Predicting the dissolution behavior of pharmaceutical tablets with NIR chemical imaging

High-throughput optimisations for 3D chemical imaging of pharmaceutical solid oral dosage forms

Chemical imaging of pharmaceutical solid oral dosage forms is a key technique for quality assurance and issue diagnosis. This technique can be further augmented using 3D chemical imaging via serial sections and image stacking. However, the additional collection time this entails means that 3D imaging is utilised for a very niche set of applications. Previous attempts have been made to optimize the process but have often compromised the quality of the resulting chemical images to achieve the gains in process time. In this study, several optimisation strategies are employed to increase the efficiency of 3D chemical image collection without sacrificing the quality of the final chemical images. The use of automated microscope macros and a kinematic mounting system allowed for rapid sample processing and efficient utilisation of equipment time. The automated macros allow the Raman microscope to collect mapping data continuously from multiple samples without the need for operator intervention steps. The kinematic mounting system allows rapid and accurate sample transfer and positioning between instruments. These optimisations resulted in a three times speed increase in collection time while keeping the same signal-to-noise ratio of the resulting chemical images. These optimisations will allow the rapid collection of statistically robust 3D chemical image data within a set time frame that is more amenable to an industrial workflow.

Towards simultaneous determination of tablet porosity and height by terahertz time-domain reflection spectroscopy

The quality control of pharmaceutical tablets is still based on testing small sample numbers using at- and off-line testing methods. Traditional in-process controls, such as tablet mass, height, mechanical strength, and disintegration time are time- and resource-consuming and poorly suited to support an effective transition towards continuous manufacturing. Another suitable parameter to monitor during production would be tablet porosity. Porosity can be linked to mechanical strength and disintegration but typically requires knowledge of tablet dimensions and mass. Tablet porosity measurements based on terahertz time-domain spectroscopy (THz-TDS) offer a fast and non-destructive approach to in-process control testing for physical tablet properties. This study presents THz-TDS reflection measurements as an alternative to the previously reported transmission setup. It is shown that the proposed method can determine porosity based on the reflected amplitude from the tablet surface, but also allows for precise determination of tablet height in the same measurement. The tablet mass can be estimated by combining the height and porosity measurements. This opens up for the opportunity to determine the tablet's mechanical strength by using the possible correlation to the determined porosity.

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.

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 ANN-based 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.

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.

Seeing Is Believing: Time-Lapse Macro-Imaging of Morphological Changes of Solid Dosages as a Teaching and Research Tool

PURPOSE

Disintegration kinetics and behaviors are critical for the quality and performance of oral solid dosages. Instead of performing standard disintegration tests, herein, we aim to visualize these kinetic processes in real time.

METHOD

A visual acquisition system is developed to capture the morphological changes of tablets under static conditions via time-lapse macro-imaging. The system consists of: i) a customized quartz chamber, ii) a metal sieve with pore sizes ranging from 1 to 2 mm in diameter to allow rapid settling of the disintegrated particles, and iii) a temperature-controlled water bath. A typical workflow consists of the following steps: i) planning of the experiment to consider the type of the active pharmaceutical ingredient and drug release mechanism; ii) acquisition of photo-imaging data from at least two cameras arranged at different angles over a predetermined time period; iii) post-processing of the image data; iv) production of video clips and image analysis.

RESULTS

Representative works are shown to demonstrate the disintegration phenomenon or the morphological changes of solid drug products of various controlled- and extended-release mechanisms.

CONCLUSION

These video clips are used as teaching materials for students majoring in pharmacy or pharmaceutical chemistry, which also provide an insightful unique perspective of the microprocess during tablet fragmentation, disintegration or drug release.

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. In our work, sustained-release tablets using hydroxypropyl methylcellulose (HPMC) as matrix polymer are prepared, the concentration and particle size of this component varied between samples. Chemical maps of HPMC are converted into histograms with two different methods, an approach based on discretizing concentration values and a wavelet analysis technique. These histograms are then subjected to Principal Component Analysis, the score value of the first two principal components was found to represent HPMC content and particle size. These values are used as input in Artificial Neural Networks which are trained to predict the dissolution profile of the tablets. As a result, accurate predictions were obtained for the test tablets (the average f₂ similarity value is higher than 59 with both methods). The presented methodology lays the foundations of the analysis of far more extensive datasets acquired with the emerging fast Raman imaging technology.

Pharmaceutical application of multivariate modelling techniques: a review on the manufacturing of tablets

The tablet manufacturing process is a complex system, especially in continuous manufacturing (CM). It includes multiple unit operations, such as mixing, granulation, and tableting. In tablet manufacturing, critical quality attributes are influenced by multiple factorial relationships between material properties, process variables, and interactions. Moreover, the variation in raw material attributes and manufacturing processes is an inherent characteristic and seriously affects the quality of pharmaceutical products. To deepen our understanding of the tablet manufacturing process, multivariable modeling techniques can replace univariate analysis to investigate tablet manufacturing. In this review, the roles of the most prominent multivariate modeling techniques in the tablet manufacturing process are discussed. The review mainly focuses on applying multivariate modeling techniques to process understanding, optimization, process monitoring, and process control within multiple unit operations. To minimize the errors in the process of modeling, good modeling practice (GMoP) was introduced into the pharmaceutical process. Furthermore, current progress in the continuous manufacturing of tablets and the role of multivariate modeling techniques in continuous manufacturing are introduced. In this review, information is provided to both researchers and manufacturers to improve tablet quality.

Potential of Raman spectroscopy in facilitating pharmaceutical formulations development - An AI perspective

Drug development is time-consuming and inherently possesses a high failure rate. Pharmaceutical formulation development is the bridge that links a new chemical entity (NCE) to pre-clinical and clinical trials, and has a high impact on the efficacy and safety of the final drug product. Further, the time required for this process is escalating as formulation techniques are becoming more complicated due to the rising demands for drug products with better efficacy and patient compliance, as well as the inherent difficulties of addressing the unfavorable properties of NCEs such as low water solubility. The advent of artificial intelligence (AI) provides possibilities to accelerate the drug development process. In this review, we first examine applications of AI methods in different types of pharmaceutical formulations and formulation techniques. Moreover, as availability of data is the engine for the advancement of AI, we then suggest a potential way (i.e. applying Raman spectroscopy) for faster high-quality data gathering from formulations. Raman techniques have the capability of analyzing the composition and distribution of components and the physicochemical properties thereof within formulations, which are prominent factors governing drug dissolution profiles and subsequently bioavailability. Thus, useful information can be obtained bridging formulation development to the final product quality.

A Fast and Non-destructive Terahertz Dissolution Assay for Immediate Release Tablets

There is a clear need for a robust process analytical technology tool that can be used for on-line/in-line prediction of dissolution and disintegration characteristics of pharmaceutical tablets during manufacture. Tablet porosity is a reliable and fundamental critical quality attribute which controls key mass transport mechanisms that govern disintegration and dissolution behavior. A measurement protocol was developed to measure the total porosity of a large number of tablets in transmission without the need for any sample preparation. By using this fast and non-destructive terahertz spectroscopy method it is possible to predict the disintegration and dissolution of drug from a tablet in less than a second per sample without the need of a chemometric model. The validity of the terahertz porosity method was established across a range of immediate release (IR) formulations of ibuprofen and indomethacin tablets of varying geometries as well as with and without debossing. Excellent correlation was observed between the measured terahertz porosity, dissolution characteristics (time to release 50% drug content) and disintegration time for all samples. These promising results and considering the robustness of the terahertz method pave the way for a fully automated at-line/on-line porosity sensor for real time release testing of IR tablets dissolution.

Prediction of drug dissolution from Toremifene 80 mg tablets by NIR spectroscopy

The aim of our study was to justify substitution of dissolution analysis for NIR measurement of Toremifene 80 mg tablets. We studied implementation of a NIRS method by integrating the method development to discrimination power of the dissolution method. Hence, we analyzed 20 DoE tablet batches and studied which of the critical formulation factors affecting dissolution were statistically significant. To study if these factors can be detected by NIRS, PLS calibration models were developed. Finally, PLS model was built to correlate NIR data with the actual dissolution results to predict the released amount of toremifene in 30 min. To obtain the data the tablet batches were measured by NIR using diffuse reflectance technique and multivariate analysis tool was used to calibrate the NIRS models. Correlations between the critical formulation factors and the NIR spectra of Toremifene 80 mg tablet were shown and it was thus justified to develop a NIRS prediction model for dissolution. Variance (R²), standard error of estimate (SEE) and standard error of prediction (SEP) of the model were 90.0%, 4.3% and 5.9%, respectively. It was thus shown that multi-phased and time consuming dissolution procedure could be substituted for fast non-invasive NIRS method.

Modeling of Disintegration and Dissolution Behavior of Mefenamic Acid Formulation Using Numeric Solution of Noyes-Whitney Equation with Cellular Automata on Microtomographic and Algorithmically Generated Surfaces

Manufacturing parameters may have a strong impact on the dissolution and disintegration of solid dosage forms. In line with process analytical technology (PAT) and quality by design approaches, computer-based technologies can be used to design, control, and improve the quality of pharmaceutical compacts and their performance. In view of shortcomings of computationally intensive finite-element or discrete-element methods, we propose a modeling and simulation approach based on numerical solutions of the Noyes-Whitney equation in combination with a cellular automata-supported disintegration model. The results from in vitro release studies of mefenamic acid formulations were compared to calculated release patterns. In silico simulations with our disintegration model showed a high similarity of release profile as compared to the experimental evaluation. Furthermore, algorithmically created virtual tablet structures were in good agreement with microtomography experiments. We conclude that the proposed computational model is a valuable tool to predict the influence of material attributes and process parameters on drug release from tablets.

Recent advances in the applications of vibrational spectroscopic imaging and mapping to pharmaceutical formulations

Vibrational spectroscopic imaging and mapping approaches have continued in their development and applications for the analysis of pharmaceutical formulations. Obtaining spatially resolved chemical information about the distribution of different components within pharmaceutical formulations is integral for improving the understanding and quality of final drug products. This review aims to summarise some key advances of these technologies over recent years, primarily since 2010. An overview of FTIR, NIR, terahertz spectroscopic imaging and Raman mapping will be presented to give a perspective of the current state-of-the-art of these techniques for studying pharmaceutical samples. This will include their application to reveal spatial information of components that reveals molecular insight of polymorphic or structural changes, behaviour of formulations during dissolution experiments, uniformity of materials and detection of counterfeit products. Furthermore, new advancements will be presented that demonstrate the continuing novel applications of spectroscopic imaging and mapping, namely in FTIR spectroscopy, for studies of microfluidic devices. Whilst much of the recently developed work has been reported by academic groups, examples of the potential impacts of utilising these imaging and mapping technologies to support industrial applications have also been reviewed.

A Rational Analysis of Uniformity Risk for Agglomerated Drug Substance Using NIR Chemical Imaging

Early risk detection and quick diagnosis of manufacturing challenges are necessary to support the accelerated development pace of drug product in the current competitive environment. Analytical tools, such as near-infrared (NIR) chemical imaging (CI), can be employed for alerting drug substance uniformity risks in intermediates and the final product of solid dosage forms. In this particular study, the ability to characterize the behavior of agglomerated drug substance throughout process development was enabled by NIR CI to identify uniformity risks with small sample sizes and short turnaround time. Using NIR chemical imaging, the drug substance distribution and cluster size in all intermediates were visualized throughout the drug product process. NIR CI enabled rapid identification of the key unit operations that produced the greatest reduction in cluster size for enhanced distribution of the drug substance. The comil acted as a high shear mixing step to disperse soft lumps prior to roller compaction. Shear forces or pressure during roller compaction was sufficient to break down and disperse the agglomerates further. Ultimately, the process was robust against a range of drug substance input properties such that the uniformity of the final blend was consistently achieved and the agglomerated drug substance had no risks to the drug product process.