At-line validation of optical coherence tomography as in-line/at-line coating thickness measurement method

Extending the Use of Optical Coherence Tomography to Scattering Coatings Containing Pigments

Coating thickness is a critical quality attribute of many coated tablets. Functional coatings ensure correct drug release kinetics or protection from light, while non-functional coatings are generally applied for cosmetic reasons. Traditionally, coating thickness is assessed indirectly via offline methods, such as weight gain or diameter growth. In the past decade, several methods, including optical coherence tomography (OCT) and Raman spectroscopy, have emerged to perform in-line measurements of various subclasses of coating formulations. However, there are some obstacles. For example, when using OCT, a major challenge is scattering pigments, such as titanium dioxide and iron oxide, which make the interface between the coating and the tablet core difficult to detect. This work explores novel OCT image evaluation techniques using unsupervised machine learning to compute image metrics. Certain image metrics of highly scattering coatings are correlated with the tablet thickness, and hence indirectly with the coating thickness. The method was demonstrated using a titanium dioxide rich coating formulation. The results are expected to be applicable to other scattering coatings and will significantly broaden the applicability of OCT to at-line and in-line coating thickness measurements of a much larger class of coating formulations.

Ultra-high-resolution optical coherence tomography for the investigation of thin multilayered pharmaceutical coatings

Optical Coherence Tomography (OCT) has recently gained attention as a promising technology for in-line monitoring of pharmaceutical film-coating processes for (single-layered) tablet coatings and end-point detection with commercial systems. An increasing interest in the investigation of multiparticulate dosage forms with mostly multi-layered coatings below 20 µm final film thickness demands advancement in OCT technology for pharmaceutical imaging. We present an ultra-high-resolution (UHR-) OCT and investigate its performance based on three different multiparticulate dosage forms with different layer structures (one single-layered, two multi-layered) with layer thicknesses in a range from 5 to 50 µm. The achieved system resolution of 2.4 µm (axial) and 3.4 µm (lateral, both in air) enables the assessment of defects, film thickness variability and morphological features within the coating, previously unattainable using OCT. Despite the high transverse resolution, the provided depth of field was found sufficient to reach the core region of all dosage forms under test. We further demonstrate an automated segmentation and evaluation of UHR-OCT images for coating thicknesses, where human experts struggle using today's standard OCT systems.

Research progress on the application of spectral imaging technology in pharmaceutical tablet analysis

Tablet as a traditional dosage form in pharmacy has the advantages of accurate dosage, ideal dissolution and bioavailability, convenient to carry and transport. The most concerned tablet quality attributes include active pharmaceutical ingredient (API) contents and polymorphic forms, components distribution, hardness, density, coating state, dissolution behavior, etc., which greatly affect the bioavailability and consistency of tablet final products. In the pharmaceutical industry, there are usually industry standard methods to analyze the tablet quality attributes. However, these methods are generally time-consuming and laborious, and lack a comprehensive understanding of the properties of tablets, such as spatial information. In recent years, spectral imaging technology makes up for the shortcomings of traditional tablet analysis methods because it provides non-contact and rich information in time and space. As a promising technology to replace the traditional tablet analysis methods, it has attracted more and more attention. The present paper briefly describes a series of spectral imaging techniques and their applications in tablet analysis. Finally, the possible application prospect of this technology and the deficiencies that need to be improved were also prospected.

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

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

Anti-counterfeiting protection, personalized medicines - Development of 2D identification methods using laser technology

Counterfeiting of the products for healing is as old as trading, and it is difficult to quantify the magnitude of the problem. It is known that substandard and/or falsified (SF) medicines are a growing global threat to health, and they cause serious social and economic damage. The EU has a strong legal framework for medicines, it is mandatory to meet the requirements of Directive 2011/62/EU. Serialisation prevents SF medicinal products from entering the legal distribution chain. The present study is an extension of the original idea and aims to develop a laser technology-based method to mark an individual traceable code on the surface of the tablet, which technology can also be used for marking personalized medicines. The method is based on the ablation of the upper layer of a double-layer, differently coloured coating. The 2D code should be formed without harming the functional layer, and anyone with a smartphone integrated with a camera should be able to authenticate these drugs with a suitable application. The present findings confirmed that KrF excimer laser and Ti:sapphire femtosecond laser are efficient and reliable for marking. These should be promising candidates for pharmaceutical companies that would like to have additional protection against drug counterfeiters.

Continuous In-Line Chromium Coating Thickness Measurement Methodologies: An Investigation of Current and Potential Technology

Coatings or films are applied to a substrate for several applications, such as waterproofing, corrosion resistance, adhesion performance, cosmetic effects, and optical coatings. When applying a coating to a substrate, it is vital to monitor the coating thickness during the coating process to achieve a product to the desired specification via real time production control. There are several different coating thickness measurement methods that can be used, either in-line or off-line, which can determine the coating thickness relative to the material of the coating and the substrate. In-line coating thickness measurement methods are often very difficult to design and implement due to the nature of the harsh environmental conditions of typical production processes and the speed at which the process is run. This paper addresses the current and novel coating thickness methodologies for application to chromium coatings on a ferro-magnetic steel substrate with their advantages and limitations regarding in-line measurement. The most common in-line coating thickness measurement method utilized within the steel packaging industry is the X-ray Fluorescence (XRF) method, but these systems can become costly when implemented for a wide packaging product and pose health and safety concerns due to its ionizing radiation. As technology advances, nanometer-scale coatings are becoming more common, and here three methods are highlighted, which have been used extensively in other industries (with several variants in their design) which can potentially measure coatings of nanometer thickness in a production line, precisely, safely, and do so in a non-contact and non-destructive manner. These methods are optical reflectometry, ellipsometry and interferometry.

Feasibility of In-line monitoring of critical coating quality attributes via OCT: Thickness, variability, film homogeneity and roughness

The feasibility of Optical Coherence Tomography (OCT) for in-line monitoring of pharmaceutical film coating processes has recently been demonstrated. OCT enables real-time acquisition of high-resolution cross-sectional images of coating layers and computation of coating thickness. In addition, coating quality attributes can be computed based on in-line data. This study assesses the in-line applicability of OCT to various coating functionalities and formulations. Several types of commercial film-coated tablets containing the most common ingredients were investigated. To that end, the tablets were placed into a miniaturized perforated drum. An in-line OCT system was used to monitor the tablet bed. This set-up resembles the final stage of an industrial pan coating process. All investigated coatings were measured, and the coating thickness, homogeneity and roughness were computed. The rotation rate was varied in a range comparable to large-scale coating operations, and no influence on the outcome was observed. The results indicate that OCT can be used to determine end-point and establish in-process control for a wide range of coating formulations. The real-time computation of coating homogeneity and roughness can support process optimization and formulation development.

Deep convolutional neural networks: Outperforming established algorithms in the evaluation of industrial optical coherence tomography (OCT) images of pharmaceutical coatings

This paper presents a novel evaluation approach for optical coherence tomography (OCT) image analysis of pharmaceutical solid dosage forms based on deep convolutional neural networks (CNNs). As a proof of concept, CNNs were applied to image data from both, in- and at-line OCT implementations, monitoring film-coated tablets as well as single- and multi-layered pellets. CNN results were compared against results from established algorithms based on ellipse-fitting, as well as to human-annotated ground truth data. Performance benchmarks used include, efficiency (computation speed), sensitivity (number of detections from a defined test set) and accuracy (deviation from the reference method). The results were validated by comparing the output of several algorithms to data manually annotated by human experts and microscopy images of cross-sectional cuts of the same dosage forms as a reference method. In order to guarantee comparability for all results, the algorithms were executed on the same hardware. Since modern OCT systems must operate under real-time conditions in order to be implemented in-line into manufacturing lines, the necessary steps are discussed on how to achieve this goal without sacrificing the algorithmic performance and how to tailor a deep CNN to cope with the high amount of image noise and alterations in object appearance. The developed deep learning approach outperforms static algorithms currently available in pharma applications with respect to performance benchmarks, and represents the next level in real time evaluation of challenging industrial OCT image data.