Prediction of the chromatographic hydrophobicity index with immobilized artificial membrane chromatography using simple molecular descriptors and artificial neural networks

Ciura, Krzesimir; Kоvаčеvić, Strаhinjа; Pastewska, Monika; Kapica, Hanna; Kornela, Martyna; Sawicki, Wiesław

doi:10.1016/j.chroma.2021.462666

Cited by 22 publications

(9 citation statements)

References 31 publications

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“…Among other separation techniques, micellar liquid chromatography (MLC) or micellar electrokinetic chromatography (MEEKC), respectively, in which the mobile phase comprises surfactants that allow better mapping of penetration of molecules through biomembranes can be also be used for lipophilicity determination [ 7 ]. The literature review shows that numerous publications have dealt with the subject of determining the lipophilicity of various bioactive compounds using chromatographic methods [ 8 , 9 , 10 , 11 , 12 , 13 , 14 , 15 , 16 , 17 , 18 , 19 , 20 , 21 , 22 , 23 , 24 , 25 , 26 , 27 , 28 ].…”

Section: Introductionmentioning

confidence: 99%

Assessment of Lipophilicity Parameters of Antimicrobial and Immunosuppressive Compounds

2023

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Lipophilicity in addition to the solubility, acid-base character and stability is one of the most important physicochemical parameters of a compound required to assess the ADMET properties (absorption, distribution, metabolism, excretion and toxicity) of a bioactive molecule. Therefore, the subject of this work was to determine the lipophilicity parameters of selected antimicrobial and immunosuppressive compounds such as delafloxacin, linezolid, sutezolid, ceftazidime, everolimus and zotarolimus using thin-layer chromatography in reversed phase system (RP-TLC). The chromatographic parameters of lipophilicity (RMW) for tested compounds were determined on different stationary phases: RP18F254, RP18WF254 and RP2F254 using ethanol, acetonitrile, and propan-2-ol as organic modifiers of mobile phases used. Chromatographically established RMW values were compared with partition coefficients obtained by different computational methods (AlogPs, AClogP, AlogP, MlogP, XlogP2, XlogP3, logPKOWWIN, ACD/logP, milogP). Both cluster and principal component analysis (CA and PCA) of the received results allowed us to compare the lipophilic nature of the studied compounds. The sum of ranking differences analysis (SRD) of all lipophilicity parameters was helpful to select the most effective method of determining the lipophilicity of the investigated compounds. The presented results demonstrate that RP-TLC method may be a good tool in determining the lipophilic properties of studied substances. Obtained lipophilic parameters of the compounds can be valuable in the design of their new derivatives as efficient antimicrobial and immunosuppressive agents.

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Section: Introductionmentioning

confidence: 99%

Assessment of Lipophilicity Parameters of Antimicrobial and Immunosuppressive Compounds

2023

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“…At this point, attention turned to artificial neural network (ANN) models. Artificial neural networks are widely used to predict drugs' bioavailability (Carracedo-Reboredo et al 2021) or properties such as an affinity for phospholipids using IAM chromatography and calculated descriptors (Ciura et al 2021). The great advantages of neural networks compared to MLR are the possibility of utilizing both linear and non-linear relationships between input data and a predicted parameter and the ability of ANNs to learn these relationships directly from the data being modeled.…”

Section: Artificial Neural Network Analysismentioning

confidence: 99%

Immobilized artificial membrane-chromatographic and computational descriptors in studies of soil-water partition of environmentally relevant compounds

Sobańska

2022

Environ Sci Pollut Res

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Chromatographic retention factor log kIAM obtained from immobilized artificial membrane (IAM) HPLC with buffered, aqueous mobile phases and calculated molecular descriptors (molecular weight — log MW; molar volume — VM; polar surface area — PSA; total count of nitrogen and oxygen atoms -(N + O); count of freely rotable bonds — FRB; H-bond donor count — HD; H-bond acceptor count — HA; energy of the highest occupied molecular orbital — EHOMO; energy of the lowest unoccupied orbital — ELUMO; dipole moment — DM; polarizability — α) obtained for a group of 175 structurally unrelated compounds were tested in order to generate useful models of solutes’ soil-water partition coefficient normalized to organic carbon log Koc. It was established that log kIAM obtained in the conditions described in this study is not sufficient as a sole predictor of the soil-water partition coefficient. Simple, potentially useful models based on log kIAM and a selection of readily available, calculated descriptors and accounting for over 88% of total variability were generated using multiple linear regression (MLR) and artificial neural networks (ANN). The models proposed in the study were tested on a group of 50 compounds with known experimental log Koc values by plotting the calculated vs. experimental values. There is a good close similarity between the calculated and experimental data for both MLR and ANN models for compounds from different chemical families (R2 ≥ 0.80, n = 50) which proves the models’ reliability.

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“…The support vector (SVR) and Partial Least Square (PLS) models are the most popular options [ 1 , 11 , 12 , 13 , 14 , 15 , 16 ], but other types of regression algorithms such as Gradient Boosting Regression (GBR), Random Forest, Neural networks, etc. have been successfully applied [ 17 , 18 , 19 , 20 ].…”

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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Prediction of the chromatographic hydrophobicity index with immobilized artificial membrane chromatography using simple molecular descriptors and artificial neural networks

Cited by 22 publications

References 31 publications

Assessment of Lipophilicity Parameters of Antimicrobial and Immunosuppressive Compounds

Assessment of Lipophilicity Parameters of Antimicrobial and Immunosuppressive Compounds

Immobilized artificial membrane-chromatographic and computational descriptors in studies of soil-water partition of environmentally relevant compounds

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

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