Process analytical technology for continuous manufacturing tableting processing: A case study

THz-TDS as a PAT tool for monitoring blend homogeneity in pharmaceutical manufacturing of solid oral dosage forms: A proof-of-concept study

The process analytical technology (PAT) framework is well established and integral to facilitate process understanding, enable a transition from batch to continuous manufacturing, and improve product quality. Near-infrared (NIR) spectroscopy has been established as a standard PAT tool for many process analytical challenges, including monitoring powder blend homogeneity. However, alternative technologies for monitoring powder blending are of interest due to the importance of the blending step in manufacturing solid oral dosage forms. Terahertz time-domain spectroscopy (THz-TDS) is therefore explored in this study as an alternative tool for monitoring blend homogeneity with the potential for endpoint control in a batch blending process. Powder blends of microcrystalline cellulose (MCC) and dibasic calcium phosphate dihydrate and blends of MCC and granulated α-lactose monohydrate were investigated non-invasively at various compositions using THz-TDS in transmission mode for acquiring spectra from samples enclosed in the blending container. It was found that attenuation- and phase-related parameters acquired with THz-TDS could reliably resolve physical changes related to the homogeneity of the blend. Further evaluations revealed that changes in the bulk density of the blend, in addition to the intrinsic optical properties of the materials, played a critical role in the observed trends for both systems. In contrast, the scattering contribution of the powder was mainly crucial for the attenuation-related parameter in blends with materials of high refractive indices. Finally, THz-TDS measurements were acquired throughout a blending process mimicking a continuous acquisition. The method could follow blending dynamics and resulted in reasonable predictive errors of the content of 0.5 - 2.5 %. Relative standard deviations for high content blends (20 %) were acceptable (3 - 7 %) whereas at low contents (5 %) significantly higher values (9 - 35 %) were found. Based on these findings, THz-TDS is a feasible PAT tool for monitoring blend homogeneity and controlling high content blend processes, although precision and accuracy is considered to improve with a more suitable interface.

Implementation of near-infrared spectroscopy and convolutional neural networks for predicting particle size distribution in fluidized bed granulation

Monitoring the particle size distribution (PSD) is crucial for controlling product quality during fluidized bed granulation. This paper proposed a rapid analytical method that quantifies the D10, D50, and D90 values using a Convolutional Block Attention Module-Convolutional Neural Network (CBAM-CNN) framework tailored for deep learning with near-infrared (NIR) spectroscopy. This innovative framework, which fuses CBAM with CNN, excels at extracting intricate features while prioritizing crucial ones, thereby facilitating the creation of a robust multi-output regression model. To expand the training dataset, we incorporated the C-Mixup algorithm, ensuring that the deep learning model was trained comprehensively. Additionally, the Bayesian optimization algorithm was introduced to optimize the hyperparameters, improving the prediction performance of the deep learning model. Compared with the commonly used Partial Least Squares (PLS), Support Vector Machine (SVM), and Artificial Neural Network (ANN) models, the CBAM-CNN model yielded higher prediction accuracy. Furthermore, the CBAM-CNN model avoided spectral preprocessing, preserved the spectral information to the maximum extent, and returned multiple predicted values at one time without degrading the prediction accuracy. Therefore, the CBAM-CNN model showed better prediction performance and modeling convenience for analyzing PSD values in fluidized bed granulation.

UV/Vis spectroscopy as an in-line monitoring tool for tablet content uniformity

Continuous manufacturing provides advantages compared to batch manufacturing and is increasingly gaining importance in the pharmaceutical industry. In particular, the implementation of tablet processes in continuous plants is an important part of current research. For this, in-line real-time monitoring of product quality through process analytical technology (PAT) tools is crucial. This study focuses on an in-line UV/Vis spectroscopy method for monitoring the active pharmaceutical ingredient (API) content in tablets. UV/Vis spectroscopy is particularly advantageous here, because it allows univariate data analysis without complex data processing. Experiments were conducted on a rotary tablet press. The tablets consisted of 7- 13 wt% theophylline monohydrate as API, lactose monohydrate and magnesium stearate. Two tablet production rates were investigated, 7200 and 20000 tablets per hour. The UV/Vis probe was mounted at the ejection position and measurements were taken on the tablet sidewall. Validation was according to ICH Q2 with respect to specificity, linearity, precision, accuracy and range. The specificity for this formulation was proven and linearity was sufficient with coefficients of determination of 0.9891 for the low throughput and 0.9936 for the high throughput. Repeatability and intermediate precision were investigated. Both were sufficient, indicated by coefficients of variations with a maximum of 6.46% and 6.34%, respectively. The accuracy was evaluated by mean percent recovery. This showed a higher accuracy at 20000 tablets per hour than 7200 tablets per hour. However, both throughputs demonstrate sufficient accuracy. Finally, UV/Vis spectroscopy is a promising alternative to the common NIR and Raman Spectroscopy.

Virtually Possible: Enhancing Quality Control of 3D-Printed Medicines with Machine Vision Trained on Photorealistic Images

Three-dimensional (3D) printing is an advanced pharmaceutical manufacturing technology, and concerted efforts are underway to establish its applicability to various industries. However, for any technology to achieve widespread adoption, robustness and reliability are critical factors. Machine vision (MV), a subset of artificial intelligence (AI), has emerged as a powerful tool to replace human inspection with unprecedented speed and accuracy. Previous studies have demonstrated the potential of MV in pharmaceutical processes. However, training models using real images proves to be both costly and time consuming. In this study, we present an alternative approach, where synthetic images were used to train models to classify the quality of dosage forms. We generated 200 photorealistic virtual images that replicated 3D-printed dosage forms, where seven machine learning techniques (MLTs) were used to perform image classification. By exploring various MV pipelines, including image resizing and transformation, we achieved remarkable classification accuracies of 80.8%, 74.3%, and 75.5% for capsules, tablets, and films, respectively, for classifying stereolithography (SLA)-printed dosage forms. Additionally, we subjected the MLTs to rigorous stress tests, evaluating their scalability to classify over 3000 images and their ability to handle irrelevant images, where accuracies of 66.5% (capsules), 72.0% (tablets), and 70.9% (films) were obtained. Moreover, model confidence was also measured, and Brier scores ranged from 0.20 to 0.40. Our results demonstrate promising proof of concept that virtual images exhibit great potential for image classification of SLA-printed dosage forms. By using photorealistic virtual images, which are faster and cheaper to generate, we pave the way for accelerated, reliable, and sustainable AI model development to enhance the quality control of 3D-printed medicines.

Terahertz time-domain spectroscopy for the investigation of tablets prepared from roller compacted granules

Roller compaction before tableting is a common unit operation to increase the processability of powders. Terahertz time-domain spectroscopy (THz-TDS) has recently been introduced as a potential process analytical technology (PAT) for measuring tablet porosity based on the refractive index of the tablet. Tablet porosity is a governing parameter for tablet disintegration and dissolution. The first aim of this study was to investigate tablets prepared from roller-compacted materials with THz-TDS to explore its usefulness for particle size evaluation of granules in tablets. Secondly, the impact of roller compaction and granule size before tablet compression on the established THz-TDS based measurement of tablet porosity was investigated. Microcrystalline cellulose and α-lactose monohydrate were roller compacted separately at five specific compaction forces (2, 4, 8, 12, and 16 kN cm-1) and fractionated into three size fractions. Tablets were prepared from the fractionated and unfractionated granules at twelve tableting pressures and subjected to THz-TDS transmission measurements. It was possible to use the scattering behaviour of the tablets at terahertz frequencies to describe the granulated materials' particle size changes during tableting. At the same time, prediction of porosity was impaired due to the deviation of the refractive index in strongly scattering samples. A correction method was introduced in which the porosity error was corrected based on the tablet's scattering behaviour, resulting in an improved prediction of tablet porosity. In conclusion, THz-TDS is considered a promising technique for the process monitoring of tableting based on its sensitivity to porosity and particle size changes within the tablet non-destructively, with a possible application as part of an in-process control strategy of the tableting of granulated or non-granulated materials.

The application of Near-Infrared Spatially Resolved Spectroscopy in scope of achieving continuous real-time quality monitoring and control of tablets with challenging dimensions

In scope of achieving real-time release of tablets, quality attributes need to be monitored and controlled through Process Analytical Technology tools such as near-infrared spectroscopy (NIRS). The authors evaluated the suitability of NIR-Spatially Resolved Spectroscopy (NIR-SRS) for continuous real-time monitoring and control of content uniformity, hardness and homogeneity of tablets with challenging dimensions. A novel user-friendly research and development inspection unit was used as standalone equipment for the analysis of small oblong tablets with deep-cut break lines. A total of 66 tablets varying in hardness and Active Pharmaceutical Ingredient (API) content were inspected, with each tablet being analysed five times and measurements repeated on three different days. Partial Least Squares (PLS) models were developed to assess content uniformity and hardness, of which the former showed higher accuracy. The authors attempted to visualize tablet homogeneity through NIR-SRS spectra by regressing all spectra obtained during a single measurement using a content uniformity PLS model. The NIR-SRS probe demonstrated its potential towards real-time release testing through its ability to quickly monitor content uniformity, hardness and visualize homogeneity, even for tablets with challenging dimensions.

Advancing non-destructive analysis of 3D printed medicines

Pharmaceutical 3D printing (3DP) has attracted significant interest over the past decade for its ability to produce personalised medicines on demand. However, current quality control (QC) requirements for traditional large-scale pharmaceutical manufacturing are irreconcilable with the production offered by 3DP. The US Food and Drug Administration (FDA) and the UK Medicines and Healthcare Products Regulatory Agency (MHRA) have recently published documents supporting the implementation of 3DP for point-of-care (PoC) manufacturing along with regulatory hurdles. The importance of process analytical technology (PAT) and non-destructive analytical tools in translating pharmaceutical 3DP has experienced a surge in recognition. This review seeks to highlight the most recent research on non-destructive pharmaceutical 3DP analysis, while also proposing plausible QC systems that complement the pharmaceutical 3DP workflow. In closing, outstanding challenges in integrating these analytical tools into pharmaceutical 3DP workflows are discussed.

Implementation of a fully integrated CM direct compression and coating process at a commercial pharmaceutical facility - Part 2: PAT and RTD results for normal operational conditions batches

This is the second of two articles detailing the continuous manufacturing (CM) development and implementation activities for an marketed product which have been realized in novel, qualified equipment, using validated control strategy elements to enable manufacture of batches under current good manufacturing practices (cGMP) and compliant with data integrity principles. Here, the application of process analytical technologies (PAT) and automation tools on batches produced under normal operational conditions is reviewed. The results from residence time distribution (RTD) models for predicting API concentration, in-line near infrared (NIR) testing of blend uniformity (BU) and at-line NIR spectroscopy analysis of core tablet concentration and tablet identity for real-time release testing (RTRT) are discussed. The influences of process equipment and design choices on NIR and RTD model variability, as well as the use of the PAT tools for monitoring the evolving properties understanding of CM process development, such as overcoming flow instabilities, is described. Results demonstrate that the RTD and NIR models developed and validated are robust to operating conditions and are critical for assuring steady state control of the continuous manufacturing process. Finally, the NIR and RTD model lifecycle, including procedures for necessary and normal model upgrades in a cGMP production environment, are presented.

Automatic system dynamics characterization of a pharmaceutical continuous production line

Continuous Manufacturing (CM) of drug products is a new approach in the pharmaceutical industry. In the presented paper, a GMP continuous wet granulation line for production of solid oral dosage forms was investigated in order to assess the system dynamics of the line and to define the best control and diversion strategy. The following steps were involved in the continuous process: dosing / feeding, blending, twin-screw wet granulation, fluid-bed drying, sieving and tableting. Two drug products with two different drug substances were compared during this study: one drug substance as model drug compound and one formulation of a currently evaluated commercial drug product. Several step tests in API concentration were performed in order to characterize the process flow and assess the process dynamics. API content was monitored in real time by Process Analytical Technologies (PAT) thanks to three Near Infrared (NIR) probes located along the process and measuring the API content after blender, after dryer and in the tablet press feed frame. The process parameter values were changed during production in order to detect the impact on the quality of the final product. An automatic residence time distribution (RTD) computation method has been developed in order automate the RTD calculation on the basis of process data to further define and monitor the system dynamics with the final aim of out of specification material diversion during the continuous production. The RTD has been seen as a process fingerprint: a change in the RTD values implies a change in the process.

Continuous Manufacturing and Molecular Modeling of Pharmaceutical Amorphous Solid Dispersions

Amorphous solid dispersions enhance solubility and oral bioavailability of poorly water-soluble drugs. The escalating number of drugs with poor aqueous solubility, poor dissolution, and poor oral bioavailability is an unresolved problem that requires adequate interventions. This review article highlights recent solubility and bioavailability enhancement advances using amorphous solid dispersions (ASDs). The review also highlights the mechanism of enhanced dissolution and the challenges faced by ASD-based products, such as stability and scale-up. The role of process analytical technology (PAT) supporting continuous manufacturing is highlighted. Accurately predicting interactions between the drug and polymeric carrier requires long experimental screening methods, and this is a space where computational tools hold significant potential. Recent advancements in data science, computational tools, and easy access to high-end computation power are set to accelerate ASD-based research. Hence, particular emphasis has been given to molecular modeling techniques that can address some of the unsolved questions related to ASDs. With the advancement in PAT tools and artificial intelligence, there is an increasing interest in the continuous manufacturing of pharmaceuticals. ASDs are a suitable option for continuous manufacturing, as production of a drug product from an ASD by direct compression is a reality, where the addition of multiple excipients is easy to avoid. Significant attention is necessary for ongoing clinical studies based on ASDs, which is paving the way for the approval of many new ASDs and their introduction into the market.

Control strategy definition for a drug product continuous wet granulation process: Industrial case study

This paper describes the specific control strategy of the commercial manufacturing process of an immediate release tablet formulation based on continuous twin-screw wet granulation. This control strategy has been defined by a multidisciplinary team using an enhanced approach, in alignment with the quality by design principles. During process development, experiments have been performed according to multivariate designs first to identify critical material attributes and critical process parameters and then, to define process conditions generating a product having the required quality. Hence, controls have been applied on critical quality attributes and on related critical process parameters and critical material attributes. Due to the specificity of the process that combines batch and continuous unit operations, a specific control strategy has been designed to ensure intermediate and end product quality. Therefore, controls including soft sensor model and in process controls have been developed to continuously monitor granules residual moisture content, assay and dissolution as granules and tablets critical attributes. In addition, process analytical technology implementation enabled increased process understanding and provided support for the development of the control strategy. This study is therefore considered as a real industrial case study of control strategy definition and implementation for an intended commercial continuous manufacturing process.

Real-time coating thickness measurement and defect recognition of film coated tablets with machine vision and deep learning

This paper presents a system, where images acquired with a digital camera are coupled with image analysis and deep learning to identify and categorize film coating defects and to measure the film coating thickness of tablets. There were 5 different classes of defective tablets, and the YOLOv5 algorithm was utilized to recognize defects, the accuracy of the classification was 98.2%. In order to characterize coating thickness, the diameter of the tablets in pixels was measured, which was used to measure the coating thickness of the tablets. The proposed system can be easily scaled up to match the production capability of continuous film coaters. With the developed technique, the complete screening of the produced tablets can be achieved in real-time resulting in the improvement of quality control.

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.

Development and Use of a Residence Time Distribution (RTD) Model Control Strategy for a Continuous Manufacturing Drug Product Pharmaceutical Process

Residence-time-distribution (RTD)-based models are key to understanding the mixing dynamics of continuous manufacturing systems. Such models can allow for material traceability throughout the process and can provide the ability for removal of non-conforming material from the finished product. These models have been implemented in continuous pharmaceutical manufacturing mainly for monitoring purposes, not as an integral part of the control strategy and in-process specifications. This paper discusses the steps taken to develop an RTD model design space and how the model was statistically incorporated into the product’s control strategy. To develop the model, experiments were conducted at a range of blender impeller speeds and total system mass flow rates. RTD parameters were optimized for each condition tested using a tank-in-series-type model with a delay. Using the experimental RTD parameters, an equation was derived relating the mean residence time to the operating conditions (i.e., blender impeller speed and mass flow rate). The RTD parameters were used in combination with real-time upstream process data to predict downstream API concentration, where these predictions allowed validation across the entire operating range of the process by comparison to measured tablet assay. The standard in-process control limits for the product were statistically tightened using the validation acceptance criteria. Ultimately, this model and strategy were accepted by regulatory authorities.

Development of a tablet press feed frame lead lag determination model using in-line and off-line NIR measurements

For continuous pharmaceutical manufacturing of oral solid dosages, it is essential that product quality is measured inline. In this application, a continuous rotary tablet press is used. The goal is a model-based assessment of the quality of the blend in the feed frame to determine whether the concentration of the active pharmaceutical ingredient (API) will be within the prescribed limits. This is to achieve a better quality assurance than by offline testing of a small sample of tablets. In this way, product quality for real-time release (RTR) could be implemented. With a near-infrared (NIR) probe, the concentration of the API in the feed chute and the feed-frame were measured, as well as the API concentration of the tablets by an offline NIR measurement. These different data sets are connected and used for the residence time distribution characterization of the mixing dynamic of the tablet press. A residence time distribution model is fitted to the data, and is further used to compute the lead-lag time. This yields information on how long it takes for a quantity of product to go from being measured in the feed frame until ending up in tablets. Further, it gives information on the occurrence of mixing in the feed-frame itself. These models allow making accurate predictions of whether tablets fall within specified concentration range in real-time. The real-time prediction can be used in combination with a control system both to maintain the quality of the blend as well as to know which tablets to discard. This real-time quality assurance will lead to less material waste and fewer declined batches of tablets.

Prediction of entire tablet formulations from pure powder components' spectra via a two-step non-linear optimization methodology

Process analytical technology in the pharmaceutical industry requires the monitoring of critical quality attributes (CQA) through calibrated models. However, the development, implementation, and maintenance of these quantitative models are both resource and time-intensive. This study proposes the implementation of a non-linear iterative optimization technology (IOT) to study the magnitude of analytical errors when the calibration tablet used to extract the λ vector deviates physically and chemically from the test samples. IOT is based on mathematical optimization of excess spectral absorbance. It requires minimum calibration effort and allows simultaneous prediction of the entire formulation instead of only the active pharmaceutical ingredient (API), with just one standard and pure component spectral data. Unlike Partial Least Squares (PLS), which requires the development of standards to incorporate variations in the process, this non-destructive methodology minimizes significant calibration effort by developing a mathematical model that uses only one standard and spectral information of pure powders present in the tablet. The method described in this study allows a fast re-calculation to include factors such as change of spectroscopic instruments, variations in raw materials, environmental conditions, and methods of tablet preparation. The robustness of the proposed approach for variation in compaction (physical changes) and variation in composition (chemical changes) was evaluated for correlated and uncorrelated formulations. For uncorrelated formulation a PLS model was also constructed to compare the robustness of the proposed methodology. The RMSEP of API in target formulation predicted using non-linear IOT method was varied from 0.17 to 1.50 depends on compaction of tablet chosen to compute λ vector. On the other hand, the RMSEP of API in target formulation predicted using PLS-based model was varied from 0.13 to 0.57 depending on compaction of tablet. The additional accuracy achieved in PLS based model required significant calibration effort of preparing 84 tablets compared to just one in proposed non-linear IOT method.

Determination and understanding of lead-lag between in-line NIR tablet press feed frame and off-line NIR tablet measurements

The influence of different tableting process parameters on lead-lag was studied by collecting in-line near-infrared (NIR) spectra in the filling chamber of the tablet press feed frame and off-line NIR tablet data. Lead-lag is defined as the difference in time and API concentration between the measured in-line feed frame NIR response and the off-line NIR tablet data. Lead-lag results from the product formulation blend undergoing additional mixing after passing the NIR probe inside the feed frame, before being filled into the dies of the tablet press. A design of experiments (DoE) was performed to evaluate the effect of the tableting process factors paddle speed, turret speed, overfill level, paddle speed ratio and feed frame type upon lead-lag. Paddle speed and turret speed were identified as the only tableting parameters affecting lead-lag. Lead-lag decreased with increasing paddle speed or turret speed and became negligible at high paddle speed and high turret speed. Overfill level, paddle speed ratio and feed frame type did not affect lead-lag, suggesting that the amount and the trajectory of the recirculating powder in the feed frame did not significantly vary and hence influence the lead-lag within the examined process factor ranges. Finally, a methodology was developed using the in-line feed frame NIR measurements for the continuous monitoring and control of blend potency and tablet content uniformity. Tablet diversion should start when the in-line feed frame monitored blend potency exceeds the predefined control limits and can discontinue when this blend potency is again within the control limits for a duration equal to the lead-lag time. A combination of continuous blend potency monitoring inside the feed frame and in-process tablet weight control allows real-time tablet content uniformity assurance. Although the findings of this study are restricted to the specific equipment, tableting parameter ranges and product formulation used, the suggested approach for lead-lag determination and continuous tablet content uniformity monitoring can be applied to any rotary tablet press and product formulation.

Indirect monitoring of ultralow dose API content in continuous wet granulation and tableting by machine vision

This paper presents new machine vision-based methods for indirect real-time quantification of ultralow drug content during continuous twin-screw wet granulation and tableting. Granulation was performed with a solution containing carvedilol (CAR) as API in the ultralow dose range (0.05w/w% in the granule) and the addition of riboflavin (RI) as a coloured tracer. An in-line calibration in the range of 0.047-0.058 w/w% was prepared for the measurement of CAR concentration using colour analysis (CA) and particle size analysis (PSA), and the validation with HPLC resulted in respective relative errors of 2.62% and 2.30% showing great accuracy. To improve the technique, a second in-line calibration was conducted in a broader CAR concentration range of 0.039-0.063 w/w% utilizing only half the amount of RI (0.045 w/w%), while doubling the output of the granulation line to 2 kg/h, producing a relative error of 4.51% and 4.29%, respectively. Finally, it was shown that the CA technique can also be carried on to monitor the CAR content of tablets in the 42-62 μg dose range with a relative error of 5.20%. Machine vision was proven to be a potent indirect method for the in-line, determination and monitoring of ultralow API content during continuous manufacturing.

A micro-extrusion 3D printing platform for fabrication of orodispersible printlets for pediatric use

3D printed pharmaceuticals offers the potential to manufacture personalized medicines for patients. Such technology is of particular benefit to pediatric populations from the offer of increased patient compliance and dose flexibility. With a bench-to-patient approach, this study established and optimized the critical parameters of the semi-solid micro-extrusion 3D printing process to guarantee the quality attributes of the final dosage form. Pediatrics orodispersible printlets of hydrochlorothiazide were manufactured through the modification of printing parameters, as well as printing surfaces materials. The printlets were characterized and the dimensions were measured using a digital caliper and computer vision algorithm. This study identified that the printing surface material and the first printing layer are critical parameters for high-resolution printlets. Following the optimization of 3D printing parameters, high quality orodispersible printlets loaded with hydrochlorothiazide - specifically tailored for pediatric patient's dosage forms - were obtained (4.62 mm × 1.90 mm). Mass and content uniformity assays demonstrated that the printlets satisfied the requirements for orodispersible printlets set by the European Pharmacopoeia. As such, in order to transition from laboratory research towards the treatment of patients, distinguishing accurate 3D printing parameters is necessary for the manufacture of medicines with key quality attributes that follow Pharmacopoeia requirements.

Process Analytical Technology Tools for Monitoring Pharmaceutical Unit Operations: A Control Strategy for Continuous Process Verification

Various frameworks and methods, such as quality by design (QbD), real time release test (RTRT), and continuous process verification (CPV), have been introduced to improve drug product quality in the pharmaceutical industry. The methods recognize that an appropriate combination of process controls and predefined material attributes and intermediate quality attributes (IQAs) during processing may provide greater assurance of product quality than end-product testing. The efficient analysis method to monitor the relationship between process and quality should be used. Process analytical technology (PAT) was introduced to analyze IQAs during the process of establishing regulatory specifications and facilitating continuous manufacturing improvement. Although PAT was introduced in the pharmaceutical industry in the early 21st century, new PAT tools have been introduced during the last 20 years. In this review, we present the recent pharmaceutical PAT tools and their application in pharmaceutical unit operations. Based on unit operations, the significant IQAs monitored by PAT are presented to establish a control strategy for CPV and real time release testing (RTRT). In addition, the equipment type used in unit operation, PAT tools, multivariate statistical tools, and mathematical preprocessing are introduced, along with relevant literature. This review suggests that various PAT tools are rapidly advancing, and various IQAs are efficiently and precisely monitored in the pharmaceutical industry. Therefore, PAT could be a fundamental tool for the present QbD and CPV to improve drug product quality.

Residence time and mixing capacity of a rotary tablet press feed frame

PURPOSE

Most rotary tablet presses contain a feed frame to provide a continuous powder flow and to feed powder into the dies. The wide residence time distribution (RTD) of these feed frames is problematic, because it negatively affects material traceability in continuous manufacturing. In a rotary tablet press, different machine settings influence the RTD, which is characterized by the mean and the width of the distribution. This study focused on the effects of the rotational speed of the feed frame paddles and the rotary tablet press throughput on the RTD.

METHODS

An in-line UV/Vis measurement method was developed for determining the RTD in the feed frame. A model based on a plug flow and a continuous stirred tank reactor was adapted to model the experimentally determined RTDs. Finally, the mixing capacity of a feed frame was evaluated and correlated with a model parameter of the RTD.

RESULTS

Overall, the developed UV/Vis measurement method was suitable and could be used to obtain process information regarding content uniformity in real time. The experimentally-determined RTDs were described well by fitting an inverse mixing and a transport time. In addition, a correlation between the location and the shape of measured RTDs and tablet press throughput was found. In contrast, rotational feed frame paddle speed did not affect the RTDs. Split-feeding experiments indicated the mixing capacity of the rotary tablet press feed frame.

CONCLUSION

The inverse mixing time can be used as an initial indicator for estimating the mixing capacity.

Benchtop NIR method development for continuous manufacturing scale to enable efficient PAT application for solid oral dosage form

A Near Infrared (NIR) method was developed using a small benchtop feed frame system to quantify Saccharin potency in a powder blend during continuous manufacturing process. A 15-point Design of Experiments (DoE) was created based on the NIR spectral response and compositions of the formulation to develop a calibration set. The calibration set was designed to create compositional and raw material lots variation using minimum resources. The calibration experiments utilized around 0.5 kg Saccharin (Active Pharmaceutical Ingredient (API) surrogate) and 1.8 kg of excipients. Partial Least Square (PLS) modeling was used to develop a quantitative NIR method from the calibration data. The NIR method was implemented during 5 test batches in two different manufacturing sites across different potency levels at a continuous manufacturing platform for direction compression. Acceptable prediction performance was achieved from the NIR method at both sites. The NIR method was robust against changes in process scale and NIR instruments. The variance information built into the calibration set was found to be critical to successful model performance. This study shows a benchtop feed frame can be used for material sparing calibration method development without operating at a full-scale process line and applied across multiple sites, instruments at different potency levels.

An agile and robust in-line NIR potency deviation detection method for monitoring and control of a continuous direct compression process

Near Infrared (NIR) method for blend potency estimation has been commonly used as an essential tool for process monitoring and control in continuous manufacturing of solid oral dosage forms. Robustness has been the main challenge for successful application of an NIR method, which often results in a long development time with frequent method update. Robustness deficiency often presents as an offset (bias) on the mean potency estimation. In this paper, the purpose of the NIR method has been redefined from estimating potency to potency deviation. This quantitative approach uses the mean centered potency to estimate potency deviations from the process mean, therefore, detects the non-conforming materials for continuous process monitoring and control. An NIR method was developed at the lab benchtop scale and directly deployed to a direct compression continuous manufacturing platform at Pfizer for mean centered potency estimation. The benchtop calibration provided a speedy and efficient NIR method development and the method showed enhanced robustness for estimating potency deviation in presence of wide process and raw material variations. Integrating with the mean centered approach, the NIR model from the lab could be implemented to different sites using different instruments without requiring model update for the established range of process conditions and raw material properties.

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.

Sample Mass Estimate for the Use of Near-Infrared and Raman Spectroscopy to Monitor Content Uniformity in a Tablet Press Feed Frame of a Drug Product Continuous Manufacturing Process

Recently, feed frame-based process analytical technology measurements used to assure product quality during continuous manufacturing processes have received significant attention. These measurements are able to accurately determine uniformity of the powder blend before compression, and in these applications, it is necessary to understand the interrogated sample volume per measurement. This understanding ensures that the blend measurement can be indicative of the uniformity of the final dosage form. A scientifically sound approach is proposed here to estimate sample mass for a continuous manufacturing process that utilizes either near infrared or Raman spectroscopy. A wide range of commercially available probes with varying spot diameters are considered. By comparing near infrared and Raman spectroscopy, an optimal range of probe spot diameters was identified in order to reach an estimated sample mass between 50 and 500 mg for pharmaceutical blends per measurement, which is equivalent to common tablet weight ranges for solid oral dosage forms currently on the market.

Approach to Establishment of Control Strategy for Oral Solid Dosage Forms Using Continuous Manufacturing

As a result of the research activities of the Japan Agency for Medical Research and Development (AMED), this document aims to show an approach to establishing control strategy for continuous manufacturing of oral solid dosage forms. The methods of drug development, technology transfer, process control, and quality control used in the current commercial batch manufacturing would be effective also in continuous manufacturing, while there are differences in the process development using continuous manufacturing and batch manufacturing. This document introduces an example of the way of thinking for establishing a control strategy for continuous manufacturing processes.

Determination of Residence Time Distribution in a Continuous Powder Mixing Process With Supervised and Unsupervised Modeling of In-line Near Infrared (NIR) Spectroscopic Data

Successful implementation of continuous manufacturing processes requires robust methods to assess and control product quality in a real-time mode. In this study, the residence time distribution of a continuous powder mixing process was investigated via pulse tracer experiments using near infrared spectroscopy for tracer detection in an in-line mode. The residence time distribution was modeled by applying the continuous stirred tank reactor in series model for achieving the tracer (paracetamol) concentration profiles. Partial least squares discriminant analysis and principal component analysis of the near infrared spectroscopy data were applied to investigate both supervised and unsupervised chemometric modeling approaches. Additionally, the mean residence time for three powder systems was measured with different process settings. It was found that a significant change in the mean residence time occurred when comparing powder systems with different flowability and mixing process settings. This study also confirmed that the partial least squares discriminant analysis applied as a supervised chemometric model enabled an efficient and fast estimate of the mean residence time based on pulse tracer experiments.

A near-infrared spectroscopy-based end-point determination method for the blending process of Dahuang soda tablets

OBJECTIVES

This study is aimed to explore the blending process of Dahuang soda tablets. These are composed of two active pharmaceutical ingredients (APIs, emodin and emodin methyl ether) and four kinds of excipients (sodium bicarbonate, starch, sucrose, and magnesium stearate). Also, the objective is to develop a more robust model to determine the blending end-point.

METHODS

Qualitative and quantitative methods based on near-infrared (NIR) spectroscopy were established to monitor the homogeneity of the powder during the blending process. A calibration set consisting of samples from 15 batches was used to develop two types of calibration models with the partial least squares regression (PLSR) method to explore the influence of density on the model robustness. The principal component analysis-moving block standard deviation (PCA-MBSD) method was used for the end-point determination of the blending with the process spectra.

RESULTS

The model with different densities showed better prediction performance and robustness than the model with fixed powder density. In addition, the blending end-points of APIs and excipients were inconsistent because of the differences in the physical properties and chemical contents among the materials of the design batches. For the complex systems of multi-components, using the PCA-MBSD method to determine the blending end-point of each component is difficult. In these conditions, a quantitative method is a more suitable alternative.

CONCLUSIONS

Our results demonstrated that the effect of density plays an important role in improving the performance of the model, and a robust modeling method has been developed.

The optimization of process analytical technology for the inline quantification of multiple drugs in fixed dose combinations during continuous processing

Complications associated with uncontrolled hypertension are considered the major cause of premature death worldwide. Fixed-dose combinations (FDCs) offer an alternative approach to polypharmacy with the aim to improve patient compliance. Process Analytical Technology (PAT) is gaining momentum as a non-invasive, predictive tool to control the quality of drugs during continuous processing. PAT offers real-time quality control that can be built into the production line. However, the vast majority of studies reported in the literature have focused on quantifying a single drug during continuous processing. The aim of this study was to develop non-destructive, predictive inline PAT tools allowing for the simultaneous quantification of two antihypertensive drugs, Hydrochlorothiazide (HCTZ) and Ramipril (RMP), during the continuous manufacture of FDCs. A calibration set composed of HCTZ and RMP at concentration ranges of 6.5 to 40 and 2.5-15 (% w/w), respectively, were manufactured using hot melt extrusion. The extrudates were analysed during the process using inline Raman spectroscopy. Optimum wavenumber regions were observed at 200-400 and 630-730 cm^-1 for HCTZ, and 980-1100 cm^-1 for RMP using principal component analysis. Partial least squares (PLS) regression was performed to establish the predictive calibration models. The PLS developed models showed excellent linearity (R2 = 0.986 and 0.974), selectivity (PC1 = 98.6% and 91.9%) and accuracy (RMSEcv = 1.586 and 0.645%) for HCTZ and RMP, respectively. Additionally, RMSEP values were reported as 1.237 and 1.007% for HCTZ and RMP, respectively, depicting good predictability for drug content in the validation set. The output of this study demonstrated that utilisation of the full potential of chemometrics, Raman spectroscopy can be used for the simultaneous inline quantification of multiple drugs in complex formulations. This facilitates the in-process quality control of FDCs and other multicomponent systems during continuous pharmaceutical production.

From powder to tablets: Investigation of residence time distributions in a continuous manufacturing process train as basis for continuous process verification

The essence of Continuous Manufacturing (CM) resides in the fact that continuous process units are directly connected to each other forming a continuous process train. The thorough understanding of material flow in this train based on suitable sensors, including on-line process analytical technologies and other sensors, is key in understanding the time-domain behavior of the system and the process. This real-time monitoring correlated with the time domain material flow behavior could be used to close control-loops. In practical terms, the implementation of such a control strategy is only feasible, if the overlying control system knows precisely what material is when and where at all times. Consequently, thorough knowledge of the residence time distribution (RTD) of the material throughout the whole manufacturing network needs to be established early on in development. Once RTD is well understood, its constant observation could also be used for continuous process verification purposes hinging on the argument that the flow pattern of the material is unchanged. As continuous processes that run over extended periods of time are susceptible to unforeseen incidents like equipment wear-out or clogging, drifts or shifts in RTD could indicate such issues early on. The presented work aims to demonstrate this proposed concept for an integrated wet-granulation CM process. To achieve this aim, three steps were completed: First, thorough RTD knowledge was generated, by inducing endogenous step-tests in active pharmaceutical ingredient (API) content in the range of ±30% at varying process conditions, and analyzing the material RTDs via NIRS analysis at four different locations in the line. Second, it was demonstrated that also low-level step tests of ±5% and even ±3% are sufficient for accurate RTD determination. This validated the possibility of continuous RTD assessment during (pre-)validation trials or even commercial manufacturing, as the drug product would comply with required quality characteristics (content uniformity, assay). In the third step, it was then demonstrated that recurring low-level step testing during routine manufacturing could be used as a way to determine the current system health, as observed changes in RTD indicated blockages and accidental material hold-up in the line. While deliberate changes in API content during commercial production might seem counter intuitive, they would actually aid in ensuring the production of quality product in a better way, than running at constant process settings over an extended period of time without the constant assessment of system health.

End-to-end continuous manufacturing of conventional compressed tablets: From flow synthesis to tableting through integrated crystallization and filtration

An end-to-end continuous pharmaceutical manufacturing process was developed for the production of conventional direct compressed tablets on a proof-of-concept level for the first time. The output reaction mixture of the flow synthesis of acetylsalicylic acid was crystallized continuously in a mixed suspension mixed product removal crystallizer. The crystallizer was directly connected to a continuous filtration carousel device, thus the crystallization, filtration and drying of acetylsalicylic acid (ASA) was carried out in an integrated 2-step process. Steady state was reached during longer operations and the interaction of process parameters was evaluated in a series of experiments. The filtered crystals were ready for further processing in a following continuous blending and tableting experiment due to the good flowability of the material. The ASA collected during the crystallization-filtration experiments was fed into a continuous twin-screw blender along with microcrystalline cellulose as tableting excipient. After continuous blending Near-Infrared spectroscopy was applied to in-line analyze the drug content of the powder mixture. A belt conveyor carried the mixture towards an eccentric lab-scale tablet press, which continuously produced 500 mg ASA-loaded compressed tablets of 100 mg dose strength. Thus, starting from raw materials, the final drug product was obtained by continuous manufacturing steps with appropriate quality.

Near infrared analysis of pharmaceutical powders with empirical target distribution optimization (ETDO)

Near infrared (NIR) spectroscopy is a well-established method for analysis of pharmaceutical products, and especially useful for process monitoring and control of continuous production due to high sample throughput. In this work, a previously established method called empirical target distribution optimization (ETDO) wherein reference sample values using information from model prediction of the calibration data was used as a tool to improve the performance of NIR partial least squares (PLS) models. Model performance was assessed using root mean square error (R²), bias and accuracy in prediction of test samples. A target value selection threshold was tested to assess the ETDO procedure for NIR analysis of powder samples. The amount of specific variation captured by the model was examined and compared for models calibrated with and without ETDO. The results reported in this work suggests that PLS models optimized with ETDO of reference values can provide more specific PLS models for NIR analysis for complex powder mixtures. In addition, the model optimization method could also be applied as a tool to verify the necessary amount of PLS components to produce robust models. The ETDO method presented in this work is an approach that could be applied in the development of continuous blending or tableting processes where robust in-line quantitative analysis of powder samples is needed.

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.

Predictive Model-Based Process Start-Up in Pharmaceutical Continuous Granulation and Drying

Continuous manufacturing (CM) is a promising strategy to achieve various benefits in the context of quality, flexibility, safety and cost in pharmaceutical production. One of the main technical challenges of CM is that the process needs to handle transient conditions such as the start-up phase before state of control operation is reached, which can potentially cause out-of-specification (OOS) material. In this context, the presented paper aims to demonstrate that suitable process control strategies during start-up of a continuous granulation and drying operation can limit or even avoid OOS material production and hence can ensure that the provided benefits of CM are not compromised by poor production yields. In detail, heat-up of the drying chamber prior the start of production can lead to thermal energy being stored inside of the stainless-steel housing, acting as an energy buffer that is known to cause over-dried granules in the first few minutes of the drying process. To compensate this issue, an automatic ramping procedure of dryer rotation speed (and hence drying time) was introduced into the plant's process control system, which counteracts the excessive drying capacity during start-up. As a result, dry granules exiting the dryer complied with the targeted intermediate critical quality attribute loss-on-drying (LOD) from the very beginning of production.

Continuous manufacturing process monitoring of pharmaceutical solid dosage form: A case study

Continuous Manufacturing (CM) of pharmaceutical drug products is a rather new approach within the pharmaceutical industry. In the presented paper, a GMP continuous wet granulation line used for clinical production of solid dosage forms was investigated with a thorough monitoring strategy regarding process performance and robustness. The line was composed of the subsequent continuous unit operations feeding - twin-screw wet-granulation - fluid-bed drying - sieving and tableting; the formulation of a new pharmaceutical entity in development was selected for this study. In detail, a Design of Experiments (DoE) was used to evaluate the impact of the three main factors (amount of water, filling rate, and shear force in twin-screw granulator) on the tablet quality. The process was monitored via in-process control (IPC) tests (e.g. weight, hardness, disintegration, and loss-on-drying), Process Analytical Technologies (PAT), and through the analysis of the process parameters (multivariate process control). The tested formulation was very robust to the large process variation of the DoE: all IPC results were in specification, the PAT probes provided stable results for the content uniformity and no critical variations can be detected in the process parameters. An adequate monitoring strategy was presented and the robustness of the process with one formulation has been demonstrated. In summary, this continuous process in combination with smart formulation development allows the robust production of constant quality tablets. The synergy between PAT, process data science and IPC creates an adequate monitoring framework of the continuous manufacturing line.

Feed frame: The last processing step before the tablet compaction in pharmaceutical manufacturing

The feed frame is a force-feeding device used in the die filling process. The die filling process is crucial within pharmaceutical manufacturing to guarantee the critical quality attributes of the tablets. In recent years, interest in this unit has increased because it can affect the properties of the powder blend and tablets, and because of the success in real time monitoring of powder blend uniformity potential for Process Analytical Technology as described in this review. The review focuses on the recent advances in understanding the powder flow behavior inside the feed frame and how the residence time distribution of the powder within the feed frame is affected by the operating conditions and design parameters. Furthermore, this review also highlights the effect of the paddle wheel design and feed frame process parameters on the tablet weight, the principal variable for measuring die filling performance.

Chemometric analysis of enantioselective Raman spectroscopy data enables enantiomeric ratio determination

In-line determination of the enantiomeric ratio is still a challenge in process analytical technology (PAT). This study combines enantioselective Raman (esR) spectroscopy with partial least-squares regression (PLSR) to determine the enantiomeric fraction of the chiral molecule (5,6)-diphenyl-morpholin-2-one diluted in dimethyl sulfoxide (DMSO) as a proof-of-concept. Morpholinone derivates are potential candidates for pharmaceutical applications. The PLS weights were carefully analyzed in order to avoid misleading regression results, e.g. caused by sample impurities. A suitable PLSR model was found with two components and it was validated by a leave-one-out cross-validation. The enantiomeric fraction ef₍₊₎ could be calculated with deviations from the prepared ef₍₊₎ in the range of -0.031 and +0.052 from the esR spectra recorded at a half-wave retarder angle of 30.0°.

Development of an In-Line Near-Infrared Method for Blend Content Uniformity Assessment in a Tablet Feed Frame

Process analytical technology (PAT) has shown great potential for in-line tableting process monitoring. The study focuses on the development and validation of an in-line near-infrared (NIR) spectroscopic method for the determination of content uniformity of blends in a tablet feed frame. An in-line NIR method was developed after careful evaluation of the impact of potential experimental factors on the robustness and model accuracy and precision. The NIR method was validated according to the principles outlined in International Conference on Harmonization-Q2 for validation of analytical procedures and was demonstrated to be suitable for monitoring blend content for the formulation under evaluation. Reliable measurements of blend homogeneity rely on representative sampling. To reach the appropriate scale of scrutiny for a unit dose, the study assessed factors that influence the effective sample size measured by NIR. Spectral averaging, integration time, and feed frame paddle wheel speed were found to influence the effective sample size measured by the NIR probe. The effective sampling size was also estimated by comparing the distribution of predicted values with the reference values. The development of a robust, in-line PAT method was facilitated by thorough understanding of the sensitivity of PAT sensors to factors affecting pharmaceutical processes and products.