Image Analysis: A Versatile Tool in the Manufacturing and Quality Control of Pharmaceutical Dosage Forms

Study on Lyophilised Orodispersible Tablets from Plant-Based Drinks as Bulking Agents

Background/Objectives: Oral administration of active pharmaceutical ingredients (APIs) is the most commonly used route of administration. As dysphagia is a prevalent problem, the size of the swallowed dosage form could negatively influence patient adherence. Orally disintegrating tablets (ODTs) are beneficial dosage forms because they disintegrate within a few seconds in the oral cavity without water. Lactose is one of the most commonly used excipients in the pharmaceutical industry; it served as the central concept of a recent publication on the formulation of milk-based ODTs despite lactose malabsorption being widespread worldwide. Consequently, the plant-based alternative market has grown exponentially and has become a prevailing food trend, with various alternatives to choose from. For this reason, the development of a nonsteroidal anti-inflammatory drug (NSAID)-containing ODT with plant-based drinks (PBDs) was assessed for its innovative potential. Methods: Different PBDs were investigated and compared to traditional and lactose-free milk. The liquids' viscosity, pH, and particle size were determined, and an electronic tongue was used for the sensory evaluation. The various ODTs were prepared with the freeze-drying method, and then the qualitative characteristics of the dosage form were investigated. Results: Our different measurements show that different plant beverages differ from each other and that these differences have an impact on the technological processing. According to the HPLC-DAD measurements, all values were in the required range. Conclusions: These measurements suggest that the soya drink is the most similar to traditional cow milk and would be the most appropriate choice among the investigated plant-based drinks to be used as a carrier system for an ibuprofen-containing ODT.

FT-NIR models for predicting film quality parameters in titanium dioxide-free tablet coatings

This study leverages Fourier Transform Near-Infrared (FT-NIR) spectroscopy to monitor the coating process of pharmaceutical tablets using PVA-based TiO2-free films, with talc and iron oxides as opacifiers. By employing a combination of multivariate analytical techniques, the correlation between film coating progression and film thickness was evaluated. Assessment of coating thickness for different coating levels was performed by optical microscopy. Additionally, using colorimetric analysis by scanner method, the color progression for different coating levels was evaluated and expressed as the a* value from CIELAB color space. The coordinate value a* showed predictable changes with the progression of the coating process and film thickness values, indicating its utility as a robust reference method for quality control and process optimization. The predictive capability of the OPLS models, validated against measured film thickness and the a* value, demonstrated low prediction errors and confirmed the models' effectiveness in distinguishing coating levels and accurately predicting film coating progression. The OPLS model used knowledge-based peaks of interest, which were further confirmed by loading and coefficient plots. The study demonstrated that film thickness, as a destructive, and a* value from CIELAB color space, as a non-destructive reference method for coating progression could be used during a controlled pharmaceutical coating process for product quality assessment and pharmaceutical process endpoint determination.

Evaluation of floatability characteristics of gastroretentive tablets using VIS imaging with artificial neural networks

Gastroretentive dosage forms are recommended for several active substances because it is often necessary for the drug to be released from the carrier system into the stomach over an extended period. Among gastroretentive dosage forms, floating tablets are a very popular pharmaceutical technology. In this study, it was investigated whether a rapid, nondestructive method can be used to characterize the floating properties of a tablet. To accomplish our objective, the same composition was compressed, and varied compression forces were applied to achieve the desired tablet. In addition to physical examinations, digital microscopic images of the tablets were captured and analyzed using image analysis techniques, allowing the investigation of the floatability of the dosage form. Image processing algorithms and artificial neural networks (ANNs) were utilized to classify the samples based on their strength and floatability. The input dataset consisted solely of the acquired images. It has been shown by our research that visible imaging coupled with pattern recognition neural networks is an efficient way to categorize these samples based on their floatability. Rapid and non-destructive digital imaging of tablet surfaces is facilitated by this method, offering insights into both crushing strength and floating properties.

Process analytical technology based quality assurance of API concentration and fiber diameter of electrospun amorphous solid dispersions

In this study, a novel quality assurance system was developed utilizing Process analytical technology (PAT) tools and artificial intelligence (AI). Our goal was to monitor the critical quality attributes (CQAs) like drug concentration, morphology and fiber diameter of electrospun amorphous solid dispersion (ASD) formulations with fast at-line techniques. Doxycycline-hyclate (DOX), a tetracycline-type antibiotic was used as a model drug with 2-hydroxypropyl-β-cyclodextrin (HP-β-CD) as the matrix excipient. The water-based formulations were electrospun with high-speed electrospinning (HSES). Raman and NIR sensors and machine vision-based color measurement techniques were employed to accurately determine the drug concentration. Given that morphology can influence the solubility of the drug, a convolutional neural network (CNN)-based AI model was developed to examine this property and detect manufacturing defects. Additionally, the diameter of electrospun fibrous samples was measured using camera images and a trained AI model, enabling rapid analysis of fiber diameter with results similar to that of scanning electron microscopy (SEM). These methods and models demonstrate potential in-line analytical tools, offering rapid, cheap and non-destructive analysis of ASD formulations.

A Novel image processing technique for weighted particle size distribution assessment

The objective of the study was to create a reliable method that could be used to evaluate the particle size distribution of samples and pre-mixes in real-world situations, particularly those consisting of typical formulation blends. The goal was to use this method to assess the uniformity of the samples and ensure that they met the required quality standards. The researchers aimed to create a method that could be easily incorporated into the manufacturing process, providing a practical and efficient solution. This study demonstrates the use of ImageJ software to analyze the particle size distribution (PSD) of powders. The technique produces qualitative data from microscopy images and quantitative data from analysis of parameters including average diameter, D10, D50, D90, and standard deviation. The method was tested with various treatments, showing differentiating outcomes in all cases. The alternate technique provides a rapid and cost-effective method for PSD analysis, surpassing the limitations of sieve analysis. Extensive testing of the method, using a variety of sample types, including typical formulation blends, was performed. The results suggest that the method can effectively assess the morphology of changing materials during batch manufacturing and characterize uniformity in blends. The methodology has the capability to identify attributes related to PSD that are typically required to be monitored during manufacturing. The technique allows for accurate and reliable quantification of the attributes through image capture technology. The technique has future potential and has important implications for material science, powder rheology, pharmaceutical formulation development, and continual process monitoring.

Basic regulatory science behind drug substance and drug product specifications of monoclonal antibodies and other protein therapeutics

In this review, we focus on providing basics and examples for each component of the protein therapeutic specifications to interested pharmacists and biopharmaceutical scientists with a goal to strengthen understanding in regulatory science and compliance. Pharmaceutical specifications comprise a list of important quality attributes for testing, references to use for test procedures, and appropriate acceptance criteria for the tests, and they are set up to ensure that when a drug product is administered to a patient, its intended therapeutic benefits and safety can be rendered appropriately. Conformance of drug substance or drug product to the specifications is achieved by testing an article according to the listed tests and analytical methods and obtaining test results that meet the acceptance criteria. Quality attributes are chosen to be tested based on their quality risk, and consideration should be given to the merit of the analytical methods which are associated with the acceptance criteria of the specifications. Acceptance criteria are set forth primarily based on efficacy and safety profiles, with an increasing attention noted for patient-centric specifications. Discussed in this work are related guidelines that support the biopharmaceutical specification setting, how to set the acceptance criteria, and examples of the quality attributes and the analytical methods from 60 articles and 23 pharmacopeial monographs. Outlooks are also explored on process analytical technologies and other orthogonal tools which are on-trend in biopharmaceutical characterization and quality control.

In-situ particle analysis with heterogeneous background: a machine learning approach

We propose a novel framework that combines state-of-the-art deep learning approaches with pre- and post-processing algorithms for particle detection in complex/heterogeneous backgrounds common in the manufacturing domain. Traditional methods, like size analyzers and those based on dilution, image processing, or deep learning, typically excel with homogeneous backgrounds. Yet, they often fall short in accurately detecting particles against the intricate and varied backgrounds characteristic of heterogeneous particle-substrate (HPS) interfaces in manufacturing. To address this, we've developed a flexible framework designed to detect particles in diverse environments and input types. Our modular framework hinges on model selection and AI-guided particle detection as its core, with preprocessing and postprocessing as integral components, creating a four-step process. This system is versatile, allowing for various preprocessing, AI model selections, and post-processing strategies. We demonstrate this with an entrainment-based particle delivery method, transferring various particles onto substrates that mimic the HPS interface. By altering particle and substrate properties (e.g., material type, size, roughness, shape) and process parameters (e.g., capillary number) during particle entrainment, we capture images under different ambient lighting conditions, introducing a range of HPS background complexities. In the preprocessing phase, we apply image enhancement and sharpening techniques to improve detection accuracy. Specifically, image enhancement adjusts the dynamic range and histogram, while sharpening increases contrast by combining the high pass filter output with the base image. We introduce an image classifier model (based on the type of heterogeneity), employing Transfer Learning with MobileNet as a Model Selector, to identify the most appropriate AI model (i.e., YOLO model) for analyzing each specific image, thereby enhancing detection accuracy across particle-substrate variations. Following image classification based on heterogeneity, the relevant YOLO model is employed for particle identification, with a distinct YOLO model generated for each heterogeneity type, improving overall classification performance. In the post-processing phase, domain knowledge is used to minimize false positives. Our analysis indicates that the AI-guided framework maintains consistent precision and recall across various HPS conditions, with the harmonic mean of these metrics comparable to those of individual AI model outcomes. This tool shows potential for advancing in-situ process monitoring across multiple manufacturing operations, including high-density powder-based 3D printing, powder metallurgy, extreme environment coatings, particle categorization, and semiconductor manufacturing.

Microparticles and multi-unit systems for advanced drug delivery

Microparticles have unique benefits in the formulation of multiparticulate and multi-unit type pharmaceutical dosage forms allowing improved drug safety and efficacy with favorable pharmacokinetics and patient centricity. On the other hand, the above advantages are served by high and well reproducible quality attributes of the medicinal product where even flexible design and controlled processability offer success as well as possible longer product life-cycle for the manufacturers. Moreover, the specific demands of patients can be taken into account, including simplified dosing regimens, flexible dosage, drug combinations, palatability, and ease of swallowing. In the more than 70 years since the first modified-release formulation appeared on the market, many new formulations have been marketed and many publications have appeared in the literature. More unique and newer pharmaceutical technologies and excipients have become available for producing tailor-made particles with micrometer dimensions and beyond. All these have contributed to the fact that the sub-units (e.g. minitablets, pellets, microspheres) that make up a multiparticulate system can vary widely in composition and properties. Some units have mucoadhesive properties and others can float to contribute to a suitable release profile that can be designed for the multiparticulate formula as a whole. Nowadays, there are some available formulations on the market, which are able to release the active substance even for several months (3 or 6 months depending on the type of treatment). In this review, the latest developments in technologies that have been used for a long time are presented, as well as innovative solutions such as the applicability of 3D printing to produce subunits of multiparticulate systems. Furthermore, the diversity of multiparticulate systems, different routes of administration are also presented, touching the ones which are capable of carrying the active substance as well as the relevant, commercially available multiparticle-based medical devices. The versatility in size from 1 µm and multiplicity of formulation technologies promise a solid foundation for the future applications of dosage form design and development.

Advanced analysis of disintegrating pharmaceutical compacts using deep learning-based segmentation of time-resolved micro-tomography images

The mechanism governing pharmaceutical tablet disintegration is far from fully understood. Despite the importance of controlling a formulation's disintegration process to maximize the active pharmaceutical ingredient's bioavailability and ensure predictable and consistent release profiles, the current understanding of the process is based on indirect or superficial measurements. Formulation science could, therefore, additionally deepen the understanding of the fundamental physical principles governing disintegration based on direct observations of the process. We aim to help bridge the gap by generating a series of time-resolved X-ray micro-computed tomography (μCT) images capturing volumetric images of a broad range of mini-tablet formulations undergoing disintegration. Automated image segmentation was a prerequisite to overcoming the challenges of analyzing multiple time series of heterogeneous tomographic images at high magnification. We devised and trained a convolutional neural network (CNN) based on the U-Net architecture for autonomous, rapid, and consistent image segmentation. We created our own μCT data reconstruction pipeline and parameterized it to deliver image quality optimal for our CNN-based segmentation. Our approach enabled us to visualize the internal microstructures of the tablets during disintegration and to extract parameters of disintegration kinetics from the time-resolved data. We determine by factor analysis the influence of the different formulation components on the disintegration process in terms of both qualitative and quantitative experimental responses. We relate our findings to known formulation component properties and established experimental results. Our direct imaging approach, enabled by deep learning-based image processing, delivers new insights into the disintegration mechanism of pharmaceutical tablets.

Topical foam as a promising carrier system for active pharmaceutical ingredients: review of clinical studies

Skin disorders are preferentially treated by topical administration of medicines or cosmetics because of the possibility of local action. However, a great concern is the delivery of topical actives with effective penetration through the stratum corneum to ensure the desired effect. Considering the search for a carrier system that allows the penetration/permeation of active pharmaceutical ingredients through this structure, searching for effective topical pharmaceutical forms is needed. Foams have been widely studied over the years due to their high capacity to favor the active to overcome the cutaneous barrier and because this form of presentation has ease of application and high acceptability by users. The objective of this review was to analyze the potential of foam as a topical pharmaceutical form for treating skin disorders, upon clinical cases reported in the literature. Foam presents technical advantages when compared to other conventional topical pharmaceutical forms due to its fast action, high tolerance, and safety, with reduction or total remission of adverse events. Regarding the patient, foam increased the rate of adherence to the treatment. Therefore, it is concluded that foam is an effective, secure, and stable topical presentation form for carrying active pharmaceutical ingredients and widely accepted by patients.

Development of pediatric orally disintegrating mini-tablets containing atomoxetine hydrochloride-β-cyclodextrin inclusion complex using experimental design

OBJECTIVE

The aim of the study was to develop and evaluate characteristics of orally disintegrating mini-tablet (ODMT) formulations including atomoxetine hydrochloride (ATO)/β-cyclodextrin (β-CD) inclusion complex for pediatric therapy of attention deficit and hyperactivity disorder (ADHD).

METHODS

Design of experiment approach was used to develop ODMTs. The ODMTs were compressed using direct compression method with two different superdisintegrants (Parteck ODT^® and Ac-Di-Sol^®) and characterized with quality control tests. In vitro dissolution and taste studies were performed.

RESULTS

The hardness and friability values of the optimized three ODMT formulations were determined as 41.7 N, 42.4 N, and 40.8 N and 0.32%, 0.29%, and 0.42%, respectively. The disintegration time of all the optimized formulations was found to be less than one minute. In addition, dissolution profiles of ATO from optimized ODMTs were determined in four different dissolution media (distilled water, pH 1.2, 6.8, and 7.4) and it was determined that the maximum dissolved ATO amount reached at the end of 20 min.

CONCLUSION

As a conclusion, the novel formulation of ODMTs with ATO/β-CD inclusion complex was successfully developed for pediatric use.

Emerging Artificial Intelligence (AI) Technologies Used in the Development of Solid Dosage Forms

Artificial Intelligence (AI)-based formulation development is a promising approach for facilitating the drug product development process. AI is a versatile tool that contains multiple algorithms that can be applied in various circumstances. Solid dosage forms, represented by tablets, capsules, powder, granules, etc., are among the most widely used administration methods. During the product development process, multiple factors including critical material attributes (CMAs) and processing parameters can affect product properties, such as dissolution rates, physical and chemical stabilities, particle size distribution, and the aerosol performance of the dry powder. However, the conventional trial-and-error approach for product development is inefficient, laborious, and time-consuming. AI has been recently recognized as an emerging and cutting-edge tool for pharmaceutical formulation development which has gained much attention. This review provides the following insights: (1) a general introduction of AI in the pharmaceutical sciences and principal guidance from the regulatory agencies, (2) approaches to generating a database for solid dosage formulations, (3) insight on data preparation and processing, (4) a brief introduction to and comparisons of AI algorithms, and (5) information on applications and case studies of AI as applied to solid dosage forms. In addition, the powerful technique known as deep learning-based image analytics will be discussed along with its pharmaceutical applications. By applying emerging AI technology, scientists and researchers can better understand and predict the properties of drug formulations to facilitate more efficient drug product development processes.

Review on Starter Pellets: Inert and Functional Cores

A significant proportion of pharmaceuticals are now considered multiparticulate systems. Modified-release drug delivery formulations can be designed with engineering precision, and patient-centric dosing can be accomplished relatively easily using multi-unit systems. In many cases, Multiple-Unit Pellet Systems (MUPS) are formulated on the basis of a neutral excipient core which may carry the layered drug surrounded also by functional coating. In the present summary, commonly used starter pellets are presented. The manuscript describes the main properties of the various nuclei related to their micro- and macrostructure. In the case of layered pellets formed based on different inert pellet cores, the drug release mechanism can be expected in detail. Finally, the authors would like to prove the industrial significance of inert cores by presenting some of the commercially available formulations.

Stereomicroscope with Imaging Analysis: A Versatile Tool for Wetting, Gel Formation and Erosion Rate Determinations of Eutectic Effervescent Tablet

Wettability, gel formation and erosion behaviors could influence the drug release pattern of solid dosage forms. Typically, these parameters are evaluated using a variety of techniques. Nonetheless, there has been no previous research on versatile tool development for evaluating several tablet characteristics with a single tool. The aim of this study was to develop the versatile tool for measuring various physical properties of eutectic effervescent tablets and also investigate the relationship between these parameters with parameters from drug dissolution. Ibuprofen (IBU)-poloxamer 407 (P407) eutectic effervescent tablets were fabricated with a direct compression method. Their wetting properties, gel formation and erosion behaviors were investigated using a stereomicroscope with imaging analysis in terms of the liquid penetration distance, gel thickness and erosion boundary diameter, respectively. In addition, the dissolution rate (k) and disintegration time of eutectic effervescent tablets in 0.1 N HCl buffer pH 1.2 were also determined. Incorporation of P407 into the IBU tablet improved the tablet wetting properties with increasing liquid penetration distance under stereoscope. CO2 liberation from effervescent agents promoted tablet surface roughness from matrix erosion. The relationship between observed physical properties and disintegration and dissolution parameters suggested that the combination of erosion by effervescent agents and gel formation by P407 had a potential influence on dissolution enhancement of the formulation. Therefore, a developed stereomicroscope with an imaging analysis technique was exhibited as an alternative versatile tool for determining the wetting properties, gel formation and erosion behaviors of pharmaceutical solid dosage forms.

A generalized image analytical algorithm for investigating tablet disintegration

Commonly used methods for analyzing tablet disintegration are based on visual observations and can thus be user-dependent. To address this, a generally applicable image analytical algorithm has been developed for machine vision-based quantification of tablet disintegration. The algorithm has been tested with a conventional immediate release tablet, as well as model compacts disintegrating mainly through erosion, and finally, with a polymeric slow-release system. Despite differences in disintegration mechanisms between these compacts, the developed image analytical algorithm demonstrated its general applicability through quantifying the extent of disintegration without adaptation of image analytical parameters. The reproducibility of the approach was estimated with commercial tablets, and further, it could differentiate a range of different model compacts. The developed image analytical algorithm mimics the human decision-making processes and the current experience-based visual evaluation of disintegration time. In doing so the algorithmic method allows a user-independent approach for development of the optimal tablet formulation as well as gaining an understanding on how the selection of excipients and manufacturing processes ultimately influences tablet disintegration.

3D printed tablets containing oxaliplatin loaded alginate nanoparticles for colon cancer targeted delivery. An in vitro/in vivo study

This study aimed to develop a colon-targeted tablet of oxaliplatin (OP) using the combination of nanotechnology and fused deposition modeling (FDM) 3D printing to improve its antitumor activity, tumor targetability, and safety profile. Eudragit L100-55 filament containing OP loaded alginate nanoparticles (OP-NPs) were fabricated using hot-melt extrusion method and printed by an FDM printer to 3D printed tablets with good uniformity in the drug content and selective release of OP in the colonic environment. The antitumor effect of 3D printed tablets containing OP-NPs in CT-26 tumor-bearing mice was evaluated compared to intravenous and oral administration of OP solution, and compressed tablets containing OP-NPs, which were prepared by direct compression method with the same formulation. The antitumor effect of 3D printed tablets containing OP-NPs was remarkable and comparable with intravenous OP solution (p ˃ 0.05) with a better safety profile, whereas compressed tablets did not show any significant antitumor effect, probably in terms of non-selective drug release in stomach and upper intestine environments. This study highlights the potential of the combination of nanotechnology and 3D printing in the preparation of colon-specific drug delivery systems of chemotherapeutic drugs with good antitumor activity, tumor targetability, and safety profile for colorectal cancer treatment.