Preformulation studies and characterization of the physicochemical properties of amorphous polymers using artificial neural networks

Ebube, Nkere K.; Owusu-Ababio, Godfried; Adeyeye, Christiana Moji

doi:10.1016/s0378-5173(99)00405-6

Cited by 47 publications

(21 citation statements)

References 17 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…This would facilitates the medium penetration into the matrix easily with more rapid release of the drug from the designed formula. [14] Panna thapa et. al.…”

Section: Determination Of the Floating Lag Time And Floating Durationmentioning

confidence: 99%

Preparation and Evaluation of Gastroretentive Systems of Telmisartan

Ashoor¹

2017

WJPPS

View full text Add to dashboard Cite

Telmisartan (TEL) is a selective angiotensin II receptor blocker used in the management of cardiovascular disorders. It is a class II drug according to biopharmaceutics classification system; with poor solubility and high permeability. Owing to its hydrophobicity, TEL demonstrates low dissolution behavior in gastro-intestinal (GIT) media ensuing deprived absorption, and later poor bioavailability. The subject of the research is to prepare a floating tablet of TEL for oral use, which related to various benefits which can be summarized by improvement in the solubility and increment in the absorption and hence better bioavailability will be obtained in addition to reduction in its side effects. The Gastroretentive Floating Tablets (GFT) were formulated by means of hydroxy propyl methyl cellulose (HPMC). Six batches were formulated by Direct Compression (DC) and Wet Granulation (WG) method by using different HPMC concentrations. In addition to that, changing the diluent type (avecil 101 and 102) to reach optimal floating and dissolution performance.These prepared formulas were examined for angle of repose, Carr's Index (CI), hardness, Floating Lag Time (FLT), Floating Duration (FD) and dissolution profile. F5 (the favored formula) comprised 120mg HPMC plus 20mg sodium bi carbonate (NaHCO 3 ) which formulated by DC displays a reasonable characteristics in release profile with FLT of 8±2 second and buoyancy of 6.5±0.5 hours. It was concluded that F5 (the favored formula) represent a hopeful one as a floating of gastroretentive drug delivery systems of Telmisartan. KEYWORDS:In addition to that, changing the diluent type (avecil 101 and 102) to reach optimal floating and dissolution performance.

show abstract

“…This would facilitates the medium penetration into the matrix easily with more rapid release of the drug from the designed formula. [14] Panna thapa et. al.…”

Section: Determination Of the Floating Lag Time And Floating Durationmentioning

confidence: 99%

Preparation and Evaluation of Gastroretentive Systems of Telmisartan

Ashoor¹

2017

WJPPS

View full text Add to dashboard Cite

show abstract

“…ANN model has been used in the design and optimization of preformulation to determine the physicochemical propertiesof amorphous polymers [49]. A study by Ebubeet.…”

Section: Preformulationmentioning

confidence: 99%

Artificial Neural Network in Drug Delivery and Pharmaceutical Research

Sutariya¹,

Groshev²,

Sadana³

et al. 2013

TOBIOIJ

View full text Add to dashboard Cite

Artificial neural networks (ANNs) technology models the pattern recognition capabilities of the neural networks of the brain. Similarly to a single neuron in the brain, artificial neuron unit receives inputs from many external sources, processes them, and makes decisions. Interestingly, ANN simulates the biological nervous system and draws on analogues of adaptive biological neurons. ANNs do not require rigidly structured experimental designs and can map functions using historical or incomplete data, which makes them a powerful tool for simulation of various non-linear systems.ANNs have many applications in various fields, including engineering, psychology, medicinal chemistry and pharmaceutical research. Because of their capacity for making predictions, pattern recognition, and modeling, ANNs have been very useful in many aspects of pharmaceutical research including modeling of the brain neural network, analytical data analysis, drug modeling, protein structure and function, dosage optimization and manufacturing, pharmacokinetics and pharmacodynamics modeling, and in vitro in vivo correlations. This review discusses the applications of ANNs in drug delivery and pharmacological research.

show abstract

“…Most applications of NNs in the field of polymer uses the Class I NN to predict end-user properties based on the molecular weight distribution of the polymer or the physical characteristic of the polymer (Al-Hiak et al, 2004;Ebube et al, 2000;Fujii et al, 2003;Hinchliffe et al, 2003;Huang & Liao, 2002;Kuroda & Kim, 2002;Simon & Fernandes, 2004;Sumpter & Noid, 1994;Sun et al, 1996;Zhang et al, 2003). This application is usually simple and does not require many d ata points to train the NN.…”

Section: Recent Achievements In the Field Of Polymerizationmentioning

confidence: 99%