Snezana Agatonovic-Kustrin

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.

Application of neural networks for response surface modeling in HPLC optimization

The usefulness of artificial neural networks for response surface modeling in HPLC optimization is compared with multiple regression methods. The results show that neural networks offer promising possibilities in HPLC method development. The predicted capacity factors of analytes were better to those obtained with multiple regression method.

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 ANNs 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.

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.

Prediction of drug transfer into human milk from theoretically derived descriptors

Abstract The goal of this study was to develop a genetic neural network (GNN) model to predict the degree of drug transfer into breast milk, depending on the molecular structure descriptors, and to compare it with the current model. A supervised network with back-propagation learning rule and multilayer perceptron (MLP) architecture was used to correlate activity with descriptors that were preselected by a genetic algorithm. The set of 60 drug compounds and their experimentally derived M / P values used in this study were gathered from literature. A total of 61 calculated structural features including constitutional, topological, chemical, geometrical and quantum chemical descriptors were generated for each of the 60 compounds. The M / P values were used as the ANNs output and calculated molecular descriptors as the inputs. The best GNN model with 26 input descriptors is presented, and the chemical significance of the chosen descriptors is discussed. Strong correlation of predicted versus experimentally derived M / P values ( R 2 >0.96) for the best ANN model (26-5-5-1) confirms that there is a link between structure and M / P values. The strength of the link is measured by the quality of the external prediction set. With the RMS error of 0.425 and a good visual plot, the external prediction set ensures the quality of the model. Unlike previously reported models, the GNN model described here does not require experimental parameters and could potentially provide useful prediction of M / P ratio of new potential drugs and reduce the need for actual compound synthesis and experimental M / P ratio determination.

Bioavailability Prediction Based on Molecular Structure for a Diverse Series of Drugs

AbstractPurpose. Radial basis function artificial neural networks and theoretical descriptors were used to develop a quantitative structure-pharmacokinetic relationship for structurally diverse drug compounds. Methods. Human bioavailability values were taken from the literature and descriptors were generated from the drug structures. All models were trained with 137 compounds and tested with a further 15, after which they were evaluated for predictive ability with an additional 15 compounds. Results. The final model possessed a 10-31-1 topology and training and testing correlation coefficients were 0.736 and 0.897, respectively. Predictions for independent compounds agreed well with experimental literature values, especially for compounds that were well absorbed and/or had high observed bioavailability. Important theoretical descriptors included solubility parameters, electronic descriptors, and topological indices. Conclusions. Useful information regarding drug bioavailability was gained from drug structure alone, reducing the need for experimental methods in drug development.

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 networks 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.

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 ANNs 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 infants response.

Prediction of drug bioavailability based on molecular structure

Oral dosing is the most common method of drug administration, and final plasma concentrations of the drug depend upon its bioavailability. In the current study, a quantitative structure–pharmacokinetic relationship (QSPR) was developed for a diverse range of compounds to allow prediction of drug bioavailability. Bioavailability data for 169 compounds was taken from the literature, and from the molecular structures 94 theoretical descriptors were generated. Stepwise regression was employed to develop a regression equation based on 159 training compounds, and predictive ability was tested on 10 compounds reserved for that purpose. The final regression equation included eight descriptors that represented electronic, steric, hydrophobic and constituent parameters of the drug molecules, all of which could be related to solubility and partitioning properties. Predicted bioavailability for the training set agreed more closely for drugs exhibiting mid-range literature bioavailability values. A correlation of 0.72 was achieved for test set bioavailability predictions when compared with literature values. The structure–pharmacokinetic relationship developed in the current study highlighted solubility and partitioning characteristics that may be useful in designing drugs with appropriate bioavailability.

Analysing the crystal purity of mebendazole raw material and its stability in a suspension formulation.

The objective of this study was to develop a simple, direct and non-destructive method to assess crystal purity of mebendazole raw material and to establish its stability in a suspension formulation using diffuse reflectance ultraviolet (DRA-UV) spectroscopy and attenuated total reflectance-Fourier transform infrared (ATR-FTIR) spectroscopy. Quantitation of mebendazole, found to exhibit polymorphism with three polymorphic forms A, B and C identified, was carried out with ATR-FTIR spectroscopy. Artificial neural network (ANN) was employed as a data-modelling tool. The developed ANN models confirmed that the characteristic absorptions in the infrared (IR) spectral region are directly proportional to the measured amounts of mebendazole crystal forms present in the samples (r(2)>0.94), which was confirmed with X-ray diffraction (XRD) at r(2)>0.97. These models also predicted that the mebendazole raw material contained 7.21+/-1.25% (ATR-FTIR data) and 10.38+/-0.18% (XRD data) of form A as an impurity. ATR-FTIR data for the suspension formulation showed some dissolution of form C and recrystalisation as the more stable form A. These quantitative results obtained for the binary crystal form mixtures clearly demonstrate the strong potential of ATR-FTIR for use in the determination of the polymorphic content not only in bulk pharmaceuticals but also in liquid formulations.