Showing papers in "Sar and Qsar in Environmental Research in 2010"

Estimation of reliability of predictions and model applicability domain evaluation in the analysis of acute toxicity (LD50).

Abstract: This study presents a new type of acute toxicity (LD(50)) prediction that enables automated assessment of the reliability of predictions (which is synonymous with the assessment of the Model Applicability Domain as defined by the Organization for Economic Cooperation and Development). Analysis involved nearly 75,000 compounds from six animal systems (acute rat toxicity after oral and intraperitoneal administration; acute mouse toxicity after oral, intraperitoneal, intravenous, and subcutaneous administration). Fragmental Partial Least Squares (PLS) with 100 bootstraps yielded baseline predictions that were automatically corrected for non-linear effects in local chemical spaces--a combination called Global, Adjusted Locally According to Similarity (GALAS) modelling methodology. Each prediction obtained in this manner is provided with a reliability index value that depends on both compound's similarity to the training set (that accounts for similar trends in LD(50) variations within multiple bootstraps) and consistency of experimental results with regard to the baseline model in the local chemical environment. The actual performance of the Reliability Index (RI) was proven by its good (and uniform) correlations with Root Mean Square Error (RMSE) in all validation sets, thus providing quantitative assessment of the Model Applicability Domain. The obtained models can be used for compound screening in the early stages of drug development and prioritization for experimental in vitro testing or later in vivo animal acute toxicity studies.

Examination of Michael addition reactivity towards glutathione by transition-state calculations

Abstract: Kinetic rate constants (k(GSH)) for the reaction of compounds acting as Michael acceptors with glutathione (GSH) were modelled by quantum chemical transition-state calculations at the B3LYP/6-31G** and B3LYP/TZVP level The data set included α, β-unsaturated aldehydes, ketones and esters, with double bonds and triple bonds, linear and cyclic systems, both with and without substituents in the α-position Predicted values for k(GSH) were found to be in good agreement with experimental k(GSH) values Factors affecting rate constants have been elucidated, especially solvent effects and the influence of steric hindrance Solvent effects were examined by adding explicit solvent molecules to the system and by using a polarizable continuum solvent model Detailed analysis of transition-state energies shows that the reaction is reversible The reactive enolic intermediate plays an important role in Michael addition to GSH, while the subsequent keto-enol-tautomerism is not rate limiting

QSAR with quantum topological molecular similarity indices: toxicity of aromatic aldehydes to Tetrahymena pyriformis

Abstract: Extensive production and utilization of aromatic aldehydes and their derivatives without proper certification is alarming with regard to environmental safety. This concern motivated our construction of predictive quantitative structure-activity relationship (QSAR) models for the toxicity of aldehydes to the ecologically important species Tetrahymena pyriformis. Quantum topological molecular similarity (QTMS) descriptors, along with the lipid-water partition coefficient (log K(o/w)), were used as predictor variables. The QTMS descriptors were calculated at different levels of theory including AM1, HF/3-21G(d), HF/6-31G(d), B3LYP/6-31 + G(d,p), B3LYP/6-311 + G(2d,p) and MP2/6-311+G(2d,p). The data set of 77 aromatic aldehydes was divided into a training set (n = 58) and a test (n = 19) set, and 58 models were developed using partial least squares (PLS) and genetic partial least squares (G/PLS). We evaluated the overall predictive capacity of the models based on leave-one-out predictions for the training set compounds and model derived predictions for the test set compounds. For both PLS and G/PLS, the models built at the HF/6-31G(d) level show better predictivity (based on overall prediction) than the models developed at any of the other five levels. Further validation was also performed utilizing (process and model) randomization tests. We show that improved predictive QSAR models for aldehydic toxicity to Tetrahymena pyriformis can be generated using QTMS descriptors along with log K(o/w).

Pharmacophore-based virtual screening and docking studies on Hsp90 inhibitors.

Abstract: Hsp90 (Heat shock protein 90) is an important therapeutic target for the treatment of cancer. To identify important chemical features for Hsp90 inhibitory activity, a 3D-QSAR pharmacophore model was developed using a set of 61 inhibitors (a training set of 31 and a test set of 30 compounds) belonging to a series of 2-amino-6-halopurine and 7′-substituted benzothiazolothio- and pyridinothiazolothio-purines. The best HypoGen model consisted of five pharmacophoric features: one hydrogen bond acceptor (HBA), one hydrogen bond donor (HBD) and three hydrophobic (HY) groups. It showed a high correlation coefficient (r = 0.943) and low root mean square deviation (RMSD = 0.751). This model was validated against 30 known Hsp90 inhibitors, where it showed a high predictive value for R 2 [ = 0.805], thus confirming that HY, HBA and HBD features are essential for Hsp90 inhibition. The validated pharmacophore model (Hypo-1) was used as a 3D query for virtual screening to retrieve potential inhibitors from the Maybridge...

Using support vector regression coupled with the genetic algorithm for predicting acute toxicity to the fathead minnow

Abstract: The potential toxicity of chemicals may present adverse effects to the environment and human health. The quantitative structure-activity relationship (QSAR) provides a useful method for hazard assessment. In this study, we constructed a QSAR model based on a highly heterogeneous data set of 571 compounds from the US Environmental Protection Agency, for predicting acute toxicity to the fathead minnow (Pimephales promelas). An approach coupling support vector regression (SVR) with the genetic algorithm (GA) was developed to build the model. The generated QSAR model showed excellent data fitting and prediction abilities: the squared correlation coefficients (r(2)) for the training set and the test set were 0.826 and 0.802, respectively. Only eight critical descriptors, most of which are closely related to the toxicity mechanism, were chosen by GA-SVR, making the derived model readily interpretable. In summary, the successful case reported here highlights that our GA-SVR approach can be used as a general machine learning method for toxicity prediction.

Toxicity of organic pollutants to seven aquatic organisms: effect of polarity and ionization.

Abstract: The toxicity of organic chemicals to Vibrio fischeri, river bacteria, algae, Daphnia magna and fishes were analysed. The results showed that the toxicity of chemicals to narcotics was dependent on hydrophobicity. A single model for both polar and non-polar narcotics was developed by inclusion of a polarity descriptor as well as the hydrophobic parameter. The highly hydrophobic polar narcotics could be treated as non-polar narcotics because their polar functional group(s) make(s) a relatively small contribution to polarity as compared with their hydrophobicity. In order to investigate the toxic mechanism of action for reactive compounds, the response-surface approach was used to develop models derived from easily calculated descriptors. The stepwise analysis selected the octanol/water partition coefficient and a polarity descriptor to parameterize bio-uptake and reactivity, respectively, for seven species. Benzoic acids can be easily absorbed into the unicellular bacteria, but this is not the case for multicellular D. magna and fish. Their toxicity to V. fischeri is much higher than that to D. magna and carp. Regression analysis was performed based on the model that we developed for ionizable compounds. Good correlations were observed by introducing the correction factor for ionizable compounds. The toxic mechanisms are discussed.

Physiologically based pharmacokinetic (PBPK) tool kit for environmental pollutants – metals

Abstract: The Agency for Toxic Substances and Disease Registry (ATSDR) is mandated by the US Congress to identify significant human exposure levels, develop methods to determine such exposures, and design strategies to mitigate them. Physiologically based pharmacokinetic (PBPK) models are increasingly being used to evaluate toxicity of environmental pollutants through multiple exposure pathways. As part of its translational research project, ATSDR is developing a human ‘PBPK model tool kit’ that consists of a series of published models re-coded in a common simulation language. The tool kit currently consists of models, at various stages of development, for priority environmental contaminants including solvents and persistent organic pollutants. Presented here are results of translational activities of re-coding models for cadmium, mercury, and arsenic. As part of this work, following re-coding each new model was evaluated for fidelity followed by sensitivity analysis. Good agreement was generally obtained for all t...

Domain of EPI suite biotransformation models.

Abstract: Knowledge of the interpolative region or applicability domain (AD) of structure–activity relationships is believed to improve predictive accuracy. The present work was undertaken to characterize the AD of EPI Suite™ biotransformation models and evaluate the performance of selected AD assessment methods. AD methods were applied to the training sets of four models representing different end-points, and the predictive accuracy was then evaluated using six independent validation sets. Two of the models estimated a continuous variable (log half-life) from fragment descriptors. For biotransformation in fish (BCFBAF™) and hydrocarbon biodegradation (BioHCwin), the approach using ranges, with preprocessing by analysis of principal components, worked reasonably well in identifying subsets of validation chemicals that have higher root mean squared error than for all validation chemicals. AD methods were also applied to two classification models, Biowin3 (which predicts the time required to achieve complete aerobic ...

QSAR classification of estrogen receptor binders and pre-screening of potential pleiotropic EDCs.

Abstract: Endocrine disrupting chemicals (EDCs) are suspected of posing serious threats to human and wildlife health through a variety of mechanisms, these being mainly receptor-mediated modes of action It is reported that some EDCs exhibit dual activities as estrogen receptor (ER) and androgen receptor (AR) binders Indeed, such compounds can affect the normal endocrine system through a dual complex mechanism, so steps should be taken not only to identify them a priori from their chemical structure, but also to prioritize them for experimental tests in order to reduce and even forbid their usage To date, very few EDCs with dual activities have been identified The present research uses QSARs, to investigate what, so far, is the largest and most heterogeneous ER binder data set (combined METI and EDKB databases) New predictive classification models were derived using different modelling methods and a consensus approach, and these were used to virtually screen a large AR binder data set after strict validation As a result, 46 AR antagonists were predicted from their chemical structure to also have potential ER binding activities, ie pleiotropic EDCs In addition, 48 not yet recognized ER binders were in silico identified, which increases the number of potential EDCs that are substances of very high concern (SVHC) in REACH Thus, the proposed screening models, based only on structure information, have the main aim to prioritize experimental tests for the highlighted compounds with potential estrogenic activities and also to design safer alternatives

Antibacterial activity and QSAR of chalcones against biofilm-producing bacteria isolated from marine waters

Abstract: Biofouling in the marine environment is a major problem. In this study, three marine organisms, namely Bacillus flexus (LD1), Pseudomonas fluorescens (MD3) and Vibrio natriegens (MD6), were isolated from biofilms formed on polymer and metal surfaces immersed in ocean water. Phylogenetic analysis of these three organisms indicated that they were good model systems for studying marine biofouling. The in vitro antifouling activity of 47 synthesized chalcone derivatives was investigated by estimating the minimum inhibitory concentration against these organisms using a twofold dilution technique. Compounds C-5, C-16, C-24, C-33, C-34 and C-37 were found to be the most active. In the majority of the cases it was found that these active compounds had hydroxyl substitutions. A quantitative structure-activity relationship (QSAR) was developed after dividing the total data into training and test sets. The statistical measures r(2), [image omitted] (>0.6) q(2) (>0.5) and the F-ratio were found to be satisfactory. Spatial, structural and electronic descriptors were found to be predominantly affecting the antibiofouling activity of these compounds. Among the spatial descriptors, Jurs descriptors showed their contribution in all the three antibacterial QSARs.

Evaluation of the OECD (Q)SAR Application Toolbox and Toxtree for predicting and profiling the carcinogenic potential of chemicals

Abstract: The OECD (Q)SAR Application Toolbox and Toxtree are software tools used in regulatory toxicology to fill gaps in (eco)toxicity data. They include different SAR and QSAR models for estimating (eco)toxicological endpoints. Among them, the Benigni/Bossa rule-based system is proposed to characterize the carcinogenic potential of chemicals. Our study evaluates the predictive performance that can be expected from the OECD (Q)SAR Toolbox and Toxtree when analysing chemicals by means of the structural alerts coded within the Benigni/Bossa rule-based system for carcinogenicity and the associated QSAR model (QSAR8). These evaluations have been carried out thanks to a large collection of chemicals retrieved from original publications and public databases. Overall, our findings confirm the performance of the system of structural alerts while suggesting that the sensitivity of QSAR8, as implemented in the two tools, is lower than what was previously reported. They also indicate that attention has to be paid when interpreting the output of the two tools because of possible malfunctions involving the coding of two-dimensional structures. A set of possible modulating factors for the structural alert identifying polycyclic aromatic hydrocarbons is also proposed together with candidates for putative new structural alerts not included in the tested tools.

Mechanism-based common reactivity pattern (COREPA) modelling of aryl hydrocarbon receptor binding affinity.

Abstract: The aryl hydrocarbon receptor is a ligand-activated transcription factor responsive to both natural and synthetic environmental compounds, with the most potent agonist being 2,3,7,8-tetrachlotrodibenzo-p-dioxin. The aim of this work was to develop a categorical COmmon REactivity PAttern (COREPA)-based structure-activity relationship model for predicting aryl hydrocarbon receptor ligands within different binding ranges. The COREPA analysis suggested two different binding mechanisms called dioxin- and biphenyl-like, respectively. The dioxin-like model predicts a mechanism that requires a favourable interaction with a receptor nucleophilic site in the central part of the ligand and with electrophilic sites at both sides of the principal molecular axis, whereas the biphenyl-like model predicted a stacking-type interaction with the aryl hydrocarbon receptor allowing electron charge transfer from the receptor to the ligand. The current model was also adjusted to predict agonistic/antagonistic properties of chemicals. The mechanism of antagonistic properties was related to the possibility that these chemicals have a localized negative charge at the molecule's axis and ultimately bind with the receptor surface through the electron-donating properties of electron-rich groups. The categorization of chemicals as agonists/antagonists was found to correlate with their gene expression. The highest increase in gene expression was elicited by strong agonists, followed by weak agonists producing lower increases in gene expression, whereas all antagonists (and non-aryl hydrocarbon receptor binders) were found to have no effect on gene expression. However, this relationship was found to be quantitative for the chemicals populating the areas with extreme gene expression values only, leaving a wide fuzzy area where the quantitative relationship was unclear. The total concordance of the derived aryl hydrocarbon receptor binding categorical structure-activity relationship model was 82% whereas the Pearson's coefficient was 0.88.

Cellular automata modelling of biomolecular networks dynamics

Abstract: The modelling of biological systems dynamics is traditionally performed by ordinary differential equations (ODEs). When dealing with intracellular networks of genes, proteins and metabolites, however, this approach is hindered by network complexity and the lack of experimental kinetic parameters. This opened the field for other modelling techniques, such as cellular automata (CA) and agent-based modelling (ABM). This article reviews this emerging field of studies on network dynamics in molecular biology. The basics of the CA technique are discussed along with an extensive list of related software and websites. The application of CA to networks of biochemical reactions is exemplified in detail by the case studies of the mitogen-activated protein kinase (MAPK) signalling pathway, the FAS-ligand (FASL)-induced and Bcl-2-related apoptosis. The potential of the CA method to model basic pathways patterns, to identify ways to control pathway dynamics and to help in generating strategies to fight with cancer is demonstrated. The different line of CA applications presented includes the search for the best-performing network motifs, an analysis of importance for effective intracellular signalling and pathway cross-talk.

Evaluation of the OECD QSAR Application Toolbox and Toxtree for estimating the mutagenicity of chemicals. Part 1. Aromatic amines

Abstract: The Ames Salmonella typhimurium mutagenicity assay is a short-term bacterial reverse mutation test that was designed to detect mutagens. For several decades, it has been used in research laboratories and by regulatory agencies throughout the world for the detection and characterization of potential mutagens among natural products and man-made chemicals. Faced with the ever-growing number of chemicals available on the market, congeneric and non-congeneric (Q)SAR models have been designed from Ames test results obtained on specific S. typhimurium strains such as TA 100 or TA 98. Such models have great potential for a quick and cheap identification and classification of large numbers of potential chemical mutagens. The OECD QSAR Application Toolbox and Toxtree, which were developed for facilitating the practical use of (Q)SAR approaches in regulatory contexts, include two mechanistic SAR models for predicting the mutagenicity of aromatic amines and - unsaturated aliphatic aldehydes. The aim of this study was to estimate the interest and limitations of the former model. The model was first re-computed to check its transparency and to verify its statistical validity. Then, it was tested on about 150 chemicals not previously used for the design of the model but belonging to its domain of application. A critical analysis of the results was performed and proposals were made for increasing the model performances.

Quantitative structure-activity relationship modelling of the carcinogenic risk of nitroso compounds using regression analysis and the TOPS-MODE approach.

Abstract: Worldwide, legislative and governmental efforts are focusing on establishing simple screening tools for identifying those chemicals most likely to cause adverse effects without experimentally testing all chemicals of regulatory concern. This is because even the most basic biological testing of compounds of concern, apart from requiring a huge number of test animals, would be neither resource nor time effective. Thus, alternative approaches such as the one proposed here, quantitative structure-activity relationship (QSAR) modelling, are increasingly being used for identifying the potential health hazards and subsequent regulation of new industrial chemicals. This paper follows up on our earlier work that demonstrated the use of the TOPological Substructural MOlecular DEsign (TOPS-MODE) approach to QSAR modelling for predictions of the carcinogenic potency of nitroso compounds. The data set comprises 56 nitroso compounds which have been bio-assayed in female rats and administered by the oral water route. The QSAR model was able to account for about 81% of the variance in the experimental activity and exhibited good cross-validation statistics. A reasonable interpretation of the TOPS-MODE descriptors was achieved by means of bond contributions, which in turn afforded the recognition of structural alerts (SAs) regarding carcinogenicity. A comparison of the SAs obtained from different data sets showed that experimental factors, such as the sex and the oral administration route, exert a major influence on the carcinogenicity of nitroso compounds. The present and previous QSAR models combined together provide a reliable tool for estimating the carcinogenic potency of yet untested nitroso compounds and they should allow the identification of SAs, which can be used as the basis of prediction systems for the rodent carcinogenicity of these compounds.

Exploring the binding features of polybrominated diphenyl ethers as estrogen receptor antagonists: docking studies

Abstract: The polybrominated diphenyl ethers (PBDEs) accumulating in nature are known to be endocrine-disrupting compounds. Of first concern are those interacting with and altering activity of the human estrogen receptor alpha (hERalpha). In this study a docking study was carried out to explore the binding modes of PBDE compounds as hERalpha antagonists. It was found that some of the PBDE compounds with antiestrogenic activity extended into the channel of the estrogen receptor (ER), which is usually occupied by the alkylamine side chain of the ER antagonists raloxifene (RAL) and 4-hydroxytamoxifen (OHT), while most PBDE compounds without antiestrogenic activity adopted binding modes similar to that of ER agonist 17beta-estradiol (E2), located in the binding cavity and which did not protrude into the channel. The present study suggests that pose comparison based on docking is useful for discriminating whether or not PBDE compounds have antiestrogenic activity. Knowing the binding modes of compounds in hERalpha can help to screen out antiestrogenic compounds and further develop descriptive and predictive models in ecotoxicology.

Development of an ecotoxicity QSAR model for the KAshinhou Tool for Ecotoxicity (KATE) system, March 2009 version

Abstract: The KAshinhou Tool for Ecotoxicity (KATE) system, including ecotoxicity quantitative structure–activity relationship (QSAR) models, was developed by the Japanese National Institute for Environmental Studies (NIES) using the database of aquatic toxicity results gathered by the Japanese Ministry of the Environment and the US EPA fathead minnow database. In this system chemicals can be entered according to their one-dimensional structures and classified by substructure. The QSAR equations for predicting the toxicity of a chemical compound assume a linear correlation between its log P value and its aquatic toxicity. KATE uses a structural domain called C-judgement, defined by the substructures of specified functional groups in the QSAR models. Internal validation by the leave-one-out method confirms that the QSAR equations, with r 2 > 0.7, RMSE ≤ 0.5, and n > 5, give acceptable q 2 values. Such external validation indicates that a group of chemicals with an in-domain of KATE C-judgements exhibits a lower root...

Internet resources for agent-based modelling.

Abstract: The use of agent-based models (ABMs) is steadily increasing in all the disciplines including environmental chemistry and toxicology. This growth is mainly driven by their ability to address problems that conventional modelling techniques cannot, such as the change of scale or the emergence of unanticipated phenomena resulting from interactions between their constitutive goal-directed agents. After a brief introduction on the basic principles of agent-based modelling and the presentation of selected case studies, the main software resources available on the Internet are presented. An attempt is made to estimate the complexity of these tools versus their potentialities and flexibility.

QSAR model for the prediction of bio-concentration factor using aqueous solubility and descriptors considering various electronic effects.

Abstract: The in silico modelling of bio-concentration factor (BCF) is of considerable interest in environmental sciences, because it is an accepted indicator for the accumulation potential of chemicals in organisms. Numerous QSAR models have been developed for the BCF, and the majority utilize the octanol/water partition coefficient (log P) to account for the penetration characteristics of the chemicals. The present work used descriptors from a variety of software packages for the development of a multi-linear regression model to estimate BCF. The modelled data set of 473 diverse compounds covers a wide range of log BCF values. In the proposed QSAR model, most of the variation is described by the calculated solubility in water. Other contributing descriptors describe, for instance, hydrophobic surface area, hydrogen bonding and other electronic effects. The model was validated internally by using a variety of statistical approaches. Two external validations were also performed. For the former validation, a subset ...

3D-QSAR studies on triclosan derivatives as Plasmodium falciparum enoyl acyl carrier reductase inhibitors

Abstract: 3D-QSAR studies were carried out on a training set of 53 structurally highly diverse analogues of triclosan to investigate the correlation of the structural properties of triclosan derivatives with the inhibition of the activity of enoyl acyl carrier protein reductase in Plasmodium falciparum (PfENR) by employing Comparative Molecular Field Analysis (CoMFA) and Comparative Molecular Similarity Indices Analysis (CoMSIA). The crystal structure bound conformation of triclosan, was used as a template for aligning molecules. The probable binding mode conformations of other inhibitors were explored according to molecular docking and molecular mechanics poisson-boltzmann surface area (MM/PBSA) solvation free energy estimation methods using grid based linear Poisson-Boltzmann calculations. Predictive 3D-QSAR models, established using routine database alignment rule based on crystallographic-bound conformation of template molecule, produced statistically significant results with cross-validated values of 0.64 and ...

A quantitative structure-activity relationship study on serotonin 5-HT6 receptor ligands: indolyl and piperidinyl sulphonamides

Abstract: The serotonin 5-HT6 binding affinity of indolyl- and piperidinyl-sulphonamide derivatives has been analysed with topological and molecular features with DRAGON software. Analysis of the structural features in conjunction with the biological endpoints in combinatorial protocol in multiple linear regression (CP-MLR) led to the identification of 25 descriptors for modelling the activity. The study clearly suggested the role of an average Randic-type eigenvector-based index from adjacency matrix, VRA2, number of secondary aliphatic amines, nNHR, the sum of the topological distance between N and O, T(N···O), ring tertiary carbon atoms, nCrHR, and CH2RX type fragment, C-006, in a molecular structure to optimize the 5-HT6 binding affinities of titled compounds. The PLS analysis confirmed the dominance of information content of CP-MLR identified descriptors for modelling the activity when compared with those of leftover ones.

Reactivity-based toxicity modelling of five-membered heterocyclic compounds: application to Tetrahymena pyriformis.

Abstract: A diverse set of 57 heterocyclic organic chemicals, consisting of a five-membered unsaturated ring of four carbon atoms and one oxygen (furans), or sulfur (thiophenes), or nitrogen (pyrroles) were evaluated for reactivity with thiol and acute aquatic toxicity assays using glutathione (GSH) as a model nucleophile and the ciliate Tetrahymena pyriformis, respectively. Reactivity was quantified by the RC₅₀ value, the concentration of test compound that produced 50% reaction of the GSH thiol groups in 2 hours. Under standard conditions, RC₅₀ values are mathematically proportional to reciprocal rate constants. Toxicity was quantified by the IGC₅₀, the concentration of the test compound that produces 50% inhibition of population growth in 40 hours. Pyrroles with polarized α,β-unsaturated substructures were found to be non-reactive with GSH and did not exhibit excess toxicity in the Tetrahymena assay. In contrast, those furans and thiophenes with polarized α,β-unsaturated substructures were reactive with GSH via the Michael addition mechanism and did exhibit excess acute aquatic toxicity in Tetrahymena. For furans and thiophenes, reactivity and toxicity varied with the number, type, and location on the ring of the π-bond-containing polarized moieties. Comparisons of reactivity and toxicity potency between furan and thiophene derivatives revealed furans to be twice as potent as thiophenes. QSAR analysis revealed that aquatic toxicity IGC₅₀ to Tetrahymena is correlated with RC₅₀ values: log (IGC₅₀(-1)) = 1.13 log (RC₅₀(-1)) + 1.43; n = 23, r²= 0.815, r²(adj) = 0.806, s = 0.41, F = 92.

Prediction of acute toxicity to mice by the Arithmetic Mean Toxicity (AMT) modelling approach.

Abstract: A modelling approach based on the structural and physicochemical similarity of chemicals to their nearest neighbours is proposed for toxicity estimation. This approach, called Arithmetic Mean Toxicity (AMT) modelling, is illustrated by means of an AMT model for predicting acute rodent toxicity. The AMT approach uses one or a few pairs of nearest structural neighbours. Each pair contains a chemical with a higher descriptor value and with a smaller descriptor value compared with the chemical of interest. Arithmetic mean toxicity values of those pairs are considered as toxicity of chemical of interest. The toxicity of the chemical of interest was not included in the development of the AMT model. The approach was applied to calculate the toxicity of chemicals to mice following intravenous injection. A toxicity data set containing 10,241 organic neutral compounds was formed from the SYMYX Toxicity database. The toxicity (log (1/LD50), mmol/kg), where LD50 is the median lethal dose, of 10,227 chemicals was calc...

Evaluation of the OECD QSAR Application Toolbox and Toxtree for estimating the mutagenicity of chemicals. Part 2. α-β unsaturated aliphatic aldehydes.

Abstract: The OECD QSAR Application Toolbox versions 1.1.01 and 1.1.02 and Toxtree version 1.60, which were developed for facilitating the practical use of (Q)SAR approaches by regulators, include a mechanistic SAR model for predicting the mutagenicity of α-β unsaturated aliphatic aldehydes. The aim of this study was to estimate the interest and limitations of this model. First, the model was re-computed to check its transparency and to verify its statistical validity. Then, the model implemented in the two software tools was tested on 34 chemicals not previously used for its design and for which experimental mutagenic activity data were available in the literature. A critical analysis of the results was performed and the practical interest of the model was discussed.

Screening for low aquatic bioaccumulation. 1. Lipinski's 'Rule of 5' and molecular size.

Abstract: Aquatic bioconcentration factors are critical in PBT assessment of industrial chemicals under REACH. Reliable indicators based on physico-chemical properties and molecular attributes of chemicals with low bioconcentration potential have been searched to de-prioritize non-accumulative chemicals in order to avoid unnecessary biotests that do not produce risk-relevant information. Developed to screen drug candidates, Lipinski's ‘Rule of 5’ identifies chemicals with poor oral absorption based on criteria in partitioning, molecular weight and hydrogen bonding. This parameter ensemble has been supplemented with molecular diameter and tested for its adequacy to filter chemicals with low bioconcentration potential. Perhaps (not) surprisingly, the application of the ‘Rule of 5’ fails to protectively identify non-accumulative compounds because other processes dominate the uptake in aquatic environments as compared with oral absorption. No robust evidence was found for cut-offs in bioconcentration related to molecul...

Comparison of prediction methods for the uptake of As, Cd and Pb in carrot and lettuce

Abstract: The New Model Framework (NMF) for uptake into crops is based on particle deposition and Transfer factors from soil to plant calculated from the BAse de donnees sur les teneurs en Elements Traces metalliques de Plantes Potageres (BAPPET) database. Besides NMF, approaches developed by the National Institute of Public Health and the Environment (RIVM), Hough, and the United States Environmental Protection Agency (US EPA), and the Contaminated Land Exposure Assessment (CLEA) approach were tested. Experimental data were assembled from the BAPPET database and Danish background data of As, Cd and Pb in soil, air and crops was collected. None of the models proved able to estimate the measured concentrations in plants from the BAPPET database with an absolute normalized error smaller than 70%. On average, the predictions had an error of 80–250%. However, when applying the models to the rural Danish background scenario, the NMF and other models predicted the concentrations in carrot and lettuce within the range of ...

Combinatorial protocol in multiple linear regression/partial least-squares directed rationale for the caspase-3 inhibition activity of isoquinoline-1,3,4-trione derivatives

Abstract: The caspase-3 inhibition activity of isoquinoline-1,3,4-trione derivatives has been analysed with the topological and molecular features from Dragon software. Analysis of the structural features in conjunction with the biological endpoints in combinatorial protocol in multiple linear regression (CP-MLR) led to the identification of 45 descriptors for modelling the activity. The study clearly suggested the role of rotatable bonds, mean information on the distance degree equality, radial centricity, bond and structural information content of five-order neighbourhood symmetry, atomic van der Waals volumes and the presence or absence of certain structural fragments to optimise the caspase-3 inhibitory activity of titled compounds. The models developed and the participating descriptors advocate that the substituent groups of the isoquinoline moiety hold scope for further modification in the optimization of the caspase-3 inhibitory activity. Analysis of these descriptors in partial least squares (PLS) highlighted their relative significance in modulating the biological response. The selected descriptors are enriched with information corresponding to the activity when compared to the remaining ones.

A new graphical representation of similarity/dissimilarity studies of protein sequences

Abstract: Based on chaos game representation, a two-dimensional-graphical representation of protein sequences is described in which 20 amino acids are rearranged in a cyclic order using a PAM250 substitution matrix. A numerical characterisation has been developed as a descriptor to compare protein sequences. Finally, an example is given in which the dehydrogenase subunit 5 (ND5) protein sequences of nine species are compared.

Role of physicochemical properties in the estimation of skin permeability: in vitro data assessment by Partial Least-Squares Regression.

Abstract: Skin provides passage for the delivery of drugs. The in vitro and in vivo testing of chemicals for estimation of dermal absorption is very time consuming, costly and has many ethical difficulties related to human and animal testing. The solution to the problem is Quantitative structure-permeability relationships. This method relates dermal penetration properties of a range of chemical compounds to their physicochemical parameters. In the present study, an effort has been made to develop models for the accurate prediction of skin permeability using a large, diverse dataset through the combination of various regression methods coupled with the Genetic Algorithm (GA)/Interval Partial Least-Squares Algorithm (iPLS). The descriptors were calculated using e-DRAGON and ADME Pharma Algorithms-Abrahams descriptors. The original dataset was divided into a training set and a testing set using the Kennard-Stone Algorithm. The selection of descriptors was made by the GA and iPLS. The model applicability domain was determined. The results showed that a three-parameter model built through Partial Least-squares Regression was most accurate with r(2) of 0.936.

QSAR modelling of bioconcentration factor using hydrophobicity, hydrogen bonding and topological descriptors.

Abstract: Bioconcentration factor (BCF) is an important step in the uptake of environmental pollutants in the food chain. It is expensive and time-consuming to measure, so predictive methods are of value. We have used an artificial neural network QSAR approach involving descriptors for hydrophobicity, hydrogen bonding and molecular topology, obtained from commercially available software, to predict the fish BCF values of a diverse data set of 624 chemicals. The training set statistics were: r 2 = 0.765, q 2 = 0.763, s = 0.610, and those of the external test set were: r 2 = 0.739, s = 0.627. The model complies with the OECD Principles for the Validation of (Q)SARs.