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Formulation and Characterization of Itraconazole as Nanosuspension Dosage Form for Enhancement of Solubility
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Abstract

            Itraconazole is a triazole antifungal given orally for the treatment of oropharyngeal and vulvovaginal candidiasis, for systemic infections including aspergillosis, candidiasis,  and for the prophylaxis of fungal infections in immunocompromised patients.

           The study aimed to formulate a practical water-insoluble Itraconazole, with insufficient bioavailability as nanosuspension to increase aqueous solubility and improve its dissolution and oral bioavailability.

          Itraconazole nanosuspension was produced by a solvent-antisolvent nanoprecipitation method in the presence of different stabilisers (Poloxamer-188, HPMCE5) at different ratios with the drug alone or combination with surfactant(tween 80, SLS).

         The results exhibit that the particle sizes of all prepared itraconazole formulations were in the nano size.  The best formula (F6) has a particle size.  ( 42  ) nm and Zeta potential of (- 21.86 ) mV.  In vitro cumulative release from the nanosuspension was (88 %) at (30) min when compared to the pure drug (13%) and lyophilized nanoparticles (98.2%) at (30)min. Effect of different parameters was investigated.

          Fourier transforms infrared spectroscopy(FTIR), Differential scanning calorimetry (DSC) and X-ray diffraction (XRD), Scanning electron microscope( SEM) was done for the optimized  nanoparticles prepared by lyophilization technique

        Thus, Nanosuspension appears to be an encouraging approach to formulate Itraconazole nanosuspension with high solubility and dissolution rate.

 

 

 

 

 

 

 

Keywords: Itraconazole, Nanoprecipitation method, Nanosuspension

         

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Publication Date
Tue Dec 15 2020
Journal Name
Journal Of Pharmaceutical And Biological Sciences
Fascinating approach for using metabolites products of living microorganisms as reducing agents for preparing silver nanoparticles
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A crucial area of research in nanotechnology is the formation of environmentally benign nanoparticles. Both unicellular and multicellular play an important role in synthesis nanoparticles through the production of inorganic materials either intracellularly or extracellularly. The agents (pigments, siderophores, cell extracted metabolites and reducing compounds) were used to prepare silver nanparticles with different sizes and shapes. The color variations (dark yellow, slightly dark yellow and golden yellow) arising from changes in the composition, size, and shape of nanoparticles, surrounding medium can be monitored using UV-visible spectrophotometer. These effects are due to the phenomena called surface plasmon resonance. The silver nanopa

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Sun Jan 01 2017
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Sun Sep 01 2019
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A New Method for the Isolation and Purification of Trigonelline as Hydrochloride from Trigonella foenum-graecum L.
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Separation of Trigonelline, the major alkaloid in fenugreek seeds, is difficult because the extract of these seeds usually contains Trigonelline, choline, mucilage, and steroidal saponins, in addition to some other substances. This study amis to isolate the quaternary ammonium alkaloid (Trigonelline) and choline from fenugreek seeds (Trigonella-foenum graecum L.) which have similar physiochemical properties by modifying of the classical method. Seeds were defatted and then extracted with methanol. The presence of alkaloids was detected by using Mayer's and Dragendorff's reagents. In this work, trigonilline was isolated with traces of choline by subsequent processes of purification using analytical and preparative TLC techniques.

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Using sewage sludge as a permeable reactive barrier for remediation of groundwater contaminated with lead and phenol
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Potential Application of Natural and Modified Orange Peel as an Eco‒friendly Adsorbent for Methylene Blue Dye
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Synthesis of nano-magnetite and magnetite/synthetic geopolymer nano-porous composite for application as a novel adsorbent
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Breast Cancer MRI Classification Based on Fractional Entropy Image Enhancement and Deep Feature Extraction
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Disease diagnosis with computer-aided methods has been extensively studied and applied in diagnosing and monitoring of several chronic diseases. Early detection and risk assessment of breast diseases based on clinical data is helpful for doctors to make early diagnosis and monitor the disease progression. The purpose of this study is to exploit the Convolutional Neural Network (CNN) in discriminating breast MRI scans into pathological and healthy. In this study, a fully automated and efficient deep features extraction algorithm that exploits the spatial information obtained from both T2W-TSE and STIR MRI sequences to discriminate between pathological and healthy breast MRI scans. The breast MRI scans are preprocessed prior to the feature

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