The current state-of-the art in pharmaceutical continuous film coating - A review.

In this decade, one of the major trends in the pharmaceutical industry is the adoption of continuous manufacturing. This requires the development of continuous equivalents of essential pharmaceutical processes such as film coating. The process of film coating is the last step of the processing of solid dosage forms and is critical because it determines the visual appearance of the end product, along with ensuring its stability and possibly even defining the rate of drug release.

Simultaneous prediction of the API concentration and mass gain of film coated tablets using Near-Infrared and Raman spectroscopy and data fusion.

This study investigates the simultaneous prediction of active pharmaceutical ingredient (API) concentration and mass gain in film-coated tablets using Partial Least Squares (PLS) regression combined with three data fusion (DF) techniques: Low-Level (LLDF), Mid-Level (MLDF), and High-Level (HLDF). Near-Infrared (NIR) and Raman spectroscopy were utilized in both reflection and transmission modes, providing four types of spectral data per tablet. Transmission models proved more effective for API prediction by capturing data from the entire tablet, while reflection models excelled in assessing mass gain by focusing on the surface layer.

Automated tablet defect detection and the prediction of disintegration time and crushing strength with deep learning based on tablet surface images.

This paper presents novel measurement methods, where deep learning was used to detect tableting defects and determine the crushing strength and disintegration time of tablets on images captured by machine vision. Five different classes of defects were used and the accuracy of the real-time defect recognition performed with the deep learning algorithm YOLOv5 was 99.2 %.

Development and integration of a continuous horizontal belt filter into drug production procedure.

In the pharmaceutical industry, filtration is traditionally carried out in batch mode. However, with the spread of continuous technologies, there is an increasing demand for robust continuous filtration strategies suitable for processing suspensions produced in continuous crystallizers. Accordingly, this study aimed to investigate a lab-scale horizontal conveyor belt filtration approach for pharmaceutical separation purposes for the first time.

In-line indirect concentration measurement of ultralow dose API during twin-screw wet granulation based on NIR and Raman spectroscopy.

Twin-screw wet granulation (TWSG) is a promising continuous alternative of pharmaceutical wet granulation. One of its benefits is that the components dissolved in the granulation liquid are distributed homogeneously in the granules. This provides an elegant way to manufacture products with ultralow drug doses.

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.

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 (TiO), 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 TiO from the film coating of tablets.

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.

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.

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.

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.

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.

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.

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.

Real-time coating thickness measurement and defect recognition of film coated tablets with machine vision and deep learning.

This paper presents a system, where images acquired with a digital camera are coupled with image analysis and deep learning to identify and categorize film coating defects and to measure the film coating thickness of tablets. There were 5 different classes of defective tablets, and the YOLOv5 algorithm was utilized to recognize defects, the accuracy of the classification was 98.2%.

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.

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.

A robust mucin-containing poly(vinyl alcohol) hydrogel model for the in vitro characterization of mucoadhesion of solid dosage forms.

Mucoadhesion testing at macroscopic scale needs a robust, convenient in vitro method as ex vivo methods suffer from poor reproducibility and ethical problems. Here we synthesized mucin-free poly(vinyl alcohol) (PVA) and mucin-containing PVA hydrogel substrates (Muc/PVA) to measure adhesion of polymer tablets. Freezing-thawing method was used for gelation to avoid chemical cross-linking and to preserve the functionality of mucin.

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.

Powder filling of electrospun material in vials: A proof-of-concept study.

The present paper reports the powder filling of milled electrospun materials in vials, which contained voriconazole and sulfobutylether-β-cyclodextrin. High-speed electrospinning was used for the production of the fibrous sample, which was divided into 6 parts. Each portion was milled using different milling methods and sizes of sieves to investigate whether the milling influences the powder and filling properties.

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.

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.

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.

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.