UV imaging for the rapid at-line content determination of different colourless APIs in their tablets with artificial neural networks.

This paper presents a novel high-resolution and rapid (50 ms) UV imaging system, which was used for at-line, non-destructive API content determination of tablets. For the experiments, amlodipine and valsartan were selected as two colourless APIs with different UV induced fluorescent properties according to the measured solid fluorescent spectra. Images were captured with a LED-based UV illumination (385-395 nm) of tablets containing amlodipine or valsartan and common tableting excipients. Blue or green colour components from the RGB colour space were extracted from the images and used as an input dataset to execute API content prediction with artificial neural networks. The traditional destructive, solution-based transmission UV measurement was applied as reference method. After the optimization of the number of hidden layer neurons it was found that the relative error of the content prediction was 4.41 % and 3.98 % in the case of amlodipine and valsartan containing tablets respectively. The results open the possibility to use the proposed UV imaging-based system as a rapid, in-line tool for 100 % API content screening in order to greatly improve pharmaceutical quality control and process understanding.

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.

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.

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.

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.

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%. In order to characterize coating thickness, the diameter of the tablets in pixels was measured, which was used to measure the coating thickness of the tablets. The proposed system can be easily scaled up to match the production capability of continuous film coaters. With the developed technique, the complete screening of the produced tablets can be achieved in real-time resulting in the improvement of quality control.

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.

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.