Babassu (Attalea glassmanii 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. PURPOSE: In the present study, the objective was to comparatively analyze the microcirculatory effects of unrefined babassu oil with olive oil in microcirculation and liver of male hamsters of the species Mesocricetus auratus, checking the permeability to macromolecules after ischemia-reperfusion (I/R) without and with topical application of histamine 5 × 10-6 M. This is an experimental study, using as model the hamster's cheek pouch, which was prepared for intravital microscopy. The hamsters were divided into seven groups and orally treated for 14 days, twice a day (at 8 AM and 4 PM), orally received treatments in the following doses: unrefined babassu oil (BO) 0.02 mL/dose (group BO-2), 0.06 mL/dose (group BO-6), and 0.18 mL/dose (BO-18 group); extra virgin olive oil (OI) 0.02 mL/dose (group OI-2), 0.06 mL/dose (group OI-6), and 0.18 mL/dose (OI-18 group); and mineral oil (MO) 0.18 mL/dose (MO-18 group). The observations were made on the 15th day on the hamsters' cheek pouch; the increase of vascular permeability induced by I/R with and without histamine application was evaluated, and in the liver the biological material was collected aseptically then fixed in 10% buffered formalin. RESULTS: Microcirculatory analyses showed a significant reduction in the number of leaks after I/R with and without the topical use of histamine in animals treated with unrefined BO 0.06 mL/dose (BO-6) and 0.18 mL/dose (BO-18) compared to animals treated with OI. The BO group (p < 0.001) presented a dose-response relationship for decreasing leaks after I/R with and without topical use of histamine. Histological liver analyses showed no fat deposition changes in any of the treatment groups. Phytochemical analyses evidenced a chemical compound (C31H60NO8) in unrefined BO but not in OI. CONCLUSIONS: This experiment demonstrates the protective effect of unrefined BO on the microcirculatory system and its greater dose effect than that of OI. Finding a chemical compound (C31H60NO8) that is present in BO but not in OI opens the possibility of investigating whether this chemical compound was responsible for the protective effect on membrane permeability.

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. The results showed that all the columns provide good estimations of the log P(o/w) for most of the compounds included in this study. The Gemini NX column has been selected to calculate log P(o/w) values of the set of studied drugs, and really good correlations between the determined log P(o/w) values and those considered as reference were obtained, proving the ability of the procedure for the lipophilicity assessment of bioactive compounds with very different structures and functionalities.

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. (logk)(0) and P(s)(N) parameters are characteristic of the chromatographic system and are determined from the retention of a selected set of 12 compounds. Then, the p value of a solute determined in a properly characterized system is easily derived from the retention factor data. Solute p values are slightly dependent on the chromatographic system but they are linearly related to those obtained in the reference system (Spherisorb ODS-2 column and acetonitrile as organic modifier). Therefore, they can be easily transferred from any experimental system to the reference one. A Quantitative Structure-Property Relationship study reveals that the p parameter in the reference chromatographic system depends, mainly, on the hydrophobicity of the compound, expressed as the n-octanol/water partition coefficient (logP(o/w)), and five additional structural descriptors which can be easily calculated through the CODESSA program from the chemical structure of the solute. In this work the p descriptors of a wide set of structurally different organic compounds have been determined in several chromatographic systems and transferred to the reference one from these and the CODESSA structural parameters. The logP(o/w) values have been determined. The obtained values agree with those determined from classical experimental techniques and validate the new method as a useful tool to determine the hydrophobicity of a wide variety of compounds in a broad logP(o/w) range.

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 0.998 (3.89), 0.999 (2.86), and 0.997 (3.90) for the training, prediction, and validation sets, respectively. Five of the descriptors of the model correspond to the amino acids and the other two to the cations; they encode information clearly related to the cation-amino acid interactions responsible for the binding affinity values analyzed. A detailed analysis of results shows that, despite the different nature of the bonding between the metal cations and the amino acids, the neural networks used are capable of predicting accurately the property studied.

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) (302.2-479.8 kJ mol(-1)), while potassium has the lowest values (115.6-192.4 kJ mol(-1)). The results of the calculations have been compared with both experimental and theoretical values from the literature when they are available. Also, an energy partitioning scheme has been used to evaluate the different factors that have an influence on the value of the amino acid-cation binding energy, mainly the preorganization energy of the ligand and the interaction energy between the cation and the different donor atoms and/or pi system of the amino acid. The procedure developed here can be used with a wide range of metal cations, including those pertaining to the first and second transition series.

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. The quality of prediction of both approaches has also been evaluated, with the second one giving more accurate results for the most lipophilic vitamins. This model allows establishing the best conditions to separate and determine simultaneously some fat-soluble vitamins in dairy foods.

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. Good results were obtained from both methodologies, being the best those from CNN, that give a rms error of 6.54 (R2 = 0.954) and an average error of 3.57% for the training set; for the prediction set the rms error is 8.61 (R2 = 0.914) and the average error 4.39%. The models derived from the two approaches contain descriptors that belong to the same classes, constitutional and electrostatic. The comparison with the results obtained from high level theoretical methods shows that the values obtained from the QSPR approach are very similar and even better, especially when the sets compared are large and contain compounds of different chemical structure. These good results shows that, despite the complexity of Li+-base interactions, the proposed models contain descriptors which encode properly the characteristics of the molecules directly related to their gas-phase basicity against the Li cations.

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. Good correlations were obtained for the four parameters studied, and the corresponding values of R(2) and standard deviations are better or similar than those derived from other theoretical bases. All models were validated by external prediction sets. The proposed QSPR models, both by MLRA and CNN, contain analogous descriptors encoding similar information, that agree with the accepted physicochemical meaning of the Abraham parameters; however, some descriptors which encode information that is not associated with this physicochemical meaning are also included in the QSPR models.

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) = 0.954, F = 889, n = 45, and SD = 0.27). The study shows that log P(o/w) and hydrogen bond acidity of the solutes are the most relevant descriptors to predict p values. This p parameter is embodied in a general equation to predict retention in reversed-phase liquid chromatography (RP-HPLC). It describes analyte retention exclusively on the basis of mobile phase/analyte/stationary phase polar interactions. Equations and procedures to determine polarity of both chromatographic phases had been successfully developed previously. Therefore, the proposed QSPR model for p estimation becomes a very useful tool in RP-HPLC optimization of procedures and methods in the everyday analytical work.

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. The average absolute relative errors are 1.37% (R(2) = 0.8978; SD: 6.67) and 1.13% (R(2) = 0.9076; SD: 4.26) for the working set (62 compounds) and the prediction set (16 compounds), respectively. These results are better than those obtained from DFT calculations, QSAR approach, and correlations with Hammet parameters.

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 2.31% and 1.93% for the working set of 340 solvents and the prediction set of 86 solvents, respectively. Semiempirical quantum methods, such as, AM1, PM3, and MNDO, gave errors of about 35%. The density functional theory (DFT) calculations give better results than the semiempirical ones but poorer results than those obtained by the additive approach.