Large Set Of Compounds Research Articles

Discovery of highly potent analgesic activity of isopulegol-derived (2R,4aR,7R,8aR)-4,7-dimethyl-2-(thiophen-2-yl)octahydro-2H-chromen-4-ol

A large set of chiral heterocyclic compounds with the octahydro-2H-chromene scaffold was first obtained by a reaction of (−)-isopulegol and (+)-neoisopulegol with furan-2-carbaldehyde, thiophene-2-carbaldehyde and their derivatives and isomers in the presence of montmorillonite K10 clay. Most of the (−)-isopulegol-derived compounds exhibited a significant analgesic activity in the acetic acid-induced writhing test. Compound 3b obtained by a reaction of (−)-isopulegol with thiophene-2-carbaldehyde demonstrated a significant analgesic effect in this test within 15 min after oral administration at the dose of 1 mg/kg and retains the effect for at least 24 h. Compound 3b exhibited analgesic activity in the hot plate test also. A change in the sulfur atom position in the aromatic ring was found to lead to the effect reversal in the hot plate test.

The Monte Carlo technique as a tool to predict LOAEL

Quantitative structure – activity relationships (QSARs) for the Lowest Observed Adverse Effect Level (LOAEL) for a large set of organic compounds (n = 341) are suggested. The molecular structures of these compounds are represented by Simplified Molecular Input-Line Entry Systems (SMILES). A criteria for the estimation quality of split into the “visible” training set (used for developing a model) and “invisible” external validation set is suggested. The correlation between the above criterion and the predictive potential of developed QSAR model (root-mean-square error for “invisible” validation set) has been detected. One-variable models are built up for several different splits into the “visible” training set and “invisible” validation set. The statistical quality of these models is quite good. Mechanistic interpretation and the domain of applicability for these models are defined according to probabilistic point of view. The methodology for defining applicability domain in QSAR modeling with SMILES notation based optimal descriptors is presented.

Solvation Structure and Quasidynamics of Biomolecules Steered with Effective Solvation Forces Obtained From Molecular Theory of Solvation

Three-dimensional reference interaction site model with Kovalenko-Hirata closure (3D-RISM-KH molecular theory of solvation) is Ornstein-Zernike type integral equation theory of liquids. Based on first principles of statistical mechanics, 3D-RISM-KH consistently accounts for effect of chemical specificities of solvent, co-solvent, ions, and ligands on biomolecules solvation structure and thermodynamics, including steric forces, hydrophobicity, hydrogen bonding, and other effective interactions. 3D-RISM-KH supplemented with the partial molar volume correction, a.k.a. “Universal Correction” (UC), provides an excellent agreement with experimental data on a large set of small compounds for solvation free energies in octanol and water, and octanol-water partition coefficients. 3D-RISM-KH structural water detection and placement are implemented in Amber Tools and Molecular Operating Environment (MOE) packages. Structural water plays critical role in protein interactions and functions; case studies include HET-s prion, Aβ oligomer and fibril formation, folding promotion in GroEL chaperonin, and ligand binding to maltose-binding protein. Multi-time-step MD of biomolecules steered with mean solvation forces obtained from 3D-RISM-KH at outer steps and treated with generalized solvation force extrapolation (GSFE) at inner steps is efficiently stabilized with the optimized isokinetic Nose-Hoover chain (OIN) thermostat. The accuracy of GSFE and efficiency of OIN allow picoseconds outer steps while accurately reproducing conformational properties, as validated on hydrated alanine dipeptide, miniprotein 1L2Y, and protein G. Due to 3D-RISM-KH statistical averaging over rare events of solvent exchange and localization in biomolecular spaces, this quasidynamics results in time scale compression of protein conformational changes coupled with solvent and so in huge acceleration of conformational sampling. This provides up to 1000-fold effective speedup of sampling, compared to conventional MD with explicit solvent, and enables to fold the miniprotein from a fully denatured state in 60ns quasidynamics, cf. 4-9μs folding time in experiment.

High-throughput log Po/w determination from UHPLC measurements: Revisiting the chromatographic hydrophobicity index

A fast and accurate lipophilicity determination is fundamental in the drug discovery process, as long as it is a relevant property in the absorption, distribution, metabolism, excretion and toxicity (ADMET) of a potential drug substance. In the present work, different models based on chromatographic retention values for a large set of compounds and some of their molecular descriptors (calculated by ACD/Labs or CODESSA programs) have been examined in order to establish reliable equations for logPo/w determination from fast chromatographic hydrophobicity index (CHI) measurements. This appears to be a very interesting high-throughput methodology for screening purposes, since CHI values can be measured by UHPLC in very short runs (<4min) and molecular descriptors can be easily computed from the structure of any compound. The selected final descriptors were Abraham’s hydrogen-bond acidity (A) and excess molar refraction (E) from ACD/Labs, and hydrogen-bond acidity HDCA-1/TMSA and HOMO-LUMO polarizability descriptors from CODESSA software. The proposed equations allow an accurate determination of logPo/w with standard errors in the range of 0.4 units.

Predictive Models for Halogen-bond Basicity of Binding Sites of Polyfunctional Molecules.

Halogen bonding (XB) strength assesses the ability of an electron-enriched group to be involved in complexes with polarizable electrophilic halogenated or diatomic halogen molecules. Here, we report QSPR models of XB of particular relevance for an efficient screening of large sets of compounds. The basicity is described by pKBI2 , the decimal logarithm of the experimental 1 : 1 (B : I2 ) complexation constant K of organic compounds (B) with diiodine (I2 ) as a reference halogen-bond donor in alkanes at 298 K. Modeling involved ISIDA fragment descriptors, using SVM and MLR methods on a set of 598 organic compounds. Developed models were then challenged to make predictions for an external test set of 11 polyfunctional compounds for which unambiguous assignment of the measured effective complexation constant to specific groups out of the putative acceptor sites is not granted. At this stage, developed models were used to predict pKBI2 of all putative acceptor sites, followed by an estimation of the predicted effective complexation constant using the ChemEqui program. The best consensus models perform well both in cross-validation (root mean squared error RMSE=0.39-0.47 logKBI2 units) and external predictions (RMSE=0.49). The SVM models are implemented on our website (http://infochim.u-strasbg.fr/webserv/VSEngine.html) together with the estimation of their applicability domain and an automatic detection of potential halogen-bond acceptor atoms.

Nontargeted identification of peptides and disinfection byproducts in water

A broad range of organic compounds are known to exist in drinking water sources and serve as precursors of disinfection byproducts (DBPs). Epidemiological findings of an association of increased risk of bladder cancer with the consumption of chlorinated water has resulted in health concerns about DBPs. Peptides are thought to be an important category of DBP precursors in water. However, little is known about the actual presence of peptides and their DBPs in drinking water because of their high sample complexity and low concentrations. To address this challenge and identify peptides and non-chlorinated/chlorinated peptide DBPs from large sets of organic compounds in water, we developed a novel high throughput analysis strategy, which integrated multiple solid phase extraction (SPE), high performance liquid chromatography (HPLC) separation, and non-target identification using precursor ion exclusion (PIE) high resolution mass spectrometry (MS). After MS analysis, structures of candidate compounds, particularly peptides, were obtained by searching against the Human Metabolome Database (HMDB). Using this strategy, we successfully detected 625 peptides (out of 17,205 putative compounds) and 617 peptides (out of 13,297) respectively in source and finished water samples. The source and finished water samples had 501 peptides and amino acids in common. The remaining 116 peptides and amino acids were unique to the finished water. From a subset of 30 putative compounds for which standards were available, 25 were confirmed using HPLC-MS analysis. By analyzing the peptides identified in source and finished water, we successfully confirmed three disinfection reaction pathways that convert peptides into toxic DBPs.

Genome and Phenotype Microarray Analyses of Rhodococcus sp. BCP1 and Rhodococcus opacus R7: Genetic Determinants and Metabolic Abilities with Environmental Relevance.

In this paper comparative genome and phenotype microarray analyses of Rhodococcus sp. BCP1 and Rhodococcus opacus R7 were performed. Rhodococcus sp. BCP1 was selected for its ability to grow on short-chain n-alkanes and R. opacus R7 was isolated for its ability to grow on naphthalene and on o-xylene. Results of genome comparison, including BCP1, R7, along with other Rhodococcus reference strains, showed that at least 30% of the genome of each strain presented unique sequences and only 50% of the predicted proteome was shared. To associate genomic features with metabolic capabilities of BCP1 and R7 strains, hundreds of different growth conditions were tested through Phenotype Microarray, by using Biolog plates and plates manually prepared with additional xenobiotic compounds. Around one-third of the surveyed carbon sources was utilized by both strains although R7 generally showed higher metabolic activity values compared to BCP1. Moreover, R7 showed broader range of nitrogen and sulphur sources. Phenotype Microarray data were combined with genomic analysis to genetically support the metabolic features of the two strains. The genome analysis allowed to identify some gene clusters involved in the metabolism of the main tested xenobiotic compounds. Results show that R7 contains multiple genes for the degradation of a large set of aromatic and PAHs compounds, while a lower variability in terms of genes predicted to be involved in aromatic degradation was found in BCP1. This genetic feature can be related to the strong genetic pressure exerted by the two different environment from which the two strains were isolated. According to this, in the BCP1 genome the smo gene cluster involved in the short-chain n-alkanes degradation, is included in one of the unique regions and it is not conserved in the Rhodococcus strains compared in this work. Data obtained underline the great potential of these two Rhodococcus spp. strains for biodegradation and environmental decontamination processes.

Simultaneous determination of hydrophobicity and dissociation constant for a large set of compounds by gradient reverse phase high performance liquid chromatography–mass spectrometry technique

Fast and reliable methods for the determination of hydrophobicity and acidity are desired in pre-clinical drug development phases to eliminate compounds with poor pharmacokinetic properties. Reversed-phase high-performance liquid chromatography (RP HPLC) coupled with time-of-flight mass spectrometry (RP HPLC–ESI-TOF–MS) is a convenient technique for that purpose. In this work we determined the chromatographic measure of hydrophobicity (logkw) and dissociation constant (pKa) simultaneously for a large and diverse group of 161 drugs. Retention times were determined by means of RP HPLC–ESI-TOF–MS for a series of pH and organic modifier gradients. We were able to measure retention times for 140 out of 161 (87%) compounds. For those analytes logkw and pKa parameters were calculated and compared with literature and ACD Labs-calculated data. The determined chromatographic measure of hydrophobicity and dissociation constant was closely related to literature and theoretically calculated values. Applied methodology achieved the medium-throughput screening rate of 100 compounds per day and proved to be a simple, fast and reliable approach of assessing important physicochemical properties of drugs. This technique has certain limitations as it is not applicable for very hydrophilic analytes (logP<0.5) and compounds with identical molar masses.

Ga(+) Basicity and Affinity Scales Based on High-Level Ab Initio Calculations.

The structure, relative stability and bonding of complexes formed by the interaction between Ga(+) and a large set of compounds, including hydrocarbons, aromatic systems, and oxygen-, nitrogen-, fluorine and sulfur-containing Lewis bases have been investigated through the use of the high-level composite ab initio Gaussian-4 theory. This allowed us to establish rather accurate Ga(+) cation affinity (GaCA) and Ga(+) cation basicity (GaCB) scales. The bonding analysis of the complexes under scrutiny shows that, even though one of the main ingredients of the Ga(+) -base interaction is electrostatic, it exhibits a non-negligible covalent character triggered by the presence of the low-lying empty 4p orbital of Ga(+) , which favors a charge donation from occupied orbitals of the base to the metal ion. This partial covalent character, also observed in AlCA scales, is behind the dissimilarities observed when GaCA are compared with Li(+) cation affinities, where these covalent contributions are practically nonexistent. Quite unexpectedly, there are some dissimilarities between several Ga(+) -complexes and the corresponding Al(+) -analogues, mainly affecting the relative stability of π-complexes involving aromatic compounds.

Linear and Nonlinear Structure-Retention Relationship Analysis of Different Classes of Pesticides Isolated From Groundwater

The present article gives an insight into the linear and nonlinear relationships between the retention behavior in reversed-phase high performance liquid chromatography (RP-HPLC) of several classes of pesticides isolated from groundwater, and their in silico physicochemical, topological and lipophilicity molecular descriptors. The quantitative structure-retention relationship (QSRR) chemometric approach was applied for this purpose on a large set of compounds (77 pesticides). The selection of the most appropriate molecular descriptors was achieved by stepwise selection procedure coupled with partial least squares method and the variance inflation in projection parameter (SS-PLS-VIP). QSRR included the linear regression (LR), multiple linear regession (MLR), and artificial neural networks regression (ANN-R). In order to select the optimal QSRR model, statistical validation parameters were used. Additionally, a relatively new chemometric method called sum of ranking differences (SRD) was applied in order to select the optimal regression model. The obtained results showed that certain models can successfully be used for precise prediction of the retention time of the studied compounds.

Rule-Based Prediction Models of Cytochrome P450 Inhibition.

Hepatotoxicity, drug-induced liver injury, and competitive Cytochrome P-450 (CYP) isozyme binding are serious problems associated with drug use. It would be favorable to avoid or to understand potential CYP inhibition at the developmental stages. However, current in silico CYP prediction models or available public prediction servers can provide only yes/no classification results for just one or a few CYP enzymes. In this study, we utilized a rule-based C5.0 algorithm with different descriptors, including PaDEL, Mold(2), and PubChem fingerprints, to construct rule-based inhibition prediction models for five major CYP enzymes-CYP1A2, CYP2C9, CYP2C19, CYP2D6 and CYP3A4-that account for 90% of drug oxidation or hydrolysis. We also developed a rational sampling algorithm for the selection of compounds in the training data set, to enhance the performance of these CYP prediction models. The optimized models include several improved features. First, the final models significantly outperformed all of the currently available models. Second, the final models can also be used for rapid virtual screening of a large set of compounds due to their ruleset-based nature. Moreover, such rule-based prediction models can provide rulesets for structural features related to the five major CYP enzymes. The five most significant rules for CYP inhibition were identified for each CYP enzymes and discussed. An example was chosen for each of the five CYP enzymes to demonstrate how rule-based models can be used to gain insights into structural features that correspond with CYP inhibitions. A newer version of the freely accessible CYP prediction server, CypRules, is presented here as a result of the aforementioned improvements.

Facile Oxime Ether Synthesis: Free Carbonyl Compound Derivatization by a Brominated O-Benzylhydroxylamine

The lipid peroxidation of fatty acids leads to secondary products, among which several carbonyl compounds are of concern in food toxicology. The detection of these reactive aldehydes for identification and evaluation is required. Derivatization is necessary to improve their stability and detection in liquid chromatography/high-resolution mass spectrometry (LC/HRMS) trace analyses. Therefore, a brominated O-benzylhydroxylamine, namely 1-((ammoniooxy)methyl)-2-bromobenzene chloride, was selected as a new probe for the mild and selective derivatization of carbonyl compounds. New oxime ethers were thus synthesized under mild reaction and workup conditions, with full analytical characterization. The relevance of the chemical reaction was assessed with nine aldehydes, especially conjugated and deuterium-labeled aldehydes, and two ketones. Virtually, the reaction should be applicable to a large set of carbonyl compounds for derivatization in complex biological samples and selective detection of the in situ–synthesized brominated oxime ethers by LC/HRMS methodology.

High-throughput in vivo genotoxicity testing: an automated readout system for the somatic mutation and recombination test (SMART).

Genotoxicity testing is an important component of toxicity assessment. As illustrated by the European registration, evaluation, authorization, and restriction of chemicals (REACH) directive, it concerns all the chemicals used in industry. The commonly used in vivo mammalian tests appear to be ill adapted to tackle the large compound sets involved, due to throughput, cost, and ethical issues. The somatic mutation and recombination test (SMART) represents a more scalable alternative, since it uses Drosophila, which develops faster and requires less infrastructure. Despite these advantages, the manual scoring of the hairs on Drosophila wings required for the SMART limits its usage. To overcome this limitation, we have developed an automated SMART readout. It consists of automated imaging, followed by an image analysis pipeline that measures individual wing genotoxicity scores. Finally, we have developed a wing score-based dose-dependency approach that can provide genotoxicity profiles. We have validated our method using 6 compounds, obtaining profiles almost identical to those obtained from manual measures, even for low-genotoxicity compounds such as urethane. The automated SMART, with its faster and more reliable readout, fulfills the need for a high-throughput in vivo test. The flexible imaging strategy we describe and the analysis tools we provide should facilitate the optimization and dissemination of our methods.

Large-scale chemical similarity networks for target profiling of compounds identified in cell-based chemical screens.

Target identification is one of the most critical steps following cell-based phenotypic chemical screens aimed at identifying compounds with potential uses in cell biology and for developing novel disease therapies. Current in silico target identification methods, including chemical similarity database searches, are limited to single or sequential ligand analysis that have limited capabilities for accurate deconvolution of a large number of compounds with diverse chemical structures. Here, we present CSNAP (Chemical Similarity Network Analysis Pulldown), a new computational target identification method that utilizes chemical similarity networks for large-scale chemotype (consensus chemical pattern) recognition and drug target profiling. Our benchmark study showed that CSNAP can achieve an overall higher accuracy (>80%) of target prediction with respect to representative chemotypes in large (>200) compound sets, in comparison to the SEA approach (60–70%). Additionally, CSNAP is capable of integrating with biological knowledge-based databases (Uniprot, GO) and high-throughput biology platforms (proteomic, genetic, etc) for system-wise drug target validation. To demonstrate the utility of the CSNAP approach, we combined CSNAP's target prediction with experimental ligand evaluation to identify the major mitotic targets of hit compounds from a cell-based chemical screen and we highlight novel compounds targeting microtubules, an important cancer therapeutic target. The CSNAP method is freely available and can be accessed from the CSNAP web server (http://services.mbi.ucla.edu/CSNAP/).

CypRules: a rule-based P450 inhibition prediction server.

Cytochrome P450 (CYPs) are the major enzymes involved in drug metabolism and bioactivation. Inhibition models were constructed for five of the most popular enzymes from the CYP superfamily in human liver. The five enzymes chosen for this study, namely CYP1A2, CYP2D6, CYP2C19, CYP2C9 and CYP3A4, account for 90% of the xenobiotic and drug metabolism in human body. CYP enzymes can be inhibited or induced by various drugs or chemical compounds. In this work, a rule-based CYP inhibition prediction online server, CypRules, was created based on predictive models generated by the rule-based C5.0 algorithm. CypRules can predict and provide structural rulesets for CYP inhibition for each compound uploaded to the server. Capable of fast execution performance, it can be used for virtual high-throughput screening (VHTS) of a large set of testing compounds. CypRules is freely accessible at http://cyprules.cmdm.tw/ and models, descriptor and program files for all compounds are publically available at http://cyprules.cmdm.tw/sources/sources.rar.

Computational NMR Methods in the Stereochemical Analysis of Organic Compounds: Are Proton or Carbon NMR Chemical Shift Data More Discriminating?

AbstractWe have investigated the 1H and 13C NMR chemical shift data of a large set of stereoisomeric organic compounds to determine whether it is possible to identify the nucleus that is more discriminating in stereochemical analyses conducted by quantum mechanical methods. By estimating the intrinsic deviation of the experimental chemical shifts of 219 pairs of diastereoisomers, we have reconsidered the concept of the match ratio as an inequality, namely 1H match ratio &gt; 13C match ratio, mathematically demonstrating that the 1H nucleus is more discriminating than the 13C nucleus for all the investigated cases. Moreover, we have also verified that this inequality is satisfied by the most often used quantum chemical methods, which suggests that 1H chemical shifts are more discriminating than 13C chemical shifts in stereochemical assignments by quantum chemical calculations.

Property-based characterization of kinase-like ligand space for library design and virtual screening

A property-based desirability scoring scheme has been developed for kinase-focused library design and ligand-based pre-screening of large compound sets.

Polymorphic Behaviour of 3-Chloromandelic acid

For several years we have been making detailed comparisons of the crystal structures of large sets of related compounds in an attempt to understand the factors determining the adoption of particular molecular arrangements within crystal structures. In all these projects, the XPac program [1] was used to identify similarity between structures in 0-dimensions (0D) (discrete molecular arrangements e.g. dimers, trimers etc),1D (chains or stacks), 2D (sheets or planes) and 3D (frameworks or full isostructurality) as a preliminary to the detailed comparison of the similarities so identified. As part of a larger crystallographic project to investigate the relationship between structure and chirality, we have synthesized and determined crystal structures of families of monosubstituted racemic mandelic acids with fluoro, chloro, bromo, iodo, trifluoromethyl, methyl and methoxy substituents at the ortho, meta and para positions. The substituted mandelic acids are polymorphically prolific and with the inclusion of unsubstituted mandelic acid, 28 structures have been compared. Of special interest has been 3-chloromandelic acid (3-ClMA), for which so far five polymorphs have been discovered: three racemic, two of which are isostructural [2], and two enantiopure. A crystal structure prediction (CSP) study of 3-ClMA has been carried out using the CrystalPredictor [3] and CrystalOptimizer [3] algorithms to generate the crystal energy landscape which is exceptionally dense with 3050 structures within 20 kJ mol-1. Many of these are more stable than the known forms, which is consistent with the complex crystallisation behaviour observed. From our observations of the crystallisation behaviour of substituted chloromandelic acids in general and the CSP study of 3-ClMA, we expect to discover further polymorphs of 3-ClMA and to this end cross-seeding experiments using crystals from differently substituted mandelic acids and a comprehensive polymorph screen are at present being undertaken.

Genomic models of short-term exposure accurately predict long-term chemical carcinogenicity and identify putative mechanisms of action.

BackgroundDespite an overall decrease in incidence of and mortality from cancer, about 40% of Americans will be diagnosed with the disease in their lifetime, and around 20% will die of it. Current approaches to test carcinogenic chemicals adopt the 2-year rodent bioassay, which is costly and time-consuming. As a result, fewer than 2% of the chemicals on the market have actually been tested. However, evidence accumulated to date suggests that gene expression profiles from model organisms exposed to chemical compounds reflect underlying mechanisms of action, and that these toxicogenomic models could be used in the prediction of chemical carcinogenicity.ResultsIn this study, we used a rat-based microarray dataset from the NTP DrugMatrix Database to test the ability of toxicogenomics to model carcinogenicity. We analyzed 1,221 gene-expression profiles obtained from rats treated with 127 well-characterized compounds, including genotoxic and non-genotoxic carcinogens. We built a classifier that predicts a chemical's carcinogenic potential with an AUC of 0.78, and validated it on an independent dataset from the Japanese Toxicogenomics Project consisting of 2,065 profiles from 72 compounds. Finally, we identified differentially expressed genes associated with chemical carcinogenesis, and developed novel data-driven approaches for the molecular characterization of the response to chemical stressors.ConclusionHere, we validate a toxicogenomic approach to predict carcinogenicity and provide strong evidence that, with a larger set of compounds, we should be able to improve the sensitivity and specificity of the predictions. We found that the prediction of carcinogenicity is tissue-dependent and that the results also confirm and expand upon previous studies implicating DNA damage, the peroxisome proliferator-activated receptor, the aryl hydrocarbon receptor, and regenerative pathology in the response to carcinogen exposure.

Characterization of extracellular polymeric substances in the biofilms of typical bacteria by the sulfur K-edge XANES spectroscopy

A combined approach of physicochemical extraction and sulfur K-edge X-ray absorption near-edge structure (XANES) spectroscopy was applied to characterize the extracellular polymeric substances (EPS) of typical bacterial biofilms in this study. Physicochemical analysis showed variation of the contents of DNA, polysaccharide and protein in different fractions of EPS in different mediums. The sulfur K-edge XANES analysis yielded a variety of spectra. Spectral fitting of the XANES spectra utilizing a large set of model compounds showed that there was more reduced sulfur in both LB-EPS (loosely bound EPS) and TB-EPS (tightly bound EPS) of all the biofilms in LB medium than in R2A medium. More oxidized sulfur was identified in LB-EPS than that in TB-EPS, suggesting different niches and physiological heterogeneity in the biofilms. Our results suggested that the sulfur K-edge XANES can be a useful tool to analyze the sulfur speciation in EPS of biofilms.