Foodinformatic prediction of the retention time of pesticide residues detected in fruits and vegetables using UHPLC/ESI Q-Orbitrap.

The present work describes the development of an in silico model to predict the retention time (tR) of a large Compound DataBase (CDB) of pesticides detected in fruits and vegetables. The model utilizes ultrahigh-performance liquid chromatography electrospray ionization quadrupole-Orbitrap (UHPLC/ESI Q-Orbitrap) mass spectrometry (MS) data. The available CDB was properly curated, and the pesticides were represented by conformation-independent molecular descriptors. In an attempt to improve the model predictions, the best four MLR models obtained were subjected to a consensus analysis. The optimal model was evaluated by means of the coefficient of determination and the residual standard deviation in calibration, validation, and prediction, along other internal and external validation criteria to accomplish the guidelines defined by the Organization for Economic Co-operation and Development. Finally, the in silico model was applied to predict the tR of an external set of 57 pesticides.

The Use of the Index of Ideality of Correlation to Build Up Models for Bioconcentration Factor.

We establish a QSPR analysis for the bioconcentration factor of 851 heterogeneous structural compounds. Linear models are proposed via two different approaches: i. the optimal descriptor method implemented in CORAL, and ii. multivariable linear regressions on the best molecular descriptors found with the Replacement Method on 44,216 structural descriptors. Such variables are derived with different freely available softwares, such as PaDEL, PyDescriptor, Mold2 , QuBiLs-MAS and ISIDA/Fragmentor. The same validation set is employed in order to compare the predictive performance between the so-obtained CORAL and RM based models. Finally, the comparison of several models for the bioconcentration factor confirms the ability of the so-called index of ideality of correlation to be a criterion of predictive potential in Quantitative Structure-Property Relationships.

Foodinformatics: Quantitative Structure-Property Relationship Modeling of Volatile Organic Compounds in Peppers.

The aim of this work was the foodinformatic (chemoinformatic) modeling of volatile organic compounds (VOCs) of different samples of peppers based on a quantitative structure-property relationship (QSPR) for the retention indices of 273 identified compounds. The experimental retention indices were measured by means of comprehensive two-dimensional gas chromatography combined with quadrupole-mass spectrometry (GC × GC/qMS) using the BPX5 and BP20 column coupled system. All the VOCs were represented by means of both conformation-independent molecular descriptors and molecular fingerprints calculated in the Dragon and PaDEL-Descriptor software. The dataset was divided into training, validation and test sets of molecules according to the Balanced Subsets Method (BSM). Subsequently, the V-WSP unsupervised variable reduction method was used to reduce the presence of multicollinearity, redundancy, and noise in the initial pool of 4,336 molecular descriptors and fingerprints. Using this method, a reduced pool of 1,664 was submitted to the supervised selection by means of the replacement method (RM) variable subset selection in order to define a four-descriptor model. The quality of the model was measured by means of the coefficient of determination and the root-mean-square deviation in fitting ( R train 2 = 0 . 879 and RMSD train = 72.1 ), validation ( R val 2 = 0 . 832 and RMSD val = 91.7 ), and prediction ( R test 2 = 0 . 915 and RMSD test = 55.4 ). The negligible differences among the parameters in the three sets indicate a stable and predictive QSPR model. This quantitative structure-activity relationship was developed keeping in mind the five principles defined by the Organization for Economic Co-operation and Development (OECD) to make it applicable. PRACTICAL APPLICATION: This predictive mathematical model developed from the retention indices of 273 volatile organic compounds (VOCs) detected in pepper samples could be useful for chromatographers working on the identification of other common VOCs in peppers or other foods by means of comprehensive two-dimensional gas chromatography combined with quadrupole-mass spectrometry (GC × GC/qMS) using a bi-dimensional stationary phase coupled system (BPX5 and BP20).

Conformation-Independent QSPR Approach for the Soil Sorption Coefficient of Heterogeneous Compounds.

We predict the soil sorption coefficient for a heterogeneous set of 643 organic non-ionic compounds by means of Quantitative Structure-Property Relationships (QSPR). A conformation-independent representation of the chemical structure is established. The 17,538 molecular descriptors derived with PaDEL and EPI Suite softwares are simultaneously analyzed through linear regressions obtained with the Replacement Method variable subset selection technique. The best predictive three-descriptors QSPR is developed on a reduced training set of 93 chemicals, having an acceptable predictive capability on 550 test set compounds. We also establish a model with a single optimal descriptor derived from CORAL freeware. The present approach compares fairly well with a previously reported one that uses Dragon descriptors.

Is there a relationship between sweet taste and seizures? Anticonvulsant and proconvulsant effects of non-nutritive sweeteners.

From a virtual screening campaign, a number of artificial and natural sweeteners were predicted as potential anticonvulsant agents with protective effects in the seizure animal model Maximal Electroshock Seizure (MES) test. In all cases, the predictions were experimentally confirmed in the aforementioned preclinical seizure model. The article reviews and expands previous reports from our group on anticonvulsant activity of those non-nutritive sweeteners, illustrating the potential of virtual screening approaches to propose new medical uses of food additives. This constitutes a particular case of knowledge-based drug repositioning, which may greatly shorten the development time and investment required to introduce novel medications to the pharmaceutical market. We also briefly overview evidence on possible molecular explanations on the anticonvulsant and proconvulsant effects of different non-nutritive sweeteners. Our analysis -based on Swanson's ABC model- suggests that group I metabotropic glutamate receptors and carbonic anhydrase isoform VII (both proposed or validated molecular targets of antiepileptic drugs) might be involved in the anticonvulsant effect of artificial sweeteners. The first hypothesis is in line with recent advances on development of selective modulators of group I metabotropic glutamate receptors as potential antiepileptic agents.

Systematic Comparison of the Performance of Different 2D and 3D Ligand-Based Virtual Screening Methodologies to Discover Anticonvulsant Drugs.

Virtual screening encompasses a wide range of computational approaches aimed at the high-throughput, cost-efficient exploration of chemical libraries or databases to discover new bioactive compounds or novel medical indications of known drugs. Here, we have performed a systematic comparison of the performance of a large number of 2D and 3D ligand-based approaches (2D and 3D similarity, QSAR models, pharmacophoric hypothesis) in a simulated virtual campaign on a chemical library containing 50 known anticonvulsant drugs and 950 decoys with no previous reports of anticonvulsant effect. To perform such comparison, we resorted to Receiver Operating Characteristic curves. We also tested the relative performance of consensus methodologies. Our results indicate that the selective combination of the individual approaches (through voting and ranking combination schemes) significantly outperforms the individual algorithms and/or models. Among the best-performing individual approaches, 2D similarity search based on circular fingerprints and 3D similarity approaches should be highlighted. Combining the results from different query molecules also led to enhanced enrichment.

The Monte Carlo method based on eclectic data as an efficient tool for predictions of endpoints for nanomaterials - two examples of application.

The theoretical predictions of endpoints related to nanomaterials are attractive and more efficient alternatives for their experimental determinations. Such type of calculations for the "usual" substances (i.e. non nanomaterials) can be carried out with molecular graphs. However, in the case of nanomaterials, descriptors traditionally used for the quantitative structure--property/activity relationships (QSPRs/QSARs) do not provide reliable results since the molecular structure of nanomaterials, as a rule, cannot be expressed by the molecular graph. Innovative principles of computational prediction of endpoints related to nanomaterials extracted from available eclectic data (technological attributes, conditions of the synthesis, etc.) are suggested, applied to two different sets of data, and discussed in this work.

QSAR models for thiophene and imidazopyridine derivatives inhibitors of the Polo-Like Kinase 1.

The inhibitory activity of 103 thiophene and 33 imidazopyridine derivatives against Polo-Like Kinase 1 (PLK1) expressed as pIC50 (-logIC50) was predicted by QSAR modeling. Multivariate linear regression (MLR) was employed to model the relationship between 0D and 3D molecular descriptors and biological activities of molecules using the replacement method (MR) as variable selection tool. The 136 compounds were separated into several training and test sets. Two splitting approaches, distribution of biological data and structural diversity, and the statistical experimental design procedure D-optimal distance were applied to the dataset. The significance of the training set models was confirmed by statistically higher values of the internal leave one out cross-validated coefficient of determination (Q2) and external predictive coefficient of determination for the test set (Rtest2). The model developed from a training set, obtained with the D-optimal distance protocol and using 3D descriptor space along with activity values, separated chemical features that allowed to distinguish high and low pIC50 values reasonably well. Then, we verified that such model was sufficient to reliably and accurately predict the activity of external diverse structures. The model robustness was properly characterized by means of standard procedures and their applicability domain (AD) was analyzed by leverage method.

Amino acid profiles and quantitative structure-property relationship models as markers for Merlot and Torrontés wines.

Quantitative structure-property relationships (QSPRs) were applied to the aminograms obtained by HPLC in our laboratories for Torrontés and Merlot wines. Dragon theoretical descriptors were derived for a set of optimized amino acid structures with the purpose of establishing QSPR models. The statistical Replacement Method was used for designing the best multi-parametric linear regression models, which included structural features selected from a pool containing 1497 constitutional, topological, geometrical or electronic molecular descriptors. Predicted QSPR results were in good agreement with experimental amino acid profiles. The developed QSPR approach showed to be of practical value for distinguishing each wine varietal, and for calculating experimentally non-available amino acid concentrations of Torrontés and Merlot wines. It was also useful for assessing wine authenticity; the models were especially suitable for Merlot and Torrontés wines.

QSAR study for carcinogenicity in a large set of organic compounds.

In our continuing efforts to find out acceptable Absorption, Distribution, Metabolization, Elimination and Toxicity (ADMET) properties of organic compounds, we establish linear QSAR models for the carcinogenic potential prediction of 1464 compounds taken from the "Galvez data set", that include many marketed drugs. More than a thousand of geometry-independent molecular descriptors are simultaneously analyzed, obtained with the softwares E-Dragon and Recon. The variable subset selection method employed is the Replacement Method, and also the improved version Enhanced Replacement Method. The established models are properly validated through an external test set of compounds, and by means of the Leave-Group-Out Cross Validation method. In addition, we apply the Y-Randomization strategy and analyze the Applicability Domain of the developed model. Finally, we compare the results obtained in present study with the previous ones from the literature. The novelty of present work relies on the development of an alternative predictive structure-carcinogenicity relationship in a large heterogeneous set of organic compounds, by only using a reduced number of geometry independent molecular descriptors.

An integrated drug development approach applying topological descriptors.

We describe the opportunities posed by computer-assisted drug design in the light of two aspects of the current drug discovery scenario: the decline of innovation due to high attrition rates at clinical stage of development and the combinatorial explosion emerging from exponential growth of feasible small molecules and genome and proteome exploration. We present an overview of recent reports from our group in the field of rational drug development, by using topological descriptors (either alone, or in combination with different 3D approaches) and a diversity of modeling techniques such as Linear Discriminant Analysis and the Replacement Method. Modeling efforts aimed at the integrated prediction of several significant molecular properties in the field of drug discovery, such as pharmacological activity, aqueous solubility, human intestinal permeability and affinity to P-glycoprotein (ABCB1, MDR1) are reviewed. The suitability of conformation-independent descriptors to explore large chemical repositories is highlighted, as well as the opportunities posed by in silico guided drug repurposing.

Topological properties of some PhSeX compounds.

A theoretical study on the series of compounds "PhSeX", where Ph = phenyl, Se = selenium and X = Cl, Br, I, CN or SCN, is reported and compared with previously reported experimental data. The molecular geometry for these PhSeX compounds was studied at the DFT/B3LYP level of calculation by means of the 6-311G(d,p) basis set. The equilibrium structures of the molecules were dependent on the method employed to compare the known solid structures. A topological study of the calculated PhSeX species, based on the AIM theory, was carried out to gain a deeper insight into the bonding nature and to find an explanation for the structural diversity exhibited by these PhSeX compounds. The results reported herein illustrate the subtle differences in the solid-state structures of PhSeX compounds.

Global metabolic response in the bile of pejerrey (Odontesthes bonariensis, Pisces) sublethally exposed to the pyrethroid cypermethrin.

The metabolic profile of Odontesthes bonariensis and its global response to the insecticide cypermethrin were studied using HPLC-MS-based metabolomics. Three experiments using either juveniles or adults of O. bonariensis were performed by exposing fish (6, 24, or 96 h) to sublethal concentrations of cypermethrin (5 or 10 µg/L). Metabolic profiling was performed on either whole bile or aqueous and organic extracts. Chromatography was performed using a C18 column and an ACN/H2O mobile phase. Electrospray ionization (ESI) and atmospheric pressure chemical ionization (APCI) interfaces were used in positive and negative modes. Full scan MS data were processed using the XCMS software, log-transformed, and analyzed using either regression analysis or principal component analysis (PCA). The highest amount of information (1163 peaks) was yielded by analyzing the whole bile with the ESI⁻ interface. Complementary information, useful for metabolite confirmation, was obtained from the aqueous and organic extracts and using the ESI⁺ and APCI interfaces. The bile metabolic profile of O. bonariensis was characterized by some abundant metabolite ions corresponding with taurine conjugated bile acids, which were useful as reference peaks. A characteristic global metabolic response to cypermethrin was identified in the bile of O. bonariensis. A ten-fold or higher variation in abundance was observed in the whole bile of exposed fish for a small group of peaks (32), and these peaks corresponded to an even smaller number of metabolites (nineteen). Both regression analysis and PCA were useful in identifying those peaks, better explaining differences between exposed and control groups, but slight differences were suggested by each of those methods. Using unsupervised PCA scores, we were able to distinguish organisms from each treatment on the basis of the metabolic changes induced by the cypermethrin, this variability being explained mainly by only one principal component (PC3, 17.7 percent total variance). Two cypermethrin metabolites were identified as major contributors within the augmented peaks: the known glucuronide of 4'-hydroxy-cypermethrin and the sulfate of 4'-hydroxycypermethrin, not previously reported in fish bile. The HPLC-MS-based metabolomic approach demonstrated to be a powerful ecotoxicological tool for identifying biological responses to pollutants, discovering new metabolic pathways and proposing specific biomarkers using non model organisms.

QSPR Study of Valproic Acid and Its Functionalized Derivatives.

This work establishes a Quantitative Structure-Property Relationships (QSPR) based analysis with the aim of interpreting both the structural and electronic properties of the polar region of valproic acid and its derivatives, in terms of stabilizing intramolecular interactions related to the involved substituents. We consider ten different calculated properties as dependent variables for the QSPR models: the bond lengths C8 O9 , C8 X10 , and the percentage of s-character of the natural hybrids forming the bonding σ orbitals of the O9 C8 X10 region. The representative descriptors are the charges transferred during donor/acceptor interactions around this function calculated at the B3LYP/6-311++G**(6d,10f) level of theory, and/or hybrid descriptors derived therefrom. The models so established result simple, predictive, and have a quite direct physical meaning.

A comparative QSAR on 1,2,5-thiadiazolidin-3-one 1,1-dioxide compounds as selective inhibitors of human serine proteinases.

Selective inhibitors of target serine proteinases have a potential therapeutic role for the treatment of various inflammatory and related diseases. We develop a comparative quantitative structure-activity relationships based analysis on compounds embodying the 1,2,5-thiadiazolidin-3-one 1,1-dioxide scaffold. By means of classical Molecular Dynamics we obtain the conformation of each lowest-energy molecular structure from which we derive more than a thousand of structural descriptors necessary for building predictive QSAR models. We resort to two different modeling approaches with the purpose of testing the consistency of our results: (a) multivariable linear regressions based on the replacement method and forward stepwise regression, and (b) the calculation of flexible descriptors with the CORAL program. All the models are properly validated by means of standard procedures. The resulting QSAR models are supposed to be of great utility for the rational search and design (including synthesis and/or in vitro biochemical studies) of new effective non-peptidyl inhibitors of serine proteinases.

Inclusion complex of 2-chlorobenzophenone with cyclomaltoheptaose (ß-cyclodextrin): temperature, solvent effects and molecular modeling.

A thermodynamic study of the inclusion process between 2-chlorobenzophenone (2ClBP) and cyclomaltoheptaose (ß-cyclodextrin, ß-CD) was performed using UV-vis spectroscopy, reversed-phase liquid chromatography (RP-HPLC), and molecular modeling (PM6). Spectrophotometric measurements in aqueous solutions were performed at different temperatures. The stoichiometry of the complex is 1:1 and its apparent formation constant (K(c)) is 3846M(-1) at 30°C. Temperature dependence of K(c) values revealed that both enthalpy (ΔH°=-10.58kJ/mol) and entropy changes (ΔS°=33.76J/Kmol) are favorable for the inclusion process in an aqueous medium. Encapsulation was also investigated using RP-HPLC (C18 column) with different mobile-phase compositions, to which ß-CD was added. The apparent formation constants in MeOH-H(2)O (K(F)) were dependent of the proportion of the mobile phase employed (50:50, 55:45, 60:40 and 65:35, v/v). The K(F) values were 419M(-1) (50% MeOH) and 166M(-1) (65% MeOH) at 30°C. The thermodynamic parameters of the complex in an aqueous MeOH medium indicated that this process is largely driven by enthalpy change (ΔH°=-27.25kJ/mol and ΔS°=-45.12J/Kmol). The results of the study carried out with the PM6 semiempirical method showed that the energetically most favorable structure for the formation of the complex is the 'head up' orientation.

Advances in the replacement and enhanced replacement method in QSAR and QSPR theories.

The selection of an optimal set of molecular descriptors from a much greater pool of such regression variables is a crucial step in the development of QSAR and QSPR models. The aim of this work is to further improve this important selection process. For this reason three different alternatives for the initial steps of our recently developed enhanced replacement method (ERM) and replacement method (RM) are proposed. These approaches had previously proven to yield near optimal results with a much smaller number of linear regressions than the full search. The algorithms were tested on four different experimental data sets, formed by collections of 116, 200, 78, and 100 experimental records from different compounds and 1268, 1338, 1187, and 1306 molecular descriptors, respectively. The comparisons showed that one of the new alternatives further improves the ERM, which has shown to be superior to genetic algorithms for the selection of an optimal set of molecular descriptors from a much greater pool. The new proposed alternative also improves the simpler and the lower computational demand algorithm RM.

Study of the structural and electronic properties of valproic acid and new derivatives used as anticonvulsant agents.

The conformational and electronic characteristics of the polar O(9)═C(8)-X(10) moiety in the anticonvulsant valproic acid (Vpa) drug and some of their amides and ester derivatives are analyzed at the B3LYP level using the 6-31+G(d,p) and 6-311++G(d,p) 6d,10f basis sets. Exploring the delocalization of the electron density of the O(9)═C(8)-X(10) moiety by means of ELF, NBO, and AIM calculations, we found that the bending away from coplanarity of the atoms in O(9)═C(8)-X(10) is accompanied by a three-dimensional arrangement of donor and acceptor proton units closing nearly planar pseudorings of four, five, and six members arising from stabilizing interactions around the O(9)═C(8)-X(10) backbone. From the structure-property relationship analysis, we explain the origin of the change in the structural parameters and atomic charges in the polar moiety.

Prediction of drug intestinal absorption by new linear and non-linear QSPR.

In order to minimize the high attrition rate that usually characterizes drug research and development projects, current medicinal chemists aim to characterize both pharmacological and ADME profiles at the beginning of drug R&D initiatives. Thus, the development of ADME High-Throughput Screening in vitro and in silico ADME models has become an important growing research area. Here we present new linear and non-linear predictive QSPR models to predict the human intestinal absorption rate, which are derived from a medium sized, balanced and diverse training set of organic compounds. The structure-property relationships so obtained involve only 4 molecular descriptors, and display an excellent ratio of number of cases to number of descriptors. Their adjustment of the training set data together with the performance achieved during the internal and external validation procedures are comparable to previously reported modeling efforts.

Spectroscopic study of solvent effects on the electronic absorption spectra of flavone and 7-hydroxyflavone in neat and binary solvent mixtures.

The solvatochromic characteristics of flavone and 7-hydroxyflavone were investigated in neat and binary solvent mixtures. The spectral shifts of these solutes were correlated with the Kamlet and Taft parameters (α, ß and π*) using linear solvation energy relationships. The multiparametric analysis indicates that both specific hydrogen bond donor ability and non-specific dipolar interactions of the solvents play an important role in absorption maxima of flavone in pure solvents. The hydrogen bond acceptor ability of the solvent was the main parameter affecting the absorption maxima of 7-hydroxyflavone. The simulated absorption spectra using a TD-DFT method were in good agreement with the experimental ones for both flavones. Index of preferential solvation was calculated as a function of solvent composition. Preferential solvation by ethanol was detected in cyclohexane-ethanol and acetonitrile-ethanol mixtures for flavone and in acetonitrile-ethanol mixtures for 7-hydroxyflavone. These results indicate that intermolecular hydrogen bonds between solute and solvent are responsible for the non-linear variation of the solvatochromic shifts on the mole fraction of ethanol in the analyzed binary mixtures.