Retention prediction using quantitative structure-retention relationships combined with the hydrophobic subtraction model in reversed-phase liquid chromatography.

The hydrophobic subtraction model (HSM) combined with quantitative structure-retention relationships (QSRR) methodology was utilized to predict retention times in reversed-phase liquid chromatography (RPLC). A selection of new analytes and new RPLC columns that had never been used in the QSRR modeling process were used to verify the proposed approach. This work is designed to facilitate early prediction of co-elution of analytes in pharmaceutical drug discovery applications where it is advantageous to predict whether impurities might be co-eluted with the active drug component.

Retention Index Prediction Using Quantitative Structure-Retention Relationships for Improving Structure Identification in Nontargeted Metabolomics.

Structure identification in nontargeted metabolomics based on liquid-chromatography coupled to mass spectrometry (LC-MS) remains a significant challenge. Quantitative structure-retention relationship (QSRR) modeling is a technique capable of accelerating the structure identification of metabolites by predicting their retention, allowing false positives to be eliminated during the interpretation of metabolomics data. In this work, 191 compounds were grouped according to molecular weight and a QSRR study was carried out on the 34 resulting groups to eliminate false positives.

Retention prediction in reversed phase high performance liquid chromatography using quantitative structure-retention relationships applied to the Hydrophobic Subtraction Model.

Quantitative Structure-Retention Relationships (QSRR) methodology combined with the Hydrophobic Subtraction Model (HSM) have been utilized to accurately predict retention times for a selection of analytes on several different reversed phase liquid chromatography (RPLC) columns. This approach is designed to facilitate early prediction of co-elution of analytes, for example in pharmaceutical drug discovery applications where it is advantageous to predict whether impurities might be co-eluted with the active drug component. The QSRR model utilized VolSurf+ descriptors and a Partial Least Squares regression combined with a Genetic Algorithm (GA-PLS) to predict the solute coefficients in the HSM.

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.

Error measures in quantitative structure-retention relationships studies.

An analysis and comparison of the use of four commonly used error measures (mean absolute error, percentage mean absolute error, root mean square error, and percentage root mean square error) for evaluating the predictive ability of quantitative structure-retention relationships (QSRR) models is reported. These error measures are used for reporting errors in the prediction of retention time of external test analytes, that is, analytes not employed during model development. The error-based validation metrics were compared using a simple descriptive statistic, the sum of squared residuals (SSR) of outliers to the edge of an error window.

Benchmarking of Computational Methods for Creation of Retention Models in Quantitative Structure-Retention Relationships Studies.

Quantitative structure-retention relationship (QSRR) models are powerful techniques for the prediction of retention times of analytes, where chromatographic retention parameters are correlated with molecular descriptors encoding chemical structures of analytes. Many QSRR models contain geometrical descriptors derived from the three-dimensional (3D) spatial coordinates of computationally predicted structures for the analytes. Therefore, it is sensible to calculate these structures correctly, as any error is likely to carry over to the resulting QSRR models.

Towards a chromatographic similarity index to establish localised Quantitative Structure-Retention Relationships for retention prediction. III Combination of Tanimoto similarity index, logP, and retention factor ratio to identify optimal analyte training sets for ion chromatography.

Retention prediction for unknown compounds based on Quantitative Structure-Retention Relationships (QSRR) can lead to rapid "scoping" method development in chromatography by simplifying the selection of chromatographic parameters. The use of retention factor ratio (or k-ratio) as a chromatographic similarity index can be a potent method to cluster similar compounds into a training set to generate an accurate predictive QSRR model provided that its limitation - that the method is impractical for retention prediction for unknown compounds - is successfully addressed. In this work, we propose a localised QSRR modelling approach with the aim of compensating the critical limitation in the otherwise successful k-ratio filter-based QSRR modelling.

Use of dual-filtering to create training sets leading to improved accuracy in quantitative structure-retention relationships modelling for hydrophilic interaction liquid chromatographic systems.

The development of quantitative structure retention relationships (QSRR) having sufficient accuracy to support high performance liquid chromatography (HPLC) method development is still a major issue. To tackle this challenge, this study presents a novel QSRR methodology to select a training set of compounds for QSRR modelling (i.e.

Towards a chromatographic similarity index to establish localised quantitative structure-retention relationships for retention prediction. II Use of Tanimoto similarity index in ion chromatography.

Quantitative Structure-Retention Relationships (QSRR) are used to predict retention times of compounds based only on their chemical structures encoded by molecular descriptors. The main concern in QSRR modelling is to build models with high predictive power, allowing reliable retention prediction for the unknown compounds across the chromatographic space. With the aim of enhancing the prediction power of the models, in this work, our previously proposed QSRR modelling approach called "federation of local models" is extended in ion chromatography to predict retention times of unknown ions, where a local model for each target ion (unknown) is created using only structurally similar ions from the dataset.

Rapid Method Development in Hydrophilic Interaction Liquid Chromatography for Pharmaceutical Analysis Using a Combination of Quantitative Structure-Retention Relationships and Design of Experiments.

A design-of-experiment (DoE) model was developed, able to describe the retention times of a mixture of pharmaceutical compounds in hydrophilic interaction liquid chromatography (HILIC) under all possible combinations of acetonitrile content, salt concentration, and mobile-phase pH with R > 0.95. Further, a quantitative structure-retention relationship (QSRR) model was developed to predict retention times for new analytes, based only on their chemical structures, with a root-mean-square error of prediction (RMSEP) as low as 0.

Retention prediction of low molecular weight anions in ion chromatography based on quantitative structure-retention relationships applied to the linear solvent strength model.

Quantitative Structure-Retention Relationships (QSRRs) represent a popular technique to predict the retention times of analytes, based on molecular descriptors encoding the chemical structures of the analytes. The linear solvent strength (LSS) model relating the retention factor, k to the eluent concentration (log k=a-blog [eluent]), is a well-known and accurate retention model in ion chromatography (IC). In this work, QSRRs for inorganic and small organic anions were used to predict the regression parameters a and b in the LSS model (and hence retention times) for these analytes under a wide range of eluent conditions, based solely on their chemical structures.

Prediction of retention in hydrophilic interaction liquid chromatography using solute molecular descriptors based on chemical structures.

Quantitative structure-retention relationship (QSRR) models are developed to predict the retention times of analytes on five hydrophilic interaction liquid chromatography (HILIC) stationary phases (bare silica, amine, amide, diol and zwitterionic), with a view to selecting the most suitable stationary phase(s) for the separation of these analytes. The study was conducted using six β-adrenergic agonists as target analytes. Molecular descriptors were calculated based only on chemical structures optimized using density functional theory.

Hydrogen from formic acid through its selective disproportionation over sodium germanate--a non-transition-metal catalysis system.

A robust catalyst for the selective dehydrogenation of formic acid to liberate hydrogen gas has been designed computationally, and also successfully demonstrated experimentally. This is the first such catalyst not based on transition metals, and it exhibits very encouraging performance. It represents an important step towards the use of renewable formic acid as a hydrogen-storage and transport vector in fuel and energy applications.

Hydrogen-atom abstraction from a model amino acid: dependence on the attacking radical.

We have used computational chemistry to examine the reactivity of a model amino acid toward hydrogen abstraction by HO•, HOO•, and Br•. The trends in the calculated condensed-phase (acetic acid) free energy barriers are in accord with experimental relative reactivities. Our calculations suggest that HO• is likely to be the abstracting species for reactions with hydrogen peroxide.

Mechanistic investigation of the oxidation of hydrazides: implications for the activation of the TB drug isoniazid.

Aryl hydrazides are oxidised to acyl radicals through a mechanism involving diimide intermediates that are prone to nucleophilic acyl substitution. This oxidation occurs regardless of the oxidant involved, however there is no evidence that the acyl radical formed undergoes further oxidation to the corresponding acylium ion, even in the presence of strong oxidants. This study may provide insight into the mechanism of isoniazid resistance in Mycobacterium tuberculosis.

Nucleophilic acyl substitution of acyl diimides.

The nucleophilic acyl substitution of the acyl diimide intermediate formed by the oxidation of isoniazid was found to involve two methanol molecules in a six-membered cyclic transition state. Calculations were performed in the gas phase at the B3LYP/6-311+G(d,p)//B3LYP/6-31G(d) level of theory and solvation effects were included both explicitly and implicitly by using CPCM. The effect of electron withdrawing and donating groups on the aryl ring was also explored.

A mechanistic study on the oxidation of hydrazides: application to the tuberculosis drug isoniazid.

Herein we report radical trapping experiments that support the formation of an acyl radical as the active species from the oxidation of isoniazid; these data provide insight into the mechanism of hydrazide oxidation.