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

Spectroscopy-Based Partial Prediction of In Vitro Dissolution Profile Using Artificial Neural Networks

In pharmaceutical industry, dissolution testing is part of the target product quality that essentials are in the approval of new products. The prediction of the dissolution profile based on spectroscopic data is an alternative to the current destructive and time-consuming method. RAMAN and Near Infrared (NIR) spectroscopy are two complementary methods, that provide information on the physical and chemical properties of the tablets and can help in predicting their dissolution profiles. This work aims to use the information collected by these methods to support the decision of how much of the dissolution profile should be measured and which methods to use, so that by estimating the remaining part, the accuracy requirement of the industry is met. Artificial neural network models were created, in which parts of the measured dissolution profiles, along with the spectroscopy data and the measured compression curves were used as an input to estimate the remaining part of the dissolution profiles. It was found that by measuring the dissolution profiles for 30 minutes, the remaining part was estimated within the acceptance limits of the f2 similarity factor. Adding further spectroscopy methods along with the measured parts of the dissolution profile significantly increased the prediction accuracy

Real-Time Monitoring of Continuous Pharmaceutical Mixed Suspension Mixed Product Removal Crystallization Using Image Analysis

In this work, we developed an in-line image analysis system for the monitoring of the continuous crystallization of an active pharmaceutical ingredient. Acetylsalicylic acid was crystallized in a mixed suspension mixed product removal crystallizer, which was equipped with overflow tubing as an outlet. A steep glass plate was placed under the outlet onto which the slurry dripped on its surface. The glass plate spread and guided the droplets toward the product collection filter. A high-speed process camera was mounted above the glass plate to capture images of the crystals. Several light sources were tested in various positions to find the appropriate experimental setup for the optimal image quality. Samples were taken during continuous operation for off-line particle size analysis in order to compare to the crystal size distributions calculated from the images. The results were in good agreement, and the trends of the process could be followed well using the images. As a next step, image analysis was operated throughout the entire continuous crystallization experiment, and a huge quantity of information was collected from the process. The crystal size distribution of the product was calculated every 30 s, which provided a thorough and detailed insight into the crystallization process

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. To this respect, data fusion strategies have been extensively applied in different sectors, such as food or chemical, to provide a more robust performance of the analytical platforms. This survey evaluates the challenges and opportunities of implementing data fusion within the PAT concept by identifying transfer opportunities from other sectors. Special attention is given to the data types available from pharmaceutical manufacturing and their compatibility with data fusion strategies. Furthermore, the integration into Pharma 4.0 is discussed

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

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. Although artificial neural networks (ANNs) have emerged as powerful tools in data-rich environments, their implementation in pharmaceutical manufacturing is hindered by their black-box nature. In this work, ANNs were developed and interpreted to demonstrate their applicability to increase process understanding by retrospective analysis of developmental or manufacturing data. The in vitro dissolution and hardness of extended-release, directly compressed tablets were predicted from manufacturing and spectroscopic data of pilot-scale development. The ANNs using material attributes and operational parameters provided better results than using NIR or Raman spectra as predictors. ANNs were interpreted by sensitivity analysis, helping to identify the root cause of the batch-to-batch variability, e.g., the variability in particle size, grade, or substitution of the hydroxypropyl methylcellulose excipient. An ANNbased control strategy was also successfully utilized to mitigate the batch-to-batch variability by flexibly operating the tableting process. The presented methodology can be adapted to arbitrary data-rich manufacturing steps from active substance synthesis to formulation to predict the quality from manufacturing or development data and gain process understanding and consistent product quality

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