Biomedical subjects

S Agatonovic-Kustrin

Publications and source records attributed to S Agatonovic-Kustrin.

At least 19 recordsLinked to original sources

PubMed2005

Prediction of drug absorption based on immobilized artificial membrane (IAM) chromatography separation and calculated molecular descriptors.

The aim of this study was to evaluate the usefulness of IAM chromatography in building a model that would allow prediction of drug absorption in humans. The human intestinal absorption values (%HIA) for 52 drugs with low to high intestinal absorption were collected from the literature. The retention (capacity factor, k') of each drug was measured by reverse-phase HPLC using an IAM.PC.DD2 column (prepared with phosphatidylcholine analogs, 12 microM, 300A, 15 cm x 4.6 mm) with an eluent of acetonitrile-0.1M phosphate buffer at pH 5.4. In addition, 76 molecular descriptors and solubility parameters for each drug were calculated using ChemSW from the 3D-molecular structures. Stepwise regression was employed to develop a regression equation that would correlate %HIA with molecular descriptors and k'. Human intestinal absorption was reciprocally correlated to the negative value of the capacity factor (-1/k') (R=0.64). The correlation was further improved with the addition of molecular descriptors representing molecular size and shape (molecular width, length and depth) solubility (solubility parameter, HLB, hydrophilic surface area) and polarity (dipole, polar surface area) (R=0.83). Experimentally measured IAM chromatography retention values and calculated molecular descriptors and solubility parameters can be used to predict intestinal absorption of drugs in humans. Developed QSAR can be used as a screening method in the designing of drugs with appropriate IA and for the selection of drug candidates in the early stage of drug discovery process.

Chromatography, High Pressure Liquid↗

PubMed2005

Artificial neural networks to optimize formulation components of a fixed-dose combination of rifampicin, isoniazid and pyrazinamide in a microemulsion.

The aim of this study to design a stable microemulsion formulation to deliver a combination of rifampicin, isoniazid and pyrazinamide in quantities suitable for administration to a paediatric population. The chemical stability of rifampicin, isoniazid and pyrazinamide alone and in various combinations was investigated in different solvents, solubilizing agents and surfactants. An artificial neural network was used to model data from the stability studies and a sensitivity analysis was applied to optimize the selection of the formulation components. Imwitor 308 and Crillet 3, exhibiting the highest overall positive sensitivity were selected to formulate the stable microemulsion. Due to drug dose specifications and solubility limitations, the final formulation contained only rifampicin and isoniazid, since the solubility of pyrazinamide in the lipid and aqueous components of the microemulsion did not achieve the required dose. The stability and solubility of rifampicin were improved in the formulation. Solubilization of the rifampicin in the lipid droplets of the internal phase and lipophilic chains of the surfactants increased the quantity of rifampicin that can be incorporated, while protecting it from oxidative degradation and also limited its contact with isoniazid, which has been shown to affect its stability. The results of this study indicate that the Artificial Neural Network can be successfully used to optimize the choice of solvents, solubilizing agents and surfactants prior to formulation of the microemulsion, limiting the amount of experiments, thus reducing the costs during the preformulation study.

Antibiotics, Antitubercular↗

PubMed2004

Strategy for the development of a thermodynamically stable oral microemulsion.

Microemulsions, being thermodynamically stable systems, with low viscosity and elegant in appearance have attracted interest not only for the delivery of single drug substances with low water solubility but for the stabilization of drugs in combination due to their preferential solubility in either the water or oil phases. Microemulsion design involves the solubilisation of an optimum amount of the dispersed phase in the continuous phase, utilizing the minimum amount of surfactant/mixture of surfactants/cosurfactants. It is the choice of the surfactant/surfactant mixture and/or cosurfactants, which poses the greatest challenge in the design of a thermodynamically stable microemulsion formulation. This paper will present a strategy for choosing surfactants to achieve a stable, dilutable microemulsion formulation for oral administration. Ternary and pseudo-ternary phase diagrams were constructed by titrating a series of mixtures [lipid (miglyol 812): cosurfactant and/or surfactant (cremophor RH, imwitor 308/742, sorbitol, brij 97, crillet 3)] with water at room temperature, the phases formed visually assessed after each addition and classified as isotropic (ME), liquid crystalline (LC) or coarse emulsions (EM). Results indicate that the surfactant combination of imwitor 308 and crillet 3 proved most successful in incorporating 25 to 30% miglyol 812 into a microemulsion formulation utilizing an intermediate quantity of surfactant and maintaining homogeneity on dilution.

Anisotropy↗

PubMed2003

Prediction of corneal permeability using artificial neural networks.

The purpose of this study was to develop a simple model for prediction of corneal permeability of structurally different drugs as a function of calculated molecular descriptors using artificial neural networks. A set of 45 compounds with experimentally derived values of corneal permeability (log C) was used to develop, test and validate a predictive model. Each compound was encoded with 1194 calculated molecular structure descriptors. A genetic algorithm was used to select a subset of descriptors that best describe corneal permeability coefficient log C and a supervised network with radial basis transfer function (RBF) was used to correlate calculated molecular descriptors with experimentally derived measures of corneal permeability. The best model, with 4 input descriptors and 12 hidden neurones was chosen, and the significance of the selected descriptors to corneal permeability was examined. Strong correlation of predicted with experimentally derived log C values (correlation coefficient greater than 0.87 and 0.83 respectively) was obtained for the training and testing data sets. The developed model could be useful for the rapid prediction of the corneal permeability of candidate drugs based on molecular structure alone as it does not require experimentally derived data.

Chemical Phenomena↗

PubMed2002

Molecular descriptors that influence the amount of drugs transfer into human breast milk.

Most drugs are excreted into breast milk to some extent and are bioavailable to the infant. The ability to predict the approximate amount of drug that might be present in milk from the drug structure would be very useful in the clinical setting. The aim of this research was to simplify and upgrade the previously developed model for prediction of the milk to plasma (M/P) concentration ratio, given only the molecular structure of the drug. The set of 123 drug compounds, with experimentally derived M/P values taken from the literature, was used to develop, test and validate a predictive model. Each compound was encoded with 71 calculated molecular structure descriptors, including constitutional descriptors, topological descriptors, molecular connectivity, geometrical descriptors, quantum chemical descriptors, physicochemical descriptors and liquid properties. Genetic algorithm was used to select a subset of the descriptors that best describe the drug transfer into breast milk and artificial neural network (ANN) to correlate selected descriptors with the M/P ratio and develop a QSAR. The averaged literature M/P values were used as the ANN's output and calculated molecular descriptors as the inputs. A nine-descriptor nonlinear computational neural network model has been developed for the estimation of M/P ratio values for a data set of 123 drugs. The model included the percent of oxygen, parachor, density, highest occupied molecular orbital energy (HOMO), topological indices (chiV2, chi2 and chi1) and shape indices (kappa3, kappa2), as the inputs had four hidden neurons and one output neuron. The QSPR that was developed indicates that molecular size (parachor, density) shape (topological shape indices, molecular connectivity indices) and electronic properties (HOMO) are the most important for drug transfer into breast milk. Unlike previously reported models, the QSPR model described here does not require experimentally derived parameters and could potentially provide a useful prediction of M/P ratio of new drugs only from a sketch of their structure and this approach might also be useful for drug information service. Regardless of the model or method used to estimate drug transfer into breast milk, these predictions should only be used to assist in the evaluation of risk, in conjunction with assessment of the infant's response.

Animals↗

PubMed2002

Application of the artificial neural network in quantitative structure-gradient elution retention relationship of phenylthiocarbamyl amino acids derivatives.

Quantitative structure-retention relationship(QSRR) method was used to model reversed-phase high-performance liquid chromatography (RP-HPLC) separation of 18 selected amino acids. Retention data for phenylthiocarbamyl (PTC) amino acids derivatives were obtained using gradient elution on ODS column with mobile phase of varying acetonitrile, acetate buffer and containing 0.5 ml/l of triethylamine (TEA). Molecular structure of each amino acid was encoded with 36 calculated molecular descriptors. The correlation between the molecular descriptors and the retention time of the compounds in the calibration set was established using the genetic neural network method. A genetic algorithm (GA) was used to select important molecular descriptors and supervised artificial neural network (ANN) was used to correlate mobile phase composition and selected descriptors with the experimentally derived retention times. Retention time values were used as the network's output and calculated molecular descriptors and mobile phase composition as the inputs. The best model with five input descriptors was chosen, and the significance of the selected descriptors for amino acid separation was examined. Results confirmed the dominant role of the organic modifier in such chromatographic systems in addition to lipophilicity (log P) and molecular size and shape (topological indices) of investigated solutes.

Amino Acids↗

PubMed2001

Theoretically-derived molecular descriptors important in human intestinal absorption.

A quantitative structure-human intestinal absorption relationship was developed using artificial neural network (ANN) modeling. A set of 86 drug compounds and their experimentally-derived intestinal absorption values used in this study was gathered from the literature and a total of 57 global molecular descriptors, including constitutional, topological, chemical, geometrical and quantum chemical descriptors, calculated for each compound. A supervised network with radial basis transfer function was used to correlate calculated molecular descriptors with experimentally-derived measures of human intestinal absorption. A genetic algorithm was then used to select important molecular descriptors. Intestinal absorption values (IA%) were used as the ANN's output and calculated molecular descriptors as the inputs. The best genetic neural network (GNN) model with 15 input descriptors was chosen, and the significance of the selected descriptors for intestinal absorption examined. Results obtained with the model that was developed indicate that lipophilicity, conformational stability and inter-molecular interactions (polarity, and hydrogen bonding) have the largest impact on intestinal absorption.

Artificial Intelligence↗

PubMed2001

The use of a response surface methodology on HPLC analysis of methyldopa, amiloride and hydrochlorothiazide in tablets.

A multifactor optimisation technique is successfully applied to develop a new HPLC method in which methyldopa, hydrochlorothiazide and amiloride were analysed and determined on a C18 column with detection at 286 nm. The optimal conditions of HPLC separation were determined with the aid of the response surface diagram -- 'window diagram'. The effect of simultaneously varying the pH, proportion aqueous acetic acidum and methanol in the mobile phase were studied to optimise the separation. The mobile phase composition that provides an acceptable resolution methyldopa, hydrochlorothiazide and amiloride in a short elution time is water--methanol (75:25) and pH 3.60. The k' values for methyldopa, hydrochlorothiazide and amiloride after optimisation were 1.40, 2.50 and 5.33, respectively. Relative retention (alpha) for ratio hydrochlorothiazide/methyldopa and amiloride/hydrochlorothiazide were 1.767 and 2.159, respectively. Correlation coefficients of the calibration curves for all analytes were greater than 0.995 and the R.S.D. values for the slope and the intercept with respect to the linearity were less than 2%. A method is applied for the quantitative analysis of Alatan tablets (Lek-Ljubljana). The powdered tablets are extracted with methanol, containing caffeine as the internal standard and assayed by comparison of peak areas after liquid chromatography. The high recovery (for all analytes about 100%) and the low R.S.D. (<2%) confirm good precision and reproducibility of the chromatographic method.

Amiloride↗

PubMed2001

Determination of polymorphic forms of ranitidine-HCl by DRIFTS and XRPD.

The identification, characterization and quantification of crystal forms are becoming increasingly important within the pharmaceutical industry. A combination of different physical analytical techniques is usually necessary for this task. In this work solid-state techniques, diffuse reflectance infrared Fourier transform spectroscopy (DRIFTS) and X-ray powder diffractometry (XRPD) were combined to analyze polymorphic purity of crystalline ranitidine-HCl, an antiulcer drug, H2 receptor antagonists. A series of 12 different mixtures of Form 1 and 2 was prepared by geometric mixing and their DRIFT spectra and XRD powder patterns were obtained and analyzed, either alone or combined together, using Artificial Neural Networks (ANNs). A standard feed-forward network, with back-propagation rule and with multi layer perceptron architecture (MPL) was chosen. A working range of 1.0-100% (w/w) of crystal Form 2 in Form 1 was established with a minimum quantifiable level (MQL) of 5.2% and limit of detection of 1.5% (w/w). The results demonstrate that DRIFTS combined with XRPD may be successfully used to distinguish between the ranitidine-HCl polymorphs and to quantify the composition of binary mixtures of the two.

Ranitidine↗

PubMed2001

ANN modeling of the penetration across a polydimethylsiloxane membrane from theoretically derived molecular descriptors.

A quantitative structure-permeability relationship was developed using Artificial Neural Network (ANN) modeling to study penetration across a polydimethylsiloxane membrane. A set of 254 compounds and their experimentally derived maximum steady state flux values used in this study was gathered from the literature. A total of 42 molecular descriptors were calculated for each compound. A genetic algorithm was used to select important molecular descriptors and supervised ANN was used to correlate selected descriptors with the experimentally derived maximum steady-state flux through the polydimethylsiloxane membrane (log J). Calculated molecular descriptors were used as the ANN's inputs and log J as the output. Developed model indicates that molecular shape and size, inter-molecular interactions, hydrogen-bonding capacity of drugs, and conformational stability could be used to predict drug absorption through skin. A 12-descriptor nonlinear computational neural network model has been developed for the estimation of log J values for a data set of 254 drugs. Described model does not require experimental parameters and could potentially provide useful prediction of membrane penetration of new drugs and reduce the need for actual compound synthesis and flux measurements.

Algorithms↗

PubMed2001

Role of genetic algorithms and artificial neural networks in predicting the phase behavior of colloidal delivery systems.

PURPOSE: A genetic neural network (GNN) model was developed to predict the phase behavior of microemulsion (ME), lamellar liquid crystal (LC), and coarse emulsion forming systems (W/O EM and O/W EM) depending on the content of separate components in the system and cosurfactant nature. METHOD: Eight pseudoternary phase triangles, containing ethyl oleate as the oil component and a mixture of two nonionic surfactants and n-alcohol or 1,2-alkanediol as a cosurfactant, were constructed and used for training, testing, and validation purposes. A total of 21 molecular descriptors were calculated for each cosurfactant. A genetic algorithm was used to select important molecular descriptors, and a supervised artificial neural network with two hidden layers was used to correlate selected descriptors and the weight ratio of components in the system with the observed phase behavior. RESULTS: The results proved the dominant role of the chemical composition, hydrophile-lipophile balance, length of hydrocarbon chain, molecular volume, and hydrocarbon volume of cosurfactant. The best GNN model, with 14 inputs and two hidden layers with 14 and 9 neurons, predicted the phase behavior for a new set of cosurfactants with 82.2% accuracy for ME, 87.5% for LC, 83.3% for the O/W EM, and 91.5% for the W/O EM region. CONCLUSIONS: This type of methodology can be applied in the evaluation of the cosurfactants for pharmaceutical formulations to minimize experimental effort.

Algorithms↗

PubMed2000

Effects of alcohols and diols on the phase behaviour of quaternary systems.

The aim of the current study was to investigate the effect of different co-surfactants on the phase behaviour of the pseudoternary system water:ethyl oleate:nonionic surfactant blend (sorbitan monolaurate/polyoxyethylene 20 sorbitan mono-oleate). Four aliphatic alcohols (1-propanol, 1-butanol, 1-hexanol and 1-octanol) and four 1, 2-alkanediols (1,2-propanediol, 1,2-pentanediol, 1,2-hexanediol and 1,2-octanediol) were used. The co-surfactant-free system forms two different colloidal structures, a water-in-oil microemulsion (w/o ME) and lamellar liquid crystals (LC) and two coarse dispersions, water-in-oil (w/o EM) and oil-in-water (o/w EM) emulsions. Microemulsion region area (%ME), liquid crystalline region area (%LC), amount of amphiphile blend required to produce a balanced microemulsion (%AMPH) and amount of water solubilised (%W) were used as assessment criteria to evaluate the co-surfactants. Seven calculated physico-chemical descriptors were used to represent the different co-surfactants. 1-butanol, 1,2-hexanediol and 1, 2-octanediol produced balanced MEs capable of solubilising a high percentage of both oil and water. A similarity was observed between the descriptors attributed to 1-butanol and 1,2-hexanediol. The requirements of a co-surfactant molecule to produce a balanced microemulsion were: HLB value 7.0-8.0, a carbon backbone of 4-6 atoms, percentage carbon of 60-65%, percentage oxygen of 20-30%, logP value 0.2-0.9 and log 1/S (S: aqueous solubility) close to zero.

1-Butanol↗

PubMed2000

Ranitidine hydrochloride X-ray assay using a neural network.

A simple X-ray powder diffractometric (XRD) method with artificial neural networks (ANNs) for data modelling was developed to recognize and quantify two crystal modifications of ranitidine HCl in mixtures and thus, provide information about the solid state of the bulk drug. The method was also used to quantify ranitidine HCl from tablets in the presence of other components. An ANN consisting of three layers of neurons was trained by using a back-propagation learning rule. A sigmoid output function was used in the hidden layer to facilitate non-linear fitting. Unlike other techniques the ANN method described here employed pattern recognition on the entire XRD pattern. Correct classification was mainly influenced by the XRD pattern resolution. It was shown that data transformations improved the quantitative performance when the XRD patterns were not contaminated by other components. Only smoothed X-ray diffractograms were required to distinguish between the two crystalline forms in a mixture. In the case of ranitidine-HCl quantification from tablets, where significant interference with tablet excipients was present, better results were obtained without data transformations. The trained ANN perfectly quantified ranitidine HCI polymorphic forms from mixtures (mean sum of squared error was less than 0.02%) and ranitidine HCl form 1 from tablets (recovery = 98.65). Excellent quantification performance of the ANN analysis. demonstrated in this study, serves as an indication of the broad potential of neural networks in pattern analysis. While the system described has been developed to interpret XRD patterns, peak detection has implications in every chemical application where the recognition of peak-shaped signals in analytical data is important.

Anti-Ulcer Agents↗

PubMed2000

Statistical optimization of a reversed-phase liquid chromatographic method for the analysis of amiloride and hydrochlorothiazide in tablets.

A method has been developed for the separation of hydrochlorothiazide and amiloride by high-performance liquid chromatographic (HPLC) method on a C18 column with detection at 280 nm. The optimal conditions of separation were determined with the aid of 'window diagram' technique of Laub and Purnell. The effect of simultaneously varying the pH, proportion aqueous acetic acid and methanol in the mobile phase were studied to optimize the separation. A response surface diagram was used to optimize the experimental conditions for the separation. The mobile phase composition that provides an acceptable resolution hydrochlorothiazide and amiloride in a short elution time is water:methanol (60:40) and pH 3.2 (pH adjusted to 3.2 with CH3COOH). A method is applied for the quantitative analysis of Moduretic tablets (Merck Sharp & Dokme International). The powdered tablets are extracted with methanol, containing caffeine as the internal standard, and assayed by comparison of peak areas after liquid chromatography.

Amiloride↗

PubMed2000

Basic concepts of artificial neural network (ANN) modeling and its application in pharmaceutical research.

Artificial neural networks (ANNs) are biologically inspired computer programs designed to simulate the way in which the human brain processes information. ANNs gather their knowledge by detecting the patterns and relationships in data and learn (or are trained) through experience, not from programming. An ANN is formed from hundreds of single units, artificial neurons or processing elements (PE), connected with coefficients (weights), which constitute the neural structure and are organised in layers. The power of neural computations comes from connecting neurons in a network. Each PE has weighted inputs, transfer function and one output. The behavior of a neural network is determined by the transfer functions of its neurons, by the learning rule, and by the architecture itself. The weights are the adjustable parameters and, in that sense, a neural network is a parameterized system. The weighed sum of the inputs constitutes the activation of the neuron. The activation signal is passed through transfer function to produce a single output of the neuron. Transfer function introduces non-linearity to the network. During training, the inter-unit connections are optimized until the error in predictions is minimized and the network reaches the specified level of accuracy. Once the network is trained and tested it can be given new input information to predict the output. Many types of neural networks have been designed already and new ones are invented every week but all can be described by the transfer functions of their neurons, by the learning rule, and by the connection formula. ANN represents a promising modeling technique, especially for data sets having non-linear relationships which are frequently encountered in pharmaceutical processes. In terms of model specification, artificial neural networks require no knowledge of the data source but, since they often contain many weights that must be estimated, they require large training sets. In addition, ANNs can combine and incorporate both literature-based and experimental data to solve problems. The various applications of ANNs can be summarised into classification or pattern recognition, prediction and modeling. Supervised 'associating networks can be applied in pharmaceutical fields as an alternative to conventional response surface methodology. Unsupervised feature-extracting networks represent an alternative to principal component analysis. Non-adaptive unsupervised networks are able to reconstruct their patterns when presented with noisy samples and can be used for image recognition. The potential applications of ANN methodology in the pharmaceutical sciences range from interpretation of analytical data, drug and dosage form design through biopharmacy to clinical pharmacy.

Algorithms↗

PubMed1999

Powder diffractometric assay of two polymorphic forms of ranitidine hydrochloride.

A simple X-ray powder diffractometric method was developed for the qualitative and quantitative assay of the two crystalline modifications of ranitidine-HCl. The main purpose of the present work was to investigate if artificial neural networks (ANNs) could be applied in quantitative X-ray diffractometric analyses. The ANN approach was compared with a conventional mixture design method. The results obtained by the ANN had a smaller standard deviation and relative error and a better precision at lower concentrations. ANNs provide a simple alternative to conventional statistical modelling methods to identify the non-linear relationship without complex equations.

Anti-Ulcer Agents↗

PubMed1999

Use of artificial neural networks to predict quaternery phase systems from limited experimental data.

The aim of the present work was to develop a method for predicting the phase behaviour of four component systems consisting of oil, water and two surfactants from a limited number of screening experiments. Investigations were conducted to asses the potential of artificial neural networks (ANNs) with back-propagation training algorithm to predict the phase behaviour of four component systems. Three inputs only (percentages of oil and water and HLB of the surfactant blend) and four outputs (oil in water emulsion, water in oil emulsion, microemulsion, and liquid crystals containing regions) were used. Samples used for training represented about 15% of the sampling space within the tetrahedron body. The network was trained by performing optimization of the number and size of the weights for neuron interconnections. The lowest error was obtained with 15 hidden neurons and after 4,500 training cycles. The trained ANN was tested on validation data and had an accuracy of 85.2-92.9%. In most cases the errors in the prediction were confined to points lying along the boundaries of regions and for the extrapolated predictions outside the ANNs 'experience'. This approach is shown to be highly successful and the ANNs have proven to be a useful tool for the prediction of the phase behaviour of quaternary systems with less experimental effort.

Hexoses↗

PubMed1999

Use of ANN modelling in structure--retention relationships of diuretics in RP-HPLC.

Structure retention relationship study, conducted by RP HPLC, was used to investigate physical chemical parameters related to the RP retention times of amiloride, hydrochlorothiazide and methyldopa in order to predict the separation of amiloride and methylclothiazide from Lometazid tablets. Retention data were obtained with an ODS column using a mobile phase methanol water (pH adjusted with phosphoric acid). Physical chemical properties were calculated directly from the molecular structure. Artificial neural networks (ANNs) were used to correlate chromatograms retention times with mobile phase composition and pH, and with physical chemical properties of amiloride, hydrochlorothiazide and methyldopa and to predict separation of amiloride and methylclothiazide from Lometazid tablets. Sensitivity analysis was performed to interpret the meaning of the descriptors included in the models. Results confirmed the dominant role of the polar modifier in such chromatographic systems. Within a series of solutes chromatographed under identical conditions, the retention parameters could be approximated by a non-linear combination of logP, logD, pKa, surface tension, parachor, molar volume and to minor extend by polarisability, reetractivity index and density. This study has demonstrated that the use ANNs techniques can result in much more efficient use of experimental information. As HPLC is the most popular analytical technique, improvements in HPLC methods development can yield significant gains in the overall analytical effort. The ANNs extension presented could be the method of choice in some advanced research settings and serves as an indication of the broad potential of neural networks in chromatography analysis.

Chromatography, High Pressure Liquid↗