Innovative Approach to Enhance Bioavailability of Birch Bark Extracts: Novel Method of Oleogel Development Contrasted with Other Dispersed Systems.

Birch outer bark extract (BBE), containing pentacyclic triterpenes such as betulin, lupeol, and betulinic acid, is a widely recognized natural product renowned for its diverse pharmacological effects. However, its limited water solubility restricts its bioavailability. Therefore, the main objective is to enhance the bioavailability of BBE for pharmaceutical use. In this study, we aimed to develop a dispersion system utilizing a unique oleogel-producing method through the recrystallization of BBE from an ethanol solution in the oil phase. We generated an oleogel that demonstrates a notable 42-80-fold improvement in betulin and lupeol peroral bioavailability from BBE in Wistar rats, respectively. A physical paste-like BBE hydrogel developed with antisolvent precipitation showed a 16-56-fold increase in the bioavailability of betulin and lupeol from BBE in rat blood plasma, respectively. We also observed that the repeated administration of the BBE oleogel did not exhibit any toxicity at the tested dose (38.5 mg/kg betulin, 5.2 mg/kg lupeol, 1.5 mg/kg betulinic acid daily for 7 days). Betulin and betulinic acid were not detected in rat heart, liver, kidney, or brain tissues after the peroral administration of the oleogel daily for 7 days. Lupeol was found in rat heart, liver, and kidney tissues.

Evaluation of Oleogels Stabilized by Particles of Birch Outer Bark Extract through a Novel Approach.

Structuring liquid oils into oleogels using natural and abundant biomass components as gelling agents is of great significance in fields such as foods, pharmaceuticals, and cosmetics. In this work, a more energy-efficient and technologically simpler approach for directly preparing birch outer bark extract particles containing oleogel was used. This method involves introducing birch outer bark extract particles into the oil phase directly from the extract solution, combining both the evaporation of solution and gel formation. As a result, stable oleogels of various vegetable oils (sunflower, almond, olive, and hemp seed) were obtained with birch outer bark extract particle contents of 4-10%. Infrared spectroscopy and particle size analysis confirmed that when increasing the content of extract particles in the oil, increasing self-assembly of extract particles via hydrogen bonding occurs, leading to the formation of a more structured network. This is consistent with gel strength values from rheological tests of oleogels, which also increased with particle concentration. The obtained oleogels showed important properties such as good tolerance to time-dependent deformation, shear thinning, and thermoreversibility.

Effect of Betulin Colloidal Particles on Proliferation and Cytokine Secretion of Human Skin Fibroblasts.

The aim of the study was to obtain untreated and treated betulin colloidal particles and assess their effect on the viability, morphology, proliferation and cytokine secretion of human dermal fibroblasts. To improve bioavailability, betulin treatment was performed by an antisolvent precipitation technique. The average particle size after treatment in the aqueous dispersion decreased from 552.9 ± 11.3 to 278.2 ± 1.6 nm. Treated betulin colloidal particles showed no cytotoxicity up to a concentration of 400 µg·mL-1 in the colorimetric tetrazolium salt viability test (CCK-8). Moreover, the cell morphology was not changed in the presence of betulin colloidal particles at a concentration range from 0.78 to 400 µg·mL-1. The obtained results also show that betulin particles induce the secretion of the proinflammatory and angiogenesis-stimulating cytokine IL-8. However, further studies would be required to clarify the mechanism of IL-8 secretion induction.

Semantic text mining in early drug discovery for type 2 diabetes.

BACKGROUND: Surveying the scientific literature is an important part of early drug discovery; and with the ever-increasing amount of biomedical publications it is imperative to focus on the most interesting articles. Here we present a project that highlights new understanding (e.g. recently discovered modes of action) and identifies potential drug targets, via a novel, data-driven text mining approach to score type 2 diabetes (T2D) relevance. We focused on monitoring trends and jumps in T2D relevance to help us be timely informed of important breakthroughs. METHODS: We extracted over 7 million n-grams from PubMed abstracts and then clustered around 240,000 linked to T2D into almost 50,000 T2D relevant 'semantic concepts'. To score papers, we weighted the concepts based on co-mentioning with core T2D proteins. A protein's T2D relevance was determined by combining the scores of the papers mentioning it in the five preceding years. Each week all proteins were ranked according to their T2D relevance. Furthermore, the historical distribution of changes in rank from one week to the next was used to calculate the significance of a change in rank by T2D relevance for each protein. RESULTS: We show that T2D relevant papers, even those not mentioning T2D explicitly, were prioritised by relevant semantic concepts. Well known T2D proteins were therefore enriched among the top scoring proteins. Our 'high jumpers' identified important past developments in the apprehension of how certain key proteins relate to T2D, indicating that our method will make us aware of future breakthroughs. In summary, this project facilitated keeping up with current T2D research by repeatedly providing short lists of potential novel targets into our early drug discovery pipeline.