Shadab Shahsavari

Background: From the perspective of drug delivery systems, oral films (OFs) have received increased attention, mainly for pediatric and geriatric applications. Beeswax, a naturally derived and FDA-approved material, is often mixed with other polymers to enhance its mechanical properties. This study presented the first use of precisely controlled, solvent-free pressure-assisted micro-syringe ...

Background and Objectives: Contamination of surface and groundwater sources with aromatic compounds such as phenolic compounds is one of the newest environmental problems that humans encountered it today. One of the ways to remove these pollutants is the use of polymeric adsorbents. In this study, chitosan-bentonite-nano-graphene oxide nanosorbent was synthesized to investigate the phenol adsorption. Material and Methods: In this research, experimental design was performed using Design-Expert 7.0 software to investigate the effect of independent variables including nano graphene oxide, chitosan and bentonite on the dependent variable (phenol removal rate). Moreover, phenol absorption isotherm, kinetics and thermodynamics, were also examined. Results: Eventually, the optimum formulation of nano-adsorbent with specifications of 0.1 g of chitosan, 0.18 g of bentonite and 0.07 g of nano-graphene oxide was investigated. Optimal adsorption conditions were also obtained at a concentration of 100 ppm, pH=5 and 45 minutes of absorption time. Conclusion: Finally, it was found that the synthesized nanosorbent follows the Langmuir isotherm, which represents physical adsorption, and also the kinetics of the adsorption process is the diffusion between particles. As a result, it was found that the optimal application of this nano-adsrbent is in wastewaters with a temperature of less than 400 °C.

Background: Chitosan nanoparticle, a potential vehicle, is used as a hydrophilic carrier system since it can deliver drugs to specific sites and also control the drug release rate. Moreover, controlled release systems are designed to minimize systemic absorption and to achieve optimum delivery of the biologically active mesalamine to the distal small intestine and the colon. Objective: The current study investigated the development of new nanoparticulate drug delivery systems based on polyurethane-chitosan copolymers. The copolymer shows good biodegradablity and biocompatiblity properties and thus can be considered as a potential carrier for drug delivery systems. Method: In this work, Polyurethane was obtained from the condensation reaction between polypropylene glycol (PPG) as prepolymerpolyol, 1, 4-butanediol (BD) as diol, dimethylol propionic acid (DMPA) as chain extender and also isophoronediisocyanate (IPDI). The synthesized polyurethane was grafted onto the prepared chitosan through a covalent binding and preparation of nanoparticles was done further through a coprecipitation process. The particle size of the prepared samples was evaluated with dynamic light scattering (DLS) technique. Results: The obtained particle size of the samples was 80±0.05 nm. Characterization of the synthesized chitosan-polyurethane copolymer was performed by FT-IR spectroscopy, 13CNMR and 11HNMR spectroscopy. The morphology of the synthesized polyurethane-chitosan copolymers and the amount of the loaded drug were also examined using SEM images and UV-visible spectroscopy, respectively. Moreover, drug release behavior was examined in PBS (pH 7.4) at 37°C. It was concluded that the mesalamine release from polyurethane-chitosan was sustained and no initial burst release (burst effect) was observed and the percentage of mesalamine released from nanoparticles was 92.19±0.2% within 72 hrs. Conclusion: The results of the drug loaded nanoparticles showed that the drug loading process was performed successfully. As a result, polyurethane-chitosan copolymer can be a good candidate for drug delivery systems.

The aim of this research was the preparation, optimization, and in-vitro characterization of acyclovir loaded chitosan nanoparticles. Experimental design D-optimal response surface methodology was used for the optimization of the nanoparticles. Therefore, the polymeric nano-drug controlled release system has been designed for varied variables such as the concentration of Acyclovir, concentration ratio of chitosan/ TPP and pH using the ionic gelation method. The optimized nanoparticles were characterized morphologically by Scanning Electron Microscopy (SEM), particle size analyzer (DLS) for determining size, zeta and PdI, Fourier Transform Infra-Red (FTIR) Spectroscopy for determination of chemical structure of nanoparticles molecules and Thermogravimetric analysis (TGA) and differential scanning calorimetry (DSC) for studying thermal behavior. The size of the optimized particles was detected to be 132 ± 24.3 nm; zeta potential was 32 ± 2.87 mV; PdI of particles was 0.159 ± 0.05; and calculated EE% was 85 ± 4.38%. SEM image shows segregated and non-aggregated nanoparticles with sub-spherical smooth morphology. An in-vitro release study of the prepared nanoparticles illustrated that the percentage of acyclovir released from the nanoparticles was 80.17 ± 2.45% within 48 hrs. Kinetic release profiles of acyclovir from nanoparticles appeared to fit best with Korsmeyer-Peppas and First-order models, the Fickian diffusion being premier phenomenon.

Formulation of controlled release acyclovir loaded chitosan nanoparticles was optimized based on the optimization technique using response surface method (RSM) and artificial neural network (ANN) simultaneously to develop a model to identify relationships between variables affecting drug nanoparticles. In this research, the goal was to create a representation of three irregular factors, i.e. concentration of acyclovir, concentration ratio of chitosan/ Tripolyphosphate (TPP) and pH on response variables. ANN was used to create a fit model of formulations via these four training algorithms including: Levenberg–Marquardt (LM), Gradient Descent (GD), Bayesian–Regularization (BR) and BFGS Quasi- Newton (BFG) were applied to train ANN containing a various hidden layer, applying the testable data as the training set. Criterion to stop training was the divergence of the RMSE (root mean squared error) between target and output values. Both methods including gradient descent and Levenberg-marquardt have showed similar results in the data formulation. Corresponding to batch back propagation (BBP)-ANN performance, a gain in pH of polymer solution reduced the size and polydispersity index (PdI) of nanoparticles. Moreover, decreases in the concentration ratio of chitosan/TPP consequently cause an increase in entrapment efficiency (%EE). The next aim of this research was to investigate the performance of predictive algorithms. For this reason each training algorithm in order to consider the accuracy of predictive ability was evaluated and the result was as follow: LM > BFGs > GD > BR.