Application of biopartitioning micellar chromatography and QSRR modeling for prediction of gastrointestinal absorption and design of novel β-hydroxy-β-arylalkanoic acids

Dobričić, Vladimir; Savić, Jelena; Nikolic, Katarina; Vladimirov, Sote; Vujić, Zorica; Brborić, Jasmina

doi:10.1016/j.ejps.2017.01.023

Cited by 10 publications

(2 citation statements)

References 27 publications

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“…Over the past decade, there has been an impressive increase in the number of publications on QSRR studies that used ANN as a modeling technique. In particular, the singlehidden layer neural nets provided a satisfactory level of prediction accuracy [46][47][48][49][50][51]. After the improvement in computer power and the rise of big data, ANNs began to flourish in the form of deep learning (DL) algorithms [52][53][54][55].…”

Section: Artificial Neural Networkmentioning

confidence: 99%

QSRR Approach: Application to Retention Mechanism in Liquid Chromatography

Krmar¹,

Svrkota²,

Đajić³

et al. 2023

Novel Aspects of Gas Chromatography and Chemometrics

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One-factor-at-a-time experimentation was used for a long time as gold-standard optimization for liquid chromatographic (LC) method development. This approach has two downsides as it requires a needlessly great number of experimental runs and it is unable to identify possible factor interactions. At the end of the last century, however, this problem could be solved with the introduction of new chemometric strategies. This chapter aims at presenting quantitative structure–retention relationship (QSRR) models with structuring possibilities, from the point of feature selection through various machine learning algorithms that can be used in model building, for internal and external validation of the proposed models. The presented strategies of QSRR model can be a good starting point for analysts to use and adopt them as a good practice for their applications. QSRR models can be used in predicting the retention behavior of compounds, to point out the molecular features governing the retention, and consequently to gain insight into the retention mechanisms. In terms of these applications, special attention was drawn to modified chromatographic systems, characterized by mobile or stationary phase modifications. Although chromatographic methods are applied in a wide variety of fields, the greatest attention has been devoted to the analysis of pharmaceuticals.

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Section: Artificial Neural Networkmentioning

confidence: 99%

QSRR Approach: Application to Retention Mechanism in Liquid Chromatography

Krmar¹,

Svrkota²,

Đajić³

et al. 2023

Novel Aspects of Gas Chromatography and Chemometrics

View full text Add to dashboard Cite

show abstract

“…< Fig. 1 > In vitro assessment of gastrointestinal absorption could be performed using various methods, such as Parallel Artificial Membrane Permeability Assay (PAMPA) [4][5][6], biopartitioning micellar chromatography [7,8] and Caco-2 permeability experiments [9,10]. PAMPA has been extensively used for gastrointestinal absorption assessment of early drug candidates.…”

Section: J O U R N a L P R E -P R O O Fmentioning

confidence: 99%

Estimation of passive gastrointestinal absorption and membrane retention using PAMPA test, quantitative structure-permeability and quantitative structure-retention relationship analyses of ethylenediamine-N,N'-di-2-(3-cyclohexyl)propanoic acid and 1,3-propanediamine-N,N'-di-2-(3-cyclohexyl)propanoic acid derivatives

Tubić

Dobričić

Poljarević

et al. 2020

Journal of Pharmaceutical and Biomedical Analysis

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Selectivity of N(2)-substituted oxotriazinoindole aldose reductase inhibitors is determined by the interaction pattern with Pro301-Arg312 loop of aldehyde reductase

Kováčiková,

Gaikwad,

Almášiová

et al. 2024

Med Chem Res

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Novel oxotriazinoindoles (OTIs) were recently reported as highly efficient and selective aldose reductase inhibitors. Here, a series of novel N(2)-substituted oxotriazinoindoles was developed with the aim to investigate molecular interactions within the aldose reductase (ALR2) inhibitor binding site. About twice increased inhibition efficacy of the most efficient derivative 14 (N(2)-CH2CH2COOH) compared to the unsubstituted lead OTI was obtained, yet at the expense of selectivity relative to anti-target aldehyde reductase (ALR1). To explain the major drop in selectivity, observed also in other N(2)-substituted derivatives, in silico molecular modeling approach revealed the role of extra interactions with the residues of Arg309, Arg312 and Met302 located in the additional C-terminal loop of ALR1 missing in ALR2, which can prevent or enhance binding in ALR1. These key findings will be used for development of the next generation of selective OTI inhibitors.

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Application of biopartitioning micellar chromatography and QSRR modeling for prediction of gastrointestinal absorption and design of novel β-hydroxy-β-arylalkanoic acids

Cited by 10 publications

References 27 publications

QSRR Approach: Application to Retention Mechanism in Liquid Chromatography

QSRR Approach: Application to Retention Mechanism in Liquid Chromatography

Selectivity of N(2)-substituted oxotriazinoindole aldose reductase inhibitors is determined by the interaction pattern with Pro301-Arg312 loop of aldehyde reductase

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