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The objective of the present work was to develop an artificial neural network (ANN) model to accurately predict the dissolution profile of immediate release tablets based on non-destructive spectral data. Six different tablet formulations with varying API (caffeine) and disintegrant (potato starch) concentrations were prepared. The near-infrared (NIR) and Raman spectra of each tablet were collected in both reflection and transmission modes, then principal component analysis (PCA) was conducted. The training of the ANN was performed at each hidden neuron number from 1 to 10 in order to determine the optimal number of neurons in the hidden layer. The best results were obtained when a small number of neurons (1-3) was used. In the case of all four spectroscopic methods, the average similarity values (f2) of the optimized ANN models were above 59 for the validation tablets, indicating that the predicted dissolution profiles were similar to the measured dissolution curves. The optimized model based on reflection Raman spectra exhibited the best predictive ability. The results demonstrated the potential of ANN models in the implementation of the real-time release testing of tablet dissolution. [ABSTRACT FROM AUTHOR]

[Display omitted] 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. [ABSTRACT FROM AUTHOR]

Generally, chemical engineering students get well acquainted with the batch synthesis of various active pharmaceutical ingredients, however, only tiny focus is provided to undergraduates on the topic of flow chemistry. In this paper, we report that students participating in the chemical engineering BSc course at the Budapest University of Technology and Economics were encouraged to perform the flow synthesis of paracetamol, a common pain painkiller. Two different synthetic routes for the continuous production of paracetamol were investigated and compared the batch and flow methods. Thus, these experiments allowed the students to discover flow chemistry for themselves under supervision: how to set up a flow system, how to carry out a reaction continuously, and to experience the advantages of flow chemistry over batch synthesis. In addition, students also got familiar with in-line Fourier transform infrared spectroscopy, as one of the reactions was monitored in real-time. [ABSTRACT FROM AUTHOR]

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. [Display omitted] [ABSTRACT FROM AUTHOR]

• An endoscopy-based direct imaging system can monitor granules during fluidization. • A convolutional neural network-based software was applied for particle detection. • In-line particle size measurement was accomplished despite dense particle flow. • The in-line measurements and off-line reference methods showed similar trends. 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. [Display omitted] [ABSTRACT FROM AUTHOR]

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. [ABSTRACT FROM AUTHOR]

Recently, concerns have been raised about the safety of titanium dioxide (TiO 2), 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 TiO 2 from the film coating of tablets. In this paper, we present a case study of a commercially produced tablet where the film coating containing TiO 2 was replaced with a coating using calcium carbonate (CaCO 3) 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 TiO 2 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 TiO 2 was missing from the coating. These results imply that photosensitivity is an issue, replacing TiO 2 will be challenging, though its absence can be tolerated when the drug does not need to be protected from light. [Display omitted] [ABSTRACT FROM AUTHOR]

[Display omitted] 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 f 2 similarity value of 62.95. Direct prediction based on the image had an f 2 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. [ABSTRACT FROM AUTHOR]