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

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 %. The system can already match the production capability of tablet presses, with still further room left for improvement. The YOLOv5 algorithm was also used to determine the disintegration time and crushing strength of tablets produced at different compression force settings based on their surface texture. With these accurate, low-cost methods, the 100 % screening of the produced tablets could be carried out, resulting in the improvement of quality control and effectiveness of pharmaceutical production.

Evaluation of polymer combinations in vaginal mucoadhesive tablets for the extended release of acyclovir

Genital herpes, caused by herpes simplex virus type 2 (HSV-2), affects nearly 500 million people, mostly women. Since the main route of transmission is sexual contact, the development of an acyclovir extended-release vaginal microbicide would be a suitable tool for the prevention of virus transmission. In this work, we evaluated the potential of three polymers with different characteristics (chitosan, xanthan gum and ethyl cellulose) for obtaining acyclovir extended-release vaginal tablets. By combining the polymers, certain useful synergies were observed to modify their mucoadhesive capacity and control drug release. In the swelling studies, it observed that a polyelectrolyte complex with more moderate swelling and sustained gelation was formed between chitosan and xanthan gum exclusively in acidic medium (simulated vaginal fluid). This complex allowed prolonging the mucoadhesion of the tablets in ex vivo studies performed with vaginal mucosa, which would translate into better retention in the vagina after administration. In addition, the combination of chitosan and xanthan gum allowed obtaining a controlled release of acyclovir for 5 days, regardless of the pH of the medium, which would guarantee that drug release continues even in the presence of seminal fluid.

Chemical Distribution Uniformity Assessment of "Intra-Tablet" by Hyperspectral Raman Imaging Analysis

PURPOSE

This study aimed to develop a new index, Distribution Uniformity Index (DUI), to assess the "intra-tablet" homogeneity.

METHODS

High-resolution hyperspectral Raman imaging was adopted to scan a tablet to get the components' distribution. The heuristic algorithm was applied to generate a Raman heatmap with RGB colors quantitatively correlated with the concentrations of each component. DUI is defined as the ratio of the area under the uniformity curve of the sample image to that of the randomized image. The accuracy and applicability of DUI were verified by constructing model images with controlled uniformity and random regions. The effects of "intra-tablet" homogeneity on the disintegration and dissolution of spironolactone tablets were investigated.

RESULTS

DUI value was directly obtained from heuristic visual analysis of macro-pixel from hyperspectral Raman images. A good linear relationship and good repeatability were confirmed between DUI and the uniformity of model images. The size of CaSO4·2H2O affected the "intra-tablet" homogeneity of spironolactone tablets, which was detected by the DUI value. The better "intra-tablet" homogeneity led to a higher disintegration and dissolution of spironolactone tablets.

CONCLUSIONS

DUI represents a novel index to evaluate the "intra-tablet" homogeneity and is beneficial for formulation research and development.

Quality study of animal-derived traditional Chinese medicinal materials based on spectral technology: Calculus bovis as a case

INTRODUCTION

Calculus bovis (C. bovis) is a typical traditional Chinese medicine (TCM) derived from animals, which has a remarkable curative effect and high price.

OBJECTIVES

Rapid identification of C. bovis from different types was realized based on spectral technology, and a rapid quantitative analysis method for the main quality control indicator bilirubin was established.

METHODS

We conducted a supervised and unsupervised pattern recognition study on 44 batches of different types of C. bovis by five spectral pretreatment methods. Three variable selection methods were used to extract the essential information, and the partial least squares regression (PLSR) quantitative model of bilirubin by near-infrared (NIR) spectroscopy was constructed.

RESULTS

The partial least squares discriminant analysis (PLS-DA) model could achieve 100% accuracy in identifying different types of C. bovis. The R2 of the NIR quantitative model was 0.979, which is close to 1, and the root mean square error of calibration (RMSEC) was 2.3515, indicating the good prediction ability of the model.

CONCLUSION

The study was carried out to further improve the basic data of quality control of C. bovis and help the high-quality development of TCM derived from animals.

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.

A case study on decentralized manufacturing of 3D printed medicines

Pharmaceutical 3D printing (3DP) is one of the emerging enabling technologies of personalised medicines as it affords the ability to fabricate highly versatile dosage forms. In the past 2 years, national medicines regulatory authorities have held consultations with external stakeholders to adapt regulatory frameworks to embrace point-of-care manufacturing. The proposed concept of decentralized manufacturing (DM) involves the provision of feedstock intermediates (pharma-inks) prepared by pharmaceutical companies to DM sites for manufacturing into the final medicine. In this study, we examine the feasibility of this model, with respect to both manufacturing and quality control. Efavirenz-loaded granulates (0-35%w/w) were produced by a manufacturing partner and shipped to a 3DP site in a different country. Direct powder extrusion (DPE) 3DP was subsequently used to prepare printlets (3D printed tablets), with mass ranging 266-371 mg. All printlets released more than 80% drug load within the first 60 min of the in vitro drug release test. An in-line near-infrared spectroscopy system was used as a process analytical technology (PAT) to quantify the printlets' drug load. Calibration models were developed using partial least squares regression, which showed excellent linearity (R² = 0.9833) and accuracy (RMSE = 1.0662). Overall, this work is the first to report the use of an in-line NIR system to perform real-time analysis of printlets prepared using pharma-inks produced by a pharmaceutical company. By demonstrating the feasibility of the proposed distribution model through this proof-of-concept study, this work paves the way for investigation of further PAT tools for quality control in 3DP point-of-care manufacturing.

Impact of Raman mapping area and intra-tablet homogeneity on the accuracy of sustained-release tablet dissolution prediction

This exploratory study investigated the minimum required Raman mapping area for predicting sustained-release tablet dissolution profiles based on intra-tablet homogeneity. The aim was to minimize scanning time while achieving reliable dissolution profile predictions. To construct the sample set, we controlled the blending time to introduce variability in the homogeneity of the tablets. The dissolution prediction models were established using the partial least squares regression under different Raman mapping area. The accuracies of the prediction results were evaluated according to the difference factor f1 and Intersection-Union two one-sided t-tests (IU TOST) methods, and the implications conveyed by the results were discussed. The results showed that the homogeneity of sustained-release tablet affects the minimum required mapping area, and the tablets with higher homogeneity show higher prediction accuracy when using the same mapping area to model the dissolution profiles of tablets.

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.

Review of the Application of Raman Spectroscopy in Qualitative and Quantitative Analysis of Drug Polymorphism

Drug polymorphism is an important factor affecting the drugs quality and clinical efficacy. Therefore, great attention should be paid to the crystal analysis of drugs with their researching and evaluating part. With the booming development of Raman spectroscopy in recent years, more and more crystal analysis investigations were based on vibrational spectroscopy. This review mainly discussed the qualitative and quantitative analysis of active pharmaceutical ingredients (API) and pharmaceutical preparation with Raman spectroscopy. On basis of the determination of the vibration mode of drug molecules and the analysis of their chemical structure, this method had the advantages of universal, non-destructive, fast determination, low samples and cost, etc. This review provides theoretical and technical support for crystal structure, which are worth popularizing. It is expected that it will be helpful to relevant government management institutions, pharmaceutical scientific research institutions and pharmaceutical manufacturers.