QSRR modeling for the chromatographic retention behavior of some β-lactam antibiotics using forward and firefly variable selection algorithms coupled with multiple linear regression

Fouad, Marwa A.; Tolba, Enas H.; El-Shal, Manal A.; Kerdawy, Ahmed M. El

doi:10.1016/j.chroma.2018.03.042

Cited by 24 publications

(5 citation statements)

References 76 publications

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“…Obtained network's predictive power was validated using internal and external validation. 28 IMP C was used for external validation and cross-verification with verification data set was applied as an internal type of validation. Low and balanced RMSE values indicated good predictive ability of the network.…”

Section: Accepted Manuscriptmentioning

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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Section: Accepted Manuscriptmentioning

confidence: 99%

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

Svrkota

Krmar

Protić

et al. 2022

JSCS

View full text Add to dashboard Cite

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“…In 2018, Fouad and coworkers 17 used 31 β ‐lactam antibiotics analyzed by means of high performance liquid chromatography (HPLC) with Nucleosil C18 column to calibrate two MLR models. Canonical SMILES (simplified molecular‐input line‐entry system) notation of molecules was retrieved from the PubChem database, 18 optimized in the Molecular Operating Environment by means of a MMFF94x force field.…”

Section: Introductionmentioning

confidence: 99%

Computational prediction of retention times of veterinary antibiotics obtained by liquid chromatography‐mass spectrometry

Rojas,

Sarmiento,

Ayora

et al. 2024

J Sci Food Agric

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BackgroundVeterinary antibiotics are chemical compounds used to kill or inhibit the growth of pathogenic bacteria associated with animal diseases. These molecules can be defined by the retention times (tR) in liquid chromatography‐mass spectrometry (LC‐MS). One strategy to predict the tR of new veterinary antibiotics is the development of predictive quantitative structure‐property relationship (QSPRs), which were used in this study.ResultsA database of 122 antibiotics was selected in which the tR was measured using a Hypersil Gold column. An optimal three‐feature model was developed by integrating the unsupervised variable reduction, replacement method variable subset selection and multiple linear regression. The negligible differences among the coefficient of determination and the root‐mean‐square error for the training set (R2 = 0.902 and RMSEC = 0.871) and test set (Q2 = 0.854 and RMSEP = 1.064) indicate a stable and predictive model. In a further step, a deeply explanation of the mechanism of action of each descriptor in predicting the tR is provided, as well as the construction of the theoretical chemical space for accurate predictions of new antibiotics.ConclusionThe in silico model developed in this work identified three molecular descriptors associated with aqueous solubility, octanol‐water partition coefficient and the presence of negative and lipophilic atom pairs. The QSPR developed here could be implemented by agricultural and food chemists to identify and monitor existing and new antibiotics within the framework of LC‐MS. The computational model was developed in accordance with the five principles outlined by the Organization for Economic Co‐operation and Development.This article is protected by copyright. All rights reserved.

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“…Therefore, it is a good practice to review the model’s validity as per Organization for Economic Co-operation and Development [ 27 ]. Although few research studies have checked applicability domain [ 18 , 28 , 29 , 30 ], it is still very rare that all QSRR models are accompanied with such validations.…”

Section: Introductionmentioning

confidence: 99%

Quantitative Structure Retention-Relationship Modeling: Towards an Innovative General-Purpose Strategy

et al. 2023

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Reversed-Phase Liquid Chromatography (RPLC) is a common liquid chromatographic mode used for the control of pharmaceutical compounds during their drug life cycle. Nevertheless, determining the optimal chromatographic conditions that enable this separation is time consuming and requires a lot of lab work. Quantitative Structure Retention Relationship models (QSRR) are helpful for doing this job with minimal time and cost expenditures by predicting retention times of known compounds without performing experiments. In the current work, several QSRR models were built and compared for their adequacy in predicting the retention times. The regression models were based on a combination of linear and non-linear algorithms such as Multiple Linear Regression, Support Vector Regression, Least Absolute Shrinkage and Selection Operator, Random Forest, and Gradient Boosted Regression. Models were built for five pH conditions, i.e., at pH 2.7, 3.5, 6.5, and 8.0. In the end, the model predictions were combined using stacking and the performances of all models were compared. The k-nearest neighbor-based application domain filter was established to assess the reliability of the prediction for further compound prioritization. Altogether, this study can be insightful for analytical chemists working with RPLC to begin with the computational prediction modeling such as QSRR to predict the separation of small molecules.

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QSRR modeling for the chromatographic retention behavior of some β-lactam antibiotics using forward and firefly variable selection algorithms coupled with multiple linear regression

Cited by 24 publications

References 76 publications

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

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

Computational prediction of retention times of veterinary antibiotics obtained by liquid chromatography‐mass spectrometry

Quantitative Structure Retention-Relationship Modeling: Towards an Innovative General-Purpose Strategy

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