Chromatographic retention behaviour of n-alkylbenzenes and pentylbenzene structural isomers on porous graphitic carbon and octadecyl-bonded silica studied using molecular modelling and QSRR

Matteis, Cristina I. De; Simpson, David A.; Doughty, Stephen W.; Euerby, Melvin R.; Shaw, P. Nicholas

doi:10.1016/j.chroma.2010.08.023

Cited by 27 publications

(10 citation statements)

References 24 publications

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“…Since analogues commonly share similar skeletons and modifying groups, a predicted‐analogue table based on summarisation of the structural characteristics and MS data of reported analogues can facilitate dereplication in favour of the targeted analogues . Chromatographic behaviour prediction based on structure–retention relationships has been used to assist in structure elucidation, especially for some isomers with similar fragmentation behaviour . The present study combined analogue prediction‐based selective filtering, fragmentation pattern analysis, and chromatographic behaviour prediction using UPLC‐ESI‐QTOF‐MS/MS for efficient identification of triterpene acid analogues in poria (Figure ).…”

Section: Introductionmentioning

confidence: 99%

A dereplication strategy for identifying triterpene acid analogues in Poria cocos by comparing predicted and acquired UPLC‐ESI‐QTOF‐MS/MS data

Zou

Long

et al. 2018

Phytochemical Analysis

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Introduction Triterpene acids from the dried sclerotia of Poria cocos (Schw.) Wolf (poria) were recently found to possess anti‐cancer activities. Identification of more triterpene acid analogues in poria is worthwhile for high throughput screening in anti‐cancer drug discovery. Objective To establish an efficient dereplication strategy for identifying triterpene acid analogues in poria based on ultra‐performance liquid chromatography with electrospray ionisation quadrupole time‐of‐flight tandem mass spectrometry (UPLC‐ESI‐QTOF‐MS/MS). Methodology The structural characteristics and mass spectrometric data profiles of known triterpene acids previously reported in poria were used to establish a predicted‐analogue database. Then, the quasi‐molecular ions of components in a poria extract were automatically compared with those in the predicted‐analogue database to highlight compounds of potential interest. Tentative structural identification of the compounds of potential interest and discrimination of isomers were achieved by assessing ion fragmentation patterns and chromatographic behaviour prediction based on structure–retention relationship. Results A total of 62 triterpene acids were unequivocally or tentatively characterised from poria, among which 17 triterpene acids were tentatively identified for the first time in poria. Conclusion This study provided more structure information of triterpene acids in poria for future high throughput screening of anti‐cancer candidates. It is suggested that this semi‐automated approach in which MS data are automatically compared to a predictive database may also be applicable for efficient screening of other herbal medicines for structural analogues of proven bioactives.

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Section: Introductionmentioning

confidence: 99%

A dereplication strategy for identifying triterpene acid analogues in Poria cocos by comparing predicted and acquired UPLC‐ESI‐QTOF‐MS/MS data

Zou

Long

et al. 2018

Phytochemical Analysis

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“…By QSRR models, the retention time of a new or untested chemical can be inferred from the molecular structure of similar compounds whose retention has already been assessed (Bodzioch et al 2010;Durcekova et al 2012). Currently, molecular modeling and computational chemistry are the inseparable parts in toxic design and discovery, and no one can talk about compounds design without having a bit of knowledge about computational methods (Bodzioch et al 2010;Durcekova et al 2012;Matteis et al 2010). Computational methods result in the saving of time and money for discovering new compounds in all steps of compound production.…”

Section: Introductionmentioning

confidence: 99%

Chemometrics analysis for investigation of retention behavior of hazardous compounds in effluents

Karimi

Farmany

Noorizadeh

2012

Environ Monit Assess

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The toxic substances, pesticides, and organic contaminants in effluents can potentially be causing damage that includes increased cancer risk; liver, kidney, stomach, nervous system, and immune system problems; reproductive difficulties; cataracts; and anemia. A quantitative structure-retention relationship (QSRR) was developed using the partial least square (PLS), kernel PLS (KPLS), and Levenberg-Marquardt artificial neural network (L-M ANN) approach for chemometrics study. The data which contained retention time (RT) of the 47 hazardous compounds in effluents were obtained by reverse-phase high-performance liquid chromatography. Genetic algorithm was employed as a factor selection procedure for PLS and KPLS modeling methods. By comparing the results, GA-PLS descriptors are selected for L-M ANN. Finally, a model with a low prediction error and a good correlation coefficient was obtained by L-M ANN. The described model does not require experimental parameters and potentially provides useful prediction for RT of new compounds. This is the first research on the QSRR of hazardous compounds in effluents using the chemometrics models.

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“…QSRR analysis is now a well established and highly respected technique to correlate chromategraphic retention time of a compound with its molecular structure, through a variety of descriptors. The basic strategy of QSRR analysis is to find optimum quantitative relationships, which can then be used for the prediction of the retention from molecular structures [18,19]. Once a reliable relation has been obtained, it is possible to use it to predict that retention for other structures not yet measured or even not yet prepared.…”

Section: P a G E |mentioning

confidence: 99%

Advanced QSRR Modeling of Organic Pollutants in Natural Water and Wastewater in Gas Chromatography Time-of-Flight Mass Spectrometry

Shahpar¹,

Esmaeilpoor

2017

Chem. Method.

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Chromatographic retention behaviour of n-alkylbenzenes and pentylbenzene structural isomers on porous graphitic carbon and octadecyl-bonded silica studied using molecular modelling and QSRR

Cited by 27 publications

References 24 publications

A dereplication strategy for identifying triterpene acid analogues in Poria cocos by comparing predicted and acquired UPLC‐ESI‐QTOF‐MS/MS data

A dereplication strategy for identifying triterpene acid analogues in Poria cocos by comparing predicted and acquired UPLC‐ESI‐QTOF‐MS/MS data

Chemometrics analysis for investigation of retention behavior of hazardous compounds in effluents

Advanced QSRR Modeling of Organic Pollutants in Natural Water and Wastewater in Gas Chromatography Time-of-Flight Mass Spectrometry

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