Synthesis of vanillin derivatives with 1,2,3-triazole fragments and evaluation of their fungicide and fungistatic activities.

Vanillin is the main component of natural vanilla extract and is responsible for its flavoring properties. Besides its well-known applications as an additive in food and cosmetics, it has also been reported that vanillin can inhibit fungi of clinical interest, such as Candida spp., Cryptococcus spp., Aspergillus spp., as well as dermatophytes. Thus, the present work approaches the synthesis of a series of vanillin derivatives with 1,2,3-triazole fragments and the evaluation of their antifungal activities against Candida albicans, Candida glabrata, Candida parapsilosis, Candida tropicalis, Cryptococcus neoformans, Cryptococcus gattii, Trichophyton rubrum, and Trichophyton interdigitale strains. Twenty-two vanillin derivatives were obtained, with yields in the range of 60%-91%, from copper(I)-catalyzed alkyne-azide cycloaddition (CuAAC) click reaction between two terminal alkynes prepared from vanillin and different benzyl azides. In general, the evaluated compounds showed moderate activity against the microorganisms tested, with minimum inhibitory concentration (MIC) values ranging from 32 to >512 µg mL-1 . Except for compound 3b against the C. gattii R265 strain, all vanillin derivatives showed fungicidal activity for the yeasts tested. The predicted physicochemical and ADMET (absorption, distribution, metabolism, excretion, and toxicity) properties for the compounds indicated favorable profiles for drug development. In addition, a four-dimensional structure-activity relationship (4D-SAR) analysis was carried out and provided useful insights concerning the structures of the compounds and their biological profile. Finally, molecular docking calculations showed that all compounds bind favorably at the lanosterol 14α-demethylase enzyme active site with binding energies ranging from -9.1 to -12.2 kcal/mol.

Leishmanicidal and cytotoxic activities and 4D-QSAR of 2-arylidene indan-1,3-diones.

The indan-1,3-dione and its derivatives are important building blocks in organic synthesis and present important biological activities. Herein, the leishmanicidal and cytotoxicity evaluation of 16 2-arylidene indan-1,3-diones is described. The compounds were evaluated against the leukemia cell lines HL60 and Nalm6, and the most effective ones were 2-(4-nitrobenzylidene)-1H-indene-1,3(2H)-dione (4) and 4-[(1,3-dioxo-1H-inden-2(3H)-ylidene)methyl]benzonitrile (10), presenting IC50 values of around 30 µmol/L against Nalm6. The leishmanicidal activity was assessed on Leishmania amazonensis, with derivative 4 (IC50 = 16.6 µmol/L) being the most active. A four-dimensional quantitative structure-activity analysis (4D-QSAR) was applied to the indandione derivatives, through partial least-squares regression. The statistics presented by the regression models built with the selected field descriptors of Coulomb (C) and Lennard-Jones (L) nature, considering the activities against L. amazonensis, HL60, and Nalm6 leukemia cells, were, respectively, R2 = 0.88, 0.92, and 0.98; Q2 = 0.83, 0.88, and 0.97. The presence of positive Coulomb descriptors near the carbonyl groups indicates that these polar groups are related to the activities. Besides, the presence of positive Lennard-Jones descriptors close to substituents R3 or R1 indicates that bulky nonpolar substituents in these positions tend to increase the activities. This study provides useful insights into the mode of action of indandione derivatives for each biological activity involved.

Four-dimensional structure-activity relationship model to predict HIV-1 integrase strand transfer inhibition using LQTA-QSAR methodology.

Despite highly active antiretroviral therapy (HAART) implementation, there is a continuous need to search for new anti-HIV agents. HIV-1 integrase (HIV-1 IN) is a recently validated biological target for AIDS therapy. In this work, a four-dimensional quantitative structure-activity relationship (4D-QSAR) study using the new methodology named LQTA-QSAR approach with a training set of 85 HIV-1 IN strand transfer inhibitors (INSTI), containing the ß-diketo acid (DKA) substructure, was carried out. The GROMACS molecular dynamic package was used to obtain a conformational ensemble profile (CEP) and LQTA-QSAR was employed to calculate Coulomb and Lennard-Jones potentials and to generate the field descriptors. The partial least-squares (PLS) regression model using 14 field descriptors and 8 latent variables (LV) yielded satisfactory statistics (R2= 0.897, SEC = 0.270, and F = 72.827), good performance in internal (QLOO2 = 0.842 and SEV = 0.314) and external prediction (Rpred2 = 0.839, SEP = 0.384, AREpred = 4.942%, k = 0.981, k' = 1.016, and |R02 ­ R0'2 = 0.0257). The QSAR model was shown to be robust (leave-N-out cross validation; average QLNO2 = 0.834) and was not built by chance (y-randomization test; R2 intercept = 0.109; Q2 intercept = -0.398). Fair chemical interpretation of the model could be traced, including descriptors related to interaction with the metallic cofactors and the hydrophobic loop. The model obtained has a good potential for aid in the design of new INSTI, and it is a successful example of application of LQTA-QSAR as an useful tool to be used in computer-aided drug design (CADD).

The receptor-dependent LQTA-QSAR: application to a set of trypanothione reductase inhibitors.

A new Receptor-Dependent LQTA-QSAR approach, RD-LQTA-QSAR, is proposed as a new 4D-QSAR method. It is an evolution of receptor independent LQTA-QSAR. This approach uses the free GROMACS package to carry out molecular dynamics simulations and generates a conformational ensemble profile for each compound. Such an ensemble is used to build molecular interaction field-based QSAR models, as in CoMFA. To show the potential of this methodology, a set of 38 phenothiazine derivatives that are specific competitive T. cruzi trypanothione reductase inhibitors, was chosen. Using a combination of molecular docking and molecular dynamics simulations, the binding mode of the phenotiazine derivatives was evaluated in a simulated induced fit approach. The ligands alignments were performed using both ligand and binding site atoms, enabling unbiased alignment. The models obtained were extensively validated by leave-N-out cross-validation and y-randomization techniques to test for their robustness and absence of chance correlation. The final model presented Q(2) LOO of 0.87 and R² of 0.92 and a suitable external prediction of [Formula: see text]= 0.78. The adapted binding site obtained is useful to perform virtual screening and ligand structure-based design and the descriptors in the final model can aid in the design new inhibitors.

The use of ANOVA-PCA and DD-SIMCA in the development of corn flour laboratory reference materials.

The development of a collaborative study as a requirement for the preparation of a laboratory reference material candidate is reported in this paper. The evaluation was performed by 13 laboratories invited to quantify the calcium, potassium, magnesium, phosphorus, copper, iron, manganese and zinc; 8 of them presented results for all the analytes under investigation. The data were statistically analyzed by applying the z-score robust technique as recommended by ISO Guide 35. For the potassium element, laboratories 4 and 13 presented questionable results. Laboratory 5 proved to be unsatisfactory for calcium and zinc. ANOVA-PCA and DD-SIMCA were also applied to evaluate stability and interlaboratory studies results, respectively. It has been demonstrated that multivariate data analysis can be successfully applied as an alternative method to the recommendations made by ISO 13528 and ISO Guide 35 with defined confidence intervals.

Multitrophic interactions in the rhizosphere microbiome of wheat: from bacteria and fungi to protists.

Plants modulate the soil microbiota by root exudation assembling a complex rhizosphere microbiome with organisms spanning different trophic levels. Here, we assessed the diversity of bacterial, fungal and cercozoan communities in landraces and modern varieties of wheat. The dominant taxa within each group were the bacterial phyla Proteobacteria, Actinobacteria and Acidobacteria; the fungi phyla Ascomycota, Chytridiomycota and Basidiomycota; and the Cercozoa classes Sarcomonadea, Thecofilosea and Imbricatea. We showed that microbial networks of the wheat landraces formed a more intricate network topology than that of modern wheat cultivars, suggesting that breeding selection resulted in a reduced ability to recruit specific microbes in the rhizosphere. The high connectedness of certain cercozoan taxa to bacteria and fungi indicated trophic network hierarchies where certain predators gain predominance over others. Positive correlations between protists and bacteria in landraces were preserved as a subset in cultivars as was the case for the Sarcomonadea class with Actinobacteria. The correlations between the microbiome structure and plant genotype observed in our results suggest the importance of top-down control by organisms of higher trophic levels as a key factor for understanding the drivers of microbiome community assembly in the rhizosphere.

Simple quantitative structure-property relationship (QSPR) modeling of 17O carbonyl chemical shifts in substituted benzaldehydes compared to DFT and empirical approaches.

The geometry of 50 substituted benzaldehydes was optimized at the semiempirical PM3 level, and various electronic and steric descriptors accounting for properties of the benzene ring, aldehyde group, and their connecting carbon-carbon bond were calculated. Quantitative structure-property relationships (QSPR) between (17)O carbonyl chemical shifts and these descriptors were established using partial least-squares regression and principal component regression. These two parsimonious QSPR models were comparable with the literature empirical model and DFT (density functional theory) and capable of predicting (17)O chemical shifts for 10 benzaldehydes. Principal component analysis, hierarchical cluster analysis, and crystal structure data retrieved from the Cambridge Structural Database were additional methods for chemical verification of the regression models. The QSPR models are recommended as being more reliable than and superior to the empirical and DFT models due to the results of all validations, simplicity, and short time that regressions need for (17)O shift prediction.

Optimization of extraction of high-ester pectin from passion fruit peel (Passiflora edulis flavicarpa) with citric acid by using response surface methodology.

A central composite design was employed to optimize the extraction of pectin with citric acid. The independent variables were citric acid concentration (0.086-2.91% w/v) and extraction time (17-102 min). The combined effect of these variables on the degree of esterification was investigated. Results have shown that the generated regression models adequately explained the data variation and significantly represented the actual relationship between the independent variables and the responses. Besides that, the citric acid concentration was the most important factor to affect the degree of esterification, as it exerted a significant influence on the dependent variable. Lower citric acid concentration increased the pectin degree of esterification. The surface response showed the relationships between the independent variables, and thus responses were generated. Through this surface, the satisfactory condition of 0.086% w/v citric acid for 60 min was established for extraction of high-ester yellow passion fruit pectin.

Classification of commercial Catuaba samples by NMR, HPLC and chemometrics.

For over a century, Catuaba has been used in Brazilian folk medicine as an aphrodisiac even though the identity of the plant material employed is often uncertain. The species recommended by the Brazilian Pharmacopeia is Anemopaegma arvense (Bignoniaceae), but many other plants, regionally known as Catuaba, are commercialised. Frequently, the quality control of such a complex system is based on chemical markers that do not supply a general idea of the system. With the advent of the metabolomics approach, a global analysis of samples becomes possible. It appears that (1)H-NMR is the most useful method for such application, since it can be used as a wide-spectrum chemical analysis technique. Unfortunately, the generated spectra is complex so a possible approach is to look at the metabolite profile as a whole using multivariate methods, for example, by application of principal component analysis (PCA). In the present paper, we describe for the first time a proton high-resolution magic angle spinning nuclear magnetic resonance ((1)H-HR-MAS NMR) method coupled with PCA for the metabolomic analysis of some commercial Catuaba samples, which provided a reduction in the time required for such analysis. A comparative study of HPLC, HR-MAS and liquid-NMR techniques is also reported.

In vitro cytotoxicity and structure-activity relationship approaches of ent-kaurenoic acid derivatives against human breast carcinoma cell line.

In this study, ent-kaurenoic acid derivatives were obtained by microbial transformation methodologies and tested against breast cancer cell lines (MCF-7). A multivariate quantitative-structure activity relationship (QSAR) analysis was performed taking into account both microbial transformation derivatives and other analogues previously reported in literature to give some insight into the main features behind the cytotoxic activity displayed by kaurane-type diterpenes against MCF-7 cells. The partial least square regression (PLS) method was employed in the training set and the best PLS model was built with a factor describing 69.92% of variance and three descriptors (logP, εHOMO and εHOMO-1) selected by the Ordered Predictors Selection (OPS) algorithm. The QSAR model provided reasonable regression (Q2 = 0.64, R2 = 0.72, SEC = 0.29 and SEV = 0.33). The model was validated by leave-N-out cross-validation, y-randomization and external validation (R2pred = 0.89 and SEP = 0.27). The selected descriptors indicated that the activity was mainly related to electronic parameters (HOMO and HOMO-1 molecular orbital energies), as well as to logP. These findings suggest that higher activity values are directly related with both higher logP and frontier orbital energy values. The positive relationship between these orbitals and the activity suggests that the ent-kaurenoic acid analogues interaction with the target involves charge displacement, which is entirely consistent with the literature. Based on these findings, three compounds were proposed and one of them was synthesized and tested. The experimental result confirmed the activity predicted by the model.

Multivariate classification of cigarettes according to their elemental content determined by inductively coupled plasma optical emission spectrometry.

In this paper we describe our study on the characterization of cigarette samples according to their mineral content. Acid digestion assisted by microwaves was employed, and inductively coupled plasma optical emission spectrometry was the analytical technique used for the determination of Al, Ba, Ca, Cu, Fe, K, Mg, Mn, Na, P and Sr in conventional, light, and flavorized cigarettes. Multivariate techniques, such as hierarchical clusters analysis (HCA) and principal-component analysis (PCA), were applied to discriminate among different types of cigarettes. Cluster analysis and principal-component analysis showed differences in cigarettes according to the type and mineral composition. The cigarette samples were divided within the 3 groups according to their mineral composition. Ca, Sr, Cu, K and Na were the most important elements for cigarette classification, and only these 5 variables were sufficient for the classification and discrimination of the evaluated types of cigarettes.

Molecular graphics approach to bacterial AcrB protein-beta-lactam antibiotic molecular recognition in drug efflux mechanism.

AcrAB-TolC is the most important multidrug efflux pump system of Gram-negative bacteria, responsible for their resistance to lipophilic and amphiphilic drugs. In this work, a molecular graphics study of the pump components AcrB and TolC, 16 beta-lactam antibiotics and 7 other substrates, as well as of AcrB-substrate complexes, was performed in order to give a mechanistic proposal for the efflux process at molecular level. AcrAB-TolC is a proton-dependent electromechanical device which opens to extrude drugs from the bacterial periplasm and perhaps cytoplasm, by means of a series of structural changes within the complex and its components AcrA, AcrB and TolC. These changes are initiated by protonation and disruption of salt bridges and certain hydrogen bonds, and are followed by conformational changes in which a number of intra- and interchain interactions are rearranged. Molecular properties of beta-lactams accounting for their lipophilicity, shape/conformation and other sterical features, polar/charge group distribution and other electronic properties, and hydrogen bonding potency determine their interaction with polar headpieces of the inner membrane, recognition and binding to receptors of AcrB and TolC. The orientation of the beta-lactam molecular dipoles with respect the efflux system is maintained during the drug efflux. Elongated cylinder-like beta-lactam antibiotics with lipophylic side chains, a significantly negative component of the dipole moment and low hydrogen bonding capacity seem to be good substrates of AcrAB-TolC.

"Omics" Prospective Monitoring of Bariatric Surgery: Roux-En-Y Gastric Bypass Outcomes Using Mixed-Meal Tolerance Test and Time-Resolved (1)H NMR-Based Metabolomics.

Roux-en-Y gastric bypass (RYGB) surgery goes beyond weight loss to induce early beneficial hormonal changes that favor glycemic control. In this prospective study, ten obese subjects diagnosed with type 2 diabetes underwent bariatric surgery. Mixed-meal tolerance test was performed before and 12 months after RYGB, and the outcomes were investigated by a time-resolved hydrogen nuclear magnetic resonance ((1)H NMR)-based metabolomics. To the best of our knowledge, no previous omics-driven study has used time-resolved (1)H NMR-based metabolomics to investigate bariatric surgery outcomes. Our results presented here show a significant decrease in glucose levels after bariatric surgery (from 159.80 ± 61.43 to 100.00 ± 22.94 mg/dL), demonstrating type 2 diabetes remission (p < 0.05). The metabolic profile indicated lower levels of lactate, alanine, and branched chain amino acids for the operated subject at fasting state after the surgery. However, soon after food ingestion, the levels of these metabolites increased faster in operated than in nonoperated subjects. The lipoprotein profile achieved before and after RYGB at fasting was also significantly different, but converging 180 min after food ingestion. For example, the very low-density lipoprotein, low-density lipoprotein, N-acetyl-glycoproteins, and unsaturated lipid levels decreased after RYGB, while phosphatidylcholine and high-density lipoprotein increased. This study provides important insights on RYGB surgery and attendant type 2 diabetes outcomes using an "omics" systems science approach. Further research on metabolomic correlates of RYGB surgery in larger study samples is called for.

Correlation of animal diet and fatty acid content in young goat meat by gas chromatography and chemometrics.

The meat fatty acids (FA) profiles of caprines submitted to different dietary treatments were determined by gas chromatography. The data were treated by Chemometrics to consider all variables together. The contents of saturated FA (SFA), monounsaturated FA (MUFA), polyunsaturated FA (PUFA), omega-3 (n-3) FA, and omega-6 (n-6) FA in 32 samples were analyzed. PUFA:SFA and n-6:n-3 ratios were also considered. The multivariate methods of hierarchical cluster analysis (HCA) and principal component analysis (PCA) were used to analyze the experimental results. HCA can group samples according to their basic composition, and PCA can explain the relationship among the dietary treatments according to the meat fatty acid composition. Treatment 1 presented the highest n-6 FA concentration, PUFA:SFA, and n-6:n-3 ratios, and the lowest MUFA and n-3 concentrations. Opposite results were observed for treatment 4. Treatments 2 and 3 were highly similar with differences mainly in SFA and MUFA concentrations.

Blood Metabolome Changes Before and After Bariatric Surgery: A (1)H NMR-Based Clinical Investigation.

Excessive body fat and obesity have adverse health effects and result in significant morbidity such as type 2 diabetes mellitus. The health burden of obesity can be reduced with the Roux-en-Y gastric bypass (RYGB) weight-loss bariatric surgery. Little is known on the molecular changes that occur at the metabolome level before and after bariatric surgery, with a view to clinical biomarker development. Hence, we employed a metabolomics approach in 10 obese diabetic patients who underwent bariatric surgery. Metabolomics data were obtained by T2- and diffusion-edited hydrogen nuclear magnetic resonance ((1)H NMR) spectra to monitor the metabolic and lipoprotein profiles, and gas chromatography-mass spectrometry (CG-MS) to access the fatty acid profile before and 12 months after RYGB. Using hierarchical partial least squares discriminant analysis, we found that RYGB induces several key metabolic alterations associated with glucose homeostasis, as well as fatty acid and amino acid metabolism. The levels of lactate (Krebs' intermediate cycle) decreased after RYGB. The leucine, isoleucine, valine, lactate, and glucose levels were higher in the samples before RYGB (p<0.05). Additionally, the levels of very low-density lipoprotein, unsaturated lipids, and N-acetyl-glycoprotein were higher before RYGB. By contrast, levels of the high-density lipoprotein and phosphatidylcholine were higher after bariatric surgery. These results collectively offer important holistic integrative biology data to develop future clinically relevant metabolomics biomarkers related to bariatric surgery in connection with obesity.

A priori molecular descriptors in QSAR: a case of HIV-1 protease inhibitors. I. The chemometric approach.

A quantitative structure-activity relationship (QSAR) study on 48 peptidic HIV-1 protease inhibitors was performed. Fourteen a priori molecular descriptors were used to build QSAR models. Hierarchical cluster analysis (HCA), principal component analysis (PCA) and partial least squares (PLS) regression were employed. PLS models with 32/16 (model I) and 48/0 (model II) molecules in the training/external validation set were constructed. The a priori molecular descriptors were related to two energetic variables using PLS. HCA and PCA on data from model II classified the inhibitors as slightly, moderately and highly active; three principal components, the chemical nature of which has been highlighted, are enough to describe the enzyme-inhibitor binding. Model I (r(2)=0.91, q(2)=0.84) is comparable to literature models obtained by various QSAR softwares, which justified the use of a priori descriptors.

A priori molecular descriptors in QSAR: a case of HIV-1 protease inhibitors. II. Molecular graphics and modeling.

Molecular graphics and modeling methods illustrated the chemical background of the a priori approach from part I, and visualized steric and electronic enzyme-inhibitor relationships at qualitative and quantitative level for 34 and its derivatives. The enzyme-inhibitor electron density overlap occurs at 1.5-5.5A cut-off distance, beyond van der Waals radii. Derivatives of 34 exhibit linear relationships between biological activity, molecular size and number of intermolecular interactions.

Structure-activity relationships (SAR) of contraceptive progestogens studied with four different methods using calculated physicochemical parameters.

Structure-activity relationships (SAR) of the contraceptive progestogens for (I) oral contraceptive activity (OCA), (II) androgenic effect, and (III) binding affinity for sex hormone binding globulin (SHBG) were studied using four different methods: principal component analysis (PCA), hierarchical cluster analysis (HCA), neural networks (NN), and electronic indices method (EIM) employing descriptors calculated by the semi-empirical Austin Model I (AM1) method. An additional set of molecules was used to check the reliability of the results obtained for OCA by PCA. Using PCA, three different sets of descriptors were found to correlate with the three different biological activities, I-III, indicating that the interaction between the receptor and the progestogen must depend on the type of biological activity. The descriptors selected by PCA were also employed for SAR analysis of the contraceptive progestogens using two other methods, HCA and NN. Both HCA and NN correctly classified high activity molecules as different from low activity ones. Thus, those descriptors selected by PCA work well in the other two methods of classification. Using the sign of p, a difference of electron densities of selected molecular orbitals in a specified region in a molecule, it was possible to discriminate high activity molecules from low activity molecules in the three different types of activities studied, I-III, with one exception.

Multiway calibration for creatinine determination in human serum using the Jaffé reaction.

Second-order calibration and multivariate spectroscopic-kinetic measurements in the visible region are proposed to improve the Jaffé method for creatinine assay. Analyses performed on synthetic mixtures containing bilirubin, glucose, and albumin confirm that second-order calibration is useful for creatinine determination in human serum. Quantitative determinations of creatinine with the parallel factor analysis (PARAFAC) and direct trilinear decomposition (TLD) methods were compared. It is shown that both methods can be used for creatinine determination in human serum, with an SEP (standard error of prediction) of 2.22 and coefficient of variability of 6.14% for PARAFAC, and an SEP of 2.38 and coefficient of variability of 6.57% for TLD [corrected].

Comparison between cachaça and rum using pattern recognition methods.

The differentiation between cachaça and rum using analytical data referred to alcohols (methanol, propanol, isobutanol, and isopentanol), acetaldehyde, ethyl acetate, organic acids (octanoic acid, decanoic acid, and dodecanoic acid), metals (Al, Ca, Co, Cu, Cr, Fe, Mg, Mn, Ni, Na, and Zn), and polyphenols (protocatechuic acid, sinapaldehyde, syringaldehyde, ellagic acid, syringic acid, gallic acid, (-)-epicatechin, vanillic acid, vanillin, p-coumaric acid, coniferaldehyde, coniferyl alcohol, kaempferol, and quercetin) is described. The organic and metal analyte contents were determined in 18 cachaça and 21 rum samples using chromatographic methods (GC-MS, GC-FID, and HPLC-UV-vis) and inductively coupled plasma atomic emission spectrometry, respectively. The analytical data of the above compounds, when treated by principal component analysis, hierarchical cluster analysis, discriminant analysis, and K-nearest neighbor analysis, provide a very good discrimination between the two classes of beverages.