Atorvastatin Entrapped Noisome (Atrosome): Green Preparation Approach for Wound Healing

The preventive effects of Saccharomyces boulardii against oxidative stress induced by lipopolysaccharide in rat brain

The brain is sensitive to oxidative stress, which can trigger microglial activation and neuroinflammation. Antioxidant therapies may provide neuroprotection against oxidative stress. In recent years antioxidant effects of probiotics and their possible mechanisms in oxidative stress-related models have been determined. In the current study, for the first time, we assessed the effects of Saccharomyces boulardii on oxidative stress provoked by lipopolysaccharide (LPS) in the rat brain. Four groups of animals were used, including the control, LPS, S. boulardii + LPS, and S. boulardii groups. All animals received either saline or S. boulardii (1010 CFU) by gavage for four weeks. Between days 14 and 22, all animals received either LPS (250 μg/kg) or saline by intraperitoneal (i.p.) injection. S. boulardii was able to inhibit lipid peroxidation and prevent the reduction of antioxidant levels, including glutathione and catalase in the model of oxidative stress induced by LPS in the rat hippocampus and cortex. Also, it increased the lowered ratio of glutathione/oxidized glutathione in both tissues. Serum levels of anti-inflammatory interleukin 10 (IL-10) and proinflammatory cytokines IL-6 and IL-8 increased and decreased, respectively. S. boulardii has potential antioxidant activities in oxidative stress-related model, possibly modulating gut microbiota, immune defense, and antioxidant enzyme activities that can be considered in preventing oxidative stress-related central nervous system (CNS) diseases.

Anti-inflammatory and tissue repair effect of cinnamaldehyde and nano cinnamaldehyde on gingival fibroblasts and macrophages

BACKGROUND

Recurrent aphthous stomatitis has a complex and inflammatory origin. Among the great variety of medications it is increasingly common to use herbal medicines due to the adverse side effects of chemical medications. Considering the anti-inflammatory properties of cinnamaldehyde and the lack of studies related to the effectiveness of its nano form; This study investigates the effect of cinnamaldehyde and nano cinnamaldehyde on the healing rate of recurrent aphthous stomatitis lesions.

METHODS

In a laboratory experiment, cinnamaldehyde was converted into niosomal nanoparticles. The niosome vesicles diameter and polydispersity index were measured at 25°C using a dynamic light scattering (DLS) Mastersizer 2000 (Malvern Panalytical technologies: UK) and Zetasizer Nano ZS system (Malvern Instruments Worcestershire: UK). After characterizing these particles, the (2,3-Bis-(2-Methoxy-4-Nitro-5-Sulfophenyl)-2H-Tetrazolium-5-Carboxanilide) [XTT] assay was used to assess the toxicity of cinnamaldehyde and nano cinnamaldehyde on gingival fibroblast (HGF) and macrophage (THP-1) cells. By determining the release of TNF-α, IL-6, and TGF-β cytokines using ELISA kits, the level of tissue repair and anti-inflammatory capabilities of these two substances were evaluated.

RESULTS

The size and loading rate of the cinnamaldehyde nanoparticles were established after its creation. The optimized nanovesicle exhibited the following characteristics: particle size of 228.75 ± 2.38 nm, PDI of 0.244 ± 0.01, the zeta potential of -10.87 ± 1.09 mV and the drug encapsulation percentage of 66.72 ± 3.93%. PDIs range was between 0.242-0.274. The zeta potential values at 25°C were from -2.67 to -12.9 mV. The results of the XTT test demonstrated that nano cinnamaldehyde exhibited dose-dependent toxicity effects. Moreover, nano cinnamaldehyde released more TGF-β and had better reparative effects when taken at lower concentrations than cinnamaldehyde.

CONCLUSION

Nano cinnamaldehyde and cinnamaldehyde are effective in repairing tissue when used in non-toxic amounts. After confirmation in animal models, it is envisaged that these substances can be utilized to treat recurrent aphthous stomatitis.

Machine learning algorithms for prediction of entrapment efficiency in nanomaterials

Exploitation of machine learning in predicting performance of nanomaterials is a rapidly growing dynamic area of research. For instance, incorporation of therapeutic cargoes into nanovesicles (i.e., entrapment efficiency) is one of the critical parameters that ensures proper entrapment of drugs in the developed nanosystems. Several factors affect the entrapment efficiency of drugs and thus multiple assessments are required to ensure drug retention, and to reduce cost and time. Supervised machine learning can allow for the construction of algorithms that can mine data available from earlier studies to predict performance of specific types of nanoparticles. Comparative studies that utilize multiple regression algorithms to predict entrapment efficiency in nanomaterials are scarce. Herein, we report on a detailed methodology for prediction of entrapment efficiency in nanomaterials (e.g., niosomes) using different regression algorithms (i.e., CatBoost, linear regression, support vector regression and artificial neural network) to select the model that demonstrates the best performance for estimation of entrapment efficiency. The study concluded that CatBoost algorithm demonstrated the best performance with maximum R2 score (0.98) and mean square error (< 10-4). Among the various parameters that possess a role in entrapment efficiency of drugs into niosomes, the results obtained from CatBoost model revealed that the drug:lipid ratio is the major contributing factor affecting entrapment efficiency, followed by the lipid:surfactant molar ratio. Hence, supervised machine learning may be applied for future selection of the components of niosomes that achieve high entrapment efficiency of drugs while minimizing experimental procedures and cost.

Nanofibrous Polycaprolactone Membrane with Bioactive Glass and Atorvastatin for Wound Healing: Preparation and Characterization

Skin wound healing is one of the most challenging processes for skin reconstruction, especially after severe injuries. In our study, nanofiber membranes were prepared for wound healing using an electrospinning process, where the prepared nanofibers were made of different weight ratios of polycaprolactone and bioactive glass that can induce the growth of new tissue. The membranes showed smooth and uniform nanofibers with an average diameter of 118 nm. FTIR and XRD results indicated no chemical interactions of polycaprolactone and bioactive glass and an increase in polycaprolactone crystallinity by the incorporation of bioactive glass nanoparticles. Nanofibers containing 5% w/w of bioactive glass were selected to be loaded with atorvastatin, considering their best mechanical properties compared to the other prepared nanofibers (3, 10, and 20% w/w bioactive glass). Atorvastatin can speed up the tissue healing process, and it was loaded into the selected nanofibers using a dip-coating technique with ethyl cellulose as a coating polymer. The study of the in vitro drug release found that atorvastatin-loaded nanofibers with a 10% coating polymer revealed gradual drug release compared to the non-coated nanofibers and nanofibers coated with 5% ethyl cellulose. Integration of atorvastatin and bioactive glass with polycaprolactone nanofibers showed superior wound closure results in the human skin fibroblast cell line. The results from this study highlight the ability of polycaprolactone-bioactive glass-based fibers loaded with atorvastatin to stimulate skin wound healing.

Formulation, and Optimization of Transdermal Atorvastatin Calcium-Loaded Ultra-flexible Vesicles; Ameliorates Poloxamer 407-caused Dyslipidemia

Atorvastatin calcium (AC), a cholesterol-lowering medication, has limited oral bioavailability (14%) and adverse impacts on the gastrointestinal tract (GIT), liver, and muscle. So, in an effort to improve the poor availability and overcome the hepatotoxicity complications attendant to peroral AC administration, transdermal transfersomal gel (AC-TFG) was developed as a convenient alternative delivery technique. The impact of utilizing an edge activator (EA) and varying the phosphatidylcholine (PC): EA molar ratio on the physico-chemical characteristics of the vesicles was optimized through a Quality by Design (QbD) strategy. The optimal transdermal AC-TFG was tested in an ex-vivo permeation study employing full-thickness rat skin, Franz cell experiments, an in-vivo pharmacokinetics and pharmacodynamics (PK/PD) evaluation, and a comparison to oral AC using poloxamer-induced dyslipidemic Wister rats. The optimized AC-loaded TF nanovesicles predicted by the 2³-factorial design strategy had a good correlation with the measured vesicle diameter of 71.72 ± 1.159 nm, encapsulation efficiency of 89.13 ± 0.125%, and cumulative drug release of 88.92 ± 3.78% over 24 hours. Ex-vivo data revealed that AC-TF outperformed a free drug in terms of permeation. The pharmacokinetic parameters of optimized AC-TFG demonstrated 2.5- and 13.3-fold significant improvements in bioavailability in comparison to oral AC suspension (AC-OS) and traditional gel (AC-TG), respectively. The transdermal vesicular technique preserved the antihyperlipidemic activity of AC-OS without increasing hepatic markers. Such enhancement was proven histologically by preventing the hepatocellular harm inflicted by statins. The results showed that the transdermal vesicular system is a safe alternative way to treat dyslipidemia with AC, especially when given over a long period of time.