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.

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.

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.

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.

QSAR study and molecular design of open-chain enaminones as anticonvulsant agents.

Present work employs the QSAR formalism to predict the ED(50) anticonvulsant activity of ringed-enaminones, in order to apply these relationships for the prediction of unknown open-chain compounds containing the same types of functional groups in their molecular structure. Two different modeling approaches are applied with the purpose of comparing the consistency of our results: (a) the search of molecular descriptors via multivariable linear regressions; and (b) the calculation of flexible descriptors with the CORAL (CORrelation And Logic) program. Among the results found, we propose some potent candidate open-chain enaminones having ED(50) values lower than 10 mg·kg(-1) for corresponding pharmacological studies. These compounds are classified as Class 1 and Class 2 according to the Anticonvulsant Selection Project.

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.

Anti-T. cruzi activities and QSAR studies of 3-arylquinoxaline-2-carbonitrile di-N-oxides.

In a continuing effort to identify new active compounds for combating Chagas disease and other neglected diseases, our research group synthesized and evaluated 23 3-arylquinoxaline-2-carbonitrile di-N-oxides against Trypanosoma cruzi. Five of them presented IC(50) values of the same magnitude as the standard drug Nifurtimox, making them valid as new lead compounds. The optimized molecular structures of 23 derivatives represented by 1497 types of DRAGON descriptors were subjected to linear regression analysis, and the derived QSAR was shown to be predictive. In this way, we achieved a rational guide for the proposal of new candidate structures whose activities still remain unknown.

Replacement method and enhanced replacement method versus the genetic algorithm approach for the selection of molecular descriptors in QSPR/QSAR theories.

We compare three methods for the selection of optimal subsets of molecular descriptors from a much greater pool of such regression variables. On the one hand is our enhanced replacement method (ERM) and on the other is the simpler replacement method (RM) and the genetic algorithm (GA). These methods avoid the impracticable full search for optimal variables in large sets of molecular descriptors. Present results for 10 different experimental databases suggest that the ERM is clearly preferable to the GA that is slightly better than the RM. However, the latter approach requires the smallest amount of linear regressions and, consequently, the lowest computation time.

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.

QSPR studies on aqueous solubilities of drug-like compounds.

A rapidly growing area of modern pharmaceutical research is the prediction of aqueous solubility of drug-sized compounds from their molecular structures. There exist many different reasons for considering this physico-chemical property as a key parameter: the design of novel entities with adequate aqueous solubility brings many advantages to preclinical and clinical research and development, allowing improvement of the Absorption, Distribution, Metabolization, and Elimination/Toxicity profile and "screenability" of drug candidates in High Throughput Screening techniques. This work compiles recent QSPR linear models established by our research group devoted to the quantification of aqueous solubilities and their comparison to previous research on the topic.

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).

New QSPR study for the prediction of aqueous solubility of drug-like compounds.

Solubility has become one of the key physicochemical screens at early stages of the drug development process. Solubility prediction through Quantitative Structure-Property Relationships (QSPR) modeling is a growing area of modern pharmaceutical research, being compatible with both High Throughput Screening technologies and limited compound availability characteristic of early stages of drug development. We resort to the QSPR theory for analyzing the aqueous solubility exhibited by 145 diverse drug-like organic compounds (0.781 being the average Tanimoto distances between all possible pairs of compounds in the training set). An accurate and generally applicable model is derived, consisting on a linear regression equation that involves three DRAGON molecular descriptors selected from more than a thousand available. Alternatively, we apply the linear QSPR to other 21 commonly employed validation compounds, leading to solubility estimations that compare fairly well with the performance achieved by previously reported Group Contribution Methods.

QSAR prediction of inhibition of aldose reductase for flavonoids.

We performed a predictive analysis based on quantitative structure-activity relationships (QSAR) of an important property of flavonoids, which is the inhibition (IC(50)) of aldose reductase (AR). The importance of AR inhibition is that it prevents cataract formation in diabetic patients. The best linear model constructed from 55 molecular structures incorporated six molecular descriptors, selected from more than a thousand geometrical, topological, quantum-mechanical, and electronic types of descriptors. As a practical application, we used the obtained QSAR model to predict the AR inhibitory effect of newly synthesized flavonoids that present 2-, 7-substitutions in the benzopyrane backbone.

QSAR modeling of the interaction of flavonoids with GABA(A) receptor.

Experimentally assigned values to binding affinity constants of flavonoid ligands towards the benzodiazepine site of the GABA(A) receptor complex were compiled from several publications, and enabled to perform a predictive analysis based on Quantitative Structure-Activity Relationships (QSAR). The best linear model established on 78 molecular structures incorporated four molecular descriptors, selected from more than a thousand of geometrical, topological, quantum-mechanical and electronic types of descriptors and calculated by Dragon software. An application of this QSAR equation was performed by estimating the binding affinities for some newly synthesized flavonoids displaying 2-,7-substitutions in the benzopyrane backbone which still do not have experimentally measured potencies.

QSAR studies for the pharmacological inhibition of glycogen synthase kinase-3.

The enzyme GSK-3 plays a central role in cells during the phosphorylation of various key regulatory proteins, and consequently pharmacological inhibitors of this enzyme potentially allow the treatment of diseases that include neurodegenerative and bipolar affective disorders, diabetes, and diseases caused by unicellular parasites. Today there is a huge number of reported empirical structure-activity relationships (SAR) that may guide a rational design of more potent and selective inhibitors. However, only a few studies based on Quantitative Structure-Activity Relationships (QSAR) are available for predicting the inhibitor potency against this specific kinase, and they involve mainly molecular modeling and 3D-QSAR. The present review deals with the recent search for a quantitative analysis of GSK-3 inhibition.

A theoretical study of a family of new quinoxaline derivatives.

Hybrid density functional calculations are performed on a series of 21 new quinoxaline derivatives, which would likely exhibit important biological activities. Optimized geometries, harmonic vibrational frequencies, and (1)H chemical shifts are reported and compared with experimental data when available. In addition, melting points of 75 derivatives are predicted resorting to the Quantitative Structure-Property Relationship (QSPR) Theory, doing the variable selection by means of the Replacement Method and using 875 theoretical descriptors obtained from Dragon 5 software. The best relationship found has seven descriptors with R=0.8818 and R(l-10%-o)=0.7705.

Mass spectrometry of beta-ketoesters. Some evidence of their tautomerism.

Mass spectrometric evidence of tautomerism is reported for beta-ketoesters. The analysis of the corresponding mass spectra has allowed specific assignment of fragment ions to tautomers. The predictive value of this methodology is supported by the influence of substitution pattern of these compounds on these equilibria. Experimental data are strongly supported by Austin Model 1 semiempirical calculations indicating that mass spectrometry could be resourced as a tool for the investigation of tautomerism of neutral species in the gas phase.

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.

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.