Search results for "Structure-Activity Relationship"

showing 10 items of 743 documents

Biopartitioning micellar chromatoraphy to predict blood to lung, blood to liver, blood to fat and blood to skin partition coefficients of drugs

2009

[EN] Biopartitioning micellar chromatography (BMC), a mode of micellar liquid chromatography that uses micellar mobile phases of Brij35 in adequate experimental conditions, has demonstrated to be useful in mimicking the drug partitioning process into biological systems. In this paper, the usefulness of BMC for predicting the partition coefficients from blood to lung, blood to liver. blood to fat and blood to skin is demonstrated. PLS2 and multiple linear regression (MLR) models based on BMC retention data are proposed and compared with other ones reported in bibliography. The proposed models present better or similar descriptive and predictive capability. (C) 2008 Elsevier B.V. All rights r…

Quantitative structure–activity relationshipBlood to skinQuantitative Structure-Activity RelationshipPredictive capabilityPartition coefficientsBiochemistryAnalytical ChemistryPharmacokineticsBlood to lungLinear regressionQUIMICA ANALITICAmedicineAnimalsHumansEnvironmental ChemistryPharmacokineticsTissue DistributionLungMicellesSpectroscopySkinLungChromatographyChemistryComputational BiologyChromatography liquidBiopartitioning micellar chromatographyRatsPartition coefficientmedicine.anatomical_structureAdipose TissueLiverPharmaceutical PreparationsMicellar liquid chromatographyLinear ModelsBlood to fatRabbitsChromatography LiquidBlood to liver
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Quantitative structure-activity relationships for the toxicity of organophosphorus and carbamate pesticides to the Rainbow trout Onchorhyncus mykiss.

2006

This study has investigated the development of quantitative structure-activity relationships (QSARs) for the toxicity to rainbow trout Onchorhyncus mykiss Walbaum of 75 organophosphorus and carbamate pesticides. The toxicity data were obtained from an openly available toxicological database and were selected to be representative of a single endpoint. A large number of physicochemical and structural descriptors were calculated for the pesticides. QSAR models were developed using multiple linear regression and partial least-squares analyses. Following the removal of a small number of outliers, predictive QSARs were developed on small numbers of mechanistically relevant descriptors. Applying m…

Quantitative structure–activity relationshipCarbamatemedicine.medical_treatmentQuantitative Structure-Activity RelationshipRisk AssessmentToxicologyOrganophosphorus CompoundsmedicineAnimalsPesticidesToxicity dataChemistryQuantitative structureGeneral MedicinePesticideCarbamate pesticidesInsect ScienceEnvironmental chemistryOncorhynchus mykissToxicityMultivariate AnalysisLinear ModelsRainbow troutCarbamatesCholinesterase InhibitorsAgronomy and Crop SciencePest management science
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Novel Cancer Chemotherapy Hits by Molecular Topology: Dual Akt and Beta-Catenin Inhibitors

2015

Background and purposeColorectal and prostate cancers are two of the most common types and cause of a high rate of deaths worldwide. Therefore, any strategy to stop or at least slacken the development and progression of malignant cells is an important therapeutic choice. The aim of the present work is the identification of novel cancer chemotherapy agents. Nowadays, many different drug discovery approaches are available, but this paper focuses on Molecular Topology, which has already demonstrated its extraordinary efficacy in this field, particularly in the identification of new hit and lead compounds against cancer. This methodology uses the graph theoretical formalism to numerically chara…

Quantitative structure–activity relationshipCell SurvivalColorectal cancerScienceQuantitative Structure-Activity RelationshipAntineoplastic AgentsComputational biologyBiologyBioinformaticsProstate cancerCell Line TumorNeoplasmsDrug DiscoverymedicineHumansProtein Kinase InhibitorsProtein kinase Bbeta CateninPI3K/AKT/mTOR pathwayBiological ProductsMultidisciplinaryMolecular StructureDrug discoveryTOR Serine-Threonine KinasesQRBiological activitymedicine.diseaseMedicineTOR Serine-Threonine KinasesProto-Oncogene Proteins c-aktSignal TransductionResearch ArticlePLOS ONE
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Use of Catalyst in a 3D-QSAR Study of the Interactions between Flavor Compounds and β-Lactoglobulin

2003

This paper reports a 3D-QSAR study using Catalyst software to explain the nature of interactions between flavor compounds and beta-lactoglobulin. A set of 35 compounds, for which dissociation constants were previously determined by affinity chromatography, was chosen. The set was divided into three subsets. An automated hypothesis generation, using HypoGen software, produced a model that made a valuable estimation of affinity and provided an explanation for the lack of correlation previously observed between the hydrophobicity of terpenes and the affinity for the protein. On the basis of these results, it appears that aroma binding to beta-lactoglobulin is caused by both hydrophobic interac…

Quantitative structure–activity relationshipChemical PhenomenaChemistry PhysicalTerpenesChemistryStereochemistryQuantitative Structure-Activity RelationshipHydrogen BondingLactoglobulinsGeneral ChemistryCatalysisDissociation constantModels ChemicalComputational chemistryOdorantsComputer SimulationDrug InteractionsGeneral Agricultural and Biological SciencesSoftwareFlavorJournal of Agricultural and Food Chemistry
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Use of molecular topology for the prediction of physico-chemical, pharmacokinetic and toxicological properties of a group of antihistaminic drugs

2002

We used molecular connectivity to search mathematical models for predicting physico-chemical (e.g. the partition coefficient, P), pharmacokinetic (e.g. the time of maximum plasma level, and toxicological properties (lethal dose, LD) for a group of antihistaminic drugs. The results obtained clearly reveal the high efficiency of molecular topology for the prediction of these properties. Randomization and cross-validation by use of leave-one-out tests were also performed in order to assess the stability and the prediction ability of the connectivity functions selected.

Quantitative structure–activity relationshipChemistryQuantitative Structure-Activity RelationshipPharmaceutical SciencePlasma levelsPharmacologyModels BiologicalLethal Dose 50Structure-Activity RelationshipPharmacokineticsPredictive Value of TestsHistamine H1 AntagonistsRegression AnalysisAntihistaminic drugsMolecular topologyBiological systemInternational Journal of Pharmaceutics
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Application of the modelling power approach to variable subset selection for GA-PLS QSAR models

2007

A previously developed function, the Modelling Power Plot, has been applied to QSARs developed using partial least squares (PLS) following variable selection from a genetic algorithm (GA). Modelling power (Mp) integrates the predictive and descriptive capabilities of a QSAR. With regard to QSARs for narcotic toxic potency, Mp was able to guide the optimal selection of variables using a GA. The results emphasise the importance of Mp to assess the success of the variable selection and that techniques such as PLS are more robust following variable selection.

Quantitative structure–activity relationshipChemistrybusiness.industryQuantitative Structure-Activity RelationshipFeature selectionFunction (mathematics)Machine learningcomputer.software_genreModels BiologicalBiochemistryPlot (graphics)Analytical ChemistryPower (physics)StatisticsPartial least squares regressionGenetic algorithmEnvironmental ChemistryArtificial intelligenceLeast-Squares AnalysisbusinesscomputerAlgorithmsSpectroscopySelection (genetic algorithm)Analytica Chimica Acta
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Determination of the hydrophobicity of organic compounds measured as logPo/w through a new chromatographic method

2009

A new chromatographic method to determine the octanol-water partition coefficient (logP(o/w)) of organic substances is proposed in this paper. This method is based on a previously reported model that relates the retention factor in reversed-phase liquid chromatography with solute (p), mobile phase (P(m)(N)) and stationary phase (P(s)(N)) polarity parameters: logk=(logk)(0)+p(P(m)(N)-P(s)(N)). P(m)(N) values are calculated through expressions that depend only on the organic solvent fraction in the mobile phase. (logk)(0) and P(s)(N) parameters are characteristic of the chromatographic system and are determined from the retention of a selected set of 12 compounds. Then, the p value of a solut…

Quantitative structure–activity relationshipChromatographyChemistryChemical structureOrganic ChemistryAnalytical chemistryQuantitative Structure-Activity RelationshipGeneral MedicineReversed-phase chromatographyHydrogen-Ion ConcentrationBiochemistryHigh-performance liquid chromatographyAnalytical ChemistryPartition coefficientchemistry.chemical_compoundPhase (matter)LipophilicityOrganic ChemicalsAcetonitrileHydrophobic and Hydrophilic InteractionsChromatography LiquidJournal of Chromatography A
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Opioid analgetics retention–pharmacologic activity models using biopartitioning micellar chromatography

2002

Opioids are drugs used in medicine for pain control. In this paper, retention-pharmacokinetics and retention-pharmacodynamics relationships of opioids are proposed and statistically validated. These models are based on the compound retention in the biopartitioning micellar chromatography system (BMC), a new methodology which has successfully been used to develop QRAR models for many other families of compounds. The obtained results are compared to the traditional QSAR models using lipophilicity data. The adequacy of QRAR models is due to the fact that the characteristics of the compounds such as the hydrophobicity, electronic charge and steric effects determine both their retention in BMC a…

Quantitative structure–activity relationshipChromatographyChemistryClinical BiochemistryAnalgesicCell BiologyGeneral MedicinePharmacologyBiochemistryAnalytical ChemistryAnalgesics OpioidStructure-Activity RelationshipModels ChemicalOpioidPain controlPharmacokineticsLipophilicitymedicineOpioid analgesicsChromatography Micellar Electrokinetic Capillarymedicine.drugJournal of Chromatography B
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QSPR prediction of chromatographic retention times of pesticides: Partition and fractal indices

2014

The high-performance liquid-chromatographic retentions of red-wine pesticide residues are modeled by structure-property relationships. The effect of different types of features is analyzed: geometric, lipophilic, etc. The properties are fractal dimensions, partition coefficient, etc., in linear and nonlinear correlation models. Biological plastic evolution is an evolutionary perspective conjugating the effect of acquired characters and relations that emerge among the principles of evolutionary indeterminacy, morphological determination and natural selection. It is applied to design the co-ordination index that is used to characterize pesticide retentions. The parameters used to calculate th…

Quantitative structure–activity relationshipChromatographyChemistryEnthalpyNonlinear correlationQuantitative Structure-Activity RelationshipGeneral MedicinePesticidePollutionFractal dimensionPartition coefficientFractalsFractalPartition (number theory)PesticidesChromatography LiquidFood ScienceJournal of Environmental Science and Health, Part B
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Modelling bioconcentration of pesticides in fish using biopartitioning micellar chromatography.

2005

Ecotoxicity assessment is essential before placing new chemical substances on the market. An investigation of the use of the chromatographic retention (log k) in biopartitioning micellar chromatography (BMC) as an in vitro approach to evaluate the bioconcentration factor (BCF) of pesticides in fish is proposed. A heterogeneous set of 85 pesticides from six chemical families was used. For pesticides exhibiting bioconcentration in fish (experimental log BCF > 2), a quantitative retention-activity relationships (QRAR) model is able to perform precise log BCF estimations of new pesticides. Considering the present data, the results based on log k seem to be more reliable than those from availabl…

Quantitative structure–activity relationshipChromatographyThreshold limit valueChemistryOrganic ChemistryFishesChromatography liquidQuantitative Structure-Activity RelationshipBioconcentrationGeneral MedicinePesticideBiochemistryHigh-performance liquid chromatographyAnalytical ChemistryEnvironmental chemistryFish <Actinopterygii>AnimalsEcotoxicityPesticidesChromatography LiquidJournal of chromatography. A
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