Application of artificial neural network to predict the retention time of drug metabolites in two‐dimensional liquid chromatography

Noorizadeh, Hadi; Sobhanardakani, Soheil; Raoofi, F.; Noorizadeh, Mehrab; Mortazavi, S. S.; Ahmadi, Taher; Pournajafi, K.

doi:10.1002/dta.325

Cited by 5 publications

(3 citation statements)

References 31 publications

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“…The algorithm consists of forward transfer of information and back propagation of error (Grunert et al 2013;Noorizadeh et al 2013). The specific steps of BPANN are as follows: (a) use particle swarm algorithm to determine the initial weight and threshold of the model.…”

Section: Bpann Modeling Strategymentioning

confidence: 99%

Development and validation of a new LC–MS/MS method for the determination of mefatinib in human plasma and its first application in pharmacokinetic studies

Chen

Shao

et al. 2022

J Anal Sci Technol

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Mefatinib (MET306) is a novel second-generation epidermal growth factor receptor-tyrosine kinase inhibitor (EGFR-TKI) designed to address the highly unmet clinical need of gefitinib-induced resistance and irreversibly bind to mutated tyrosine kinase domain of EGFR and human epidermal growth factor receptor 2 (HER2). In this study, a liquid chromatography–tandem mass spectrometry method was established and validated for determining MET306 in non-small cell lung cancer patients and a backpropagation artificial neural network was developed and constructed to predict the pharmacokinetic process. The mobile phase was water containing 5 mM ammonium acetate and acetonitrile at a flow rate of 0.3 mL min−1, within a 4.5 min run time. MET306 was separated on a Hypersil Gold-C18 at 40 °C and subjected to mass analysis using positive electrospray ionization. A total of 524 data were used as development groups and 145 data were used as testing groups. The final established Northern Goshawk Optimization-Backpropagation Artificial Neural Network (NGO-BPANN) model consisted of one input layer with 6 neurons, 1 hidden layer with 10 nodes, and 1 output layer with one node processed by MATLAB2021a.The calibration range of MET306 was 0.5–200 ng mL−1 with the correlation coefficient r ≥ 0.99. Accuracies ranged from 97.20 to 110.80% and the inter- and intra-assay precision were less than 15%. The ranges of extraction recoveries were 104.95% to 112.09% for analyte and internal standard and there was no significant matrix effect. The storage stability under different conditions was in accordance with the bioanalytical guidelines. The time-concentration profiles of the measured and predicted concentrations of MET306 by NGO-BPANN agree well. An NGO-BPANN model was developed to predict the plasma concentration and pharmacokinetic parameters of MET306 in the first time.

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Section: Bpann Modeling Strategymentioning

confidence: 99%

Development and validation of a new LC–MS/MS method for the determination of mefatinib in human plasma and its first application in pharmacokinetic studies

Chen

Shao

et al. 2022

J Anal Sci Technol

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“…Secondly, a maturing understanding of reverse phase LC retention mechanisms allows for reliable Quantitative Structure–Retention Relationship (QSRR) modeling between calculated molecular descriptors and experimental RTs of authentic compounds [7, 12]. The QSRR concept has been applied to drug metabolite identification, as is reported in the literature [13-17], and software packages include this type of modeling as a feature [12]. As an example, Herre and Pragst [15] proposed a biotransformation RT shift using data from C8 column chromatography to report 55 parameters for 29 biotransformations.…”

Section: Introductionmentioning

confidence: 99%

Using LC Retention Times in Organic Structure Determination: Drug Metabolite Identification

Fitch

Khojasteh²,

Aliagas

et al. 2018

DML

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“…5,6 ANN models have been widely used in tasks in the pharmaceutical sciences ranging from drug discovery and the detection of potential drugdrug interactions to predicting the risk factors of some diseases. [7][8][9][10][11] Recently, ANNs have been used to predict the plasma concentrations of several drugs and have been reported to be superior to multiple linear regression analysis. 12,13 Several studies have reported that ANN models have a predictive capability that is similar to or even better than nonlinear mixed effects modeling (NONMEM; ICON plc, Dublin, Ireland).…”

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confidence: 99%

Application of a Backpropagation Artificial Neural Network in Predicting Plasma Concentration and Pharmacokinetic Parameters of Oral Single‐Dose Rosuvastatin in Healthy Subjects

Lou

Chen

et al. 2020

Clinical Pharm in Drug Dev

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A backpropagation artificial neural network (BPANN) model was established for the prediction of the plasma concentration and pharmacokinetic parameters of rosuvastatin (RVST) in healthy subjects. The data (demographic characteristics and results of clinical laboratory tests) were collected from 4 bioequivalence studies using reference 10-mg RVST calcium tablets. After the data were cleaned using extreme gradient boosting, 13 important factors were extracted to construct the BPANN model. The model was fully validated, and mean impact values (MIVs) were calculated. The model was used to predict the plasma concentration and pharmacokinetic parameters of oral single-dose RVST in healthy subjects under fasting and fed conditions. The predicted and measured values were compared in order to evaluate the accuracy of prediction. The constructed model performed well in validation. The top 3 factors ranked by MIV related to RVST concentration are fasting/fed, time, and creatinine clearance. The time-concentration profiles of the measured and predicted data agreed well. There were no significant differences (P > .05) in the area under the concentration-time curve from 0 to the last measurable concentration (AUC 0-t) and extrapolated to infinity (AUC 0-∞), half-time of elimination, peak concentration, and time to peak concentration of the measured data and data predicted by BPANN. The BPANN model has an accurate prediction ability and can be used to predict RVST concentration and pharmacokinetic parameters in healthy subjects.

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Application of artificial neural network to predict the retention time of drug metabolites in two‐dimensional liquid chromatography

Cited by 5 publications

References 31 publications

Development and validation of a new LC–MS/MS method for the determination of mefatinib in human plasma and its first application in pharmacokinetic studies

Development and validation of a new LC–MS/MS method for the determination of mefatinib in human plasma and its first application in pharmacokinetic studies

Using LC Retention Times in Organic Structure Determination: Drug Metabolite Identification

Application of a Backpropagation Artificial Neural Network in Predicting Plasma Concentration and Pharmacokinetic Parameters of Oral Single‐Dose Rosuvastatin in Healthy Subjects

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