Prediction of HPLC retention times of tebipenem pivoxyl and its degradation products in solid state by applying adaptive artificial neural network with recursive features elimination

Mizera, Mikołaj; Talaczyńska, Alicja; Zalewski, Przemysław; Skibiński, Robert; Cielecka‐Piontek, Judyta

doi:10.1016/j.talanta.2015.01.032

Cited by 15 publications

(5 citation statements)

References 22 publications

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“…According to data in the literature, TP is susceptible to degradation when exposed to physicochemical factors in aqueous solutions and in the solid state [13]. Depending on affecting factors, different degradation products were formed; therefore, the isocratic, short HPLC-DAD method was compared to the HPLC-DAD gradient method in order to confirm the selectivity of determination of the main analyte [33]. The biological property, such as permeability of the TP-β-CD complex through the Caco-2-cell monolayer, was determined by using the HPLC-DAD when changes in the TP concentrations in acceptor and donor solvents were monitored.…”

Section: Resultsmentioning

confidence: 99%

Cyclodextrins as multifunctional excipients: Influence of inclusion into β-cyclodextrin on physicochemical and biological properties of tebipenem pivoxil

et al. 2019

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A novel approach for drug design based on the oral carbapenem analog tebipenem pivoxil (TP) has been proposed. The formation of the tebipenem pivoxil-β-cyclodextrin (TP-β-CD) complex resulted in changes concerning physicochemical properties of TP, which is significant for planning the development of an innovative pharmaceutical formulation as well as in the modifications of biological activity profile of the studied delivery system. The inclusion of TP into β-cyclodextrin (β-CD) was confirmed by spectral (infrared and Raman spectroscopies) and thermal method (differential scanning calorimetry). Precise indications of TP domains responsible for interaction with β-CD were possible through a theoretical approach. The most important physicochemical modifications obtained as an effect of TP inclusion were changes in solubility and its rate depending on acceptor fluids, and an increase in chemical stability in the solid state. Biologically essential effects of TP and β-CD interactions were decreased TP permeability through Caco–2 cell monolayers with the use of efflux effect inhibition and increased antibacterial activity. The proposed approach is an opportunity for development of the treatment in resistant bacterial infections, in which along with physicochemical modifications induced by a drug carrier impact, a carrier synergy with a pharmacological potential of an active pharmaceutical substance could be used.

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Section: Resultsmentioning

confidence: 99%

Cyclodextrins as multifunctional excipients: Influence of inclusion into β-cyclodextrin on physicochemical and biological properties of tebipenem pivoxil

et al. 2019

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“…Prediction of retention times in RPLC using a QSRR approach coupled with an ANN (i.e., the chemometric tool used to relate analyte molecular descriptors to observed retention times (see Figure ), in this case, is an ANN) has been applied quite frequently during the review period, e.g., refs , , , , and − . These publications generally apply a similar approach, with some variations in the number and nature of the molecular descriptors used as the input layer of the ANN and also the architecture of the ANN itself (i.e., the number of neurons in input, hidden, and output layers of the ANN).…”

Section: Prediction Of Retention In Rplcmentioning

confidence: 99%

“…A number of further examples of ANNs used in QSRR modeling have appeared over the review period. ,, Mizera et al have applied a 6-4-1 architecture adaptive ANN with recursive features elimination to the prediction of retention times of unstable tebipenem pivoxyl and its degradation products on a RPLC core–shell column. Descriptors were calculated using CDK Descriptor Calculator 1.3.9 with feature selection undertaken by PCA.…”

Section: Prediction Of Retention In Rplcmentioning

confidence: 99%

Prediction of Analyte Retention Time in Liquid Chromatography

2020

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“…Machine learning-based QSAR models can be successfully applied in pharmaceutical research related to drug discovery [ 19 ], drug formulation [ 20 ], and pharmaceutical analysis [ 21 , 22 ]. The applicability of QSAR modeling to predict the selectivity of CB receptors has also been reported in the literature.…”

Section: Introductionmentioning

confidence: 99%

Virtual Screening of C. Sativa Constituents for the Identification of Selective Ligands for Cannabinoid Receptor 2

Mizera

Latek

Cielecka‐Piontek

2020

IJMS

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The selective targeting of the cannabinoid receptor 2 (CB2) is crucial for the development of peripheral system-acting cannabinoid analgesics. This work aimed at computer-assisted identification of prospective CB2-selective compounds among the constituents of Cannabis Sativa. The molecular structures and corresponding binding affinities to CB1 and CB2 receptors were collected from ChEMBL. The molecular structures of Cannabis Sativa constituents were collected from a phytochemical database. The collected records were curated and applied for the development of quantitative structure-activity relationship (QSAR) models with a machine learning approach. The validated models predicted the affinities of Cannabis Sativa constituents. Four structures of CB2 were acquired from the Protein Data Bank (PDB) and the discriminatory ability of CB2-selective ligands and two sets of decoys were tested. We succeeded in developing the QSAR model by achieving Q2 5-CV > 0.62. The QSAR models helped to identify three prospective CB2-selective molecules that are dissimilar to already tested compounds. In a complementary structure-based virtual screening study that used available PDB structures of CB2, the agonist-bound, Cryogenic Electron Microscopy structure of CB2 showed the best statistical performance in discriminating between CB2-active and non-active ligands. The same structure also performed best in discriminating between CB2-selective ligands from non-selective ligands.

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Prediction of HPLC retention times of tebipenem pivoxyl and its degradation products in solid state by applying adaptive artificial neural network with recursive features elimination

Cited by 15 publications

References 22 publications

Cyclodextrins as multifunctional excipients: Influence of inclusion into β-cyclodextrin on physicochemical and biological properties of tebipenem pivoxil

Cyclodextrins as multifunctional excipients: Influence of inclusion into β-cyclodextrin on physicochemical and biological properties of tebipenem pivoxil

Prediction of Analyte Retention Time in Liquid Chromatography

Virtual Screening of C. Sativa Constituents for the Identification of Selective Ligands for Cannabinoid Receptor 2

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