Quantitative structure–retention relationships applied to development of liquid chromatography gradient-elution method for the separation of sartans

Golubović, Jelena; Protić, Ana; Otašević, Biljana; Zečević, Mira

doi:10.1016/j.talanta.2015.12.035

Cited by 20 publications

(14 citation statements)

References 26 publications

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“…The most commonly used methods for this purpose were chromatographic methods such as HPTLC and TLC (5,6), LC (7,8), HPLC (9) and CE (10,11). The UPLC-MS/MS method was applied for simultaneous determination of telmisartan and hy- Brought to you by | MIT Libraries Authenticated Download Date | 5/9/18 2:15 PM drochlorotiazide in human plasma (12).…”

Section: Valsartan (Val) (S)-3-methyl-2-(n-{[2'mentioning

confidence: 99%

Spectrophotometric method for simultaneous determination of valsartan and substances from the group of statins in binary mixtures

et al. 2017

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Applicability of derivative spectrophotometry for the determination of valsartan in the presence of a substance from the group of statins was checked. The obtained results indicate that the proposed method may be effective by using appropriate derivatives: for valsartan and fluvastatin -D1, D2 and D3, for valsartan and pravastatin -D1 and D3, for valsartan and atorvastatin -D2 and D3. The method was characterized by high sensitivity and accuracy. Linearity was maintained in the following ranges: 9.28-32.48 mg mL -1 for valsartan, 8.16-28.56 mg mL -1 for fluvastatin, 14.40-39.90 mg mL -1 for atorvastatin and 9.60-48.00 mg mL -1 for pravastatin. Determination coefficients were in the range of 0.989-0.999 depending on the analyte and the order of derivative. The precision of the method was high with RSD from 0.1 to 2.5 % and recovery of individual components was within the range of 100 ± 5 %. The developed method was successfully applied to the determination of valsartan combined with fluvastatin, atorvastatin and pravastatin in laboratory prepared mixtures and in pharmaceutical preparations.

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Section: Valsartan (Val) (S)-3-methyl-2-(n-{[2'mentioning

confidence: 99%

Spectrophotometric method for simultaneous determination of valsartan and substances from the group of statins in binary mixtures

et al. 2017

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“…3 Explicitly, QSRR models have the possibility of relating chromatographic retention behaviour to numerically expressed chemical information embedded in molecule structure in a form of molecular descriptors (MDs). 4 As an implication, the most common QSRR models are limited at predicting the retention behaviour only at constant experimental parameters (EP) values. Therefore, when QSRR studies are motivated by practical goals, such as method optimization, EPs have to be included in the model as relevant variables (predictors or inputs).…”

Section: Introductionmentioning

confidence: 99%

“…5 Models referred to as mixed QSRR models are more complex than the aforementioned classical ones since they explain retention behaviour in context of both, EPs and MDs. 4,6 A significant part of QSRR model building is the technique used for determining the mathematical relationship between MDs and retention behaviour. Most widely applied model-building techniques are Multiple Linear Regression (MLR) and Artificial Neural Networks (ANN).…”

Section: Introductionmentioning

confidence: 99%

Optimization of chromatographic separation of aripiprazole and impurities: Quantitative structure-retention relationship approach

Svrkota

Krmar

Protić

et al. 2022

JSCS

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New optimization strategy based on mixed Quantitative Structure-Retention Relationship (QSRR) model was proposed for improving the RP-HPLC separation of aripiprazole and its impurities (IMP A-E). Firstly, experimental parameters (EPs) (mobile phase composition and flow rate) were varied according to Box-Behnken Design and afterwards, artificial neural network (ANN) as QSRR model was built correlating EPs and selected molecular descriptors (ovality, torsion energy and non-1,4-Van der Waals energy) with analytes log-transformed retention time. Values of root mean square error (RMSE) were used for ANNs quality estimation (0.0227, 0.0191 and 0.0230 for training, verification and test set, respectively). Separations of critical peak pairs on chromatogram (IMP A-B and IMP D-C) were optimized using ANNs for which EPs served as inputs and log-transformed separation criteria s as outputs. They were validated applying leave-one-out cross-validation (RMSE values 0.065 and 0.056, respectively). Obtained ANNs were used for plotting response surfaces upon which analyses chromatographic conditions resulting in optimal analytes retention behaviour and optimal values of separation criteria s were defined and they comprised of 54 % of methanol at the beginning and 79 % of methanol at the end of gradient elution programme with mobile phase flow rate of 460 ?L min-1.

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“…The ANN-based applications in retention prediction include the development of quantitative structure–retention relationships (QSSRs) [17,18], modeling of the combined effects of solute structure and separation conditions (column, eluent, or both) [19,20], and transfer of retention data between different columns or eluent types [21,22,23]. ANN models based simultaneously on molecular descriptors and instrumental conditions associated with the elution mode were used to predict the retention times of diverse sets of organic compounds in gradient RP-HPLC [24,25,26,27]. We previously used ANN regression to model the retention times of 16 selected purines, pyrimidines, and nucleosides under the application of multilinear φ gradients [28].…”

Section: Introductionmentioning

confidence: 99%

Artificial Neural Network Prediction of Retention of Amino Acids in Reversed-Phase HPLC under Application of Linear Organic Modifier Gradients and/or pH Gradients

D’Archivio

2019

Molecules

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A multi-layer artificial neural network (ANN) was used to model the retention behavior of 16 o-phthalaldehyde derivatives of amino acids in reversed-phase liquid chromatography under application of various gradient elution modes. The retention data, taken from literature, were collected in acetonitrile–water eluents under application of linear organic modifier gradients (φ gradients), pH gradients, or double pH/φ gradients. At first, retention data collected in φ gradients and pH gradients were modeled separately, while these were successively combined in one dataset and fitted simultaneously. Specific ANN-based models were generated by combining the descriptors of the gradient profiles with 16 inputs representing the amino acids and providing the retention time of these solutes as the response. Categorical “bit-string” descriptors were adopted to identify the solutes, which allowed simultaneously modeling the retention times of all 16 target amino acids. The ANN-based models tested on external gradients provided mean errors for the predicted retention times of 1.1% (φ gradients), 1.4% (pH gradients), 2.5% (combined φ and pH gradients), and 2.5% (double pH/φ gradients). The accuracy of ANN prediction was better than that previously obtained by fitting of the same data with retention models based on the solution of the fundamental equation of gradient elution.

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Quantitative structure–retention relationships applied to development of liquid chromatography gradient-elution method for the separation of sartans

Cited by 20 publications

References 26 publications

Spectrophotometric method for simultaneous determination of valsartan and substances from the group of statins in binary mixtures

Spectrophotometric method for simultaneous determination of valsartan and substances from the group of statins in binary mixtures

Optimization of chromatographic separation of aripiprazole and impurities: Quantitative structure-retention relationship approach

Artificial Neural Network Prediction of Retention of Amino Acids in Reversed-Phase HPLC under Application of Linear Organic Modifier Gradients and/or pH Gradients

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