Biomedical subjects

J Devillers

Publications and source records attributed to J Devillers.

At least 19 recordsLinked to original sources

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↗

PubMed1997

European Union System for the Evaluation of Substances (EUSES). Principles and structure.

In the European Union, Directive 92/32/EC and EC Council Regulation (EC) 793/93 require the risk assessment of new and existing substances, respectively. Principles for this risk assessment have been laid down, supported by a detailed package of Technical Guidance Documents. Against this background the European Union System for the Evaluation of Substances (EUSES) has been developed. This software can be used to carry out tiered risk assessments of increasing complexity on the basis of increasing data requirements. The exposure assessment, effects assessment and risk characterisation are carried out for environmental populations as well as for human beings, including workers, consumers and man exposed through the environment. EUSES is the result of a co-ordinated effort of EU Member States, the European Commission and the European Chemical Industry.

Animals↗

PubMed1997

Modeling the environmental fate of atrazine.

Mathematical simulation models of fate and transport of chemicals have been identified by researchers and regulators as potentially valuable tools for improving the understanding of the environmental behavior of chemicals which may be released to the environment as a consequence of routine (i.e., normal manufacturing, use, disposal) and non-routine (e.g., accidental spillage) events. In this context, CHEMFRANCE, a regional fugacity model level III, which calculates the environmental distribution of organic chemicals in 12 defined regions of France, or France as a whole, has been designed. The aim of this study is to show that CHEMFRANCE provides valuable simulation results for understanding the environmental fate behavior of atrazine.

Atrazine↗

PubMed1997

Occupational exposure modelling with ease.

This article presents a validation exercise performed from eight practical case studies on EASE (version 2.0), a knowledge-based system allowing to estimate the workplace exposure to chemicals. Our results show that EASE represents a valuable simulation tool in occupational hygiene. However, it requires to be refined and extended to more realistic and precise situations to be easily used in practice.

Artificial Intelligence↗

PubMed1994

A nonlinear map of substituent constants for selecting test series and deriving structure-activity relationships. 1. Aromatic series.

A nonlinear mapping (NLM) analysis was performed on a set of 166 aromatic substituents described by six variables encoding hydrophobic (pi), steric (MR), and electronic effects (HBA, HBD, F, and R). NLM allowed to easily summarize the main information contained in the original data table. By means of collections of graphs, it was possible to relate the structure of the substituents to their pi, MR, HBA, HBD, F, and R values. The proposed approach provides a useful and easy tool for the selection of test series and for deriving structure-activity relationships.

Alanine↗

PubMed1994

A nonlinear map of substituent constants for selecting test series and deriving structure-activity relationships. 2. Aliphatic series.

A nonlinear mapping (NLM) analysis was performed on a set of 103 aliphatic substituents described by five variables encoding hydrophobic (Fr), steric (MR), and electronic effects (HBA, HBD, and F). NLM allowed to easily summarize the main information contained in the original data table. By means of collections of graphs, it was possible to relate the structure of the aliphatic substituents to their Fr, MR, HBA, HBD, and F values. The proposed approach provides a useful and easy tool for the selection of test series and for deriving structure-activity relationships.

Antifungal Agents↗

PubMed1994

Multivariate analysis of the first 10 MEIC chemicals.

Nonlinear mapping coupled to powerful graphical tools was used to compare the toxicological responses of 32 in vivo and in vitro test systems to the first 10 MEIC chemicals. The obtained results clearly underline the usefulness of our methodological approach for the comparison of the different endpoints and the selection of a battery of in vitro toxicity tests allowing to estimate the possible harmful effects of chemicals in vivo.

3T3 Cells↗

PubMed1993

Chemometrical evaluation of multispecies-multichemical data by means of graphical techniques combined with multivariate analyses.

A new method combining graphical displays with principal component analysis has been used to evaluate published data on the toxicity of seven chemicals to 14 species (17 testing procedures) of aquatic biota. The results reflect the usefulness of simple graphical approaches for analyzing the structure of environmental data sets. Thus, the study indicates the importance of end point selection and underlines some relationships among the species and chemicals.

Animals↗

PubMed1993

Nonlinear dependence of fish bioconcentration on n-octanol/water partition coefficient.

The log-log relationship between the bioconcentration tendency of organic chemicals in fish and the n -octanol/water partition coefficients breaks down for very hydrophobic compounds. The use of parabolic and bilinear models allows this problem to be overcome. The QSAR equation log BCF = 0.910 log P - 1.975 log (6.8 10(-7) P + 1) - 0.786 (n = 154; r = 0.950; s = 0.347; F = 463.51) was found to be a good predictor of bioconcentration in fish.

1-Octanol↗

PubMed1993

Neural modelling of the biodegradability of benzene derivatives.

The aim of this paper was to explore the usefulness of a backpropagation neural network (BNN) to estimate the biodegradability of benzene derivatives. 127 chemicals selected from the BIODEG data bank (Syracuse Research Corporation, 1992) were described by means of 20 structural descriptors taking into account the nature and position of the substituents on the benzene ring. Three classes of biodegradability were selected and modelled from the BNN. A 20/5/3 BNN (alpha = 0.8 and eta = 0.5) correctly classified 92% (104/113) of the training and 86% (12/14) of the testing sets. The results were compared to those produced by the BIODEG probability program (Syracuse Research Corporation, Version 2.13).

Algorithms↗

PubMed1993

Estimating pesticide field half-lives from a backpropagation neural network.

The field half-lives of 110 pesticides were modelled using a backpropagation neural network (NN). The molecules were described by means of the frequency of 17 structural fragments. Before training the NN, different scaling transformations were assayed. Best results were obtained with correspondence factor analysis which also allowed a reduction of dimensionality. The training and testing sets of the NN analysis gave 95.5% and 84.6% of good classifications, respectively. Comparison with discriminant factor analysis showed that a backpropagation NN was more appropriate to model the field half-lives of pesticides.

Discriminant Analysis↗

PubMed1990

Environmental and health risks of hydroquinone.

Hazard assessment of hydroquinone has been evaluated from bibliographical and original data on the physicochemical properties, the environmental behavior, and the biological effects of this aromatic compound. Hydroquinone, which is produced in large amounts and widely used, must be considered as an environmental contaminant. However, it is not persistent. The ecotoxicity of this molecule, which must be linked to its physicochemical properties, varies from species to species. Its acute and chronic toxicity toward higher terrestrial organisms is moderate. Hydroquinone is estimated to be nonmutagenic by the Ames test but induces chromosome aberrations or karyotypic effects in eucaryotic cells. Carcinogenic and teratogenic potentials have been at present inadequately studied. The study underlines the complementarity of QSAR models and experimental approaches when an attempt is made to obtain ecotoxicological profiles of pollutants.

Animals↗

PubMed1989

Heuristic potency of the minimum spanning tree (MST) method in toxicology.

A rapid and manual mathematical method for comparing ecotoxicologic data has been investigated. It uses a classification procedure based on the calculation of chi 2 distances between the different elements of a data matrix. The classification is carried out using a simple graph-theoretical procedure (Kruskal's algorithm), allowing the construction of minimum spanning tree (MST). From an ecotoxicologic data base of 18 bacterial tests carried out on eight heavy metals the construction of the MST is explained in detail. Even if the results obtained are directly dependent on the data set chosen, they illustrate the heuristic potency of the minimum spanning tree method in comparing and estimating the dependence between environmental data.

Bacteria↗

PubMed1989

The stochastic regression analysis as a tool in ecotoxicological QSAR studies.

Correspondence factor analysis (CFA) was used in conjunction with linear regression analysis to examine the structure-activity relationships of 50 benzene derivatives tested on Pimephales promelas. From nine molecular descriptions (numbers of C, H, O, N, Br, Cl, NO2, OH, and NH2 included in the molecules), CFA made it possible to define five new independent variables which were introduced in a stepwise regression analysis procedure to describe the acute toxicity (96-h LC50) of the aromatic compounds. The model log 1/C = -0.727F1 + 1.248F3 + 4.052 (r = 0.918; s = 0.270) is more relevant to describe the ecotoxicological behavior of the studied compounds on the fathead minnow than that obtained with principal components (log 1/C = 0.151 PC1 -0.271 PC2 + 4.124; r = 0.737; s = 0.460). The heuristic potency of this particular statistical analysis, which is called stochastic regression analysis, is discussed in detail.

Animals↗