Application of planar chromatographic descriptors to the prediction of physicochemical properties and biological activity of compounds

Sobańska, Anna W.

doi:10.1080/10826076.2018.1447886

Cited by 15 publications

(6 citation statements)

References 81 publications

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“…The subtle differences in behavior must be multiplied to become traceable and expressed quantitatively. The application of a chromatographic method gives such possibilities both in studying the CNS bioavailability [29,33,34] and in other areas of medicinal chemistry [50]. The analytical models are used to study and compare the solutes of a great diversity of physicochemical properties.…”

Section: Stepwise Multiple Regression Analysismentioning

confidence: 99%

Prediction of the Blood-Brain Barrier Permeability Using RP-18 Thin Layer Chromatography

2019

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The Blood-Brain Barrier (BBB) permeability is an important factor governing a drug’s ability to act upon the Central Nervous System. The measure of the BBB permeability used throughout this study is the log BB (the blood/brain partitioning coefficient) measured in vivo or calculated. Useful yet simple models of the BBB permeability were developed by Stepwise Multiple Regression Analysis based on the chromatographic parameters Rf and Rf/PSA obtained by RP-18 TLC with acetonitrile - pH 7.4 phosphate buffered saline 70:30 (v/v) as mobile phase, combined with descriptors - the number of H-bond donors (HD), the number of H-bond acceptors (HA), energy of the highest occupied molecular orbital – (eH), energy of the lowest unoccupied molecular orbital (eL). The ability of the solutes to cross the BBB has been studied qualitatively using Discriminant Function Analysis. Almost all compounds with the known BB vivo parameter were correctly classified as CNS+/-. The classification functions based on Rf/PSA have been verified using an external group. The results of the chromatographic analysis proposed in this study (RP-18 TLC) are a source of valuable information on the BBB permeability of compounds available even on a very small scale.

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Section: Stepwise Multiple Regression Analysismentioning

confidence: 99%

Prediction of the Blood-Brain Barrier Permeability Using RP-18 Thin Layer Chromatography

2019

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“…Reversed-phase thin layer chromatography has been used to predict physicochemical properties and bioavailability of compounds for many years [ 26 ] and the chromatographic parameter considered in these investigations is usually the R M value defined by Bate-Smith and Westall (Equation (2)) [ 42 ]: R M = log (1/ R f − 1) …”

Section: Resultsmentioning

confidence: 99%

“…Chromatography is a powerful technique for acquiring measurements of physicochemical and biological properties of solutes, including the ability of compounds to cross different biological barriers [ 26 , 27 , 28 ]. It is superior to techniques based on excised human skin, animal experiments or even cell cultures because it is more reproducible and it usually involves commercially available chromatographic supports.…”

Section: Introductionmentioning

confidence: 99%

Application of RP-18 TLC Retention Data to the Prediction of the Transdermal Absorption of Drugs

2021

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Several chromatographic parameters (RM0 and S obtained from RP-18 TLC with methanol—pH 7.4 phosphate buffer mobile phases by extrapolation to zero concentration of methanol; Rf and RM obtained from RP-18 TLC with acetonitrile—pH 7.4 phosphate buffer 70:30 v/v as a mobile phase) and calculated molecular descriptors (molecular weight—MW; molar volume—VM; polar surface area—PSA; total count of nitrogen and oxygen atoms—(N+O); H-bond donor count—HD; H-bond acceptor count—HA; distribution coefficient—log D; total energy—ET; binding energy—Eb; hydration energy—Eh; energy of the highest occupied molecular orbital—EHOMO; energy of the lowest unoccupied orbital—ELUMO; electronic energy—Ee; surface area—Sa; octanol-water partition coefficient—log P; dipole moment—DM; refractivity—R, polarizability—α) and their combinations (Rf/PSA, RM/MW, RM/VM) were tested in order to generate useful models of solutes’ skin permeability coefficient log Kp. It was established that neither RM0 nor S obtained in the conditions used in this study is a good predictor of the skin permeability coefficient. The chromatographic parameters Rf and Rf/PSA were also unsuitable for this purpose. A simple and potentially useful, purely computational model based on (N+O), log D and HD as independent variables and accounting for ca. 83% of total variability was obtained. The evaluation of parameters derived from RM (RM, RM/MW, RM/VM) as independent variables in log Kp models proved that RM/VM is the most suitable descriptor belonging to this group. In a search for a reliable log Kp model based on this descriptor two possibilities were considered: a relatively simple model based on 5 independent variables: (N+O), log D, RM/VM, ET and Eh and a more complex one, involving also Eb, MW and PSA.

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“…If gradient runs are performed instead of isocratic runs, the

\log k_{w}

found with three gradient runs agreed better with the

\log k_{w}

determined with isocratic runs than if two gradient runs were used. Next to RPLC, Sobánska argues that TLC can serve as an alternative for lipophilicity determination, since it is inexpensive, fast, and readily available [166].…”

Section: Methodsmentioning

confidence: 99%

Recent applications of retention modelling in liquid chromatography

Uijl

Schoenmakers

Pirok

et al. 2020

J of Separation Science

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Recent applications of retention modelling in liquid chromatography (2015–2020) are comprehensively reviewed. The fundamentals of the field, which date back much longer, are summarized. Retention modeling is used in retention‐mechanism studies, for determining physical parameters, such as lipophilicity, and for various more‐practical purposes, including method development and optimization, method transfer, and stationary‐phase characterization and comparison. The review focusses on the effects of mobile‐phase composition on retention, but other variables and novel models to describe their effects are also considered. The five most‐common models are addressed in detail, i.e. the log‐linear (linear‐solvent‐strength) model, the quadratic model, the log–log (adsorption) model, the mixed‐mode model, and the Neue–Kuss model. Isocratic and gradient‐elution methods are considered for determining model parameters and the evaluation and validation of fitted models is discussed. Strategies in which retention models are applied for developing and optimizing one‐ and two‐dimensional liquid chromatographic separations are discussed. The review culminates in some overall conclusions and several concrete recommendations.

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Application of planar chromatographic descriptors to the prediction of physicochemical properties and biological activity of compounds

Cited by 15 publications

References 81 publications

Prediction of the Blood-Brain Barrier Permeability Using RP-18 Thin Layer Chromatography

Prediction of the Blood-Brain Barrier Permeability Using RP-18 Thin Layer Chromatography

Application of RP-18 TLC Retention Data to the Prediction of the Transdermal Absorption of Drugs

Recent applications of retention modelling in liquid chromatography

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