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.

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.

In this study, an artificial intelligence-based machine vision system was developed for in-line particle size analysis during the pellet layering process. Drug-layered pellets were produced by coating microcrystalline cellulose cores with an ibuprofen-containing layering liquid until the target drug content was achieved. Drug content increases with pellet size; therefore, particle size monitoring can ensure product safety and quality. The direct imaging system, consisting of a rigid endoscope, a light source, and a high-speed camera, provides real-time information about pellet size and layer uniformity, enabling timely intervention in the case of out-of-spec products. A convolutional neural network-based instance segmentation algorithm was employed to detect particles in focus, ensuring that pellet size could be accurately determined despite the dense flow of the particles. After training the model, the performance of the developed system was assessed by analysing the particle size distribution of pellet cores with variable sizes within the 250–850 μm size range. The endoscopic system was tested in-line at a larger scale during the drug layering of inert pellet cores. The particle size data acquired in real time with the endoscopic imaging system corresponded with the reference methods, demonstrating the feasibility of the proposed machine vision-based method as a process analytical technology tool for in-line process monitoring.

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 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.

Artemisia annua is an important medical plant that produces artemisinin used for its antimalarial, antibacterial and antifungal effects in modern medicine. The high demand and low artemisinin content in plants (0.01-2 %) has led to studies about alternative methods to increase yield. Biofertilizers (beneficial microbes and/or biological products that colonize roots, improve plant nutrition and growth) have been reported affecting secondary metabolism and the production of active ingredients of herbs. The purpose of this paper is to draw attention to the current status of the research on mutualistic and endophytic microorganism of A. annua that have the potential to increase the quality and quantity of the crude drugs, derived from the herb. Scientific papers in this field focus on the effects on inoculation with different microorganisms (arbuscular micorrhizal fungi, endophytic bacteria and fungi) and the isolation of endophytes from A. annua. Bioinoculants can affect biomass, artemisinin and essential oil concentration, disease resistance, nutrient status, phosphatase activity, foliar glandular trichome density, leaf chlorophyll content, guaiacol peroxidase enzyme concentration, stomatal conductance and transpiration rate, and plant growth parameters (total weight, leaf yield, height, seed yield). The endophytes isolated from the plant are potential artemisinin content and plant stress resistance enhancers.

This study investigates the use of UV imaging combined with artificial neural networks (ANNs) to estimate the dissolution behaviour of extended-release hydroxypropyl methylcellulose (HPMC) matrix tablets. UV illumination enables the detection of HPMC, allowing polymer-specific optical information to be extracted. Caffeine was used as the model active pharmaceutical ingredient, and HPMC served as the matrix-forming polymer. Formulations were prepared with HPMC contents ranging from 5% to 35%. The ANN models were trained using colourimetric information extracted from UV images of the tablets. Among all models evaluated, the one based on the blue channel of the RGB colour space showed the best performance, achieving an average f₂ similarity factor of 80.68 on the dissolution curves of the external validation set. To evaluate the performance of the trained model on an external test set under dynamic conditions, powder blends with different HPMC contents were introduced sequentially, resulting in gradual changes in tablet composition. The trained model reflected these formulation changes in the predicted dissolution profiles, capturing the shifts in release behaviour associated with the varying HPMC levels throughout the experiment. UV imaging provided relevant input for dissolution modelling and enabled the rapid, non-destructive assessment of formulation-dependent drug release. The results therefore extend the applicability of imaging-based methods to excipient-level formulation changes.