Biomedical subjects

J Devillers

Publications and source records attributed to J Devillers.

At least 19 recordsLinked to original sources

PubMed2006

SAR and QSAR modeling of endocrine disruptors.

A number of xenobiotics by mimicking natural hormones can disrupt crucial functions in wildlife and humans. These chemicals termed endocrine disruptors are able to exert adverse effects through a variety of mechanisms. Fortunately, there is a growing interest in the study of these structurally diverse chemicals mainly through research programs based on in vitro and in vivo experimentations but also by means of SAR and QSAR models. The goal of our study was to retrieve from the literature all the papers dealing with structure-activity models on endocrine disruptor xenobiotics. A critical analysis of these models was made focusing our attention on the quality of the biological data, the significance of the molecular descriptors and the validity of the statistical tools used for deriving the models. The predictive power and domain of application of these models were also discussed.

Endocrine Disruptors↗

PubMed2006

Homology model of the rainbow trout estrogen receptor (rtERalpha) and docking of endocrine disrupting chemicals (EDCs).

A model for rainbow trout (Oncorhynchus mykiss) estrogen receptor (rtERa) was built by homology with the human estrogen receptor (hERa). A high level of sequence conservation between the two receptors was found with 64% and 80% of identity and similarity, respectively. Selected endocrine disrupting chemicals were docked into the ligand binding domain (LBD) of rtERa and the corresponding free binding energies Delta(DeltaG(bind)) values were calculated. A Quantitative Structure-Activity Relationship (QSAR) model between the relative binding affinity data and the Delta(DeltaG(bind)) values was derived in order to predict which further organic pollutants are likely to bind to rtERa.

Amino Acid Sequence↗

PubMed2006

Validation of counter propagation neural network models for predictive toxicology according to the OECD principles: a case study.

The OECD has proposed five principles for validation of QSAR models used for regulatory purposes. Here we present a case study investigating how these principles can be applied to models based on Kohonen and counter propagation neural networks. The study is based on a counter propagation network model that has been built using toxicity data in fish fathead minnow for 541 compounds. The study demonstrates that most, if not all, of the OECD criteria may be met when modeling using this neural network approach.

Animal Use Alternatives↗

PubMed2005

Comparative sublethal toxicity of nine pesticides on olfactory learning performances of the honeybee Apis mellifera.

Using a conditioned proboscis extension response (PER) assay, honeybees (Apis mellifera L.) can be trained to associate an odor stimulus with a sucrose reward. Previous studies have shown that observations of conditioned PER were of interest for assessing the behavioral effects of pesticides on the honeybee. In the present study, the effects of sublethal concentrations of nine pesticides on learning performances of worker bees subjected to the PER assay were estimated and compared. Pesticides were tested at three concentrations. The highest concentration of each pesticide corresponded to the median lethal dose value (48-h oral LD50), received per bee and per day, divided by 20. Reduced learning performances were observed for bees surviving treatment with fipronil, deltamethrin, endosulfan, and prochloraz. A lack of behavioral effects after treatment with lambda-cyalothrin, cypermethrin, tau-fluvalinate, triazamate, and dimethoate was recorded. No-observed-effect concentrations (NOECs) for the conditioned PER were derived for the studied pesticides. Our study shows that the PER assay can be used for estimating sublethal effects of pesticides on bees. Furthermore, comparisons of sensitivity as well as the estimation of NOECs, useful for regulatory purposes, are possible.

Animals↗

PubMed2005

A new strategy for using supervised artificial neural networks in QSAR.

A new type of environmental QSAR model is presented for the common situation in which the biological activity of molecules mainly depends on their 1-octanol/water partition coefficient (log P). In a first step, a classical regression equation with log P is derived. The residuals obtained with this simple linear equation are then modeled from a supervised artificial neural network including different molecular descriptors as input neurons. Finally, results produced by the linear and nonlinear models are both considered for calculating the activity values, which are compared with the initial actual activity values. A heterogeneous database of 569 organic compounds with 96-h LC50s measured to the fathead minnow (Pimephales promelas), randomly divided into a training set of 484 chemicals and a testing set of 85 chemicals, was used as illustrative example to show the potentialities of this new modeling strategy Finally, practical suggestions are given for designing this type of hybrid QSAR model.

Databases, Factual↗

PubMed2004

Typology of secondary cyanobacterial metabolites from minimum spanning tree analysis.

Recently, two main events have spurred a rapid increase in cyanobacteria chemical, toxicological, and ecological research. The first deals with the interest in isolating compounds from these organisms as source of active products with potential therapeutic applications. The second pertains the crucial problem of harmful cyanobacterial blooms in the aquatic environments. In this context, 594 secondary metabolites belonging to more than 30 genera of cyanobacteria were retrieved from literature. In order to perform their typology, they were first associated with 87 different molecular archetypes and two orphan classes. These 89 groups of molecular structures were then confronted to minimum spanning tree analysis. Attempts were made to graphically derive chemotaxonomical relationships. The interest of QSAR models for estimating the potential pharmacological interest of the cyanobacterial secondary metabolites was also discussed.

Biological Factors↗

PubMed2004

Linear versus nonlinear QSAR modeling of the toxicity of phenol derivatives to Tetrahymena pyriformis.

Quantitative structure-activity relationship (QSAR) models were derived from a structurally heterogeneous set of 200 phenol derivatives for which the 50% growth inhibition concentration (IGC(50)) values to the ciliated protozoan Tetrahymena pyriformis were available. Each molecule was described by means of physicochemical descriptors and structural features. Partial least squares (PLS) regression analysis and a three-layer perceptron were used as statistical engine. The performances of the linear and nonlinear models were estimated from an external testing set of 50 chemicals. Despite hard constraints voluntarily imposed in the design of the neural network models, they provided better simulation results than the PLS models.

Animals↗

PubMed2003

Effects of ethylene glycol ethers on the reproduction of Ceriodaphnia dubia.

Seven-day static renewal tests with Ceriodaphnia dubia were used to document the chronic toxicity of ethylene glycol ethers and acetates to this invertebrate. The 7-d EC10 (effective concentrations inducing an inhibition of 10% of the reproduction of the tested organisms) values ranged from 0.06 to 1025 mg/l. While a survey of the literature showed that the acute toxicity of these chemicals appeared negligible, our results clearly revealed the potential chronic effects of some of them to this organism occupying an important trophic level in the aquatic ecosystems. The usefulness of this kind of test to better estimate the adverse effects of glycol ethers was stressed.

Animals↗

PubMed2003

A decade of research in environmental QSAR.

This year marks the 10th anniversary of SAR and QSAR in Environmental Research. In this occasion, an attempt was made to highlight the particularities of the journal since its launch in 1993. The 306 papers already published were encoded by means of 21 selected descriptive parameters such as geographical origin, type of endpoint, molecular descriptor, statistical technique and so on. A linear multivariate analysis was used to graphically analyze the obtained data matrix. Specific domains of research to continue to favor in the journal for the next decade were also underlined.

Endpoint Determination↗

PubMed2002

Chemometrical analysis of 18 metallic and nonmetallic elements found in honeys sold in france.

The elemental analysis of 86 honeys sold in France was performed with an inductively coupled plasma atomic emission spectrometer in order to measure significant concentrations of Ag, Ca, Cr, Co, Cu, Fe, Li, Mg, Mn, Mo, P, S, Zn, Al, Cd, Hg, Ni, and Pb. Principal component analysis, correspondence factor analysis, and hierarchical cluster analysis were used to rationalize and interpret the analytical data. Crude relationships were found between the elemental profiles of the honeys and their botanical origin. Some honeys were highly polluted by heavy metals and/or other xenobiotics. Explanations for these contaminations are proposed.

Analysis of Variance↗

PubMed2002

Structure-toxicity modeling of pesticides to honey bees.

A quantitative structure-activity relationship (QSAR) model was derived for estimating the acute toxicity of pesticides on the honey bee. Chemicals were described by means of autocorrelation descriptors encoding lipophilicity (H), molar refractivity (MR) and the H-bonding acceptor ability (HBA) of the pesticides. A three-layer feedforward neural network trained by the back-propagation algorithm was used as statistical engine for deriving a powerful QSAR model. The root mean square residual (RMSR) values for the training and testing sets were 0.430 and 0.386, respectively. The practical interest of this original model was discussed.

Animals↗

PubMed2002

PLS-QSAR of the adult and developmental toxicity of chemicals to Hydra attenuata.

Autocorrelation descriptors encoding lipophilicity, molar refractivity, the H-bonding acceptor and donor ability of the molecules and also indicator variables were used to describe 30 organic chemicals characterized by their adult and developmental toxicities to Hydra attenuata. A PLS regression analysis was successfully employed to derive a QSAR model allowing the simulation of both endpoints. Comparisons were made with orthogonal regression analysis and different nonlinear regression analyses.

Age Factors↗

PubMed2002

QSAR modeling of the adult and developmental toxicity of glycols, glycol ethers and xylenes to Hydra attenuata.

Autocorrelation descriptors encoding lipophilicity, molar refractivity, the H-bonding acceptor ability and H-bonding donor ability of the molecules were used to describe glycols, glycol ethers, and xylenes characterized by their adult and developmental toxicities to Hydra attenuata. A PLS regression analysis was employed to derive a QSAR model allowing to simulate both endpoints. Comparisons were made with classical regression analyses using the 1-octanol/water partition coefficient as molecular descriptor.

Age Factors↗

PubMed2001

A general QSAR model for predicting the acute toxicity of pesticides to Lepomis macrochirus.

A Quantitative Structure-Activity Relationship (QSAR) model was derived for estimating the acute toxicity of pesticides against Lepomis macrochirus under varying experimental conditions. Chemicals were described by means of autocorrelation descriptors encoding lipophilicity (H(0) to H(5)) and the H-bonding acceptor ability (HBA(0)) and H-bonding donor ability (HBD(0)) of the pesticides. A three-layer feedforward neural network trained by the back-propagation algorithm was used as statistical engine for deriving a powerful QSAR model accounting for the weight of the fish, time of exposure, temperature, pH, and water hardness.

Animals↗

PubMed2001

QSAR modeling of large heterogeneous sets of molecules.

In aquatic toxicology, QSAR models are generally designed for chemicals presenting the same mode of toxic action. Their proper use provides good simulation results. Problems arise when the mechanism of toxicity of a chemical is not clearly identified. Indeed, in that case, the inappropriate application of a specific QSAR model can lead to a dramatic error in the toxicity estimation. With the advent of powerful computers and easy access to them, and the introduction of soft modeling and artificial intelligence in SAR and QSAR, radically different models, designed from large noncongeneric sets of chemicals have been proposed. Some of these new QSAR models are reviewed and their originality, advantages, and limitations are stressed.

Forecasting↗

PubMed2000

A general QSAR model for predicting the acute toxicity of pesticides to Oncorhynchus mykiss.

A Quantitative Structure-Activity Relationship (QSAR) model was derived for estimating the acute toxicity of pesticides against Oncorhynchus mykiss under varying experimental conditions. Chemicals were described by means of autocorrelation descriptors encoding lipophilicity (H0 to H5) and the H-bonding acceptor ability (HBA0) and H-bonding donor ability (HBD0) of the pesticides. A three-layer feedforward neural network trained by the back-propagation algorithm was used as statistical engine for deriving a powerful QSAR model accounting for the weight of the fish, time of exposure, temperature, pH, and hardness.

Algorithms↗

PubMed1998

Simulating lipophilicity of organic molecules with a back-propagation neural network.

From a training set of 7200 chemicals, a back-propagation neural network (BNN) model was developed for calculating the 1-octanol/water partition coefficient (log P) of molecules containing nitrogen, oxygen, halogen, phosphorus, and/or sulfur atoms. Chemicals were described by means of autocorrelation vectors encoding hydrophobicity, molar refractivity, H-bonding acceptor ability, and H-bonding donor ability. A 35/32/1 composite network composed of four configurations was selected as the final model (root-mean-square error (RMS) = 0.37, r = 0.97) because it provided the best simulation results (RMS = 0.39, r = 0.98) on an external testing set of 519 molecules. This final model compared favorably with a recently published BNN model using variables (atoms and bonds) derived from connection matrices.

Computer Simulation↗

PubMed1998

Nonlinear neural mapping analysis of the adverse effects of drugs.

Numerous drugs have been identified as presenting adverse effects towards the driving of vehicles. A large set of these drugs was compiled and classified into ten categories. Nonlinear neural mapping (N2M) was used to derive a typology of these molecules and also to link their adverse effects to therapeutic categories and structural information.

Automobile Driving↗