Hydrogen bonding part 46: a review of the correlation and prediction of transport properties by an lfer method: physicochemical properties, brain penetration and skin permeability

Abraham, Michael H.; Chadha, Harpreet S.; Martins, Filomena; Mitchell, Robert E.; Bradbury, M. W. B.; Gratton, J

doi:10.1002/(sici)1096-9063(199901)55:1<78::aid-ps853>3.0.co;2-7

Cited by 94 publications

(16 citation statements)

References 34 publications

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“…Hence the following brief account of the application of Eq. (4) to biological systems [41] is not meant to be a rigorous illustration of the indirect method shown in Fig.1 Another measure of the ability of compounds to cross the blood-brain barrier is the rate of perfusion from the internal carotid artery into the brain. Unlike distribution studies, the time scale of perfusion experiments is very small, so reducing the possibility of biological degradation of the compound during the course of the experiment.…”

Section: Rp-hplc Datasupporting

confidence: 58%

Connection between chromatographic data and biological data

Abraham

Gola

Kumarsingh

et al. 2000

Journal of Chromatography B: Biomedical Sciences and Applicatio

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There are no previous references to the direct use of GLC data in the correlation of biological processes, but we show that GLC retention data can be used in the correlation of several such processes involving gaseous solutes. There are a number of reports of RP-HPLC and MEKC data being used in the correlation of biological processes, but they are mostly restricted as to the number and type of solute studied.We show that if chromatographic data are used to obtain solvation descriptors for solutes, and if these descriptors are then used in the correlation of biological processes, that this indirect connection is a much more powerful and generally applicable method than is the direct connection between chromatographic data and biological data.

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Section: Rp-hplc Datasupporting

confidence: 58%

Connection between chromatographic data and biological data

Abraham

Gola

Kumarsingh

et al. 2000

Journal of Chromatography B: Biomedical Sciences and Applicatio

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“…The relationships identified here are unsurprising, being similar to those for permeability of epithelia and biological membranes (e.g., skin, intestine, blood-brain barrier, cornea) where lipophilicity, hydrogen bonding potential, and MW are known to be key determinants. [51,55,57,58] They are also entirely consistent with Lipinski's rules for drug-like small molecules, which emphasizes the requirement for low molecular weight (<500), low numbers of hydrogen bond donors and acceptors (<5 and <10, respectively), and a log P <5 for optimal intestinal absorption, which is a balance between aqueous solubility and membrane permeability. [54] While Eqn 1 provides a potentially useful tool for estimating tumour tissue diffusion coefficients computationally, given that all the required parameter values can be readily calculated for virtual compounds, some cautions need to be sounded.…”

Section: Discussionmentioning

confidence: 99%

“…For the set of 71 compounds, we tested the dependence of D mcl on log P 7.4 , HD, and HA since these parameters are expected to influence diffusion through lipoidal membranes and thus access to the transcellular route. [51] Including the expected MW dependence, based on its relationship with Stoke's radius, an excellent correlation was obtained using a sigmoidal dependence on log P 7.4 as previously for neutral compounds [32] modified by HD and HA as shown in Eqn 1 and demonstrated in Fig. 4.…”

Section: Relationship Between Physicochemical Parameters and Diffusiomentioning

confidence: 94%

“…[52] The lower contribution of solute hydrogen-bond acidity is due to the fact that water and wet octanol have the same hydrogen-bond basicity. [51] In other words, log P 7.4 in Eqn 1 already partially takes account of hydrogen bond basicity, hiding some of the effect of HA. Second, there is an energy cost due to the loss of favourable electrostatic interactions and hydrogen bonds when leaving the bulk water phase for the hydrophobic core of the bilayer of the cell membrane.…”

Section: Sensitivity Analysismentioning

confidence: 99%

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Prediction of Tumour Tissue Diffusion Coefficients of Hypoxia-Activated Prodrugs from Physicochemical Parameters

et al. 2008

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The therapeutic activity of anticancer agents depends critically on their ability to penetrate through tumour tissue to reach their target cells, a requirement that is especially important for hypoxia-activated prodrugs. Here we use multicellular layers (MCL) grown in vitro from HT29 colon carcinoma cells to measure tissue diffusion coefficients (D mcl ) of 67 structurally diverse benzotriazine di-N-oxides (analogues of the hypoxia-activated prodrug tirapazamine) plus four miscellaneous compounds. An algorithm was developed to predict D mcl from physicochemical parameters (molecular weight, octanol/water partition coefficient at pH 7.4, number of hydrogen bond donors and acceptors); the fitted multivariate relationship had an explained variance (R 2 ) of 0.907 and predictive power (Q 2 ) of 0.879. Using a subset of nine compounds tested as a single cassette, the algorithm was shown to apply, with some adjustment of coefficients, to MCLs from three other tumour cell lines with differing cell packing densities (SiHa, HCT8-Ea, and HCT8-Ra). The demonstrated relationships provide tools for optimizing extravascular transport of anticancer agents during lead optimization.

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“…This is often carried out through the use of QSAR where skin permeation profile is related to the molecular properties of compounds, given that the skin permeation is measured at consistent experimental conditions. QSAR has been efficiently used to model skin permeation of chemicals from simple systems such as saturated aqueous solutions (El Tayar et al, 1991;Abraham et al, 1995Abraham et al, , 1999Ghafourian and Fooladi, 2001;Moss and Cronin, 2002). Potts and Guy (1992) developed the first widely accepted QSAR model for predicting skin permeability coefficient (k p ), a linear regression model that consisted of lipophilicity measured by octanol/water partition coefficient and molecular weight.…”

Section: Introductionmentioning

confidence: 99%

Modelling the effect of mixture components on permeation through skin

Ghafourian¹,

Samaras²,

Brooks³

et al. 2010

International Journal of Pharmaceutics

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A vehicle influences the concentration of penetrant within the membrane, affecting its diffusivity in the skin and rate of transport. Despite the huge amount of effort made for the understanding and modelling of the skin absorption of chemicals, a reliable estimation of the skin penetration potential from formulations remains a challenging objective. In this investigation, quantitative structure-activity relationship (QSAR) was employed to relate the skin permeation of compounds to the chemical properties of the mixture ingredients and the molecular structures of the penetrants. The skin permeability dataset consisted of permeability coefficients of 12 different penetrants each blended in 24 different solvent mixtures measured from finite-dose diffusion cell studies using porcine skin. Stepwise regression analysis resulted in a QSAR employing two penetrant descriptors and one solvent property. The penetrant descriptors were octanol/water partition coefficient, log P and the ninth order path molecular connectivity index, and the solvent property was the difference between boiling and melting points. The negative relationship between skin permeability coefficient and log P was attributed to the fact that most of the drugs in this particular dataset are extremely lipophilic in comparison with the compounds in the common skin permeability datasets used in QSAR. The findings show that compounds formulated in vehicles with small boiling and melting point gaps will be expected to have higher permeation through skin. The QSAR was validated internally, using a leave-many-out procedure, giving a mean absolute error of 0.396. The chemical space of the dataset was compared with that of the known skin permeability datasets and gaps were identified for future skin permeability measurements.

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Hydrogen bonding part 46: a review of the correlation and prediction of transport properties by an lfer method: physicochemical properties, brain penetration and skin permeability

Cited by 94 publications

References 34 publications

Connection between chromatographic data and biological data

Connection between chromatographic data and biological data

Prediction of Tumour Tissue Diffusion Coefficients of Hypoxia-Activated Prodrugs from Physicochemical Parameters

Modelling the effect of mixture components on permeation through skin

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