based system for predicting the in vitro dissolution of meloxicam-containing tablets. The alteration of the dissolution profiles of the samples required different levels of the critical process parameters, including compression force, particle size and content of the API. These process parameters were predicted non-destructively by multivariate analysis of UV/VIS images taken from the tablets. The dissolution profile prediction was also executed using solely the image data and applying artificial neural networks. The prediction error (RMSE) of the dissolution profile points was less than 5%. The alteration of the API content directly affected the maximum concentrations observed at the end of the dissolution tests. This parameter was predicted with a relative error of less than 10% by PLS models that are based on the color components of UV and VIS images. In conclusion, this paper presents a modern, non-destructive PAT solution for real-time testing of the dissolution of tablets.

UV-VIS imaging-based investigation of API concentration fluctuation caused by the sticking behaviour of pharmaceutical powder blends

Surface powder sticking in pharmaceutical mixing vessels poses a risk to the uniformity and quality of drug formulations. This study explores methods for evaluating the amount of pharmaceutical powder mixtures adhering to the metallic surfaces. Binary powder blends consisting of amlodipine and microcrystalline cellulose (MCC) were used to investigate the effect of the mixing order on the adherence to the vessel wall. Elevated API concentrations were measured on the wall and within the dislodged material from the surface, regardless of the mixing order of the components. UV imaging was used to determine the particle size and the distribution of the API on the metallic surface. The results were compared to chemical maps obtained by Raman chemical imaging. The combination of UV and VIS imaging enabled the rapid acquisition of chemical maps, covering a substantially large area representative of the analysed sample. UV imaging was also applied in tablet inspection to detect tablets that fail to meet the content uniformity criteria. The results present powder adherence as a possible source of poor content uniformity, highlighting the need for 100% inspection of pharmaceutical products to ensure product quality and safety.

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.

Image-based simultaneous particle size distribution and concentration measurement of powder blend components with deep learning and machine vision

This work presents a system, where deep learning was used on images captured with a digital camera to simultaneously determine the API concentration and the particle size distribution (PSD) of two components of a powder blend. The blend consisted of acetylsalicylic acid (ASA) and calcium hydrogen phosphate (CHP), and the predicted API concentration was found corresponding with the HPLC measurements. The PSDs determined with the method corresponded with those measured with laser diffraction particle size analysis. This novel method provides fast and simple measurements and could be suitable for detecting segregation in the powder. By examining the powders discharged from a batch blender, the API concentrations at the top and bottom of the container could be measured, yielding information about the adequacy of the blending and improving the quality control of the manufacturing process.

In-line particle size measurement during granule fluidization using convolutional neural network-aided process imaging

This paper presents a machine learning-based image analysis method to monitor the particle size distribution of fluidized granules. The key components of the direct imaging system are a rigid fiber-optic endoscope, a light source and a high-speed camera, which allow for real-time monitoring of the granules. The system was implemented into a custom-made 3D-printed device that could reproduce the particle movement characteristic in a fluidized-bed granulator. The suitability of the method was evaluated by determining the particle size distribution (PSD) of various granule mixtures within the 100-2000 μm size range. The convolutional neural network-based software was able to successfully detect the granules that were in focus despite the dense flow of the particles. The volumetric PSDs were compared with off-line reference measurements obtained by dynamic image analysis and laser diffraction. Similar trends were observed across the PSDs acquired with all three methods. The results of this study demonstrate the feasibility of performing real-time particle size analysis using machine vision as an in-line process analytical technology (PAT) tool.

Comparing the Performance of Raman and Near-Infrared Imaging in the Prediction of the In Vitro Dissolution Profile of Extended-Release Tablets Based on Artificial Neural Networks

In this work, the performance of two fast chemical imaging techniques, Raman and near-infrared (NIR) imaging is compared by utilizing these methods to predict the rate of drug release from sustained-release tablets. Sustained release is provided by adding hydroxypropyl methylcellulose (HPMC), as its concentration and particle size determine the dissolution rate of the drug. The chemical images were processed using classical least squares; afterwards, a convolutional neural network was applied to extract information regarding the particle size of HPMC. The chemical images were reduced to an average HPMC concentration and a predicted particle size value; these were used as inputs in an artificial neural network with a single hidden layer to predict the dissolution profile of the tablets. Both NIR and Raman imaging yielded accurate predictions. As the instrumentation of NIR imaging allows faster measurements than Raman imaging, this technique is a better candidate for implementing a real-time technique. The introduction of chemical imaging in the routine quality control of pharmaceutical products would profoundly change quality assurance in the pharmaceutical industry.

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.

AI-based Analysis of In-line Process Endoscope images for Real-time Particle Size Measurement in a Continuous Pharmaceutical Milling Process

This paper presents a case study on the first in-line application of AI-based image analysis for real-time pharmaceutical particle size measurement in a continuous milling process. An AI-based imaging system, which utilises a rigid endoscope, was tested for the real-time particle size measurement of solid NaCl powder used as a model API in the range of 200-1000µm. After creating a dataset containing annotated images of NaCl particles, it was used to train an AI model for detecting particles and measuring their size. The developed system could analyse overlapping particles without dispersing air, thus broadening its applicability. The performance of the system was evaluated by measuring pre-sifted NaCl samples with the imaging tool, after which it was installed into a continuous mill for in-line particle size measurement of a milling process. By analysing ∼100 particles/s, the system was able to accurately measure the particle size of sifted NaCl samples and detect particle size reduction when applied in the milling process. The Dv50 values and PSDs measured real-time with the AI-based system correlated well with the reference laser diffraction measurements (<6% mean absolute difference over the measured samples). The AI-based imaging system shows great potential for in-line particle size analysis, which, in line with the latest pharmaceutical QC trends, can provide valuable information for process development and control.

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.

Oligonucleotide Formulations Prepared by High-Speed Electrospinning: Maximizing Loading and Exploring Downstream Processability

The aim of this study was to develop antisense oligonucleotide tablet formulations using high-speed electrospinning. Hydroxypropyl-beta-cyclodextrin (HPβCD) was used as a stabilizer and as an electrospinning matrix. In order to optimize the morphology of the fibers, electrospinning of various formulations was carried out using water, methanol/water (1:1), and methanol as solvents. The results showed that using methanol could be advantageous due to the lower viscosity threshold for fiber formation enabling higher potential drug loadings by using less excipient. To increase the productivity of electrospinning, high-speed electrospinning technology was utilized and HPβCD fibers containing 9.1% antisense oligonucleotide were prepared at a rate of ~330 g/h. Furthermore, to increase the drug content of the fibers, a formulation with a 50% drug loading was developed. The fibers had excellent grindability but poor flowability. The ground fibrous powder was mixed with excipients to improve its flowability, which enabled the automatic tableting of the mixture by direct compression. The fibrous HPβCD–antisense oligonucleotide formulations showed no sign of physical or chemical degradation over the 1-year stability study, which also shows the suitability of the HPβCD matrix for the formulation of biopharmaceuticals. The obtained results demonstrate possible solutions for the challenges of electrospinning such as scale-up and downstream processing of the fibers.

Artificial Intelligence-based Prediction of In Vitro Dissolution Profile of Immediate Release Tablets with Near-infrared and Raman Spectroscopy

The objective of the present work was to develop an artificial neural network (ANN) model to accurately predict the dissolution profile of immediate release tablets based on non-destructive spectral data. Six different tablet formulations with varying API (caffeine) and disintegrant (potato starch) concentrations were prepared. The near-infrared (NIR) and Raman spectra of each tablet were collected in both reflection and transmission modes, then principal component analysis (PCA) was conducted. The training of the ANN was performed at each hidden neuron number from 1 to 10 in order to determine the optimal number of neurons in the hidden layer. The best results were obtained when a small number of neurons (1–3) was used. In the case of all four spectroscopic methods, the average similarity values (f2) of the optimized ANN models were above 59 for the validation tablets, indicating that the predicted dissolution profiles were similar to the measured dissolution curves. The optimized model based on reflection Raman spectra exhibited the best predictive ability. The results demonstrated the potential of ANN models in the implementation of the real-time release testing of tablet dissolution.

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%).

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.

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 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.

Real-time amino acid and glucose monitoring system for the automatic control of nutrient feeding in CHO cell culture using raman spectroscopy

An innovative, Raman spectroscopy-based monitoring and control system is introduced in this paper for designing dynamic feeding strategies that allow the maintenance of key cellular nutrients at an ideal level in Chinese hamster ovary cell culture. The Partial Least Squares calibration models built for glucose, lactate and 16 (out of 20) individual amino acids had very good predictive power with low root mean square errors values and high square correlation coefficients. The developed models used for real-time measurement of nutrient and by-product concentrations allowed us to gain better insight into the metabolic behavior and nutritional consumption of cells. To establish a more beneficial nutritional environment for the cells, two types of dynamic feeding strategies were used to control the delivery of two-part multi-component feed media according to the prediction of Raman models (glucose or arginine). As a result, instead of high fluctuations, the nutrients (glucose together with amino acids) were maintained at the desired level providing a more balanced environment for the cells. Moreover, the use of amino acid-based feeding control enabled to prevent the excessive nutrient replenishment and was economically beneficial by significantly reducing the amount of supplied feed medium compared to the glucose-based dynamic fed culture. This article is protected by copyright. All rights reserved.

In-line particle size measurement based on image analysis in a fully continuous granule manufacturing line for rapid process understanding and development

The present paper serves as a demonstration how an in-line PAT tool can be used for rapid and efficient process development in a fully continuous powder to granule line consisting of an interconnected twin-screw wet granulator, vibrational fluid bed dryer, and a regranulating mill. A new method was investigated for the periodic in-line particle size measurement of high mass flow materials to obtain real-time particle size data of the regranulated product. The system utilises a vibratory feeder with periodically altered feeding intensity in order to temporarily reduce the mass flow of the material passing in front of the camera. This results in the drastic reduction of particle overlapping in the images, making image analysis a viable tool for the in-line particle size measurement of high mass-flow materials. To evaluate the performance of the imaging system, the effect of several milling settings and the liquid-to-solid ratio was investigated on the product's particle size in the span of a few hours. The particle sizes measured with the in-line system were in accordance with the expected trends as well as with the results of the off-line reference particle size measurements. Based on the results, the in-line imaging system can serve as a PAT tool to obtain valuable real-time information for rapid process development or quality assurance.

Digital twin of low dosage continuous powder blending - Artificial neural networks and residence time distribution models

In this paper we present a thorough description of the digital twin development for a continuous pharmaceutical powder blending process in accordance with the Process Analytical Technologies (PAT) and Quality by Design (QbD) guidelines. A low-dosage system of caffeine active pharmaceutical ingredient (API) and dextrose excipient was examined via continuous blending experiments. Near infrared (NIR) spectroscopy-based process analytics were applied; quantitative evaluation of spectra was achieved using multivariate data analysis. The blending system was represented with mechanistic residence time distribution (RTD) models and two types of recurrent artificial neural networks (ANN), experimental datasets were used for model training or fitting and validation. Detailed comparison of the two modelling approaches, the optimization of the model-based digital twin, and efficiency of the soft sensor-based process monitoring is presented through several validating simulations. Both RTD models and nonlinear autoregressive neural networks demonstrated excellent predictive power for the low dosage blending process. RTD models can prove to be more advantageous in industrial development as they are less resource-intensive to develop and prediction accuracy on low concentration levels lacks dependency from the precision of chemometric calibration. Reduced material costs and limited development timeframe render the digital twin an efficient tool in technological development.

Indirect monitoring of ultralow dose API content in continuous wet granulation and tableting by machine vision

This paper presents new machine vision-based methods for indirect real-time quantification of ultralow drug content during continuous twin-screw wet granulation and tableting. Granulation was performed with a solution containing carvedilol (CAR) as API in the ultralow dose range (0.05w/w% in the granule) and the addition of riboflavin (RI) as a coloured tracer. An in-line calibration in the range of 0.047-0.058 w/w% was prepared for the measurement of CAR concentration using colour analysis (CA) and particle size analysis (PSA), and the validation with HPLC resulted in respective relative errors of 2.62% and 2.30% showing great accuracy. To improve the technique, a second in-line calibration was conducted in a broader CAR concentration range of 0.039-0.063 w/w% utilizing only half the amount of RI (0.045 w/w%), while doubling the output of the granulation line to 2 kg/h, producing a relative error of 4.51% and 4.29%, respectively. Finally, it was shown that the CA technique can also be carried on to monitor the CAR content of tablets in the 42-62 μg dose range with a relative error of 5.20%. Machine vision was proven to be a potent indirect method for the in-line, determination and monitoring of ultralow API content during continuous manufacturing.

Sulfobutylether-beta-cyclodextrin-enabled antiviral remdesivir: Characterization of electrospun- and lyophilized formulations

Veklury™ by Gilead Sciences, Inc., containing antiviral drug, remdesivir (REM) has received emergency authorization in the USA and in Europe for COVID-19 therapy. Here, for the first time, we describe details of the non-covalent, host-guest type interaction between REM and the solubilizing excipient, sulfobutylether-beta-cyclodextrin (SBECD) that results in significant solubility enhancement. Complete amorphousness of the cyclodextrin-enabled REM formulation was demonstrated by X-ray diffraction, thermal analysis, Raman chemical mapping and electron microscopy/energy dispersive spectroscopy. The use of solubilizing carbohydrate resulted in a 300-fold improvement of the aqueous solubility of REM, and enhanced dissolution rate of the drug enabling the preparation of stable infusion solutions for therapy. 2D ROESY NMR spectroscopy provided information on the nature of REM-excipient interaction and indicated the presence of inclusion phenomenon and the electrostatic attraction between anionic SBECD and nitrogen-containing REM in aqueous solution.

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.

Integrated twin-screw wet granulation, continuous vibrational fluid drying and milling: A fully continuous powder to granule line

Highly homogeneous low-dose (50 μg) tablets were produced incorporating perfectly free-flowing granules prepared by a fully integrated Continuous Manufacturing (CM) line. The adopted CM equipment consisted of a Twin-Screw Wet Granulator (TSWG), a Continuous Fluid Bed Dryer (CFBD) and a Continuous Sieving (CS) unit. Throughout the experiments a pre-blend of lactose-monohydrate and corn starch was gravimetrically dosed with 1 kg/h into the TSWG, where they were successfully granulated with the drug containing water-based PVPK30 solution. The wet mass was subsequently dried in the CFBD on a vibratory conveyor belt and finally sieved in the milling unit. Granule production efficiency was maximized by determining the minimal Liquid-to-Solid (L/S) ratio (0.11). Design of Experiments (DoE) were carried out in order to evaluate the influence of the drying process parameters of the CFBD on the Loss-on-Drying (LOD) results. The manufactured granules were compressed into tablets by an industrial tablet rotary press with excellent API homogeneity (RSD < 3%). Significant scale-up was realized with the CM line by increasing the throughput rate to 10 kg/h. The manufactured granules yielded very similar results to the previous small-scale granulation runs. API homogeneity was demonstrated (RSD < 2%) with Blend Uniformity Analysis (BUA). The efficiency of TSWG granulation was compared to High-Shear Granulation (HSG) with the same L/S ratio. The final results have demonstrated that both the liquid distribution and more importantly API homogeneity was better in case of the TSWG granulation (RSD 1.3% vs. 4.5%).

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.

Raman-based dynamic feeding strategies using real-time glucose concentration monitoring system during adalimumab producing CHO cell cultivation

The use of Process Analytical Technology tools coupled with chemometrics has been shown great potential for better understanding and control of mammalian cell cultivations through real-time process monitoring. In-line Raman spectroscopy was utilized to determine the glucose concentration of the complex bioreactor culture medium ensuring real-time information for our process control system. This work demonstrates a simple and fast method to achieve a robust partial least squares calibration model under laboratory conditions in an early phase of the development utilizing shake flask and bioreactor cultures. Two types of dynamic feeding strategies were accomplished where the multi-component feed medium additions were controlled manually and automatically based on the Raman monitored glucose concentration. The impact of these dynamic feedings was also investigated and compared to the traditional bolus feeding strategy on cellular metabolism, cell growth, productivity, and binding activity of the antibody product. Both manual and automated dynamic feeding strategies were successfully applied to maintain the glucose concentration within a narrower and lower concentration range. Thus, besides glucose, the glutamate was also limited at low level leading to reduced production of inhibitory metabolites, such as lactate and ammonia. Consequently, these feeding control strategies enabled to provide beneficial cultivation environment for the cells. In both experiments, higher cell growth and prolonged viable cell cultivation were achieved which in turn led to increased antibody product concentration compared to the reference bolus feeding cultivation.

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.

Continuous Manufacturing of Homogeneous Ultralow-Dose Granules by Twin-Screw Wet Granulation

Homogeneous ultralow-dose (30 mg) tablets were prepared from perfectly free-flowing granules manufactured by continuous Twin-Screw Wet Granulation. The gravimetrically fed mixture of lactose and potato starch of low particle size was successfully agglomerated and the size enlargement technology proved to be very robust. Since the incorporated drug was dissolved in ethanol-based granulation liquid, the resulting homogeneity largely depended on the dosing of the applied liquid administering units.A peristaltic pump generated higher deviation of the drug content in tablets (Relative Standard Deviation (RSD): 7.7 %) compared to a syringe pump (RSD: 2.3 %) or a piston pump (RSD: 4.6 %). This is due to the pulsation of the liquid flow generated by the peristaltic pump according to the real-time measured mass of the fed liquid. A good correlation was found between the RSD of the liquid mass flow (calculated from the recorded masses) and the RSD of the drug content. Based on the results, the goodness of Content Uniformity, as the most relevant critical quality attribute of low-dose products, was determined by the characteristics of the applied dosing units. The feeding characteristic of the different pumps could be easily measured by the introduced balance-based method and therefore, the applicability of the pumps could be evaluated.

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.

Data fusion strategies for performance improvement of a Process Analytical Technology platform consisting of four instruments: An electrospinning case study

The aim of this work was to develop a PAT platform consisting of four complementary instruments for the characterization of electrospun amorphous solid dispersions with meloxicam. The investigated methods, namely NIR spectroscopy, Raman spectroscopy, Colorimetry and Image analysis were tested and compared considering the ability to quantify the active pharmaceutical ingredient and to detect production errors reflected in inhomogeneous deposition of fibers. Based on individual performance the calculated RMSEP values ranged between 0.654% and 2.292%. Mid-level data fusion consisting of data compression through latent variables and application of ANN for regression purposes proved efficient, yielding an RMSEP value of 0.153%. Under these conditions the model could be validated accordingly on the full calibration range. The complementarity of the PAT tools, demonstrated from the perspective of captured variability and outlier detection ability, contributed to model performance enhancement through data fusion. To the best of the author's knowledge, this is the first application of data fusion in the field of PAT for efficient handling of big-analytical-data provided by high-throughput instruments.

Corona alternating current electrospinning: A combined approach for increasing the productivity of electrospinning

Corona alternating current electrospinning (C-ACES), a scaled-up productivity electrospinning method was developed by combining the intense forces of the alternating electrostatic field and a sharp-edged spinneret design with increased free surface. C-ACES reached two orders of magnitude higher productivity (up to 1200 mL/h) than the classical single needle direct current electrospinning (DCES) without any alteration of fiber properties. Polyvinylpyrrolidone K90 (PVPK90), a water soluble high molecular weight nonionic polymer was processed for the first time with single needle alternating current electrospinning (ACES) and C-ACES in order to prepare fast dissolving amorphous solid dispersions of spironolactone (SPIR), a poorly water-soluble antihypertensive model drug. The limited spinnability of PVPK90 with AC high voltage could only be resolved by optimizing the solution conductivity with organophilic salts such as sodium dodecyl sulfate (SDS) demonstrating the importance of conductivity during ACES. The effects of varied solution properties (composition and conductivity) and scaling-up were investigated by SEM imaging. Solid state analyses revealed that SPIR was dispersed in an amorphous form in the fibrous mats. In vitro dissolution tests showed ultrafast drug release in case of the amorphous formulations even when prepared with scaled-up C-ACES. Besides the enhancement of conductivity SDS also prevents SPIR from precipitation from the dissolution media due to its solubilization ability.

The applicability of pharmaceutical polymeric blends for the fused deposition modelling (FDM) 3D technique: Material considerations-printability-process modulation, with consecutive effects on in vitro release, stability and degradation

The three dimensional printing (3DP) in the pharmaceutical domain constitutes an alternative, innovative approach compared to the conventional production methods. Fused deposition modelling (FDM), is a simple, cost-effective 3DP technique, however the range of pharmaceutical excipients that can be applied for this methodology is restricted. The study set to define the requirements of the FDM printability, using as technical support custom made, pharmaceutical polymer based filaments and to evaluate if these new dosage forms can live up to the current GMP/GCP quality standards. Formulation rationale was assessed in accordance to the apparatus functionality. Blends were pre-screened based on the processability under the API (carvedilol) thermogravimetric analysis determined critical limit. The technological process implied the use of FDM coupled with hot melt extrusion (HME), while printability was defined by means of thermal, rheological and mechanical measurements. From the pharmaceutical standpoint, the consistency of the in vitro dissolution kinetics was monitored 'at release' and 'in stability', while the print process impact was evaluated based on the previously determined processability potential. Results showed that FDM printability is multifactorial, with brittleness and melt viscosity as primary limitation factors. The increase in shear-thinning and flexural modulus can enable broader processability intervals, which in turn proved to be essential in limiting degradation product formation. The 3DP tablets released the API in an extended rate, however the temperature and humidity along production and storage should be carefully considered as it may affect the final product quality in time. In conclusion, HME + FDM can be considered as an alternative production methodology, with prospects of applicability in the clinical sector, however for some formulations extensive packaging development will be necessary before confirming their suitability.

Raman Spectroscopy for Process Analytical Technologies of Pharmaceutical Secondary Manufacturing

As the process analytical technology (PAT) mindset is progressively introduced and adopted by the pharmaceutical companies, there is an increasing demand for effective and versatile real-time analyzers to address the quality assurance challenges of drug manufacturing. In the last decades, Raman spectroscopy has emerged as one of the most promising tools for non-destructive and fast characterization of the pharmaceutical processes. This review summarizes the achieved results of the real-time application of Raman spectroscopy in the field of the secondary manufacturing of pharmaceutical solid dosage forms, covering the most common secondary process steps of a tablet production line. In addition, the feasibility of Raman spectroscopy for real-time control is critically reviewed, and challenges and possible approaches to moving from real-time monitoring to process analytically controlled technologies (PACT) are discussed.

Homogenization of Amorphous Solid Dispersions Prepared by Electrospinning in Low-Dose Tablet Formulation

Low-dose tablet formulations were produced with excellent homogeneity based on drug-loaded electrospun fibers prepared by single-needle as well as scaled-up electrospinning (SNES and HSES). Carvedilol (CAR), a BCS II class compound, served as the model drug while poly (vinylpyrrolidone-co-vinyl acetate) (PVPVA64) was adopted as the fiber-forming polymer. Scanning electron microscopy (SEM) imaging was used to study the morphology of HSES and SNES samples. Different homogenization techniques were compared to maximize homogeneity: mixing in plastic bags and in a high-shear granulator resulting in low-shear mixing (LSM) and high-shear mixing (HSM). Drug content and homogeneity of the tablets were measured by UV-Vis spectrometry, the results revealed acceptably low-dose fluctuations especially with formulations homogenized with HSM. Sieve analysis was used on the final LSM and HSM powder mixtures in order to elucidate the observed differences between tablet homogeneity. Tablets containing drug-loaded electrospun fibers were also studied by Raman mapping demonstrating evenly distributed CAR within the corpus.

A high-throughput multi-microfluidic crystal generator (MMicroCryGen) platform for facile screening of polymorphism and crystal morphology for pharmaceutical compounds

In this work, a novel multi-microfluidic crystallization platform called MMicroCryGen is presented, offering a facile methodology for generating individual crystals for fast and easy screening of the polymorphism and crystal habit of solid compounds. The MMicroCryGen device is capable of performing 8 × 10 cooling crystallization experiments in parallel using 8 disposable microcapillary film strips, each requiring less than 25 μL of solution. Compared to traditional microfluidic systems, the MMicroCryGen platform does not require complex fluid handling; it can be directly integrated with a 96-well microplate and it can also work in a "dipstick" mode. The produced crystals can be safely and directly observed inside the capillaries by optical and spectroscopic techniques. The platform was validated by performing a number of independent experimental runs for: (1) polymorph and hydrate screening of ortho-aminobenzoic acid, succinic acid and piroxicam; (2) co-crystal form screening of the p-toluenesulfonamide/triphenylphosphine oxide system; (3) studying the effect of o-toluic acid on ortho-aminobenzoic cooling crystallization (effect of structurally related additives). In all three cases, all known solid forms were identified with a single experiment using ∼200 μL of solvent and just a few micrograms of the solid material. The MMicroCryGen is simple to use, inexpensive and it provides increased flexibility compared to traditional crystallization techniques, being an effective new microfluidic solution for solid form screening in pharmaceutical, fine chemicals, food and agrochemical industries.

Application of hydroxypropyl methylcellulose as a protective agent against magnesium stearate induced crystallization of amorphous itraconazole

Itraconazole is a fungicide drug which has low bioavailability due to its poor water solubility. Amorphous solid dispersion (ASD) is a tool that has the potential to greatly increase the dissolution rate and extent of compounds. In this work, the dissolution of tablets containing the ASD of itraconazole with either hydroxypropyl methylcellulose (HPMC) or vinylpyrrolidone-vinyl acetate copolymer (PVPVA) was compared in order to find a formulation which can prevent the drug from the precipitation caused by magnesium stearate. Formulations containing the PVPVA-based ASD with HPMC included in various forms could reach 90% dissolution in 2 h, while HPMC-based ASDs could release 100% of the drug. However, HPMC-based ASD had remarkably poor grindability and low bulk density, which limited its processability and applicability. The latter issue could be resolved by roller compacting the ASD, which significantly increases the bulk density and the flowability of the powder blends used for tableting. This roller compaction step might be a base for the industrial application of HPMC-based, electrospun ASDs.

Medicated Straws Based on Electrospun Solid Dispersions

Novel medicated straws were developed based on drug-loaded electrospun fibers prepared by direct current electrospinning (DCES) and high-speed electrospinning (HSES) of scaled-up productivity. Good quality micro- and nanofibers were electrospun using both techniques despite the multiple times higher throughput rate of HSES based on the scanning electron microscopic imaging (SEM). Solid state analyses revealed that the poorly soluble model drug carvedilol (CAR) was dispersed in an amorphous form in the electrospun polyvinylpyrrolidone (PVPK30) fibers. In vitro dissolution studies revealed ultrafast drug release from the prepared fibrous formulations inserted into plastic straws. Based on the results the developed drug delivery system is suitable for storing the formulation in a solid dosage form and in situ turning it into liquid form when administered.

Investigation of Deteriorated Dissolution of Amorphous Itraconazole: Description of Incompatibility with Magnesium Stearate and Possible Solutions

Disadvantageous crystallization phenomenon of amorphous itraconazole (ITR) occurring in the course of dissolution process was investigated in this work. A perfectly amorphous form (solid dispersion) of the drug was generated by the electroblowing method (with vinylpyrrolidone-vinyl acetate copolymer), and the obtained fibers were formulated into tablets. Incomplete dissolution of the tablets was noticed under the circumstances of the standard dissolution test, after which a precipitated material could be filtered. The filtrate consisted of ITR and stearic acid since no magnesium content was detectable in it. In parallel with dissolution, ITR forms an insoluble associate, stabilized by hydrogen bonding, with stearic acid deriving from magnesium stearate. This is why dissolution curves do not have the plateaus at 100%. Two ways are viable to tackle this issue: change the lubricant (with sodium stearyl fumarate >95% dissolution can be accomplished) or alter the polymer in the solid dispersion to a type being able to form hydrogen bonds with ITR (e.g., hydroxypropyl methylcellulose). This work draws attention to one possible phenomenon that can lead to a deterioration of originally good dissolution of an amorphous solid dispersion.

In-line Raman spectroscopic monitoring and feedback control of a continuous twin-screw pharmaceutical powder blending and tableting process

The integration of Process Analytical Technology (PAT) initiative into the continuous production of pharmaceuticals is indispensable for reliable production. The present paper reports the implementation of in-line Raman spectroscopy in a continuous blending and tableting process of a three-component model pharmaceutical system, containing caffeine as model active pharmaceutical ingredient (API), glucose as model excipient and magnesium stearate as lubricant. The real-time analysis of API content, blend homogeneity, and tablet content uniformity was performed using a Partial Least Squares (PLS) quantitative method. The in-line Raman spectroscopic monitoring showed that the continuous blender was capable of producing blends with high homogeneity, and technological malfunctions can be detected by the proposed PAT method. The Raman spectroscopy-based feedback control of the API feeder was also established, creating a 'Process Analytically Controlled Technology' (PACT), which guarantees the required API content in the produced blend. This is, to the best of the authors' knowledge, the first ever application of Raman-spectroscopy in continuous blending and the first Raman-based feedback control in the formulation technology of solid pharmaceuticals.

Novel Alternating Current Electrospinning of Hydroxypropylmethylcellulose Acetate Succinate (HPMCAS) Nanofibers for Dissolution Enhancement: The Importance of Solution Conductivity

Novel, high-yield alternating current electrospinning (ACES) and direct current electrospinning methods were investigated to prepare high-quality hydroxypropylmethylcellulose acetate succinate (HPMCAS) fibers for the dissolution enhancement of poorly soluble spironolactone. Although HPMCAS is of great pharmaceutical importance as a carrier of marketed solid dispersion-based products, it was found to be unprocessable using electrospinning. Addition of small amounts of polyethylene oxide as aid polymer provided smooth fibers with direct current electrospinning but strongly beaded products with ACES. Solution characteristics were thus modified by introducing further excipients. In the presence of sodium dodecyl sulfate, high-quality, HPMCAS-based fibers were obtained even at higher throughput rates of ACES owing to the change in conductivity (rather than surface tension). Replacement of sodium dodecyl sulfate with non-surface-active salts (calcium chloride and ammonium acetate) maintained the fine quality of nanofibers, confirming the importance of conductivity in ACES process. The HPMCAS-based fibers contained spironolactone in an amorphous form according to differential scanning calorimetry and X-ray powder diffraction. In vitro dissolution tests revealed fast drug release rates depending on the salt used to adjust conductivity. The presented results signify that ACES can be a prospective process for high-scale production of fibrous solid dispersions in which conductivity of solution has a fundamental role.