Michael H. Abraham

Solubility Interactions and the Design of Chemically Selective Sorbent Coatings for Chemical Sensors and Arrays

Abstract A continual challenge in the field of chemical detection is the development of approaches to designing microsensors and microsensor-based detectors with high selectivity. No single science exists to unite all sensor technologies with a common set of principles and a common approach to achieving selectivity. Nevertheless, in the area of vapor detection, sorption phenomena and solubility interactions can be identified as common features of many sensors. In designing microsensors for vapor detection, interactive coating materials must be chosen that will collect and concentrate analyte molecules at the sensors surface. The sensitivity and selectivity of each individual sensor is controlled by tailoring the chemical and physical properties of the coating material to maximize particular solubility interactions. The selection of coatings for the complete sensor array is logically made through a systematic variation of the solubility properties of the coating materials, so that each sensor is selective for a different balance of solubility interactions. This account discusses solubility in great detail. Parameters and methodologies for characterizing analyte solubility properties, sensor coating material solubility properties, and their interactions are presented. Specific functional groups are recommended for inclusion in sensor coating materials in order to maximize particular interactions. In addition, the treatment of coating material properties is integrated with consideration of the factors that influence chemical selectivity in sensor arrays. Methods for choosing coatings inteligently for sensor arrays, and for optimizing sensor arrays for particular analytes are considered.

Linear solvation energy relations

Solvents have been parameterized by scales of dipolarity/polarizability π*, hydrogen-bond donor (HBD) strength α, and hydrogen-bond acceptor strength β. Linear dependence (LSERs) on these solvent parameters are used to correlate and predict a wide variety of solvent effects, as well as to provide an analysis in terms of knowledge and theoretical concepts of molecular structural effects. Some recent applications utilizing this approach are presented. Included are analyses of solvent effects on (a) the free energies of transfer of tetraalkylammonium halide ion pairs and dissociated ions, (b) rates of nucleophilic substitution reactions, (c) the contrast in solvent effects of water (HBD) and dimethyl sulfoxide (non-HBD) on the acidities of m- and p-substituted phenols, (d) partition coefficients of non-HBD solutes between solvent bilayers, and (e) family relationships between proton transfer (and non-protonic Lewis acid) basicities and corresponding β values for monomer HBA. A comprehensive summary of LSER with references is given.

Rate-limited steps of human oral absorption and QSAR studies

AbstractPurpose. To classify the dissolution and diffusion rate-limited drugs and establish quantitative relationships between absorption and molecular descriptors. Methods. Absorption consists of kinetic transit processes in which dissolution, diffusion, or perfusion processes can become the rate-limited step. The absorption data of 238 drugs have been classified into either dissolution or diffusion rate-limited based on an equilibrium method developed from solubility, dose, and percentage of absorption. A nonlinear absorption model derived from first-order kinetics has been developed to identify the relationship between percentage of drug absorption and molecular descriptors. Results. Regression analysis was performed between percentage of absorption and molecular descriptors. The descriptors used were ClogP, molecular polar surface area, the number of hydrogen-bonding acceptors and donors, and Abraham descriptors. Good relationships were found between absorption and Abraham descriptors or ClogP. Conclusions. The absorption models can predict the following three BCS (Biopharmaceutics Classification Scheme) classes of compounds: class I, high solubility and high permeability; class III, high solubility and low permeability; class IV, low solubility and low permeability. The absorption models overpredict the absorption of class II, low solubility and high permeability compounds because dissolution is the rate-limited step of absorption.

Classification of stationary phases and other materials by gas chromatography

Abstract The origin and evolution of solute descriptors for use in the solvation parameter model applied to the classification of stationary phases and other materials by gas chromatography are described. The model system constants provide a breakdown of solute–stationary phase interactions in terms of the contribution to retention of cavity formation and dispersion interactions, lone-pair electron interactions, interactions of a dipole-type, and hydrogen-bonding interactions. The solvation properties of additional stationary phases with useful complementary selectivity to existing phases for method development in gas chromatography are identified. The influence of temperature on system selectivity and stationary phase classification is discussed. The contribution of interfacial adsorption to the estimation of retention in method development in gas chromatography is outlined. In addition, for materials characterization, it is shown that the solvation parameter model provides a conceptual mechanism for the evaluation of the sorption properties of a wide range of materials compatible with the operation characteristics of gas chromatography.

Hydrogen bonding: XVI. A new solute salvation parameter, π2H, from gas chromatographic data

Abstract The general salvation equation, log VG0 (or log L) = c + rR2 + sπ2H + aα2H + bβ2H + l log L16 has been used to set up a new π2H parameter of solute dipolarity-polarisability, mainly through the extensive data of McReynolds and Patte et al. Values of π2H are tabulated for several hundred solutes, and two simple rules have been formulated to enable π2H to be estimated for many types of aliphatic functionally substituted compounds. A coherent set of effective solvation parameters, Σπ2H, Σα2H, Σβ2H, and also R2 and log L16, allows the application of the general solvation equation to the characterisation of any gas-liquid chromatographic stationary phase.

Study of retention in reversed-phase liquid chromatography using linear solvation energy relationships. I. The stationary phase

Abstract The applicability of linear solvation energy relationships (LSERs) to reversed-phase liquid chromatography (RPLC) was studied by examining the retention of a wide variety of aliphatic and aromatic compounds over the range of 20–50% (v/v) acetonitrile, methanol and tetrahydrofuran. The role of cavity formation, dispersion interaction, polarity/polarizability, hydrogen bond acidity, and hydrogen bond basicity in determining the retention behavior as the mobile phase composition was changed has been investigated. The LSER coefficients were then examined in terms of the corresponding properties of the mobile phase (cohesive energy density, surface tension, the Abraham solvophobic parameter, polarity/polarizability, hydrogen bond basicity, and hydrogen bond acidity) and from these the influence of mobile phase and stationary phase on the retention behavior was explored. In order to chemically interpret the RPLC retention results we compared them to alkane–water and octanol–water partition coefficients.

Hydrogen bonding. Part 13. A new method for the characterisation of GLC stationary phases—the laffort data set

A number of equations for the correlation of retention data for a series of solutes on a given stationary phase (or solvent) have been investigated with the aim of characterising stationary phases. The two most successful equations are, SP =c+dδ2+sπ2*+aα2H+bβ2H+I log L16(a), SP =c+rR2+sπ2*+aα2H+bβ2H+I log L16(b) In the present case the dependent variable SP is log L– log LDecane and the explanatory variables are solute parameters as follows: δ2 is an empirical polarisability correction term, R2 is a polarisability parameter that reflects the ability of a solute to interact with a solvent through π and n electron pairs, α2H is the solute hydrogen–bond acidity, β2H is the solute hydrogen–bond basicity, π2* is the solute dipolarity/polarisability, and L16 is the Ostwald solubility coefficient of the solute on n-hexadecane at 298 K. The constants c, r, s, a, b, and l in the more useful equation (b) are found by the method of multiple linear regression analysis, and serve to characterise a solvent phase in terms of specific solute/solvent interactions. Application of equation (b) to the five stationary phases examined by Laffort et al. shows that the magnitude of these constants is in accord with general chemical principles, and that the present procedure constitutes a new, general method for the characterisation of gas chromatographic stationary phases.

Hydrogen bonding. Part 34. The factors that influence the solubility of gases and vapours in water at 298 K, and a new method for its determination

The solubility of 408 gaseous compounds in water at 298 K has been correlated through eqn. (i), where the solubility is expressed as the Ostwald solubility coefficient, Lw, and the solute explanatory variables are R2 an excess molar refraction, π2H the dipolarity/polarizability, Σα2H and Σβ2H the effective hydrogen-bond acidity and basicity, and Vx the McGowan characteristic volume. A similar equation using the log L16 parameter instead of Vx can also be used; L16 is the Ostwald solubility coefficient on hexadecane at 298 K. log Lw=–0.994 + 0.577R2+ 2.549 π2H+ 3.813Σα2H+ 4.841Σβ2H– 0.869 Vx(i), n= 408 ρ= 0.9976 sd = 0.151 F= 16810 The main factors leading to increased solubility are solute π2H, Σα2H and Σβ2H values; conversely, the corresponding properties of water are dipolarity/polarizability, hydrogen-bond basicity and hydrogen-bond acidity. Solute size plays a minor role, and slightly decreases solubility, contrary to observations on all non-aqueous solvents. It is shown that this peculiar behaviour of water is due to (a) a greater increase in the unfavourable cavity effect with increase in solute size, for solvent water, and (b) a smaller increase in the favourable general dispersion interaction with size, for solvent water.A new method for the determination of log Lw values is put forward, using the relationship Lw=L16/P where L16 is as above, and P is either the water–hexadecane partition coefficient or the water–alkane partition coefficient. For 14 solutes using the former P-value, agreement with values calculated through eqn. (i) is 0.08 log units on average and for 45 solutes using the latter P-value, the corresponding agreement is 0.15 log units, with log Lw values ranging up to 8 log units.

Prediction of Solubility of Drugs and Other Compounds in Organic Solvents

We have set out a procedure for the prediction of solubilities of drugs and other compounds in a wide range of solvents, based on the Abraham solvation equations. The method requires a knowledge of solubilities of a given compound in a few solvents, as shown by our own experimental data on apocynin, diapocynin, dehydrodivanillin, and dehydrodi(methyl vanillate). The procedure is especially useful for very hydrophobic compounds such as cholesteryl acetate and cholesterol that we give as examples. Other examples include vanillin and 3,4-dichlorobenzoic acid. If the solubility in water is available, then this alone is sufficient to predict solubilities in organic solvents, provided that the Abraham descriptors are available for the compound. Predictions can be made for solubilities in some 85 solvents.

Correlation and prediction of a large blood-brain distribution data set: an LFER study

We report linear free energy relation (LFER) models of the equilibrium distribution of molecules between blood and brain, as log BB values. This method relates log BB values to fundamental molecular properties, such as hydrogen bonding capability, polarity/polarisability and size. Our best model of this form covers 148 compounds, the largest set of log BB data yet used in such a model, resulting in R(2)=0.745 and e.s.d.=0.343 after inclusion of an indicator variable for carboxylic acids. This represents rather better accuracy than a number of previously reported models based on subsets of our data. The model also reveals the factors that affect log BB: molecular size and dispersion effects increase brain uptake, while polarity/polarisability and hydrogen-bond acidity and basicity decrease it. By splitting the full data set into several randomly selected training and test sets, we conclude that such a model can predict log BB values with an accuracy of less than 0.35 log units. The method is very rapid-log BB can be calculated from structure at a rate of 700 molecules per minute on a silicon graphics O(2).