Determination of partitioning of drug molecules using immobilized liposome chromatography and chemometrics methods

Noorizadeh, Hadi; Farmany, Abbas

doi:10.1002/dta.262

Cited by 10 publications

(3 citation statements)

References 35 publications

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“…To select the most relevant descriptors with GA, the evolution of the population was simulated 25–27. Each individual of the population, defined by a chromosome of binary values, represented a subset of descriptors.…”

Section: Computationalmentioning

confidence: 99%

QSRR using evolved artificial neural network for 52 common pharmaceuticals and drugs of abuse in hair from UPLC–TOF‐MS

Noorizadeh

Farmany

Narimani

et al. 2011

Drug Testing and Analysis

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A quantitative structure-retention relationship (QSRR) study based on an artificial neural network (ANN) was carried out for the prediction of the ultra-performance liquid chromatography-Time-of-Flight mass spectrometry (UPLC-TOF-MS) retention time (RT) of a set of 52 pharmaceuticals and drugs of abuse in hair. The genetic algorithm was used as a variable selection tool. A partial least squares (PLS) method was used to select the best descriptors which were used as input neurons in neural network model. For choosing the best predictive model from among comparable models, square correlation coefficient R(2) for the whole set calculated based on leave-group-out predicted values of the training set and model-derived predicted values for the test set compounds is suggested to be a good criterion. Finally, to improve the results, structure-retention relationships were followed by a non-linear approach using artificial neural networks and consequently better results were obtained. This also demonstrates the advantages of ANN.

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Section: Computationalmentioning

confidence: 99%

QSRR using evolved artificial neural network for 52 common pharmaceuticals and drugs of abuse in hair from UPLC–TOF‐MS

Noorizadeh

Farmany

Narimani

et al. 2011

Drug Testing and Analysis

Self Cite

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show abstract

“…To select the most relevant descriptors with GA, the evolution of the population was simulated. [25][26][27] Each individual of the population, defined by a chromosome of binary values, represented a subset of descriptors. The number of the genes at each chromosome was equal to the number of the descriptors.…”

Section: Molecular Modelling and Theoretical Molecular Descriptorsmentioning

confidence: 99%

Application of artificial neural network to predict the retention time of drug metabolites in two‐dimensional liquid chromatography

Noorizadeh

Sobhanardakani

Raoofi

et al. 2011

Drug Testing and Analysis

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Genetic algorithm and partial least square (GA-PLS) and Levenberg-Marquardt artificial neural network (L-M ANN) techniques were used to investigate the correlation between retention time and descriptors for drug metabolites which obtained by two-dimensional liquid chromatography. The applied internal (leave-group-out cross validation (LGO-CV)) and external (test set) validation methods were used for the predictive power of four models. Both methods resulted in accurate prediction whereas more accurate results were obtained by L-M ANN model. The best model obtained from L-M ANN showed a good R(2) value (determination coefficient between observed and predicted values) for all compounds, which was superior to GA-PLS models.

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“…However, data on the solubility of analytes in octanol give only a very rough estimation of its membrane interaction properties . There are several chromatographic methods available for studying solute–membrane interactions, including free liposome partitioning systems, immobilized artificial membrane (IAM) chromatography, immobilized liposome chromatography, and CE techniques . There are only four IAM HPLC stationary phases commercially available in columns of various lengths (10–150 mm).…”

Section: Introductionmentioning

confidence: 99%

Capillary electromigration techniques for studying interactions between analytes and lipid dispersions

Wiedmer

Lokajová

2012

J of Separation Science

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CE has matured into a well-known and much used separation technique, with applications covering more or less all types of samples. EKC, which originally was developed for the separation of uncharged compounds, is still today under much development, with main focus on finding the perfect or ideal carriers (pseudo-stationary phase) for a broad range of analytes. In this review, the use of lipid dispersions as pseudostationary phases in EKC performed in capillaries and microchips, in addition to CE frontal analysis and partial filling EKC using lipid dispersions is discussed. Various types of lipid dispersions including liposomes, PEG-stabilized aggregates, proteoliposomes, lipid-based nanoparticles, and commercial lipid emulsions are described. The purpose of the review is to give the reader an overview of how EKC, CE frontal analysis, and partial filling EKC, have been applied to the study of interactions between analytes and lipid membranes.

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Determination of partitioning of drug molecules using immobilized liposome chromatography and chemometrics methods

Cited by 10 publications

References 35 publications

QSRR using evolved artificial neural network for 52 common pharmaceuticals and drugs of abuse in hair from UPLC–TOF‐MS

QSRR using evolved artificial neural network for 52 common pharmaceuticals and drugs of abuse in hair from UPLC–TOF‐MS

Application of artificial neural network to predict the retention time of drug metabolites in two‐dimensional liquid chromatography

Capillary electromigration techniques for studying interactions between analytes and lipid dispersions

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