Harpreet S. Chadha

The Factors that Influence Skin Penetration of Solutes

In this study, human skin permeation data are analysed using a number of physicochemical descriptors.

Determination of solute lipophilicity, as log P(octanol) and log P(alkane) using poly(styrene–divinylbenzene) and immobilised artificial membrane stationary phases in reversed-phase high-performance liquid chromatography

Abstract A number of RP–HPLC systems have been characterized by the linear free energy relationship: (i) log SP=c+r.R 2 +s.π H 2 +a.∑α H 2 +b.∑β 2 +v.V x Here, SP is either log k′ or log kw for a series of solutes in a given system, where k′ is the capacity factor and kw is the capacity factor extrapolated to l00% water, and the solute descriptors are, R2 an excess molar refraction, π2H the dipolarity/polarizability, ∑α2H and ∑β2 the overall or effective hydrogen-bond acidity and basicity, and Vx the McGowan characteristic volume. Comparison of the coefficients in Eq. (1) with those for water-solvent partitions confirms that the modified electrostatically coated C18 phase of Pagliara et al. (J. Liq. Chromatogr., 18 (1995) 1721) can be used to obtain solute lipophilicities, as log Poct. For RP–HPLC systems based on poly(styrene–divinylbenzene), the coefficients in Eq. (i) are nearer those for the correlation of water–alkane partition coefficients, as log Palk, than for the correlation of log Poct, suggesting that the RP–HPLC systems with poly(styrene–divinylbenzene) phases could be used as a rapid method for determination of solute lipophilicity, as log Palk or as log Pcyc, where the latter is the water–cyclohexane partition coefficient. Eq. (i) has also been applied to RP–HPLC log k′ values obtained with an immobilized artificial membrane (IAM) phase. A good regression equation was obtained, but the coefficients in this equation are substantially different to those for regressions with log Poct, log Palk, or log Pcyc as the dependent variable. On the other hand, log k′ values from the RP–HPLC system of Miyake al. [J. Chromatogr., 389 (1987) 47], consisting of silica gel coated with dipalmitoyl phosphatidyl choline as a stationary phase, with aqueous acetonitrile mobile phases, yielded coefficients in Eq. (i) very similar to those for log Poct.

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†

A number of solute descriptors that relate to the ability of a solute to take part in solute-solvent interactions have been identified, quantified and incorporated into a multiple linear regression equation. This general solvation equation can then be used for the correlation and prediction of solute effects in transport processes, that is, processes in which the main step is either the equilibrium transfer, or the rate of transfer, of a solute from one phase to another. Examples discussed include the solubility of gases and vapours in water, various water-solvent partitions, blood-brain distribution, brain perfusion, and skin permeability.

Molecular factors influencing drug transfer across the blood-brain barrier

A recently reported approach to the prediction of blood‐brain drug distribution uses the general linear free energy equation to correlate equilibrium blood‐brain solute distributions (logBB) with five solute descriptors: R2 an excess molar refraction term; π2H, solute dipolarity or polarizability; α2H and β2H, the hydrogen bond acidity or basicity, and VX, the solute McGowan volume. In this study we examine whether the model can be used to analyse kinetic transfer rates across the blood‐brain barrier in the rat.

Hydrogen bonding. Part 40. Factors that influence the distribution of solutes between water and sodium dodecylsulfate micelles

The partition of 132 assorted compounds between water and sodium dodecyl sulfate (SDS) micelles at 298 K has been correlated through eqn. (i). The mol fraction water–SDS micelle partition coefficient is denoted as Kx, and the solute explanatory variables, or descriptors, are R2 the excess molar refraction, πH2 the dipolarity/polarizability, ∑αH2 and ∑βO2 the hydrogen-bond acidity and basicity, and Vx the McGowan characteristic volume. The number of solutes is denoted as n, the correlation coefficient as ρ, the standard deviation as sd, and the F-statistic as F. log Kx= 1.201 + 0.542 R2– 0.400 πH2– 0.133 ∑αH2– 1.580 ∑βO2+ 2.793 VX(i), n= 132, ρ= 0.9849, sd = 0.171, F= 817.The two main factors that influence partition are solute hydrogen-bond basicity that reduces partition into micelles, and solute volume that increases partition. It may be deduced from eqn. (i) that SDS micelles behave as though they are highly polar, of quite high hydrogen-bond acidity (although not as high as water) and of about the same hydrogen-bond basicity as water. Comparison with water–alcohol partitions indicates that SDS micelles are as hydrophobic as water-saturated isobutanol. It is also shown that water–octanol partition coefficients, as log Koct do not correlate well with log Kx for the 132 varied solutes, but that a double regression in log Koct and Vx is a useful equation for the estimation of log Kx values. log Kx= 1.129 + 0.504 log Koct+ 1.216 Vx(ii), n= 132, ρ= 0.9755, sd = 0.215, F= 1269.

PHYSICOCHEMICAL ANALYSIS OF THE FACTORS GOVERNING DISTRIBUTION OF SOLUTES BETWEEN BLOOD AND BRAIN

Abstract An equation is described that relates the equilibrium distribution of compounds between blood and brain to various solute descriptors, for 57 varied compounds. It is shown that the main factors influencing the distribution are solute size that favours brain, and solute dipolarity/polarisability, hydrogen-bond acidity and hydrogen-bond basicity that favour blood. The descriptors can be obtained from measurements on compound substructures, so that the blood-brain distribution can be predicted for drug molecules without the necessity for synthesis.

Hydrogen bonding. Part 41.1 Factors that influence thedistribution of solutes between water and hexadecylpyridinium chloridemicelles

The partition of 46 compounds between water and hexadecylpyridinium chloride (CPC) micelles at 298 K has been correlated through eqn. (i), using the water–CPC partition coefficients of Christian and co-workers. These 46 compounds include cyclohexane, pentan-1-ol and 44 varied aromatic compounds. The water–CPC micelle partition coefficient is denoted as K(CPC), and the solute explanatory variables, or descriptors, are R2 the excess molar refraction, π2H the dipolarity/polarizability, Σα2H and Σβ2O the hydrogen-bond acidity and basicity, and Vx the McGowan characteristic volume in units of (cm3 mol-1)/100. The number of solutes is n, the correlation coefficient is r, the standard deviation is sd, and the F-statistic is F. The main factors that influence partition are solute hydrogen-bond basicity that reduces partition into the CPC micelles, and solute volume that increases partition. It can be deduced from eqn. (i) that CPC micelles behave as though they are highly polar, of very high hydrogen-bond basicity and of moderate hydrogen-bond acidity and hydrophobicity. Comparison with water–alcohol partitions indicates that CPC micelles are as hydrophobic as water-saturated pentanol. Analysis of log K values for water to CPC and to sodium dodecyl sulfate (SDS) micelles, using scaled particle theory, shows that the main factor leading to larger log K values in the CPC system is an increase in dispersion interactions between solute and the CPC pseudophase.The coefficients in eqn. (i) are the same in sign and similar in magnitude to those previously reported by Quina and co-workers for partition between water and hexadecyltrimethylammonium bromide micelles and for partition between water and dodecyltrimethylammonium bromide micelles.