Prediction of Chromatographic Retention of Pyrazine and Alkylpyrazines in RP-LC

Retention prediction models for a group of pyrazines chromatographed under reversed-phase mode were developed using multiple linear regression (MLR) and artificial neural networks (ANNs). Using MLR, the retention of the analytes were satisfactorily described by a two-predictor model based on the logarithm of the partition coefficient of the analytes (log P) and the percentage of the organic modifier in the mobile phase (ACN or MeOH). ANN prediction models were also derived using the predictors derived from MLR as inputs and log k as outputs. The best network architecture was found to be 2-2-1 for both ACN and MeOH data sets. The optimized ANNs showed better predictive properties than the MLR models especially for the ACN data set. In the case of the MeOH data set, the MLR and ANN models have comparable predictive performance.     

Determination of the n‐octanol/water partition coefficients of weakly ionizable basic compounds by reversed‐phase high‐performance liquid chromatography with neutral model compounds

A strategy to utilize neutral model compounds for lipophilicity measurement of ionizable basic compounds by reversed‐phase high‐performance liquid chromatography is proposed in this paper. The applicability of the novel protocol was justified by theoretical derivation. Meanwhile, the linear relationships between logarithm of apparent n‐octanol/water partition coefficients (logK_ow′′) and logarithm of retention factors corresponding to the 100% aqueous fraction of mobile phase (logk_w) were established for a basic training set, a neutral training set and a mixed training set of these two. As proved in theory, the good linearity and external validation results indicated that the logK_ow′′–logk_w relationships obtained from a neutral model training set were always reliable regardless of mobile phase pH. Afterwards, the above relationships were adopted to determine the logK_ow of harmaline, a weakly dissociable alkaloid. As far as we know, this is the first report on experimental logK_ow data for harmaline (logK_ow = 2.28 ± 0.08). Introducing neutral compounds into a basic model training set or using neutral model compounds alone is recommended to measure the lipophilicity of weakly ionizable basic compounds especially those with high hydrophobicity for the advantages of more suitable model compound choices and convenient mobile phase pH control.     

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.     

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.     

Estimation of the thermal conductivity λ(T,P) of ionic liquids using a neural network optimized with genetic algorithms

In this study, an artificial neural network was optimized using a genetic algorithm in order to estimate the thermal conductivity of ionic liquids at different temperatures and pressures. Experimental thermal conductivity data of 41 ionic liquids (400 experimental data points) in the range from 0.10 to 0.22 W m⁻¹ K⁻¹ were used to obtain the proposed method for the temperature range of 273–390 K and the pressure range of 100–20,000 kPa. In addition, the molecular mass M and structure of molecules, represented by the number of well-defined groups forming the molecule, were provided as input parameters in order to characterize the different molecules of ionic liquids. A heterogeneous set of ionic liquids includes cations such as imidazolium, ammonium, phosphonium, pyrrolidinium, and pyridinium. It also includes anions such as halides, sulfonates, tosylates, imides, borates, phosphates, acetates, and amino acids. The whole dataset was divided into a training set with 300 experimental data points and a prediction set with 100 experimental data points. Several architectures were studied, and the optimum weights for the network were determined. The results showed that the proposed method to estimate the thermal conductivity of ionic liquids at different temperatures and pressures presented a good accuracy with lower deviations such as AARD less than 0.91% and R² of 0.9969 for the training set, and AARD less than 0.84% with R² of 0.9963 for the prediction set.     

The SAMPL6 challenge on predicting aqueous p<Emphasis Type="Italic">K</Emphasis><Subscript>a</Subscript> values from EC-RISM theory

The “embedded cluster reference interaction site model” (EC-RISM) integral equation theory is applied to the problem of predicting aqueous pK_a values for drug-like molecules based on an ensemble of tautomers. EC-RISM is based on self-consistent calculations of a solute’s electronic structure and the distribution function of surrounding water. Following-up on the workflow developed after the SAMPL5 challenge on cyclohexane-water distribution coefficients we extended and improved the methodology by taking into account exact electrostatic solute–solvent interactions taken from the wave function in solution. As before, the model is calibrated against Gibbs energies of hydration from the “Minnesota Solvation Database” and a public dataset of acidity constants of organic acids and bases by adjusting in total 4 parameters, among which only 3 are relevant for predicting pK_a values. While the best-performing training model yields a root-mean-square error (RMSE) of 1 pK unit, the corresponding test set prediction on the full SAMPL6 dataset of macroscopic pK_a values using the same level of theory exhibits slightly larger error (1.7 pK units) than the best test set model submitted (1.7 pK units for corresponding training set vs. test set performance of 1.6). Post-submission analysis revealed a number of physical optimization options regarding the numerical treatment of electrostatic interactions and conformational sampling. While the experimental test set data revealed after submission was not used for reparametrizing the methodology, the best physically optimized models consequentially result in RMSEs of 1.5 if only improved electrostatic interactions are considered and of 1.1 if, in addition, conformational sampling accounts for quantum-chemically derived rankings. We conclude that these numbers are probably near the ultimate accuracy achievable with the simple 3-parameter model using a single or the two best-ranking conformations per tautomer or microstate. Finally, relations of the present macrostate approach to microstate pK_a results are discussed and some illustrative results for microstate populations are presented.     

Optimisation of high performance liquid chromatography separation of neuroprotective peptides. Fractional experimental designs combined with artificial neural networks

The study of experimental design conjunction with artificial neural networks for optimisation of isocratic ion-pair reverse phase HPLC separation of neuroprotective peptides is reported. Different types of experimental designs (full-factorial, fractional) were studied as suitable input and output data sources for ANN training and examined on mixtures of humanin derivatives. The independent input variables were: composition of mobile phase, including its pH, and column temperature. In case of a simple mixture of two peptides, the retention time of the most retentive component and resolution were used as the dependent variables (outputs). In case of a complex mixture with unknown number of components, number of peaks, sum of resolutions and retention time of ultimate peak were considered as output variables. Fractional factorial experimental design has been proved to produce sufficient input data for ANN approximation and thus further allowed decreasing the number of experiments necessary for optimisation. After the optimal separation conditions were found, fractions with peptides were collected and their analysis using off-line matrix assisted laser desorption/ionisation time of flight mass spectrometry (MALDI-TOF-MS) was performed.     

Optimization of HPLC Analysis of Biogenic Amines with Reference to Molecular Structure

Abstract

The selectivity and molecular connectivity indices (x) up to the sixth order were calculated and compared with measured reversed-phase high pressure liquid chromatographic (HPLC) retention data for biogenic amines common in foodstuffs and animal feed. The separation of seventeen dansylated amines including primary, secondary and polyamines, was investigated using an isoselective multisolvent gradient (IMGE), reversed-phase HPLC method. The mobile phase was optimized with the “PRISMA” model testing thirteen selectivity points. The compounds were divided into two groups according to their retention; the low-order valence level indices best described the retention. As the high correlations between the calculated and observed retention indicate, retention could be predicted in different selectivity points with a high degree of accuracy by the molecular connectivity indices.     

Optimization of a stability-indicating HPLC method for the simultaneous determination of rifampicin, isoniazid, and pyrazinamide in a fixed-dose combination using artificial neural networks

The aim of this study is to develop and optimize a simple and reliable high-performance liquid chromatography (HPLC) method for the simultaneous determination of rifampicin (RIF), isoniazid (INH), and pyrazinamide (PZA) in a fixed-dose combination. The method is developed and optimized using an artificial neural network (ANN) for data modeling. Retention times under different experimental conditions (solvent, buffer type, and pH) and using four different column types (referred to as the input and testing data) are used to train, validate, and test the ANN model. The developed model is then used to maximize HPLC performance by optimizing separation. The sensitivity of the separation (retention time) to the changes in column type, concentration, and type of solvent and buffer in the mobile phase are investigated. Acetonitrile (ACN) as a solvent and tetrabutylammonium hydroxide (tBAH), used to adjust pH, have the greatest influence on the chromatographic separation of PZA and INH and are used for the final optimization. The best separation and reasonably short retention times are produced on the micro-bondapak C18, 4.6 x 250-mm column, 10 microm/125 A using ACN-tBAH (42.5:57.5, v/v) (0.0002M) as the mobile phase, and optimized at a final pH of 3.10.     

Determination of chromatographic dissociation constants of some carbapenem group antibiotics and quantification of these compounds in human urine

The dissociation constant values (_s^spK_a) of some carbapenem group drugs (ertapenem, meropenem, doripenem) in different percentages of methanol–water binary mixtures (18, 20 and 22%, v/v) were determined from the mobile phase pH dependence of their retention factor. Evaluation of these data was performed using the NLREG program. From calculated pK_a values, the aqueous pK_a values of these subtances were calculated by different approaches. Moreover, the correlation established between retention factor and the pH of the water–methanol mobile phase was used to determine the optimum separation conditions. In order to validate the optimized conditions, these drugs were studied in human urine. The chromatographic separation was realized using a Gemini NX C₁₈ column (250 × 4.6 mm i.d., 5 µm particles) and UV detector set at 220 and 295 nm. Copyright © 2013 John Wiley & Sons, Ltd.