Babassu ( Zona) Nut Oil Is More Effective than Olive Oil for Treating Ischemia-Reperfusion Injury.

Background: Cardiovascular disease (CVD) is the leading cause of death in Western civilizations. The type of fatty acid which makes up the diet is related to the cardiovascular morbimortality and the formation of atheromas. Populations with high consumption of oils and fats have a higher number of deaths from CVD.

Lipophilicity assessment of basic drugs (log P(o/w) determination) by a chromatographic method.

A previously reported chromatographic method to determine the 1-octanol/water partition coefficient (log P(o/w)) of organic compounds is used to estimate the hydrophobicity of bases, mainly commercial drugs with diverse chemical nature and pK(a) values higher than 9. For that reason, mobile phases buffered at high pH to avoid the ionization of the solutes and three different columns (Phenomenex Gemini NX, Waters XTerra RP-18 and Waters XTerra MS C(18)) with appropriate alkaline-resistant stationary phases have been used. Non-ionizable substances studied in previous works were also included in the set of compounds to evaluate the consistency of the method.

Determination of the hydrophobicity of organic compounds measured as logP(o/w) through a new chromatographic method.

A new chromatographic method to determine the octanol-water partition coefficient (logP(o/w)) of organic substances is proposed in this paper. This method is based on a previously reported model that relates the retention factor in reversed-phase liquid chromatography with solute (p), mobile phase (P(m)(N)) and stationary phase (P(s)(N)) polarity parameters: logk=(logk)(0)+p(P(m)(N)-P(s)(N)). P(m)(N) values are calculated through expressions that depend only on the organic solvent fraction in the mobile phase.

Quantitative structure-property relationship estimation of cation binding affinity of the common amino acids.

The quantitative structure-property relationship (QSPR) methodology is applied to estimate the binding affinity of lithium, sodium, potassium, copper, and silver cations to the 20 common amino acids. The proposed model, nonlinearly derived from computational neural networks (CNN), contains seven descriptors and was validated by an external prediction set. Good results are obtained with correlation coefficients, R(2), and root-mean-square errors (rms) (kJ/mol) of 0.

A comparison of the binding affinity of the common amino acids with different metal cations.

A theoretical model, based in density functional theory with the B3LYP functional and the DZVP basis set from Salahub, has been applied for the calculation of the binding affinity and cation basicity between the 20 common amino acids and the monovalent cations Li+, Na+, K+, Cu+ and Ag+. These magnitudes have been calculated for every combination of the five cations with the twenty amino acids, thus totalling 100 reactions. The highest binding affinities correspond to copper(I) (302.

Application of a polarity parameter model to the separation of fat-soluble vitamins by reversed-phase HPLC.

The retention behavior of a series of fat-soluble vitamins has been established on the basis of a polarity retention model: log k = (log k)(0) + p (P(m) (N) - P(s) (N)), with p being the polarity of the solute, P(m) (N) the mobile phase polarity, and (log k)(0) and P(m) (N) two parameters for the characterization of the stationary phase. To estimate the p-values of solutes, two approaches have been considered. The first one is based on the application of a QSPR model, derived from the molecular structure of solutes and their log P(o/w), while in the second one, the p-values are obtained from several experimental measurements.

Determination of lithium cation basicity from molecular structure.

A quantitative structure-property relationship (QSPR) is developed to calculate the Lithium Cationic Basicity (LCB) of a large set of 229 compounds, of very different chemical nature. The proposed models derived from multiple linear regression analysis (MLRA) and computational neural networks (CNN) contain seven descriptors calculated solely from the molecular structure of compounds. The models were validated by an external prediction set.

Determination of Abraham solute parameters from molecular structure.

The Abraham solute parameters are well-known factors for the quantitative description of solute/solvent interactions. A quantitative structure-property relationship (QSPR) is reported for the E, S, A, and B parameters of a large set of 457 solutes, of very different chemical nature. The proposed models, derived from multilinear regression analysis (MLRA) and computational neural networks (CNN), contain five descriptors calculated solely from the molecular structure of compounds.

A QSPR study of the p solute polarity parameter to estimate retention in HPLC.

A Quantitative Structure-Property Relationship (QSPR) model is developed to calculate the solute polarity parameter p of a set of 233 compounds of a very different chemical nature. The proposed model, derived from multiple linear regression, contains four descriptors calculated from the molecular structure and the well-known hydrophobicity parameter log P(o/w). According to the statistics obtained with the prediction set, the model has a very good prediction capacity (R(2) = 0.

A QSPR study of O-H bond dissociation energy in phenols.

A Quantitative Structure-Property Relationship (QSPR) is developed for the O-H bond dissociation energy (BDE) of a set of 78 phenols. The data set was composed of monosubstituted, disubstituted, and polysubstituted phenolic derivatives containing substituents with different steric and electronic effects in the ortho-, meta-, and para-positions of the aromatic ring. The proposed model, derived from multiple linear regression, contains seven descriptors calculated solely from the molecular structure of compounds.

Polarizabilities of solvents from the chemical composition.

From the experimental polarizability values, alpha, of a large set of solvents containing 426 compounds with very different chemical characteristics, an additive model for the estimation of the polarizability is proposed. The derived average atomic polarizability of 10 elements, C, H, O, N, S, P, F, Cl, Br, and I, allows the calculation of the molecular polarizability of solvents from their chemical composition alone, without any other structural consideration. The average errors are 2.