Enabling real time release testing by NIR prediction of dissolution of tablets made by continuous direct compression (CDC)

Real-time release testing of in vitro dissolution and blend uniformity in a continuous powder blending process by NIR spectroscopy and machine vision

Continuous manufacturing is gaining increasing interest in the pharmaceutical industry, also requiring real-time and non-destructive quality monitoring. Multiple studies have already addressed the possibility of surrogate in vitro dissolution testing, but the utilization has rarely been demonstrated in real-time. Therefore, in this work, the in-line applicability of an artificial intelligence-based dissolution surrogate model is developed the first time. NIR spectroscopy-based partial least squares regression and artificial neural networks were developed and tested in-line and at-line to assess the blend uniformity and dissolution of encapsulated acetylsalicylic acid (ASA) - microcrystalline cellulose (MCC) powder blends in a continuous blending process. The studied blend is related to a previously published end-to-end manufacturing line, where the varying size of the ASA crystals obtained from a continuous crystallization significantly affected the dissolution of the final product. The in-line monitoring was suitable for detecting the variations in the ASA content and dissolution caused by the feeding of ASA with different particle sizes, and the at-line predictions agreed well with the measured validation dissolution curves (f2 = 80.5). The results were further validated using machine vision-based particle size analysis. Consequently, this work could contribute to the advancement of RTRT in continuous end-to-end processes.

Prediction of in-vitro dissolution and tablet hardness from optical porosity measurements

Advanced manufacturing technologies such as continuous processing require fast information on the quality of intermediates and products. Process analytical technologies (PAT) to monitor many critical quality attributes (CQAs) have been developed and successfully implemented in pharmaceutical industry. However, there are some CQAs, which still have to be measured off-line with significant effort due to the lack of suitable PAT sensors. Two prominent examples are the in-vitro dissolution and the tablet hardness. Both are obtained via destructive measurement, and the dissolution is tedious and time-consuming to determine. In this study, these two CQAs were predicted via correlation with the optical porosity of tablets. The optical porosity was measured via a novel combination of gas in scattering media absorption spectroscopy (GASMAS) and photon time of flight spectroscopy (pTOFS) with a SpectraPore instrument. The approach was tested in a continuous tableting line and showed promising results in predicting the amount of drug released after specific dissolution times as well as the tablet hardness. This indicates that the measurement of optical porosity can support control strategies within the real-time release testing (RTRT) concept.

Analytical comparison between batch and continuous direct compression processes for pharmaceutical manufacturing using an innovative UV-Vis reflectance method and chemometrics

Advancements in industrial technologies and the application of quality by design (QbD) guidelines are shifting the attention of manufacturers towards innovative production techniques. In the pharmaceutical field, there is a significant focus on the implementation of continuous processes, in which the production stages are carried out continuously, without the need to interrupt the process and store the production intermediates, as in traditional batch production. Such innovative production techniques also require the development of proper analytical methods able to analyze the products in-line, while still being processed. The present study aims to compare a traditional batch manufacturing process with an alternative continuous one. To this end, a real pharmaceutical formulation was used, substituting the active pharmaceutical ingredient (API) with riboflavin, at the concentration of 2 %w/w. Moreover, a direct and non-destructive analytical method based on UV-Vis reflectance spectroscopy was applied for the quantification of riboflavin in the final tablets, and compared with a traditional absorbance analysis. Good results were obtained in the comparison of both the two manufacturing processes and the two analytical methods, with R2 higher than 0.9 for all the calculated calibration models and predicted riboflavin concentrations that never significantly overcame the 15 % limits recommended by the pharmacopeia. The continuous production method demonstrated to be as reliable as the batch one, allowing to save time and money in the production step. Moreover, UV-Vis reflectance was proved to be an interesting alternative to absorption spectroscopy, which, with the proper technology, could be implemented for in-line process control.

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.

At-line porosity sensing for non-destructive disintegration testing in immediate release tablets

Fully automated at-line terahertz time-domain spectroscopy in transmission mode is used to measure tablet porosity for thousands of immediate release tablets. The measurements are rapid and non-destructive. Both laboratory prepared tablets and commercial samples are studied. Multiple measurements on individual tablets quantify the random errors in the terahertz results. These show that the measurements of refractive index are precise, with the standard deviation on a single tablet being about 0.002, with variation between measurements being due to small errors in thickness measurement and from the resolution of the instrument. Six batches of 1000 tablets each were directly compressed using a rotary press. The tabletting turret speed (10 and 30 rpm) and compaction pressure (50, 100 and 200 MPa) were varied between the batches. As expected, the tablets compacted at the highest pressure have far lower porosity than those compacted at the lowest pressure. The turret rotation speed also has a significant effect on porosity. This variation in process parameters resulted in batches of tablets with an average porosity between 5.5 and 26.5%. Within each batch, there is a distribution of porosity values, the standard deviation of which is in the range 1.1 to 1.9%. Destructive measurements of disintegration time were performed in order to develop a predictive model correlating disintegration time and tablet porosity. Testing of the model suggested it was reasonable though there may be some small systematic errors in disintegration time measurement. The terahertz measurements further showed that there are changes in tablet properties after storage for nine months in ambient conditions.

Regulatory Experience with Continuous Manufacturing and Real Time Release Testing for Dissolution in New Drug Applications

Regulatory submissions involving the use of continuous manufacturing (CM)1 and/or real-time release testing for dissolution (RTRT-D) to the United States Food and Drug Administration (FDA) were identified spanning several years. The submissions were for orally administered IR tablets and they were examined from a biopharmaceutics perspective to highlight commonly occurring issues which the FDA's assessment teams identified with the proposed use of CM and/or RTRT-D. The objective of this study is to provide recommendations for best practices that will help advance the field by (i) generating greater opportunities for (drug) Applicants2 to benefit from the implementation of advanced manufacturing approaches, (ii) improving high quality regulatory submissions involving CM and RTRT-D, and thus (iii) lessening the regulatory review burden. This paper has identified several common deficiencies, such as inadequate strategies for stratified sampling of drug product (DP) units, inappropriate design of experiments (DoE), inability of the proposed RTRT-D model to account for dissolution variability and to predict the entire time course of dissolution, insufficient documentation, and unsuitable in vitro dissolution methods.

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.

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.

Towards a real-time release of blends and tablets using NIR and Raman spectroscopy at commercial scales

Near Infrared and Raman spectroscopy-based Process Analytical Technology tools were used for monitoring blend uniformity (BU) and content uniformity (CU) for solid oral formulations. A quantitative Partial Least Square model was developed to monitor BU as real-time release testing at a commercial scale. The model having the R2, and root mean square error of 0.9724 and 2.2047, respectively can predict the target concentration of 100% with a 95% confidence interval of 101.85-102.68% even after one year. The tablets from the same blends were investigated for CU using NIR and Raman techniques both in reflection and transmission mode. Raman reflection technique was found to be the best and the PLS model was developed using tablets compressed at different concentrations, hardness, and speed. The model with R2 and RMSE of 0.9766 and 1.9259, respectively was used for the quantification of CU. Both the BU and CU models were validated for accuracy, precision, specificity, linearity, and robustness. The accuracy was proved against the HPLC method with a relative standard deviation of less than 3%. The equivalency for BU by NIR and CU by Raman was evaluated using Schuirmann's Two One-sided tests and found equivalent to HPLC within a 2% acceptable limit.

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.

Semi-mechanistic reduced order model of pharmaceutical tablet dissolution for enabling Industry 4.0 manufacturing systems

We propose a generalization of the Weibull dissolution model, referred to as generalized Weibull dissolution model, that seamlessly captures all three fractional dissolution rates experimentally observed in pharmaceutical solid tablets, namely decreasing, increasing, and non-monotonic rates. This is in contrast to traditional reduced order models, which capture at most two fractional dissolution rates and, thus, are not suitable for a wide range of product formulations hindering, for example, the adoption of knowledge management in the context of Industry 4.0. We extend the generalized Weibull dissolution model further to capture the relationship between critical process parameters (CPPs), critical materials attributes (CMAs), and dissolution profile to, in turn, facilitate real-time release testing (RTRT) and quality-by-control (QbC) strategies. Specifically, we endow the model with multivariate rational polynomials that interpolate the mechanistic limiting behavior of tablet dissolution as CPPs and CMAs approach certain values of physical significance (such as the upper and lower bounds of tablet porosity or lubrication conditions), thus the semi-mechanistic nature of the reduced order model. Restricting attention to direct compaction and using various case studies from the literature, we demonstrate the versatility and the capability of the semi-mechanistic ROM to estimate changes in dissolution due to process disturbances in tablet weight, porosity, lubrication conditions (i.e., the total amount of shear strain imparted during blending), and moisture content in the powder blend. In all of the cases considered in this work, the estimations of the model are in remarkable agreement with experimental data.

Releasing fast and slow: Non-destructive prediction of density and drug release from SLS 3D printed tablets using NIR spectroscopy

Selective laser sintering (SLS) 3D printing is a revolutionary 3D printing technology that has been found capable of creating drug products with varied release profiles by changing the laser scanning speed. Here, SLS 3D printed formulations (printlets) loaded with a narrow therapeutic index drug (theophylline) were produced using SLS 3D printing at varying laser scanning speeds (100-180 mm/s). The use of reflectance Fourier Transform - Near Infrared (FT-NIR) spectroscopy was evaluated as a non-destructive approach to predicting 3D printed tablet density and drug release at 2 h and 4 h. The printed drug products formulated with a higher laser speed exhibited an accelerated drug release and reduced density compared with the slower laser scanning speeds. Univariate calibration models were developed based on a baseline shift in the spectra in the third overtone region upon changing physical properties. For density prediction, the developed univariate model had high linearity (R² value = 0.9335) and accuracy (error < 0.029 mg/mm³). For drug release prediction at 2 h and 4 h, the developed univariate models demonstrated a linear correlation (R² values of 0.9383 and 0.9167, respectively) and accuracy (error < 4.4%). The predicted vs. actual dissolution profiles were found to be statistically similar (f₂ > 50) for all of the test printlets. Overall, this article demonstrates the feasibility of SLS 3D printing to produce drug products containing a narrow therapeutic index drug across a range of drug release profiles, as well as the potential for FT-NIR spectroscopy to predict the physical characteristics of SLS 3D printed drug products (drug release and density) as a non-destructive quality control method at the point-of-care.

State-of-the-art and emerging trends in analytical approaches to pharmaceutical-product commercialization

The biopharmaceutical landscape continues to evolve rapidly, and associated modality complexity and the need to improve molecular understanding require concomitant advances in analytical approaches used to characterize and release the product. The Product Quality Attribute Assessment (PQAA) and Quality Target Product Profile (QTPP) frameworks help catalog and translate molecular understanding to process and product-design targets, thereby enabling reliable manufacturing of high-quality product. The analytical target profile forms the basis of identifying best-fit analytical methods for attribute measurement and continues to be successfully used to develop robust analytical methods for detailed product characterization as well as release and stability testing. Despite maturity across multiple testing platforms, advances continue to be made, several with the potential to alter testing paradigms. There is an increasing role for mass spectrometry beyond product characterization and into routine release testing as seen by the progress in multi-attribute methods and technologies, applications to aggregate measurement, the development of capillary zone electrophoresis (CZE) coupled with mass spectrometry (MS) and capillary isoelectric focusing (CIEF) with MS for measurement of glycans and charged species, respectively, and increased application to host cell protein measurement. Multitarget engaging multispecific modalities will drive advances in bioassay platforms and recent advances both in 1- and 2-D NMR approaches could make it the method of choice for characterizing higher-order structures. Additionally, rigorous understanding of raw material and container attributes is necessary to complement product understanding, and these collectively can enable robust supply of high-quality product to patients.

Continuous direct compression: Development of an empirical predictive model and challenges regarding PAT implementation

In this study, an empirical predictive model was developed based on the quantitative relationships between blend properties, critical quality attributes (CQA) and critical process parameters (CPP) related to blending and tableting. The blend uniformity and API concentration in the tablets were used to elucidate challenges related to the processability as well as the implementation of PAT tools. Thirty divergent ternary blends were evaluated on a continuous direct compression line (ConsiGma™ CDC-50). The trials showed a significant impact of the impeller configuration and impeller speed on the blending performance, whereas a limited impact of blend properties was observed. In contrast, blend properties played a significant role during compression, where changes in blend composition significantly altered the tablet quality. The observed correlations allowed to develop an empirical predictive model for the selection of process configurations based on the blend properties, reducing the number of trial runs needed to optimize a process and thus reducing development time and costs of new drug products. Furthermore, the trials elucidated several challenges related to blend properties that had a significant impact on PAT implementation and performance of the CDC-platform, highlighting the importance of further process development and optimization in order to solve the remaining challenges.

Application of chemometrics using direct spectroscopic methods as a QC tool in pharmaceutical industry and their validation

This present review described the application of chemometrics using direct spectroscopic methods at the quality control (QC) laboratory of Pharmaceutical Industries. Using chemometrics methods, all QC assessments during the fabrication processes of the drug preparations can be well performed. Chemometrics methods have some advantages compared to the conventional methods, i.e., non-destructive, can be performed directly to intake samples without any extractions, unnecessary performing stability studies, and cost-effective. To achieve reliable results of analyses, all methods must be validated first prior to routine applications. According to the current Pharmacopeia, the validation parameters are specificity/selectivity, accuracy, repeatability, intermediate precision, range, detection limit, quantification limit and robustness. These validation data must meet the acceptance criteria, that have been described by the analytical target profile (ATP) of the drug preparations.

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.

Review: Continuous Manufacturing of Small Molecule Solid Oral Dosage Forms

Continuous manufacturing (CM) is defined as a process in which the input material(s) are continuously fed into and transformed, and the processed output materials are continuously removed from the system. CM can be considered as matching the FDA's so-called 'Desired State' of pharmaceutical manufacturing in the twenty-first century as discussed in their 2004 publication on 'Innovation and Continuous Improvement in Pharmaceutical Manufacturing'. Yet, focused attention on CM did not really start until 2014, and the first product manufactured by CM was only approved in 2015. This review describes some of the benefits and challenges of introducing a CM process with a particular focus on small molecule solid oral dosage forms. The review is a useful introduction for individuals wishing to learn more about CM.

Control strategy and methods for continuous direct compression processes

We presented a control strategy for tablet manufacturing processes based on continuous direct compression. The work was conducted by the experts of pharmaceutical companies, machine suppliers, academia, and regulatory authority in Japan. Among different items in the process, the component ratio and blended powder content were selected as the items requiring the control method specific to continuous manufacturing different from the conventional batch manufacturing. The control and management of the Loss in Weight (LIW) feeder were deemed the most important, and the Residence Time Distribution (RTD) model were regarded effective for setting the control range and for controlling of the LIW feeder. Based on these ideas, the concept of process control using RTD was summarized. The presented contents can serve as a solid fundament for adopting a new control method of continuous direct compression processes in and beyond the Japanese market.

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.

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.

Real-time determination and visualization of two independent quantities during a manufacturing process of pharmaceutical tablets by near-infrared hyperspectral imaging combined with multivariate analysis

During pharmaceutical manufacturing, line-scan hyperspectral imaging enables us to collect several electromagnetic spectra at each pixel in a two-dimensional plane for each tablet. The present study quantitatively determines two independent values of the active pharmaceutical ingredient (API) content in a tablet and the amount of coating on a surface of the same tablet simultaneously; the process is visualized by means of a near-infrared hyperspectral imaging (NIR-HSI) system combined with multivariate data analysis at a typical manufacturing speed of 4,000 tablets per minute. The API content and the amount of coating were controlled to be in the range 80-120% and 0-7 mg, respectively. The results of the cross validation of regression models demonstrated a coefficient of determination (R²) of 0.942, a root-mean-square error of cross validation (RMSECV) of 3.48% for the API content, an R² of 0.939, and an RMSECV of 0.46 mg for the amount of coating. These results demonstrated that the API content in a tablet as well as the amount of coating on the surface of the same tablet can be simultaneously determined with sufficient accuracy. This technique is practically applicable to process analytical technology in pharmaceutical manufacturing.

Process Control of Drug Product Continuous Manufacturing Operations—a Study in Operational Simplification and Continuous Improvement

Purpose

The purpose of this manuscript is to demonstrate that implementation of gravimetric measurements provides the same assurance of product quality and process control as spectroscopic measurements (1) for control of drug content in a fixed-dose combination (FDC) tablet and (2) for identification of non-conforming material.

Methods

A wet granulation continuous tableting line was used to make the FDC drug product batches. Comparative data was generated for ten batches using near-infrared (NIR) spectroscopy for core tablets, and gravimetric in-process control measurements (IPCs) applied to the ratio control of intra- and extra-granular blend (IG and EG). HPLC reference data were collected to further demonstrate uniformity at each stage of the production process, including IG, final blend, and core tablets. All possible sources of variation not directly detectable by the gravimetric measurements were considered and quantified.

Results

The two IPC measurement techniques showed excellent agreement where both were within 2% of the target drug concentrations and within 2% of each other for the ten comparative batches. The NIR was more sensitive to material and process variations than the gravimetric IPCs; thus, it was more variable within and across batches. Gravimetric IPCs were demonstrated as an effective replacement for spectroscopic measurements for continuous tableting operations, capable of ensuring on target manufacturing and detection of non-conforming material.

Conclusions

As pharmaceutical companies continue to push toward operational simplicity and sustainable manufacturing processes, soft-sensor and gravimetric controls as alternatives to their spectroscopic counterparts will be applied more broadly for process monitoring and control.

The Comparison of Two Challenging Low Dose APIs in a Continuous Direct Compression Process

Segregation is a common problem in batch-based direct compression (BDC) processes, especially with low-dose tablet products, as is the preparation of a homogenous mixture. The scope of the current work was to explore if a continuous direct compression (CDC) process could serve as a solution for these challenges. Furthermore, the principle of a platform formulation was demonstrated for low dose tablets. The combination of filler excipients and the API in the formulation used was suitable for direct compression, but also prone to induce segregation in BDC process. The CDC process was found to be very promising; it was shown that tablets with the desired quality parameters could be manufactured successfully with both of the APIs studied. Powder analysis indicated that the APIs display some fundamental differences in their physical properties, which was also reflected in powder mixture properties and, hence, eventually in processing. However, process parameters, especially mixer impeller speed, were not found to have any significant influence on end product quality. The study suggests that a CDC process can be a viable solution to resolve the challenges described. Moreover, manufacturing by using a universal platform formulation seems to be a feasible way for producing low-dose tablets.

Recent progress in continuous manufacturing of oral solid dosage forms

Continuous drug product manufacturing is slowly being implemented in the pharmaceutical industry. Although the benefits related to the quality and cost of continuous manufacturing are widely recognized, several challenges hampered the widespread introduction of continuous manufacturing of drug products. Current review presents an overview of state-of-the art research, equipment, process analytical technology implementations and advanced control strategies. Additionally, guidelines and regulatory viewpoints on implementation of continuous manufacturing in the pharmaceutical industry are discussed.

Determining key parameters of continuous wet granulation for tablet quality and productivity: A case in ethenzamide

This paper aims to determine key parameters that affect tablet quality and productivity in continuous tablet manufacturing. Experiments were performed based on design of experiments using a continuous high-shear granulator and ethenzamide as the active pharmaceutical ingredient. To guide a systematic and comprehensive parameter analysis, a parameter framework was defined that comprised five input parameters on raw material properties and process parameters, 11 intermediate parameters on granule properties, and 11 output parameters on tablet quality and productivity. The interrelationships were analyzed statistically and were described as matrix functions. The liquid/solid ratio was the key parameter that affected circularity, density, and flowability as the granule properties, and disintegration and dissolution as the tablet quality. The maximum acceptable manufacturing rate that governs productivity was also affected by the liquid/solid ratio. Circularity was found to affect disintegration and dissolution. This result was specific to the setup of the study, but suggested development opportunities for a new process analytical technology system/quality-by-design application based on circularity. In addition, practical findings were obtained as follows: (1) high-speed manufacturing favored a lower liquid/solid ratio, and (2) high circularity slowed down disintegration/dissolution. This obtained knowledge will enhance the applicability of continuous technology in an actual manufacturing environment.

Feeding of particle-based materials in continuous solid dosage manufacturing: a material science perspective

The pharmaceutical industry today is experiencing a paradigm shift from batch to continuous manufacturing, which promises greater flexibility to target diverse populations, as well as more-consistent product quality to ensure best efficacy. However, shifting to continuous processing means that even basic process steps, such as feeding, can become unexpected but are crucially important. In this review, we will present the fundamental differences between dispensing (batch) and feeding (continuous) and how they impact the formulation design space. We will further outline our rational development approach, applicable to continuous unit operations in general, which includes standardized material and process characterization, as well as predictive modeling based on advanced, multidomain simulation tools.

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.

Fast, Spectroscopy-Based Prediction of In Vitro Dissolution Profile of Extended Release Tablets Using Artificial Neural Networks

The pharmaceutical industry has never seen such a vast development in process analytical methods as in the last decade. The application of near-infrared (NIR) and Raman spectroscopy in monitoring production lines has also become widespread. This work aims to utilize the large amount of information collected by these methods by building an artificial neural network (ANN) model that can predict the dissolution profile of the scanned tablets. An extended release formulation containing drotaverine (DR) as a model drug was developed and tablets were produced with 37 different settings, with the variables being the DR content, the hydroxypropyl methylcellulose (HPMC) content and compression force. NIR and Raman spectra of the tablets were recorded in both the transmission and reflection method. The spectra were used to build a partial least squares prediction model for the DR and HPMC content. The ANN model used these predicted values, along with the measured compression force, as input data. It was found that models based on both NIR and Raman spectra were capable of predicting the dissolution profile of the test tablets within the acceptance limit of the f2 difference factor. The performance of these ANN models was compared to PLS models using the same data as input, and the prediction of the ANN models was found to be more accurate. The proposed method accomplishes the prediction of the dissolution profile of extended release tablets using either NIR or Raman spectra.

Comparison between integrated continuous direct compression line and batch processing - The effect of raw material properties

There is a current trend in pharmaceutical manufacturing to shift from traditional batch manufacture to continuous manufacturing. The purpose of this study was to test the ability of an integrated continuous direct compression (CDC) line, in relation to batch processing, to achieve consistent tablet quality over long processing periods for formulations with poor flow properties or with a tendency to segregate. The study design included four industrially relevant formulations with different segregation indices and flow properties induced through different grades of the Active Pharmaceutical Ingredient (API), paracetamol, and major filler as well as varying the amount of API. The performance metrics investigated were content, uniformity of content, tablet weight, and tablet strength. The overall process stability over time was significantly improved with the CDC line as compared to the batch process. For all the formulations with a high API content, the CDC line provided better or equal uniformity of content and tablet weight as compared to batch. The CDC line was especially efficient in providing a stable content and tablet weight for poorly flowing formulations containing the standard, cohesive, grade of API. The only formulation that performed better in the batch process was the formulation with a low API content. Thus, for this formulation, the batch process achieved lower variation in tablet content since maintaining a low feed rate for the API proved challenging in the CDC line. In addition, some of the API became stuck in the CDC line between feeding and tableting, most likely at the funnel in the mixer inlet, highlighting the need for properly designed interfaces between units. The insensitivity of the CDC line towards poor flow indicates that one could use direct compression at high drug load compositions of poorly flowing powder blends that could not be processed via batch manufacturing.

First-Principles and Empirical Approaches to Predicting In Vitro Dissolution for Pharmaceutical Formulation and Process Development and for Product Release Testing

This manuscript represents the perspective of the Dissolution Working Group of the International Consortium for Innovation and Quality in Pharmaceutical Development (IQ) and of two focus groups of the American Association of Pharmaceutical Scientists (AAPS): Process Analytical Technology (PAT) and In Vitro Release and Dissolution Testing (IVRDT). The intent of this manuscript is to show recent progress in the field of in vitro predictive dissolution modeling and to provide recommended general approaches to developing in vitro predictive dissolution models for both early- and late-stage formulation/process development and batch release. Different modeling approaches should be used at different stages of drug development based on product and process understanding available at those stages. Two industry case studies of current approaches used for modeling tablet dissolution are presented. These include examples of predictive model use for product development within the space explored during formulation and process optimization, as well as of dissolution models as surrogate tests in a regulatory filing. A review of an industry example of developing a dissolution model for real-time release testing (RTRt) and of academic case studies of enabling dissolution RTRt by near-infrared spectroscopy (NIRS) is also provided. These demonstrate multiple approaches for developing data-rich empirical models in the context of science- and risk-based process development to predict in vitro dissolution. Recommendations of modeling best practices are made, focused primarily on immediate-release (IR) oral delivery products for new drug applications. A general roadmap is presented for implementation of dissolution modeling for enhanced product understanding, robust control strategy, batch release testing, and flexibility toward post-approval changes.

Assessment of blend uniformity in a continuous tablet manufacturing process

Blend uniformity was monitored throughout a continuous manufacturing (CM) process by near infrared (NIR) spectroscopic measurements of flowing blends and compared to the drug concentration in the tablets. The NIR spectra were obtained through the chute after the blender and within the feed frame, while transmission spectra were obtained for the tablets. The CM process was performed with semi-fine acetaminophen blends at 10.0% (w/w). The blender was operated at 250 RPM, for best performance, and 106 and 495 rpm where a lower mixing efficiency was expected. The variation in blender RPM increased the variation in drug concentration at the chute but not at the feed frame. Statistical results show that the drug concentration of tablets can be predicted, with great accuracy, from blends within the feed frame. This study demonstrated a mixing effect within the feed frame, which contribute to a 60% decrease in the relative standard deviation of the drug concentration, when compared to the chute. Variographic analysis showed that the minimum sampling and analytical error was five times less in the feed frame than the chute. This study demonstrates that the feed frame is an ideal location for monitoring the drug concentration of powder blends for CM processes.

Process analytical technologies and injectable drug products: Is there a future?

Parametric release was the first subset of real time release testing (RTRT), applied to terminally sterilised injectable drug products. The objective was to offer the industry an alternative to the time and money consuming sterility testing, without compromising the sterility of the products. The rationale was that quality cannot be tested into products, instead it must be planned (the principle of quality by design, QbD). This can be implemented by setting appropriate in-process controls supported on process analytical technologies (PAT). Two of the most versatile and promising PAT tools are the near infrared spectroscopy (NIRS) and the Raman spectroscopy. However, their application to injectable drug product development and manufacturing has been scarce. This review has the objective to provide a framework for the practical implementation of the QbD approach to injectable formulations, including application of diverse risk assessment and factorial design tools. Finally, the actual application of PAT, namely NIRS and Raman spectroscopy, to injectable drug product analysis is addressed.

FIP Guidelines for Dissolution Testing of Solid Oral Products

Dissolution testing is an important physiochemical test for the development of solid oral dosage forms, tablets, and capsules. As a quality control test, the dissolution test is used for assessment of drug product quality and is specified for batch release and regulatory stability studies. In vitro dissolution test results can often be correlated with the biopharmaceutical behavior of a product.This article provides a summary of views from major global agencies (Europe, Japan, United States), pharmacopoeias, academia, and industry. Based on available guidance and literature, this article summarizes highlights for development and validation of a suitable dissolution method, setting appropriate specifications, in vitro-in vivo comparison, and how to obtain a biowaiver.