scholarly journals Optimization of self-emulsifying drug delivery system of cefuroxime axetil

2021 ◽  
Vol 66 (2) ◽  
pp. 67-79
Author(s):  
Eleonora Trajanovska ◽  
Maja Simonoska Crcarevska ◽  
Miroslav Mirchev ◽  
Frosina Jovanovikj ◽  
Ana Atanasova ◽  
...  
Keyword(s):  
Neural Network ◽  
Drug Delivery ◽  
Artificial Neural Network ◽  
Experimental Design ◽  
Olive Oil ◽  
Tween 80 ◽  
Cefuroxime Axetil ◽  
Peg 400 ◽  
Artificial Neural Network Ann ◽  
Mixture Experimental Design

Abstract Overcoming solubility problems is the greatest challenge during formulation of poorly soluble active pharmaceutical ingredients (API’s) into oral solid dosage forms. Different formulation approaches were used to surpass this problem and enhance their solubility in the gastrointestinal (GI) fluids, in order to achieve a faster dissolution and better absorption, which will directly influence their therapeutic effect. In this paper, an evaluation of the potential of a self-emulsifying drug delivery system (SEDDS) to improve the solubility of the active ingredient cefuroxime axetil (CA) was done. Screening of the solubility of the API in different excipients was done, and Tween 80, PEG 400, and Olive oil as a surfactant, co-solvent, and oil, respectively, were chosen as the most convenient system constituents. An optimal self-emulsification and solubilization ability of this system was assessed using mixture experimental design statistical tools based on the response surface methodology (RSM). The prepared CA-SEDDS were evaluated for droplet size (d10, d50, d90 in µm), droplet size distribution (Span factor), and absorbance. As a complementary approach, for better representation of the non-linear relationship between the formulation compositions and the observed dispersion characteristics an artificial neural network (ANN) was used. Optimal formulation that consists of 10% (w/w) Tween 80 as surfactant, 80% (w/w) PEG 400 as co-solvent and 10% (w/w) Olive oil, was obtained. Both, mixture experimental design and ANN were combined for a comprehensive evaluation of CA-SEDDS and the obtained results suggested that formulation of SEDDS is a useful approach for improving the solubility of the CA. Keywords: self-emulsifying drug delivery systems (SEDDS), cefuroxime axetil, design of experiment, artificial neural network (ANN)

scholarly journals OPTIMIZATION OF SELF-NANOEMULSIFYING DRUG DELIVERY SYSTEMS OF LEMONGRASS (CYMBOPOGON CITRATUS) ESSENTIAL OIL

2019 ◽  
Vol 11 (1) ◽  
pp. 144
Author(s):  
Tri Ujilestari ◽  
Bambang Ariyadi ◽  
Ronny Martien ◽  
Zuprizal . ◽  
Nanung Danar Dono
Keyword(s):  
Drug Delivery ◽  
Essential Oil ◽  
Coconut Oil ◽  
Tween 80 ◽  
Cymbopogon Citratus ◽  
Peg 400 ◽  
Transmission Electron ◽  
Poorly Soluble ◽  
Optimum Formula ◽  
Mixture Experimental Design

Objective: Focus of this study was to optimize and to characterize the self-Nano emulsifying drug delivery system using lemongrass (Cymbopogon citratus) essential oil.Methods: The optimum formulas were analyzed using a D-Optimal mixture experimental design and performed using a Design Expert® Ver. 7.1.5. Formulation variables which include in the design were: oil component X1 (a mixture of Cymbopogon citratus essential oil and virgin coconut oil/VCO), surfactant X2 (Tween 80), and co-surfactant (PEG 400), while emulsification time in a sec (Y1) and transmittance in percent (Y2) as responses.Results: The optimum formula for SNEDDS in the current study were: Cymbopogon citratus essential oil (7.147%), VCO (7.147%), Tween 80 (71.417%), and PEG 400 (14.290%). From the optimizing formula can be shown that the mean of droplet size, polydispersity-index, zeta potential, and viscosity were: 13.17±0.06 nm, 0.17±0.05,-20.90±1.47 mV, 200±0mPa. s (n=3), respectively. Furthermore, the optimized formula has passed the thermodynamic stability test; meanwhile, transmission electron microscopy displayed spherical shape.Conclusion: The optimized SNEDDS formula was improving solubility of poorly soluble Cymbopogon citratus essential oil.

Application of the Artificial Neural Network (ANN) Method as MPPT Photovoltaic for DC Source Storage

2019 ◽  
Vol 12 (3) ◽  
pp. 145 ◽  
Author(s):  
Epyk Sunarno ◽  
Ramadhan Bilal Assidiq ◽  
Syechu Dwitya Nugraha ◽  
Indhana Sudiharto ◽  
Ony Asrarul Qudsi ◽  
...  
Keyword(s):  
Neural Network ◽  
Artificial Neural Network ◽  
Artificial Neural ◽  
Artificial Neural Network Ann

Random Forest (RF) and Artificial Neural Network (ANN) Algorithms for LULC Mapping

2020 ◽  
Vol 38 (4A) ◽  
pp. 510-514
Author(s):  
Tay H. Shihab ◽  
Amjed N. Al-Hameedawi ◽  
Ammar M. Hamza
Keyword(s):  
Neural Network ◽  
Remote Sensing ◽  
Artificial Neural Network ◽  
Random Forest ◽  
Satellite Image ◽  
Landsat 8 ◽  
Optical Remote Sensing ◽  
Data Set ◽  
Artificial Neural ◽  
Artificial Neural Network Ann

In this paper to make use of complementary potential in the mapping of LULC spatial data is acquired from LandSat 8 OLI sensor images are taken in 2019.  They have been rectified, enhanced and then classified according to Random forest (RF) and artificial neural network (ANN) methods. Optical remote sensing images have been used to get information on the status of LULC classification, and extraction details. The classification of both satellite image types is used to extract features and to analyse LULC of the study area. The results of the classification showed that the artificial neural network method outperforms the random forest method. The required image processing has been made for Optical Remote Sensing Data to be used in LULC mapping, include the geometric correction, Image Enhancements, The overall accuracy when using the ANN methods 0.91 and the kappa accuracy was found 0.89 for the training data set. While the overall accuracy and the kappa accuracy of the test dataset were found 0.89 and 0.87 respectively.

Detection of Biomedical Images by Using Bio-inspired Artificial Intelligent

2020 ◽  
Vol 38 (2A) ◽  
pp. 255-264
Author(s):  
Hanan A. R. Akkar ◽  
Sameem A. Salman
Keyword(s):  
Neural Network ◽  
Artificial Neural Network ◽  
Mean Square Error ◽  
Average Rate ◽  
Mean Square ◽  
Performance Measurements ◽  
Artificial Intelligent ◽  
Average Mean Square Error ◽  
Artificial Neural ◽  
Artificial Neural Network Ann

Computer vision and image processing are extremely necessary for medical pictures analysis. During this paper, a method of Bio-inspired Artificial Intelligent (AI) optimization supported by an artificial neural network (ANN) has been widely used to detect pictures of skin carcinoma. A Moth Flame Optimization (MFO) is utilized to educate the artificial neural network (ANN). A different feature is an extract to train the classifier. The comparison has been formed with the projected sample and two Artificial Intelligent optimizations, primarily based on classifier especially with, ANN-ACO (ANN training with Ant Colony Optimization (ACO)) and ANN-PSO (training ANN with Particle Swarm Optimization (PSO)). The results were assessed using a variety of overall performance measurements to measure indicators such as Average Rate of Detection (ARD), Average Mean Square error (AMSTR) obtained from training, Average Mean Square error (AMSTE) obtained for testing the trained network, the Average Effective Processing Time (AEPT) in seconds, and the Average Effective Iteration Number (AEIN). Experimental results clearly show the superiority of the proposed (ANN-MFO) model with different features.

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