UV-VIS imaging-based investigation of API concentration fluctuation caused by the sticking behaviour of pharmaceutical powder blends.

Surface powder sticking in pharmaceutical mixing vessels poses a risk to the uniformity and quality of drug formulations. This study explores methods for evaluating the amount of pharmaceutical powder mixtures adhering to the metallic surfaces. Binary powder blends consisting of amlodipine and microcrystalline cellulose (MCC) were used to investigate the effect of the mixing order on the adherence to the vessel wall. Elevated API concentrations were measured on the wall and within the dislodged material from the surface, regardless of the mixing order of the components. UV imaging was used to determine the particle size and the distribution of the API on the metallic surface. The results were compared to chemical maps obtained by Raman chemical imaging. The combination of UV and VIS imaging enabled the rapid acquisition of chemical maps, covering a substantially large area representative of the analysed sample. UV imaging was also applied in tablet inspection to detect tablets that fail to meet the content uniformity criteria. The results present powder adherence as a possible source of poor content uniformity, highlighting the need for 100% inspection of pharmaceutical products to ensure product quality and safety.

Effects of omitting titanium dioxide from the film coating of a pharmaceutical tablet - An industrial case study of attempting to comply with EU regulation 2022/63.

Recently, concerns have been raised about the safety of titanium dioxide (TiO2), a commonly used component of pharmaceutical film coatings. The European Union has recently prohibited the application of this material in the food industry, and it is anticipated that the same will happen in the pharmaceutical industry. For this reason, pharmaceutical manufacturers have to consider the possible impact of removing TiO2 from the film coating of tablets. In this paper, we present a case study of a commercially produced tablet where the film coating containing TiO2 was replaced with a coating using calcium carbonate (CaCO3) or with a transparent coating. The performance of the coatings was compared by measuring the moisture absorption rate and the dissolution profile of the tablets. In these regards, there were negligible differences between the coating types. The tablets contained a highly photosensitive drug, the ability of the coatings to protect the drug was evaluated through environmental stability and photostability measurements. The HPLC results showed that the inclusion of TiO2 does not provide additional benefits, when humidity and thermal stress is applied, however its role was vital in protecting the drug from external light. There were several decomposition products which appeared in large quantities when TiO2 was missing from the coating. These results imply that photosensitivity is an issue, replacing TiO2 will be challenging, though its absence can be tolerated when the drug does not need to be protected from light.

Image-based simultaneous particle size distribution and concentration measurement of powder blend components with deep learning and machine vision.

This work presents a system, where deep learning was used on images captured with a digital camera to simultaneously determine the API concentration and the particle size distribution (PSD) of two components of a powder blend. The blend consisted of acetylsalicylic acid (ASA) and calcium hydrogen phosphate (CHP), and the predicted API concentration was found corresponding with the HPLC measurements. The PSDs determined with the method corresponded with those measured with laser diffraction particle size analysis. This novel method provides fast and simple measurements and could be suitable for detecting segregation in the powder. By examining the powders discharged from a batch blender, the API concentrations at the top and bottom of the container could be measured, yielding information about the adequacy of the blending and improving the quality control of the manufacturing process.

In-line particle size measurement during granule fluidization using convolutional neural network-aided process imaging.

This paper presents a machine learning-based image analysis method to monitor the particle size distribution of fluidized granules. The key components of the direct imaging system are a rigid fiber-optic endoscope, a light source and a high-speed camera, which allow for real-time monitoring of the granules. The system was implemented into a custom-made 3D-printed device that could reproduce the particle movement characteristic in a fluidized-bed granulator. The suitability of the method was evaluated by determining the particle size distribution (PSD) of various granule mixtures within the 100-2000 µm size range. The convolutional neural network-based software was able to successfully detect the granules that were in focus despite the dense flow of the particles. The volumetric PSDs were compared with off-line reference measurements obtained by dynamic image analysis and laser diffraction. Similar trends were observed across the PSDs acquired with all three methods. The results of this study demonstrate the feasibility of performing real-time particle size analysis using machine vision as an in-line process analytical technology (PAT) tool.

Convolutional neural network-based evaluation of chemical maps obtained by fast Raman imaging for prediction of tablet dissolution profiles.

In this work, the capabilities of a state-of-the-art fast Raman imaging apparatus are exploited to gain information about the concentration and particle size of hydroxypropyl methylcellulose (HPMC) in sustained release tablets. The extracted information is utilized to predict the in vitro dissolution profile of the tablets. For the first time, convolutional neural networks (CNNs) are used for the processing of the chemical images of HPMC distribution and to directly predict the dissolution profile based on the image. This new method is compared to wavelet analysis, which gives a quantification of the texture of HPMC distribution, carrying information regarding both concentration and particle size. A total of 112 training and 32 validation tablets were used, when a CNN was used to characterize the particle size of HPMC, the dissolution profile of the validation tablets was predicted with an average f2 similarity value of 62.95. Direct prediction based on the image had an f2 value of 54.2, this demonstrates that the CNN is capable of recognizing the patterns in the data on its own. The presented methods can facilitate a better understanding of the manufacturing processes, as detailed information becomes available with fast measurements.

pH-dependent disintegration of insulin amyloid fibrils monitored with atomic force microscopy and surface-enhanced Raman spectroscopy.

Aggregation of insulin into amyloid fibrils is characterized by the conversion of the native secondary structure of the peptide into an enriched ß-sheet conformation. In vitro, the growth or disintegration of amyloid fibrils can be influenced by various external factors such as pH, temperature etc. While current studies mainly focus on the influence of environmental conditions on the growth process of insulin fibrils, the present study investigates the effect of pH changes on the morphology and secondary structure of mature fibrils. In the experiments, insulin is fibrillated at pH 2.5 and the grown mature fibrils are suspended in pH 4-7 solutions. The obtained structures are analyzed by atomic force microscopy (AFM) and surface-enhanced Raman spectroscopy (SERS). Initially grown mature fibrils from pH 2.5 solutions show a long and intertwined morphology. Increasing the solution pH initiates the gradual disintegration of the filamentous morphology into unordered aggregates. These observations are supported by SERS experiments, where the spectra of the mature fibrils show mainly a ß-pleated sheet conformation, while the amide I band region of the amorphous aggregates indicate exclusively α-helix/unordered structures. The results demonstrate that no complex reagent is required for the disintegration of insulin fibrils. Simply regulating the pH of the environment induces local changes in the protonation state within the peptide chains. This effectively disrupts the well-ordered ß-sheet structure network based on hydrogen bonds.

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.

Hypotensive Snake Venom Components-A Mini-Review.

Hypertension is considered a major public health issue due to its high prevalence and subsequent risk of cardiovascular and kidney diseases. Thus, the search for new antihypertensive compounds remains of great interest. Snake venoms provide an abundant source of lead molecules that affect the cardiovascular system, which makes them prominent from a pharmaceutical perspective. Such snake venom components include bradykinin potentiating peptides (proline-rich oligopeptides), natriuretic peptides, phospholipases A2, serine-proteases and vascular endothelial growth factors. Some heparin binding hypotensive factors, three-finger toxins and 5' nucleotidases can also exert blood pressure lowering activity. Great advances have been made during the last decade regarding the understanding of the mechanism of action of these hypotensive proteins. Bradykinin potentiating peptides exert their action primarily by inhibiting the angiotensin-converting enzyme and increasing the effect of endogenous bradykinin. Snake venom phospholipases A2 are capable of reducing blood pressure through the production of arachidonic acid, a precursor of cyclooxygenase metabolites (prostaglandins or prostacyclin). Other snake venom proteins mimic the effects of endogenous kallikrein, natriuretic peptides or vascular endothelial growth factors. The aim of this work was to review the current state of knowledge regarding snake venom components with potential antihypertensive activity and their mechanisms of action.