Engineering atorvastatin loaded Mg-Mn/LDH nanoparticles and their composite with PLGA for bone tissue applications.

The impact of mixing method in conventional co-precipitation synthesis of layered double hydroxides (LDHs), on particle size, size distribution and drug loading capacity is reported. Synthesis of Mg (II)/Mn (III)-LDH nano-platelets was performed at constant pH using three different mixing systems, magnetic stirrer, mechanical mixer, and homogenizer at ambient temperature and a fixed Mg/Mn ratio of 3/1. The LDH characterization results showed that mechanical mixing and homogenization lead to production of very fine LDH nano-platelets (about 90-140 nm), with narrow particle size distribution. Amount of the intercalated drug was determined as about 60% and showed a significant increase in loading capacity of the LDH through homogenization and mechanical mixing compared to that of the magnetic stirring (about 35%). Our results also showed that in LDH preparation via co-precipitation, the mixing system plays a more influential role in particle size, size distribution, and drug loading control, than the mixing speed of each system. Drug loaded-LDH/PLGA composites were prepared via electrospinning to afford a bioactive/osteoinductive scaffold. A remarkable degree of cell viability on the scaffolds (drug-loaded-LDH/PLGA composite) was confirmed using MTT assay. Osteogenic differentiation of human ADMSCs, as shown by alkaline phosphatase activity and Alizarin Red staining assays, indicated that the scaffold with 5% drug loaded LDH(Mn-Mg-LDH/PLGA/AT5%) induced a remarkably higher level of the markers compared to the PLGA scaffold and therefore, it could be a valuable candidate for bone tissue engineering applications.

ChABC-loaded PLGA nanoparticles: A comprehensive study on biocompatibility, functional recovery, and axonal regeneration in animal model of spinal cord injury.

Spinal Cord Injury (SCI) is one of the leading causes of physical disability. In this study, spherical PLGA nanoparticles (NPs) containing ChABC enzyme were manufactured and fully characterized for SCI therapy. The NPs were used in the rat's contused spinal cord to assess the functional improvement and scar digestion. Twenty-three adult male Wistar rats (275 ± 25 g) were assigned into four groups of control, sham, blank-treated particle, and ChABC-treated particle. Throughout the survey, the BBB scores were obtained for all the groups. Finally, the injured sections of animals were dissected, and histological studies were conducted using Luxol fast blue and Bielschowsky. The biocompatibility and non-toxicity effects of the NPs on olfactory ensheathing cells (OECs) were confirmed by the MTT test. The flow-cytometry revealed the purity of cultured OECs with p75+/GFAP+ at around 87.9 ± 2.4%. Animals in the control and the blank-treated groups exhibited significantly lower BBB scores compared with the ChABC-treated particle group. Histological results confirmed the induced contusion models in the injured site. Myelin was observed in the treated groups, especially when the ChABC-loaded nanoparticles were utilized. The immunohistochemistry results indicated the scar glial degradation in animals treated by the ChABC-loaded particles. According to this study, the loaded particles can potentially serve as a suitable candidate for spinal cord repair, functional recovery and axonal regeneration.

Development of bioactive fish gelatin/chitosan nanoparticles composite films with antimicrobial properties.

The objective of this work was to develop active bio-based nanocomposite films from fish gelatin (FG) and chitosan nanoparticles (CSNPs) incorporated with Origanum vulgare L. essential oil (OEO). CSNPs were obtained by ionic gelation of chitosan with sodium tripolyphosphate, which presented a spherical morphology with size range of 40-80nm. Remarkable differences in the surface morphology were observed between the control and bioactive nanocomposite films as revealed by SEM and AFM images. FTIR results confirmed that an interaction between polymer matrix and essential oil had occurred, as shown by an increase in the amplitude of peaks at wavenumbers 1242cm(-1) and 1451cm(-1). Meanwhile, XRD peaks of OEO-containing films were more intense, indicating that the introduction of essential oil into the film matrix induces an increase in crystallinity. TGA analysis demonstrated that the addition of OEO had no impact on thermal stability of the films. Inclusion of OEO in the film matrix resulted in less resistant and more flexible films, with a decrease in water vapor permeability (WVP). The FG/CSNPs bioactive films exhibited distinctive antimicrobial activity against four test food pathogens, namely Staphylococcus aureus, Listeria monocytogenes, Salmonella enteritidis and Escherichia coli.

A novel image analysis approach for evaluation of mixing uniformity in drug-filled silicone rubber matrix.

Nowadays, there is a lot of interest in developing long-acting drug delivery devices for human or veterinary applications including monolithic systems. Drug content uniformity of a monolithic device is highly dependent on the uniform distribution of drug particles within the polymeric matrix both in dispersion and distribution levels. Here, a range of formulations were prepared which consisted of progesterone (1%w/w) and estradiol benzoate (0.1%w/w) dispersed in a silicone rubber matrix. Blend uniformity of the compounds was analyzed by image analysis of SEM micrographs obtained from the cross-sections of the devices by a new image processing approach. Efficiency of mixing was investigated at the dispersion level by plotting the relative frequency of drug particles versus "projected area diameter" of their aggregates. Based on the particle size distribution results, a significant improvement was observed in the dispersion pattern of drug particles by adding silicone oil (9%w/w). Distribution pattern of the particles was investigated by transforming the micrographs into algebraic matrices. An "ideal matrix" was developed by assumption of uniform localization of the drug particles. Real matrices obtained for all of the formulations were compared with this ideal matrix as a reference. Closer similarity between the two matrices was observed for silicone oil-containing (9%w/w) samples showing the best dispersive and distributive mixing quality.

Artificial neural networks for bilateral prediction of formulation parameters and drug release profiles from cochlear implant coatings fabricated as porous monolithic devices based on silicone rubber.

OBJECTIVES: The coating of cochlear implants for topical delivery of drugs, for example, corticosteroids, or antibiotics is a novel approach to manage post-surgical complications associated with cochlear implantation surgery like inflammation or infections. Many variables, including formulation parameters, can be changed to modulate the amount and duration of drug release from these devices. Mathematical modeling of drug release profile from a delivery system may be helpful to accelerate formulations in a more cost-efficient way. To attain specific in vitro drug release characteristics, a model should be capable to provide good estimates on the initial formulation parameters, for example, composition, geometry and drug loading vice versa. Here, artificial neural networks (ANNs) are used to predict dexamethasone (DEX) release profile and formulation parameters, bilaterally, from cochlear implant coatings designed as porous, monolithic silicone rubber-based matrices. METHODS: The devices were fabricated as monolithic dispersions of DEX in a silicone rubber matrix containing porogens. A newly developed mathematical function was fitted on the experimental DEX release curves, and the function coefficients were fed into the network as input variables to simulate drug release profile from the porous devices. Formulation variables consisted of drug loading percentage (0.05-0.5% w/w), porogen type (dextran (dext) or sodium chloride particles) and porogen content (5-40% w/w). The ANN was also examined to determine optimal levels of the formulation parameters to provide a specifically desired drug release profile. KEY FINDINGS: The results showed that DEX release profile from porous cochlear implant devices can be modelled accurately and precisely using ANN in order to predict optimal levels for the formulation parameters to provide a specific drug release profile vice versa. CONCLUSIONS: The developed ANNs were used to achieve shorter formulation development process, and to provide tailor-made drug delivery regimens. ANNs were also successfully simulated non-linear relationships present between the initial formulation variable(s) and predict the subsequent drug release patterns.

Dexamethasone-releasing cochlear implant coatings: application of artificial neural networks for modelling of formulation parameters and drug release profile.

OBJECTIVES: Over the past few decades, mathematical modelling and simulation of drug delivery systems has been steadily gained interest as a focus for academic and industrial attention. Here, simulation of dexamethasone (DEX, a corticosteroid anti-inflammatory agent) release profile from drug-eluting cochlear implant coatings is reported using artificial neural networks. METHODS: The devices were fabricated as monolithic dispersions of the pharmaceutically active ingredient in a silicone rubber matrix. A two-phase exponential model was fitted on the experimentally obtained DEX release profiles. An artificial neural network (ANN) was trained to determine formulation parameters (i.e. DEX loading percentage, the devices surface area and their geometry) for a specific experimentally obtained drug release profile. In a reverse strategy, an ANN was trained for determining expected drug release profiles for the same set of formulation parameters. KEY FINDINGS: An algorithm was developed by combining the two previously developed ANNs in a serial manner, and this was successfully used for simulating the developed drug-eluting cochlear implant coatings. The models were validated by a leave-one-out method and performing new experiments. CONCLUSIONS: The developed ANN algorithms were capable to bilaterally predict drug release profile for a known set of formulation parameters or find out the levels for input formulation parameters to obtain a desired DEX release profile.

Two-step method for encapsulation of oregano essential oil in chitosan nanoparticles: preparation, characterization and in vitro release study.

In this study, oregano essential oil (OEO) has been encapsulated in chitosan nanoparticles by a two-step method, i.e., oil-in-water emulsion and ionic gelation of chitosan with sodium tripolyphosphate (TPP). The success of OEO encapsulation was confirmed by Fourier transform infrared (FT-IR) spectroscopy, UV-vis spectrophotometry, thermogravimetric analysis (TGA) and X-ray diffraction (XRD) techniques. The obtained nanoparticles exhibited a regular distribution and spherical shape with size range of 40-80 nm as observed by scanning electron microscopy (SEM) and atomic force microscopy (AFM). As determined by TGA technique, the encapsulation efficiency (EE) and loading capacity (LC) of OEO-loaded chitosan nanoparticles were about 21-47% and 3-8%, respectively, when the initial OEO content was 0.1-0.8 g/g chitosan. In vitro release studies showed an initial burst effect and followed by a slow drug release.

Curing behavior of silicone elastomer in the presence of two corticosteroid drugs.

Silicone rubbers are widely used as carriers for delivery of drugs intended for parenteral administration as implantable devices. Drugs having different functional groups can significantly affect curing profile of silicone rubber, which in turn may negatively affect their biological applicability due to the loss in mechanical and drug retaining properties. To this end, the effects of two corticosteroid analogs (up to 2 %w/w of drug loading) that is, dexamethasone (DEX) and its sodium phosphate ester (DSP) on curing behavior of a non-restricted, two part RTV silicone rubber was studied using different characterization techniques including spectroscopic (FTIR), calorimetric (DSC), oscillating disk rheometry, and swelling studies. The results showed that curing time extends longer for DSP-loaded samples compared to the non-loaded silicone rubber. The presence of DSP in the formulation interferes in the curing of silicone elastomers, probably due to the thermal decomposition of DSP according to the spectral changes observed in FTIR spectra as confirmed by DSC analysis. Rheometric studies showed depreciated properties for silicone elastomers upon compounding with DSP. Swelling measurements indicated to lowered crosslink density for networks and increasing M(c) upon adding DSP to the formulations which can be attributed to disruption in crosslinking reaction by sodium phosphate moieties of DSP.