Overview of PAT process analysers applicable in monitoring of film coating unit operations for manufacturing of solid oral dosage forms

In-situ monitoring of in vitro drug release processes in tablets using optical coherence tomography

Film-coated modified-release tablets are an important dosage form amenable to targeted, controlled, or delayed drug release in the specific region of the gastrointestinal (GI) tract. Depending on the film composition and interaction with the GI fluid, such coated products can modulate the local bioavailability, systemic absorption, protection as an enteric barrier, etc. Although the interaction of a dosage form with the surrounding dissolution medium is vital for the resulting release behavior, the underlying physicochemical phenomena at the film and core levels occurring during the drug release process have not yet been well described. In this work, we attempted to tackle this limitation by introducing a novel in vitro test based on optical coherence tomography (OCT) that allows an in-situ investigation of the sub-surface processes occurring during the drug release. Using a commercially available tablet based on osmotic-controlled release oral delivery systems (OROS), we demonstrated the performance of the presented prototype in terms of monitoring the membrane thickness and thickness variability, the surface roughness, the core swelling behavior, and the porosity of the core matrix throughout the in vitro drug release process from OROS. The superior spatial (micron scale) and temporal (less than 10 ms between the subsequent tomograms) resolution achieved in the proposed setup provides an improved understanding of the dynamics inside the microstructure at any given time during the dissolution procedure with the previously unattainable resolution, offering new opportunities for the design and testing of patient-centric dosage forms.

Fixed-Dose Combination Formulations in Solid Oral Drug Therapy: Advantages, Limitations, and Design Features

Whilst monotherapy is traditionally the preferred treatment starting point for chronic conditions such as hypertension and diabetes, other diseases require the use of multiple drugs (polytherapy) from the onset of treatment (e.g., human immunodeficiency virus acquired immunodeficiency syndrome, tuberculosis, and malaria). Successful treatment of these chronic conditions is sometimes hampered by patient non-adherence to polytherapy. The options available for polytherapy are either the sequential addition of individual drug products to deliver an effective multi-drug regimen or the use of a single fixed-dose combination (FDC) therapy product. This article intends to critically review the use of FDC drug therapy and provide an insight into FDC products which are already commercially available. Shortcomings of FDC formulations are discussed from multiple perspectives and research gaps are identified. Moreover, an overview of fundamental formulation considerations is provided to aid formulation scientists in the design and development of new FDC products.

Optimization of radial extrusion and pellet coating processes using PAT approaches

FDA's initiative Pharmaceutical CGMPs for the 21st century opened the door for introduction of several risk based approaches in pharmaceutical industry. One significant advancement that has emerged is the implementation of process analytical technology (PAT), which has opened doors for understanding and controlling complex technological processes. Two such processes, radial extrusion and pellet coating, offer a solid foundation for the application of PAT tools due to their numerous critical process parameters. The aim of the first part of the study was to optimize the neutral pellet production to produce the pellets with properties desired for successful film coating using design of experiments (DoE). In the second part the optimized pellets underwent film coating and the coating quantity was predicted in real or near real-time using in-line and at-line NIR probes and the performance of both probes was compared. The desired properties of the pellets, narrow particle size distribution, high sphericity and high process yield, were successfully achieved. Models for film coating quantity prediction using in-line and at-line NIR probe were successfully calibrated and tested by coating two additional batches. Despite the limited sample size for model calibration, at-line NIR exhibited excellent prediction performance and enabled accurate determination of process end-point. The coating quantity determined by UV/VIS spectroscopy in both test batches deviated by less than 2.0 % from the target value. However, the in-line NIR probe, primarily due to its inferior spectral resolution, displayed a slightly lower quality of the calibrated model and notable overprediction for the tested batches.

Ultra-high-resolution optical coherence tomography for the investigation of thin multilayered pharmaceutical coatings

Optical Coherence Tomography (OCT) has recently gained attention as a promising technology for in-line monitoring of pharmaceutical film-coating processes for (single-layered) tablet coatings and end-point detection with commercial systems. An increasing interest in the investigation of multiparticulate dosage forms with mostly multi-layered coatings below 20 µm final film thickness demands advancement in OCT technology for pharmaceutical imaging. We present an ultra-high-resolution (UHR-) OCT and investigate its performance based on three different multiparticulate dosage forms with different layer structures (one single-layered, two multi-layered) with layer thicknesses in a range from 5 to 50 µm. The achieved system resolution of 2.4 µm (axial) and 3.4 µm (lateral, both in air) enables the assessment of defects, film thickness variability and morphological features within the coating, previously unattainable using OCT. Despite the high transverse resolution, the provided depth of field was found sufficient to reach the core region of all dosage forms under test. We further demonstrate an automated segmentation and evaluation of UHR-OCT images for coating thicknesses, where human experts struggle using today's standard OCT systems.

Application of digital-intelligence technology in the processing of Chinese materia medica

Processing of Chinese Materia Medica (PCMM) is the concentrated embodiment, which is the core of Chinese unique traditional pharmaceutical technology. The processing includes the preparation steps such as cleansing, cutting and stir-frying, to make certain impacts on the quality and efficacy of Chinese botanical drugs. The rapid development of new computer digital technologies, such as big data analysis, Internet of Things (IoT), blockchain and cloud computing artificial intelligence, has promoted the rapid development of traditional pharmaceutical manufacturing industry with digitalization and intellectualization. In this review, the application of digital intelligence technology in the PCMM was analyzed and discussed, which hopefully promoted the standardization of the process and secured the quality of botanical drugs decoction pieces. Through the intellectualization and the digitization of production, safety and effectiveness of clinical use of traditional Chinese medicine (TCM) decoction pieces were ensured. This review also provided a theoretical basis for further technical upgrading and high-quality development of TCM industry.

Application of digital-intelligence technology in the processing of Chinese materia medica

Processing of Chinese Materia Medica (PCMM) is the concentrated embodiment, which is the core of Chinese unique traditional pharmaceutical technology. The processing includes the preparation steps such as cleansing, cutting and stir-frying, to make certain impacts on the quality and efficacy of Chinese botanical drugs. The rapid development of new computer digital technologies, such as big data analysis, Internet of Things (IoT), blockchain and cloud computing artificial intelligence, has promoted the rapid development of traditional pharmaceutical manufacturing industry with digitalization and intellectualization. In this review, the application of digital intelligence technology in the PCMM was analyzed and discussed, which hopefully promoted the standardization of the process and secured the quality of botanical drugs decoction pieces. Through the intellectualization and the digitization of production, safety and effectiveness of clinical use of traditional Chinese medicine (TCM) decoction pieces were ensured. This review also provided a theoretical basis for further technical upgrading and high-quality development of TCM industry.

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.

Evaluating Spatially Offset Low-Frequency Anti-Stokes Raman Spectroscopy (SOLFARS) for Detecting Subsurface Composition below an Emissive Layer: A Proof of Principle Study Using a Model Bilayer System

This work explores the potential use of spatially offset low-frequency anti-Stokes Raman spectroscopy (SOLFARS) to detect subsurface composition below an emissive surface. A range of bilayer tablets were used to evaluate this approach. Bilayer tablets differed in both the underlying layer composition (active pharmaceutical ingredient to excipient ratio, celecoxib: α-lactose monohydrate) and the upper layer thickness of the fluorescent coating (polyvinylpyrrolidone mixture with sunset yellow FCF dye). Two low- (<300 cm^-1) plus mid- (300 to 1800 cm^-1) frequency Raman instrumental setups, with lateral displacements for spatial analysis of solid dosage forms, using different excitation wavelengths were explored. The 532 nm system was used to illustrate how the low-frequency anti-Stokes Raman approach works with samples exhibiting extreme fluorescence/background emission interference, and the 785 nm system was used to demonstrate the performance when less extreme fluorescence/emission is present. Qualitative and quantitative chemometric analyses were performed to evaluate the performance of individual spectral domains and their combinations for the determination of the composition of the subsurface layer as well as the coating layer thickness. Overall, the commonly used midfrequency region (300-1800 cm^-1) proved superior when using 785 nm incident laser for quantifying the coating thickness (amorphous materials), whereas a combined Stokes and anti-Stokes low-frequency region was found to be superior for quantifying underlying crystalline materials. When exploring individual spectral regions for subsurface composition using spatially offset measurements, the anti-Stokes LFR spectral window performed best. The anti-Stokes low-frequency range also demonstrated an advantage for models composed of data exhibiting high levels of fluorescence (e.g., data collected using 532 nm incident laser), as the Stokes scattering was masked by fluorescence. Transmission measurements were also explored for comparison and showed the best applicability for both upper and lower layer analysis, attributed to the inherently larger bulk sampling volume of this setup. From a practical perspective, these results highlight the potential adjustments that can be made to already existing (in-line) Raman setups to facilitate similar analysis in pharmaceutical industry-based settings.

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.

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.

Visualising liquid transport through coated pharmaceutical tablets using Terahertz pulsed imaging

Dissolution of pharmaceutical tablets is a complex process, especially for coated tablets where layered structures form an additional barrier for liquid transport into the porous tablet matrix. A better understanding of the role of the coating structure in the mass transport processes that govern drug release, starting with the wetting of the coating layer by the dissolution medium, can benefit the formulation design and optimisation of the production. For this study, terahertz pulsed imaging was used to investigate how dissolution medium can penetrate coated tablets. In order to focus on the fundamental process, the model system for this proof-of-principle study consisted of tablet cores made from pure microcrystalline cellulose compacted to a defined porosity coated with Opadry II, a PVA-based immediate release coating blend. The coating was applied to a single side of flat-faced tablets using vacuum compression moulding. It was possible to resolve the hydration of the coating layer and the subsequent liquid ingress into the dry tablet core. The analysis revealed a discontinuity in density at the interface between coating and core, where coating polymer could enter the pore space at the immediate surface of the tablet cores during the coating process. This structure affected the liquid transport of the dissolution medium into the core. We found evidence for the formation of a gel layer upon hydration of the coating polymer. The porosity of the tablet core impacted the quality of coating and thus affected its dissolution performance (r =  0.6932 for the effective liquid penetration rate RP_eff and the core porosity). This study established a methodology and can facilitate a more in-depth understanding of the role of coating on tablet dissolution.

Ascertain a minimum coating thickness for acid protection of enteric coatings by means of optical coherence tomography

Enteric coatings are designed to protect active pharmaceutical ingredients (APIs) against untimely release in the stomach. Acid protection of such coatings depends on the coating layer thickness and integrity, which must be determined in an accurate and reliable way to ensure the final product's desired performance. Our work addresses the use of optical coherence tomography (OCT) for characterizing the coating thickness and variability of an enteric-coated drug product and linking them to resistance against gastric fluid. In this study, three batches of enteric-coated tablets drawn during the manufacturing process were investigated. An industrial OCT system was used to establish the coating thickness variability of single tablets (intra-tablet), all tablets in a batch (inter-tablet) and between the batches (inter-batch). Based on the large amount of OCT data, we calculated a critical coating thickness for the investigated film coating, which was found to be 27.4 µm. The corresponding distribution has a mean coating thickness of 44.3 µm ± 7.8 µm. The final coated product has a final mean coating thickness of 63.4 µm ± 8.7 µm, guaranteeing that all tablets meet the quality criterion (i.e., acid protection). Based on the measured thickness distributions, already known distribution functions were considered and an additional, new function was proposed for characterizing the coating thickness distributions in the early stages of industrial coating processes. The proposed approach can be transferred to in-line monitoring of the tablet coating processes, which could drastically improve the production efficiency by ultimately allowing real-time release testing (RTRT).

Quality-by-design in pharmaceutical development: From current perspectives to practical applications

Current pharmaceutical research directions tend to follow a systematic approach in the field of applied research and development. The concept of quality-by-design (QbD) has been the focus of the current progress of pharmaceutical sciences. It is based on, but not limited, to risk assessment, design of experiments and other computational methods and process analytical technology. These tools offer a well-organized methodology, both to identify and analyse the hazards that should be handled as critical, and are therefore applicable in the control strategy. Once implemented, the QbD approach will augment the comprehension of experts concerning the developed analytical technique or manufacturing process. The main activities are oriented towards the identification of the quality target product profiles, along with the critical quality attributes, the risk management of these and their analysis through in silico aided methods. This review aims to offer an overview of the current standpoints and general applications of QbD methods in pharmaceutical development.

A Novel Method for Rapid Particle Size Analysis of Ibuprofen Using Near-infrared Spectroscopy

Particle size distribution (PSD) is often considered as critical material attribute for active pharmaceutical ingredients (APIs), and the need for regular evaluation stands as an important quality control parameter in the pharmaceutical industry. Near-infrared (NIR) spectroscopy, used routinely for API identification, was introduced as analytical tool for simultaneous determination of particle size of ibuprofen. The demonstrated potential was highlighted by the development of rapid, robust, and noninvasive method coupled with multivariate data analysis (MVA), which can be easily transferred in QC laboratories for routine analysis. Principal component analysis (PCA) and partial least squares (PLS) regression analyses were performed on a calibration set of 61 ibuprofen samples, which differed in their median particle size Dv(50). The score scatterplots revealed evident clustering of ibuprofen samples according to their particle size, as well as occurrence of a distinctive outlying group of ibuprofen samples originating from one manufacturer. Further testing by means of mid-infrared spectroscopy, X-ray powder diffraction, and particle morphology analysis pinpointed particle morphology being responsible for the observed outlying group. Consequently, PLS class modeling based on particle morphology was introduced, which delivered two separate PLS regression models: one for blade-like ibuprofen crystals and another for irregular plate-like ibuprofen crystals. The former regression model exhibited high correlation coefficients and satisfactory predictive power (R²X = 0.999, R²Y = 0.917, Q² = 0.901), whereas the latter demonstrated lower statistical indicators (R²X = 0.99, R²Y = 0.72, Q² = 0.55). Additionally, the study underlines the importance of particle shape evaluation and sample classification according to particle morphology similarity prior to building NIRS-based regression models for PSD determination.

In-line monitoring and endpoint determination of percolation process of herbal medicine using ultraviolet spectroscopy combined with convolutional neural network

OBJECTIVES

As a common step in the herbal medicine production process, percolation usually lacks effective process monitoring methods and is often conducted with fixed process parameters. In this study, an in-line ultraviolet (UV) spectroscopy was used for monitoring the Caulis Sinomenii percolation process.

METHODS

The spectra and concentration data of 156 percolation samples from five batches were collected. Convolutional neural networks (CNNs) were used to develop quantitative calibration models. The mean squared error (MSE), mean absolute percentage error (MAPE) and mean absolute error (MAE) were compared to select the proper loss function for developing the CNN models. Meanwhile, partial least square regression (PLSR) was also used to develop calibration models for performance comparison.

KEY FINDINGS

The CNN models with MAPE or MAE as the loss function could provide accurate predictions for all samples. However, CNN models adopting MSE as the loss function tended not to predict low-concentration samples accurately. The CNN models mostly achieved satisfactory results without any preprocessing techniques and surpassed PLSR models in all the performance metrics.

CONCLUSIONS

An in-line UV spectroscopy system combining the CNN algorithm was implemented to monitor the percolation process of Caulis Sinomenii. The system can accurately determine the endpoint of the percolation process.

In-line cosmetic end-point detection of batch coating processes for colored tablets using multivariate image analysis

In this study, an in-line Process Analytical Technology (PAT) for cosmetic (non-functional) coating unit operations is developed using images of the tablet bed acquired in real-time by an inexpensive industrial camera and lighting system. The cosmetic end-point of multiple batches, run under different operating conditions, is automatically computed from these images using a Multivariate Image Analysis (MIA) methodology in conjunction with a stability determination strategy. The end-points detected by the algorithm differed, on average, by 3% in terms of total batch time from those identified visually by a trained operator. Since traditional practice typically relies on a coating overage to ensure full batch aspect homogeneity in the face of disturbances, the current in-line method can be used to reduce coating material and processing time (over 40% for the operating policy adopted in this work). Additionally, monitoring of the color features calculated by the algorithm allowed the identification of abnormal process conditions affecting visible coating uniformity. This work also addresses practical challenges related to image acquisition in the harsh environment of a pan coater, bringing this tool closer to a state of maturity for implementation in production units and opening the path for their optimization, monitoring, and automatic control.

Applicability of Raman and near-infrared spectroscopy in the monitoring of freeze-drying injectable ibuprofen

The freeze-drying process is an expensive, time-consuming and rather complex process. Therefore, process analytical technology (PAT) tools have been introduced to develop an optimized process and control critical process parameters, which affect the final product quality. The aim of the present work was to study the applicability of at-line near-infrared (NIR) and Raman spectroscopy approach in the monitoring of the freeze-drying process. Freeze-dried powders, which were developed previously, were manufactured as a multi-component system, containing ibuprofen (IBP). The NIR proved to be a useful tool for the monitoring of the freeze-drying process, since it was able to determine residual moisture content (RMC) and hence predict its values by using the partial least square (PLS) model. In addition, the evaluation of the correlation between the NIR and off-line HPLC IBP content results showed that NIR spectra were consistent with the HPLC measurements, even though overlapping absorption bands in multi-component system were observed. This research also studied the ability of using the at-line Raman measurements for the evaluation of the crystallinity and polymorphic transformations during the process, such as IBP ionization and mannitol polymorphism. The results were in correlation with XRPD results, but parameters of PLS models were not optimal. Nevertheless, this approach still assured better process understanding. To conclude, high applicability of the at-line NIR in the monitoring of the freeze-dried powder production was successfully demonstrated, suggesting that it can be used as a single tool to monitor RMC and IBP content as well as process deviations during the freeze-drying process.

Rapid Prototyping of Miniaturized Powder Mixing Geometries

Continuous manufacturing is an important element of future manufacturing solutions enabling for both high product quality and streamlined development process. The increasing possibilities with computer simulations allow for innovating novel mixing principles applicable for continuous manufacturing. However, these innovative ideas based on simulations need experimental validation. The use of rapid prototyping based on additive manufacturing opens a possibility to evaluate these ideas at a low cost. In this study, a novel powder mixing geometry was prototyped using additive manufacturing and further, interfaced with an in-line near-IR spectrometer allowing for investigating the residence time distribution (RTD) in this geometry.

Continuous blending monitored and feedback controlled by machine vision-based PAT tool

In a continuous powder blending process machine vision is utilized as a Process Analytical Technology (PAT) tool. While near-infrared (NIR) and Raman spectroscopy are reliable methods in this field, measurements become challenging when concentrations below 2 w/w% are quantified. However, an active pharmaceutical ingredient (API) with an intense color might be quantified in even lower quantities by images recorded with a digital camera. Riboflavin (RI) was used as a model API with orange color, its Limit of Detection was found to be 0.015 w/w% and the Limit of Quantification was 0.046 w/w% using a calibration based on the pixel value of images. A calibration for in-line measurement of RI concentration was prepared in the range of 0.2-0.45 w/w%, validation with UV/VIS spectrometry showed great accuracy with a relative error of 2.53 %. The developed method was then utilized for a residence time distribution (RTD) measurement in order to characterize the dynamics of the blending process. Lastly, the technique was applied in real-time feedback control of a continuous powder blending process. Machine vision based direct or indirect API concentration determination is a promising and fast method with a great potential for monitoring and control of continuous pharmaceutical processes.

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.

Novel NIR modeling design and assignment in process quality control of Honeysuckle flower by QbD

Honeysuckle flower is a common edible-medicinal food with significant anti-inflammatory efficacy. Process quality control of its ethanol precipitation is a topical issue in the pharmaceutical field. Near infrared (NIR) spectroscopy is commonly used for process quality analysis. However, establishing a robust and reliable quantitative model of complex process remains a challenge in industrial applications of NIR. In this paper, modeling design based on quality by design concept (QbD) was implemented for the ethanol precipitation process quality control of Honeysuckle flower. According to the 56 models' performances and 25 contour plots, quadratic model was the best with R_adj² increasing from 0.1395 to 0.9085, indicating the strong interaction among spectral pre-processing methods, variable selection methods, and latent factors. SG9 and CARS was an appropriate combination for modeling. Furthermore, spectral assignment method was creatively introduced for variable selection. Another 56 models' performances and 25 contour plots were established. Compared with the chemometric variable selection method, spectral assignment combined with QbD concept made a higher R_pre² and a lower RMSEP. When the latent factors of PLS was small, R_pre² of the model by spectral assignment increased from 0.9605 to 0.9916 and RMSEP decreased from 0.1555 mg/mL to 0.07134 mg/mL. This result suggests that the variable selected by spectral assignment is more representative and precise. This provided a novel modeling guideline for process quality control in PAT.

Process analytical technology tools for process control of roller compaction in solid pharmaceuticals manufacturing

This article presents an overview of using process analytical technology in monitoring the roller compaction process. In the past two decades, near-infrared spectroscopy, near-infrared spectroscopy coupled with chemical imaging, microwave resonance technology, thermal effusivity and various particle imaging techniques have been used for developing at-, off-, on- and in-line models for predicting critical quality attributes of ribbons and subsequent granules and tablets. The common goal of all these methods is improved process understanding and process control, and thus improved production of high-quality products. This article reviews the work of several researchers in this field, comparing and critically evaluating their achievements.

Image-Based Artificial Intelligence Methods for Product Control of Tablet Coating Quality

Mimicking the human decision-making process is challenging. Especially, many process control situations during the manufacturing of pharmaceuticals are based on visual observations and related experience-based actions. The aim of the present work was to investigate the use of image analysis to classify the quality of coated tablets. Tablets with an increasing amount of coating solution were imaged by fast scanning using a conventional office scanner. A segmentation routine was implemented to the images, allowing the extraction of numeric image-based information from individual tablets. The image preprocessing was performed prior to utilization of four different classification techniques for the individual tablet images. The support vector machine (SVM) technique performed superior compared to a convolutional neural network (CNN) in relation to computational time, and this approach was also slightly better at classifying the tablets correctly. The fastest multivariate method was partial least squares (PLS) regression, but this method was hampered by the inferior classification accuracy of the tablets. Finally, it was possible to create a numerical threshold classification model with an accuracy comparable to the SVM approach, so it is evident that there exist multiple valid options for classifying coated tablets.

Pharmaceutical Application of Tablet Film Coating

Tablet film coating is a common but critical process providing various functionalities to tablets, thereby meeting diverse clinical needs and increasing the value of oral solid dosage forms. Tablet film coating is a technology-driven process and the evolution of coated dosage forms relies on advancements in coating technology, equipment, analytical techniques, and coating materials. Although multiple coating techniques are developed for solvent-based or solvent-free coating processes, each method has advantages and disadvantages that may require continuous technical refinement. In the film coating process, intra- and inter-batch coating uniformity of tablets is critical to ensure the quality of the final product, especially for active film coating containing active pharmaceutical ingredients in the coating layer. In addition to experimental evaluation, computational modeling is also actively pursued to predict the influence of operation parameters on the quality of the final product and optimize process variables of tablet film coating. The concerted efforts of experiments and computational modeling can save time and cost in optimizing the tablet coating process. This review provides a brief overview of tablet film coating technology and modeling approaches with a focus on recent advancements in pharmaceutical applications.

Review of Terahertz Pulsed Imaging for Pharmaceutical Film Coating Analysis

Terahertz pulsed imaging (TPI) was introduced approximately fifteen years ago and has attracted a lot of interest in the pharmaceutical industry as a fast, non-destructive modality for quantifying film coatings on pharmaceutical dosage forms. In this topical review, we look back at the use of TPI for analysing pharmaceutical film coatings, highlighting the main contributions made and outlining the key challenges ahead.

Real-time process quality control of ramulus cinnamomi by critical quality attribute using microscale thermophoresis and on-line NIR

Real-time process quality control of ramulus cinnamomi (cassia twig) is still a challenge in pharmaceutical industry. Rapid critical quality attribute (CQA) determination of ramulus cinnamomi is essential for quality control. Microscale thermophoresis (MST) was used to investigate the CQA of ramulus cinnamomi by the interaction with biomacromolecule. There was a good affinity between cinnamaldehyde and human serum albumin (HSA) with K_a as 2.1722×10³mol/L. It was an excellent combination of similarity to ibuprofen with same binding force as discovered as hydrogen bond and van der Waals force. Furthermore, regarding cinnamaldehyde as CQA, on-line near-infrared was used to monitor pilot extraction process of ramulus cinnamomi combined with high performance liquid chromatography (HPLC). Quantitative model was established with R_pre² as 0.9798 and RMSECV as 0.0993, suggesting the NIR model was so robust and accurate for pilot process quality control. This method provided a perfect guideline for rapid CQA determination and real-time process quality control of Chinese materia medica (CMM) based on a vital CQA.

Product Development, Manufacturing, and Packaging of Solid Dosage Forms Under QbD and PAT Paradigm: DOE Case Studies for Industrial Applications

An integrated approach based on QbD and PAT provides a systematic and innovative framework for product development, manufacturing, and quality risk management. In this context, the significance of the outcome of design of experiments (DOEs) to the selection of the product design, robust commercial manufacturing process, design space, and overall control strategy remains vital for the success of a drug product throughout its life cycle. This paper aims at discussing selected recent DOE case studies conducted during QbD-based and integrated QbD/PAT-based development of solid oral formulations and process improvement studies. The main focus of this paper is to highlight the rationales and importance of design selection during development and applications of mathematical models and statistical tools in analyzing DOE and PAT data for developing a design space, control strategy, and improved process monitoring. A total of 25 case studies (includes 9 PAT application studies) have been discussed in this paper which cover 11 manufacturing processes commonly utilized for solid dosage forms. Two case studies relevant to selection of packaging design for solid dosage forms are also briefly discussed to complete the scope. Overall, for a successful modern QbD approach, it is highly important that DOEs are conducted and analyzed in a logical sequence which involves designs that are phase-appropriate and quality-driven and facilitate both statistical and chemometric thinking at each development stage. This approach can result into higher regulatory flexibility along with lower economic burden during life cycle of a product, irrespective of regulatory path used (NDA or ANDA).

Monitoring microsphere coating processes using PAT tools in a bench scale fluid bed

Among the factors that influence adherence to medication within the pediatric population, taste/irritation has been identified as a critical barrier to patient compliance. With the goal of improving compliance, microspheres (matrix systems within which the drug is dispersed) can be coated with a reverse enteric polymer that will prevent the release of the drug in the oral cavity while maintaining an immediate release once the drug product reaches the stomach, thereby achieving a taste neutral profile. In this work, the in-line performance of three process analytical technology (PAT) tools is evaluated in order to monitor the microsphere coating process. These tools are Raman spectroscopy, near-infrared spectroscopy and focused beam reflectance measurements, together with process data and raw material attributes. The ability of these different sources of information to predict the coating's barrier performance is evaluated by using a combined-data-approach: multiblock partial least squares (MBPLS). Results show that Raman spectroscopy has a superior predictive performance and that it has the potential to monitor the coating process of the microspheres as well as to detect process discrepancies (such as spray rate changes), demonstrating its usefulness for the monitoring of fluid bed coating processes. It was also demonstrated that Raman can be used to clearly differentiate batches with significantly difference in-vitro dissolution performance. This monitoring is considered critical to ensure consistent coating performance for this thin film barrier membrane that is essential to patient compliance.

A Review of the Applications of OCT for Analysing Pharmaceutical Film Coatings

Optical coherence tomography (OCT) has recently attracted a lot of interest in the pharmaceutical manufacturing industry as a fast, contactless and non-destructive modality for quantifying thin film coatings on pharmaceutical dosage forms, which cannot be resolved easily with other techniques. In this topical review, we present an overview of the research that has been performed to date, highlighting key differences between systems and outlining major challenges ahead.

Raman Spectroscopy for Process Analytical Technologies of Pharmaceutical Secondary Manufacturing

As the process analytical technology (PAT) mindset is progressively introduced and adopted by the pharmaceutical companies, there is an increasing demand for effective and versatile real-time analyzers to address the quality assurance challenges of drug manufacturing. In the last decades, Raman spectroscopy has emerged as one of the most promising tools for non-destructive and fast characterization of the pharmaceutical processes. This review summarizes the achieved results of the real-time application of Raman spectroscopy in the field of the secondary manufacturing of pharmaceutical solid dosage forms, covering the most common secondary process steps of a tablet production line. In addition, the feasibility of Raman spectroscopy for real-time control is critically reviewed, and challenges and possible approaches to moving from real-time monitoring to process analytically controlled technologies (PACT) are discussed.