Development and optimization of an HPLC analysis of citalopram and its four nonchiral impurities using experimental design methodology

In this study, the RP-HPLC method was investigated for the separation of citalopram and its four impurities by use of statistical experimental design. Initially, the influence of different experimental conditions (buffer pH, flow rate, and column temperature) on the chromatographic behavior of citalopram and its four impurities was investigated by use of partial least squares regression (PLSR) and multilayer perceptron (MLP) artificial neural networks (ANNs) trained by back-propagation. The developed models and the corresponding response surface plots were used to select the optimal HPLC conditions, buffer pH 7.0, flow rate 1.0 mL/ min, and column temperature 25 degrees C, for an efficient separation of citalopram and its four impurities. The elaborated HPLC method was found to be linear, specific, sensitive, precise, accurate, and robust. Retention times of citalopram and its impurities, obtained with the developed HPLC method, and the computed molecular parameters of the examined compounds were used in a quantitative structure retention relationship (QSRR) study. The PLSR and ANN algorithms were applied for the development of the QSRR methods. The MLP-two layers-ANN-QSRR model with root mean square error of prediction 0.105 and r(2) (observed versus predicted) 0.978 was selected. Since many different reaction conditions are applied for the synthesis of citalopram, different impurities and degradation products can be formed. Therefore, the developed QSRR model can be extended to the prediction of the retention times with the other citalopram impurities, degradation products, and metabolites.     

Development of an inorganic cations retention model in ion chromatography by means of artificial neural networks with different two-phase training algorithms

This paper describes development of artificial neural network (ANN) retention model, which can be used for method development in variety of ion chromatographic applications. By using developed retention model it is possible both to improve performance characteristic of developed method and to speed up new method development by reducing unnecessary experimentation. Multilayered feed forward neural network has been used to model retention behaviour of void peak, lithium, sodium, ammonium, potassium, magnesium, calcium, strontium and barium in relation with the eluent flow rate and concentration of methasulphonic acid (MSA) in eluent. The probability of finding the global minimum and fast convergence at the same time were enhanced by applying a two-phase training procedure. The developed two-phase training procedure consists of both first and second order training. Several training algorithms were applied and compared, namely: back propagation (BP), delta-bar-delta, quick propagation, conjugate gradient, quasi Newton and Levenberg-Marquardt. It is shown that the optimized two-phase training procedure enables fast convergence and avoids problems arisen from the fact that every new weight initialization can be regarded as a new starting position and yield irreproducible neural network if only second order training is applied. Activation function, number of hidden layer neurons and number of experimental data points used for training set were optimized in order to insure good predictive ability with respect to speeding up retention modelling procedure by reducing unnecessary experimental work. The predictive ability of optimized neural networks retention model was tested by using several statistical tests. This study shows that developed artificial neural network are very accurate and fast retention modelling tool applied to model varied inherent non-linear relationship of retention behaviour with respect to mobile phase parameters.     

Evaluation of separation in gradient elution ion chromatography by combining several retention models and objective functions

In this work, three different methods for modeling of gradient retention were combined with several optimization objective functions in order to find the most appropriate combination to be applied in ion chromatography method development. The system studied was a set of seven inorganic anions (fluoride, chloride, nitrite, sulfate, bromide, nitrate, and phosphate) with a KOH eluent. The retention modeling methods tested were multilayer perceptron artificial neural network (MLP-ANN), radial-basis function artificial neural network (RBF-ANN), and retention model based on transfer of data from isocratic to gradient elution mode. It was shown that MLP retention model in combination with the objective function based on normalized retention difference product was the most adequate tool for optimization purposes.     

Artificial neural networks in analysis of indinavir and its degradation products retention

Artificial neural networks (ANN) are biologically inspired computer programs designed to simulate the way in which the human brain processes the information. In the past few years, coupling of experimental design (ED) and ANN became useful tool in the method optimization. This paper presents the application of ED-ANN in analysis of chromatographic behavior of indinavir and its degradation products. According to preliminary study, full factorial design 2⁴ was chosen to set input variables for network training. Experimental data (inputs) and results for retention factors from experiments (outputs) were used to train the ANN with aim to define correlation among variables. For networks training multi-layer perceptron (MLP) with back propagation (BP) algorithm was used. Network with the lowest root mean square (RMS) had 4-8-3 topology. Predicted data were in good agreement with experimental data (correlation was higher than 0.9713 for training set). Regression statistics confirmed good ability of trained network to predict compounds retention.     

Application of artificial neural networks for gradient elution retention modelling in ion chromatography

Gradient elution in ion chromatography (IC) offers several advantages: total analysis time can be significantly reduced, overall resolution of a mixture can be increased, peak shape can be improved (less tailing) and effective sensitivity can be increased (because there is little variation in peak shape). More importantly, it provides the maximum resolution per time unit. The aim of this work was the development of a suitable artificial neural network (ANN) gradient elution retention model that can be used in a variety of applications for method development and retention modelling of inorganic anions in IC. Multilayer perceptron ANNs were used to model the retention behaviour of fluoride, chloride, nitrite, sulphate, bromide, nitrate and phosphate in relation to the starting time of gradient elution and the slope of the linear gradient elution curve. The advantage of the developed model is the application of an optimized two-phase training algorithm that enables the researcher to make use of the advantages of first- and second-order training algorithms in one training procedure. This results in better predictive ability, with less time required for the calculations. The number of hidden layer neurons and experimental data points used for the training set were optimized in terms of obtaining a precise and accurate retention model with respect to minimization of unnecessary experimentation and time needed for the calculation procedures. This study shows that developed, ANNs are the method of first choice for retention modelling of inorganic anions in IC.     

Artificial Neural Networks in water analysis: Theory and applications

Artificial Neural Networks (ANNs) have seen an explosion of interest over the last two decades and have been successfully applied in all fields of chemistry and particularly in analytical chemistry. Inspired from biological systems and originated from the perceptron, i.e. a program unit that learns concepts, ANNs are capable of gradual learning over time and modelling extremely complex functions. In addition to the traditional multivariate chemometric techniques, ANNs are often applied for prediction, clustering, classification, modelling of a property, process control, procedural optimisation and/or regression of the obtained data. This paper aims at presenting the most common network architectures such as Multi-layer Perceptrons (MLPs), Radial Basis Function (RBF) and Kohonen's self-organisations maps (SOM). Moreover, back-propagation (BP), the most widespread algorithm used today and its modifications, such as quick-propagation (QP) and Delta-bar-Delta, are also discussed. All architectures correlate input variables to output variables through non-linear, weighted, parameterised functions, called neurons. In addition, various training algorithms have been developed in order to minimise the prediction error made by the network. The applications of ANNs in water analysis and water quality assessment are also reviewed. Most of the ANNs works are focused on modelling and parameters prediction. In the case of water quality assessment, extended predictive models are constructed and optimised, while variables correlation and significance is usually estimated in the framework of the predictive or classifier models. On the contrary, ANNs models are not frequently used for clustering/classification purposes, although they seem to be an effective tool. ANNs proved to be a powerful, yet often complementary, tool for water quality assessment, prediction and classification.     

Artificial neural networks modeling in ultra performance liquid chromatography method optimization of mycophenolate mofetil and its degradation products

The study of experimental design in conjunction with artificial neural networks for optimization of isocratic ultra performance liquid chromatography method for separation of mycophenolate mofetil and its degradation products has been reported. Experimental design showed to be suitable for selection of experimental scheme, while Kennard‐Stone algorithm was used for selection of training data set. The input variables were column temperature and composition of mobile phase including percentage of acetonitrile, concentration of ammonium acetate in buffer, and its pH value. The retention factor of the most retentive component and selectivity factors were used as the dependent variables (outputs). In this way, artificial neural network has been applied as a predictable tool in solving a method optimization problem using small number of experiments. Network architecture and training parameters were optimized to the lowest root‐mean‐square error values, and the network with 5‐4‐4‐4 topology has been selected as the most predictable one. Predicted data were in good agreement with experimental data, and regression statistics confirmed good ability of trained network to predict compounds retention. The optimal chromatographic conditions included column temperature of 40°C, flow rate of 700 µl min⁻¹, 26% of acetonitrile and 9 mM ammonium acetate in mobile phase, and buffer pH of 5.87. The chromatographic analysis has been achieved within 5.2 min. The validation of the proposed method was also performed considering selectivity, linearity, accuracy, precision, limit of detection, and limit of quantification, and the results indicated that the method fulfilled all required criteria. The method was successfully applied to the analysis of commercial dosage form. Copyright © 2014 John Wiley & Sons, Ltd.     

Factorial Design in Optimization of Chromatographic Separation of Ramipril and Its Impurities

In this paper optimization of chromatographic retention of ramipril and its five impurities employing factorial design is presented. On the basis of preliminary experiments three factors were chosen as inputs (acetonitrile content, pH of the mobile phase and buffer concentration) and retention factor as output. As optimal full factorial design 2³ was chosen, factors were examined at two different levels “low” and “high”. Three replications at zero level were added in order to check linearity and complete statistical tests. Relationship between inputs and output is presented in form of second order interaction model. Adequacy of model was explained using analysis of variance. After analysis of results optimal chromatographic conditions were set. Separations were conducted on a C₁₈ column with a mixture of acetonitrile and water phase (TEA in potassium dihydrogen phosphate) in ratio 23:77 v/v. Finally, the LC method was validated and applied for quality control analysis of commercially available tablets. The proposed method is simpler and faster as compared to existing official methods and therefore more adequate for routine control of ramipril during shelf life. Also a general approach which includes factorial design in method optimization offers a possibility for predicting and following the chromatographic behavior of such complex mixtures.     

Monitoring of fosinopril sodium impurities by liquid chromatography-mass spectrometry including the neural networks in method evaluation

In this paper, the mass spectrometry (MS) detection has been applied for screening of fosinopril sodium impurities which arise during forced stress study. Before MS analysis, liquid chromatographic method with suitable mobile phase composition was developed. The separation was done on SunFire 100 mm x 4.6 mm 3.5 microm particle size column. The mobile phases which consisted of methanol-ammonium acetate buffer-acetic acid, in different ratios, were used in a preliminary study. Flow rate was 0.3 mL min(-1). Under these conditions, percent of methanol, concentration of ammonium acetate buffer and acetic acid content were tested simultaneously applying central composite design (CCD) and artificial neural network (ANN). The combinations of experimental design (ED) and ANN present powerful technique in method optimization. Input and output variables from CCD were used for network training, verification and testing. Multiple layer perceptron (MLP) with back propagation (BP) algorithm was chosen for network training. When the optimal neural topology was selected, network was trained by adjusting strength of connections between neurons in order to adapt the outputs of whole network to be closer to the desired outputs, or to minimize the sum of the squared errors. From the method optimization the following mobile phase composition was selected as appropriate: methanol-10 mM ammonium acetate buffer-acidic acid (80:19.5:0.5 v/v/v). This mobile phase was used as inlet for MS. According to molecular structure and literature data, electrospray positive ion mode was applied for analysis of fosinopril sodium and its impurities. The proposed method could be used for screening of fosinopril sodium impurities in bulk and pharmaceuticals, as well as for tracking the degradation under stress conditions.     

Heteroscedasticity of retention factor and adequate modeling in micellar liquid chromatography

The two concepts of micelle formation (pseudo-phase and mass-action) could be the basis of retention models in micellar liquid chromatography (MLC). The separation of 4-hydroxybenzoic acid esters and seven polyaromatic hydrocarbons were performed to study the repeatability of retention factor in MLC. The full two factor experimental design was used for studying the dependence of retention factor variance on mobile phase composition (sodium dodecylsulfate, 1-butanol). The experimentally observed heteroscedasticity and perturbations after linearization were taken into account by using statistical weights obtained on the basis of errors propagation law and the modeling of retention by non-weighted and weighted least squares method was performed. The mechanistical retention models based on pseudo-phase and mass-action concepts of micelle formation were compared by fitting quality and prediction capability and high robustness of bilogarithmic dependence was observed. The significance of retention factor heteroscedasticity for retention hydrophobicity relationships was shown.     

Chromatographic Behavior of Aromatic Diamines in Hydro-Organic, Micellar and Submicellar Reversed Phase Liquid Chromatographic Modes

The chromatographic behavior (retention, elution strength, efficiency, peak asymmetry and selectivity) of some aromatic diamines in the presence of methanol with or without anionic surfactant SDS in the four different reversed phased liquid chromatographic (RPLC) modes, i.e., hydro-organic, micellar (MLC), low submicellar (LSC) and high submicellar (HSC), was investigated. In the three surfactant-mediated modes, the surfactant monomers coat the stationary phase even up to 70 % methanol; this results in the suppression of peak tailing (by masking the silanol groups on the stationary phase). In MLC and HSC, the solute retention decreases by increasing the surfactant concentration, while this situation was reversed in LSC. In the region between MLC and HSC modes (25–50 % methanol), retention of late eluting solutes was increased by increasing methanol content which is seemingly due to disaggregation of SDS micelles. Changes in selectivity were observed after changing the concentrations of SDS and methanol, in a greater extent when concentration of SDS was changed. Among the four studied RPLC modes, HSC showed the best efficiency with nearly symmetrical peaks. Prediction of retention of solutes in HSC based on a mechanistic retention model combined with Pareto-optimality method allowed the full resolution of target diamines in practical analysis times.     

Optimization of artificial neural networks used for retention modelling in ion chromatography

The aim of this work is the development of an artificial neural network model, which can be generalized and used in a variety of applications for retention modelling in ion chromatography. Influences of eluent flow-rate and concentration of eluent anion (OH-) on separation of seven inorganic anions (fluoride, chloride, nitrite, sulfate, bromide, nitrate, and phosphate) were investigated. Parallel prediction of retention times of seven inorganic anions by using one artificial neural network was applied. MATLAB Neural Networks ToolBox was not adequate for application to retention modelling in this particular case. Therefore the authors adopted it for retention modelling by programming in MATLAB metalanguage. The following routines were written; the division of experimental data set on training and test set; selection of data for training and test set; Dixon's outlier test; retraining procedure routine; calculations of relative error. A three-layer feed forward neural network trained with a Levenberg-Marquardt batch error back propagation algorithm has been used to model ion chromatographic retention mechanisms. The advantage of applied batch training methodology is the significant increase in speed of calculation of algorithms in comparison with delta rule training methodology. The technique of experimental data selection for training set was used allowing improvement of artificial neural network prediction power. Experimental design space was divided into 8-32 subspaces depending on number of experimental data points used for training set. The number of hidden layer nodes, the number of iteration steps and the number of experimental data points used for training set were optimized. This study presents the very fast (300 iteration steps) and very accurate (relative error of 0.88%) retention model, obtained by using a small amount of experimental data (16 experimental data points in training set). This indicates that the method of choice for retention modelling in ion chromatography is the artificial neural network.     

Hybridized particle swarm algorithm for adaptive structure training of multilayer feed-forward neural network: QSAR studies of bioactivity of organic compounds

The multilayer feed-forward ANN is an important modeling technique used in QSAR studying. The training of ANN is usually carried out only to optimize the weights of the neural network and without paying attention to the network topology. Some other strategies used to train ANN are, first, to discover an optimum structure of the network, and then to find weights for an already defined structure. These methods tend to converge to local optima, and may also lead to overfitting. In this article, a hybridized particle swarm optimization (PSO) approach was applied to the neural network structure training (HPSONN). The continuous version of PSO was used for the weight training of ANN, and the modified discrete PSO was applied to find appropriate the network architecture. The network structure and connectivity are trained simultaneously. The two versions of PSO can jointly search the global optimal ANN architecture and weights. A new objective function is formulated to determine the appropriate network architecture and optimum value of the weights. The proposed HPSONN algorithm was used to predict carcinogenic potency of aromatic amines and biological activity of a series of distamycin and distamycin-like derivatives. The results were compared to those obtained by PSO and GA training in which the network architecture was kept fixed. The comparison demonstrated that the HPSONN is a useful tool for training ANN, which converges quickly towards the optimal position, and can avoid overfitting in some extent.     

Isolation of oxidative degradation products of atorvastatin with supercritical fluid chromatography

The isolation of four oxidative degradation products of atorvastatin using preparative high‐performance liquid chromatography applying at least two chromatographic steps is known from the literature. In this paper it is shown that the same four impurities could be isolated from similarly prepared mixtures in only one step using supercritical fluid chromatography. The methods for separation were developed and optimized. The preparation of the mixtures was altered in such a way as to enhance the concentration of desired impurities. Appropriate solvents were applied for collection of separated impurities in order to prevent degradation. The structures of the isolated impurities were confirmed and their purity determined. The preparative supercritical fluid chromatography has proven to be superior to preparative HPLC regarding achieved purity of standards applying fewer chromatographic as well as isolation steps. Copyright © 2015 John Wiley & Sons, Ltd.     

Influence of selected mobile phase properties on the TLC retention behavior of ziprasidone and its impurities

ABSTRACT

Influence of nine different solvents, either alone or in a mixture, on the retention behavior of ziprasidone and its five impurities were examined by normal-phase thin-layer chromatography. Migration distances of the examined compounds obtained under the examined chromatographic conditions were correlated with calculated mobile phase properties, such as Snyder polarity and Hansen solubility. Linear or second-order polynomial relationships with high correlation coefficients were established between investigated variables. The obtained mathematical functions and statistical results indicated that selected mobile phase properties can be used for the prediction of the retention behavior of ziprasidone and its five impurities.     

制备液相色谱法分离纯化碘帕醇

基于制备液相色谱法,开发与优化了碘帕醇的分离纯化工艺,制备得到高纯度碘帕醇样品。实验首先在分析水平发展碘帕醇的反相分离方法,考察了两种不同键合量的反相C18固定相、柱温和上样量对碘帕醇的保留、分离度和峰形等的影响。结果表明,碘帕醇在键合量为13.7%的反相C18-1分析柱（250 mm×4.6 mm,10 μm）上保留较好,且可与杂质有效分离;柱温升高,碘帕醇保留变弱,和杂质之间的分离度降低,最终选用20~25℃作为分离纯化的温度;上样量增加,碘帕醇出峰时间提前,不利于前杂的去除。在制备水平上,以水和甲醇为洗脱剂,在20℃条件下使用装填C18-1固定相的制备柱（270 mm×50 mm,10 μm）对碘帕醇进行分离纯化,制备的碘帕醇样品的色谱纯度可达98.97%,回收率为93.44%,各项有关物质均符合限量规定。该方法可以在保证高回收率的条件下有效降低杂质水平,为碘帕醇分离纯化生产工艺的开发提供新方法。