Investigation of retention behaviour of non-steroidal anti-inflammatory drugs in high-performance liquid chromatography by using quantitative structure–retention relationships

QSRR modeling of the chromatographic retention behavior of some quinolone and sulfonamide antibacterial agents using firefly algorithm coupled to support vector machine

Quinolone and sulfonamide are two classes of antibacterial agents with an opulent history of medicinal chemistry features that contribute to their bacterial spectrum, efficacy, pharmacokinetics, and adverse effect profiles. The urgent need for their use, combined with the escalating rate of their resistance, necessitates the development of suitable analytical methods that accelerate and facilitate their analysis. In this study, the advanced firefly algorithm (FFA) coupled with support vector regression (SVR) was used to select the most significant descriptors and to construct two quantitative structure-retention relationship (QSRR) models using a series of 11 selected quinolone and 13 sulfonamide drugs, respectively, to predict their retention behavior in HPLC. Precisely, the effect of the pH value and acetonitrile composition in the mobile phase on the retention behavior of quinolones and sulfonamides, respectively, were studied. The obtained QSRR models performed well in both internal and external validations, demonstrating their robustness and predictive ability. Y-randomization validation demonstrated that the obtained models did not result by statistical chance. Moreover, the obtained results shed the light on the molecular features that influence the retention behavior of these two classes under the current chromatographic conditions.

A comprehensive study on retention of selected model substances in β-cyclodextrin-modified high performance liquid chromatography

The quantitative structure-retention relationship (QSRR) models are not only employed in retention behaviour prediction, but also in an in-depth understanding of complex chromatographic systems. The goal of the present research is to enable the comprehensive understanding of retention underlying the separation in β-cyclodextrin (CD) modified reversed-phase high performance liquid chromatography (RP-HPLC) systems, through the development of mixed QSRR models. Moreover, the amount of β-CD adsorbed on the stationary phase surface (β-CD_A) is added as the model's input in order to evaluate its contribution to both model performances and retention. Nuclear magnetic resonance (NMR) experiments were conducted to confirm the predicted inclusion complex structures and support the application of in silico tools. The most significant descriptors revealed that retention is governed by the steric factors 7.5 Å distant from the geometrical centre of a molecule, 3D arrangement of atoms determining the molecular size and shape, lipophilicity indicated by topological distances, as well as the unbound system's energy, related to the inclusion complex formation. In addition, a notable effect of the pH of the aqueous phase on the retention of ionizable analytes was shown. In the case of pH of the aqueous phase and β-CD_A the change in retention behaviour of the studied analytes was observed only at the highest β-CD_A value (5.17 μM/m²), but it was not related to the ionization state of analytes. When the analytes did not change the ionization form across the investigated studied pH range, and the acetonitrile content in the mobile phase was 25% (v/v), the retention factor had low values regardless of the β-CD_A; under these circumstances the retention is probably acetonitrile driven.

Phytohormone treatment induces generation of cryptic peptides with antimicrobial activity in the Moss Physcomitrella patens

BACKGROUND

Cryptic peptides (cryptides) are small bioactive molecules generated via degradation of functionally active proteins. Only a few examples of plant cryptides playing an important role in plant defense have been reported to date, hence our knowledge about cryptic signals hidden in protein structure remains very limited. Moreover, little is known about how stress conditions influence the size of endogenous peptide pools, and which of these peptides themselves have biological functions is currently unclear.

RESULTS

Here, we used mass spectrometry to comprehensively analyze the endogenous peptide pools generated from functionally active proteins inside the cell and in the secretome from the model plant Physcomitrella patens. Overall, we identified approximately 4,000 intracellular and approximately 500 secreted peptides. We found that the secretome and cellular peptidomes did not show significant overlap and that respective protein precursors have very different protein degradation patterns. We showed that treatment with the plant stress hormone methyl jasmonate induced specific proteolysis of new functional proteins and the release of bioactive peptides having an antimicrobial activity and capable to elicit the expression of plant defense genes. Finally, we showed that the inhibition of protease activity during methyl jasmonate treatment decreased the secretome antimicrobial potential, suggesting an important role of peptides released from proteins in immune response.

CONCLUSIONS

Using mass-spectrometry, in vitro experiments and bioinformatics analysis, we found that methyl jasmonate acid induces significant changes in the peptide pools and that some of the resulting peptides possess antimicrobial and regulatory activities. Moreover, our study provides a list of peptides for further study of potential plant cryptides.

Chemometric-assisted method development in hydrophilic interaction liquid chromatography: A review

With an enormous growth in the application of hydrophilic interaction liquid chromatography (HILIC), there has also been significant progress in HILIC method development. HILIC is a chromatographic method that utilises hydro-organic mobile phases with a high organic content, and a hydrophilic stationary phase. It has been applied predominantly in the determination of small polar compounds. Theoretical studies in computer-aided modelling tools, most importantly the predictive, quantitative structure retention relationship (QSRR) modelling methods, have attracted the attention of researchers and these approaches greatly assist the method development process. This review focuses on the application of computer-aided modelling tools in understanding the retention mechanism, the classification of HILIC stationary phases, prediction of retention times in HILIC systems, optimisation of chromatographic conditions, and description of the interaction effects of the chromatographic factors in HILIC separations. Additionally, what has been achieved in the potential application of QSRR methodology in combination with experimental design philosophy in the optimisation of chromatographic separation conditions in the HILIC method development process is communicated. Developing robust predictive QSRR models will undoubtedly facilitate more application of this chromatographic mode in a broader variety of research areas, significantly minimising cost and time of the experimental work.

Predicting retention times of naturally occurring phenolic compounds in reversed-phase liquid chromatography: a Quantitative Structure-Retention Relationship (QSRR) approach

Quantitative structure-retention relationships (QSRRs) have successfully been developed for naturally occurring phenolic compounds in a reversed-phase liquid chromatographic (RPLC) system. A total of 1519 descriptors were calculated from the optimized structures of the molecules using MOPAC2009 and DRAGON softwares. The data set of 39 molecules was divided into training and external validation sets. For feature selection and mapping we used step-wise multiple linear regression (SMLR), unsupervised forward selection followed by step-wise multiple linear regression (UFS-SMLR) and artificial neural networks (ANN). Stable and robust models with significant predictive abilities in terms of validation statistics were obtained with negation of any chance correlation. ANN models were found better than remaining two approaches. HNar, IDM, Mp, GATS2v, DISP and 3D-MoRSE (signals 22, 28 and 32) descriptors based on van der Waals volume, electronegativity, mass and polarizability, at atomic level, were found to have significant effects on the retention times. The possible implications of these descriptors in RPLC have been discussed. All the models are proven to be quite able to predict the retention times of phenolic compounds and have shown remarkable validation, robustness, stability and predictive performance.

A QSRR Modeling of Hazardous Psychoactive Designer Drugs Using GA-PlS and L-M ANN

The hazardous psychoactive designer drugs are compounds in which part of the molecular structure of a stimulant or narcotic has been modified. A quantitative structure-retention relationship (QSRR) study based on a Levenberg-Marquardt artificial neural network (L-M ANN) was carried out for the prediction of the capacity factor (k′) of hazardous psychoactive designer drugs that contain Tryptamine, Phenylethylamine and Piperazine. The genetic algorithm-partial least squares (GA-PLS) method was used as a variable selection tool. A PLS method was used to select the best descriptors and the selected descriptors were used as input neurons in neural network model. For choosing the best predictive model from among comparable models, square correlation coefficient (R2) for the whole set is suggested to be a good criterion. Finally, to improve the results, structure-retention relationships were followed by nonlinear approach using artificial neural networks and consequently better results were obtained. Also this demonstrates the advantages of L-M ANN. This is the first research on the QSRR of the designer drugs using the GA-PLS and L-M ANN.

Prediction of liquid chromatographic retention for differentiation of structural isomers

A liquid chromatography (LC) retention time prediction software, ACD/ChromGenius, was employed to calculate retention times for structural isomers, which cannot be differentiated by accurate mass measurement techniques alone. For 486 drug compounds included in an in-house database for urine drug screening by liquid chromatography/quadrupole time-of-flight mass spectrometry (LC/Q-TOFMS), a retention time knowledge base was created with the software. ACD/ChromGenius calculated retention times for compounds based on the drawn molecular structure and given chromatographic parameters. The ability of the software for compound identification was evaluated by calculating the retention order of the 118 isomers, in 50 isomer groups of 2-5 compounds each, included in the database. ACD/ChromGenius predicted the correct elution order for 68% (34) of isomer groups. Of the 16 groups for which the isomer elution order was incorrectly calculated, two were diastereomer pairs and thus difficult to distinguish using the software. Correlation between the calculated and experimental retention times in the knowledge base tested was moderate, r(2)=0.8533. The mean and median absolute errors were 1.12 min, and 0.84 min, respectively, and the standard deviation was 1.04 min. The information generated by ACD/ChromGenius, together with other in silico methods employing accurate mass data, makes the identification of substances more reliable. This study demonstrates an approach for tentatively identifying compounds in a large target database without a need for primary reference standards.

A self-adaptive genetic algorithm-artificial neural network algorithm with leave-one-out cross validation for descriptor selection in QSAR study

Based on the quantitative structure-activity relationships (QSARs) models developed by artificial neural networks (ANNs), genetic algorithm (GA) was used in the variable-selection approach with molecule descriptors and helped to improve the back-propagation training algorithm as well. The cross validation techniques of leave-one-out investigated the validity of the generated ANN model and preferable variable combinations derived in the GAs. A self-adaptive GA-ANN model was successfully established by using a new estimate function for avoiding over-fitting phenomenon in ANN training. Compared with the variables selected in two recent QSAR studies that were based on stepwise multiple linear regression (MLR) models, the variables selected in self-adaptive GA-ANN model are superior in constructing ANN model, as they revealed a higher cross validation (CV) coefficient (Q(2)) and a lower root mean square deviation both in the established model and biological activity prediction. The introduced methods for validation, including leave-multiple-out, Y-randomization, and external validation, proved the superiority of the established GA-ANN models over MLR models in both stability and predictive power. Self-adaptive GA-ANN showed us a prospect of improving QSAR model.

Adsorption of s-triazines onto polybenzimidazole: a quantitative structure-property relationship investigation

The adsorption of 25 symmetric triazines (s-triazines) on polybenzimidazole (PBI) beads is investigated under equilibrium (batch) conditions. The observed adsorption isotherms of the selected compounds are accurately described by the Freundlich model, while the agreement between the Langmuir model and the experimental data is moderately worse, which seems to reflect the heterogeneous meso- and micro-porosity of PBI and polydispersion in the interaction mechanism. Methylthio- and methoxytriazines exhibit a greater adsorption tendency as compared with chlorotriazines, moreover, progressive dealkylation of amino groups results in a progressive increase of triazine uptake on PBI. Based on these evidences, the adsorption mechanism seems to be governed by a combination of pi-pi and hydrogen-bonding interactions. Genetic algorithm (GA) variable selection and multilinear regression (MLR) are combined in order to describe the effect of triazine structure on the extraction performance of PBI according to the quantitative structure-property relationship (QSPR) method. q(max), the amount of triazine adsorbed per weight unit of PBI assuming homogeneous monolayer (Langmuir) mechanism, exhibits a great variability within the set of investigated triazines and is the quantity here modelled by QSPR. On the other hand, the Freundlich constant, KF, which expresses the adsorption efficiency under multilayer heterogeneous conditions, even if markedly increases passing from chloro- to methylthio- or methoxytriazines, is less noticeably affected by the fine details of the adsorbate structure, as the number or nature of alkyl fragments bound to the amino groups. To quantitatively relate q(max) with the triazine structure GA-MLR analysis is performed on the set of 1664 theoretical molecular descriptors provided by the software Dragon. Finally, a four-dimensional QSPR model is selected based on leave-one-out cross-validation and its prediction ability is further tested on four representative triazines excluded from model calibration. The four descriptors selected by GA-MLR, all belonging to the class of three-dimensional GETAWAY (GEometry, Topology, and Atom-Weights AssemblY) descriptors, adequately represent the structural factors influencing the affinity of triazines to PBI in the batch extraction process.

Current mathematical methods used in QSAR/QSPR studies

This paper gives an overview of the mathematical methods currently used in quantitative structure-activity/property relationship (QASR/QSPR) studies. Recently, the mathematical methods applied to the regression of QASR/QSPR models are developing very fast, and new methods, such as Gene Expression Programming (GEP), Project Pursuit Regression (PPR) and Local Lazy Regression (LLR) have appeared on the QASR/QSPR stage. At the same time, the earlier methods, including Multiple Linear Regression (MLR), Partial Least Squares (PLS), Neural Networks (NN), Support Vector Machine (SVM) and so on, are being upgraded to improve their performance in QASR/QSPR studies. These new and upgraded methods and algorithms are described in detail, and their advantages and disadvantages are evaluated and discussed, to show their application potential in QASR/QSPR studies in the future.