Showing papers in "Sar and Qsar in Environmental Research in 2004"
TL;DR: It was found that although the extent of biodegradation of parent compounds could reach 60%, persistent metabolites could be formed in significant quantities and a trend was observed where PFCs are transformed to more bioaccumulative and more toxic products.
Abstract: Perfluorinated chemicals (PFCs) form a special category of organofluorine compounds with particularly useful and unique properties. Their large use over the past decades increased the interest in the study of their environmental fate. Fluorocarbons may have direct or indirect environmental impact through the products of their decomposition in the environment. It is a common knowledge that biodegradation is restricted within non-perfluorinated part of molecules: however, a number of studies showed that defluorination can readily occur during biotransformation. To evaluate the fate of PFCs in the environment a set of principal transformations was developed and implemented in the simulator of microbial degradation using the catabolite software engine (CATABOL). The simulator was used to generate metabolic pathways for 171 perfluorinated substances on Canada's domestic substances list. It was found that although the extent of biodegradation of parent compounds could reach 60%, persistent metabolites could be formed in significant quantities. During the microbial degradation a trend was observed where PFCs are transformed to more bioaccumulative and more toxic products. Perfluorooctanoic acid and perfluorooctanesulfonate were predicted to be the persistent biodegradation products of 17 and 27% of the perfluorinated sulphonic acid and carboxylic acid containing compounds, respectively.
86 citations
TL;DR: A novel 2-D graphical representation of proteins in which individual nucleic acids are represented as “spots” within a square frame distributed according to specific construction rules to facilitate visual comparison of similarities and dissimilarities between lengthy protein sequences and offer a way for mathematical characterization of protein sequences.
Abstract: We consider a novel 2-D graphical representation of proteins in which individual nucleic acids are represented as “spots” within a square frame distributed according to specific construction rules. The resulting “images” of proteins can not only serve to facilitate visual comparison of similarities and dissimilarities between lengthy protein sequences, but also offer a way for mathematical characterization of protein sequences, analogous to similar considerations for lengthy DNA sequences. Basically the approach is based on the concept of virtual genetic code, which is a hypothetical string of RNA nucleic acid bases, A, C, U and G, which generates reported protein sequences, without the knowledge of the actual genetic code that produces the protein.
80 citations
TL;DR: A highly compact graphical representation of DNA is considered, which allows visual inspection and numerical characterization of DNA sequences having a large number of nucleic acid bases and makes possible graphical Representation of protein sequences, which have hitherto evaded similar 2D visual representations.
Abstract: Most 2D graphical representations of primary DNA sequences, while offering visual geometrical patterns for depicting sequences, do require considerable space if enough details of such representations are to be visible. In this contribution, we consider a highly compact graphical representation of DNA, which allows visual inspection and numerical characterization of DNA sequences having a large number of nucleic acid bases. The approach is illustrated on the DNA sequences of the first exon of human β-globin. The same graphical approach not only allows one to depict differences in composition within a single DNA, but makes possible graphical representation of protein sequences, which have hitherto evaded similar 2D visual representations.
64 citations
TL;DR: The different scientific and regulatory purposes for which reliable (Q)SARs could be used are reviewed, and the current work of the JRC is described in providing scientific support for the development, validation and implementation of (Q), as well as ways of increasing the use of chemical category approaches.
Abstract: Recent policy developments in the European union (EU) and within the Organisation for Economic Cooperation and Development (OECD) have placed increased emphasis on the use of structure-activity relationships (SARs) and quantitative structure-activity relationships (QSARs), collectively referred to as (Q)SARs, within various regulatory programmes for the assessment of chemicals and products. The most significant example within the EU is the European commission's proposal (of 29 October 2003) to introduce a new system for managing chemicals (called REACH), which calls for an increased use of (Q)SARs and other non-animal methods, especially for the assessment of low production volume chemicals. Another development within the EU is the Seventh Amendment to the Cosmetics Directive, which foresees the phasing out of animal testing on cosmetics, combined with the imposition of marketing bans on cosmetics that have been tested on animals after certain deadlines. At the same time, the Existing Chemicals programme ...
59 citations
TL;DR: Modelling of QT-prolongation using data for 19 structurally diverse hERG K+ channel blocking drugs taken from literature produced a two parameter, interpretable and transparent QSAR with good statistical fit, including log D and the maximum diameter of molecules.
Abstract: Modelling of QT-prolongation has been performed using data for 19 structurally diverse hERG K+ channel blocking drugs taken from literature The modelling used hydrophobicity corrected for ionisation (log D) and various 2D and 3D physico-chemical molecular descriptors Stepwise regression produced a two parameter, interpretable and transparent QSAR with good statistical fit, including log D and the maximum diameter of molecules (D max) Two strategies were applied for model validation: (i) a scrambling procedure, ie, training the total set of 19 chemicals after randomising the hERG K+ channel blocking activity data and (ii) use of external validation sets Validation of the models showed them to be stable and statistically significant The effect of molecular size on QT-prolongation side effect is discussed
48 citations
TL;DR: Using toxicity data for 30 aliphatic polarized α,β-unsaturated derivatives of esters, aldehydes, and ketones, a series of six structure–toxicity relationships were evaluated, finding that homologues with straight-chain hydrocarbon moieties were more toxic than those with branched groups.
Abstract: Using toxicity data for 30 aliphatic polarized alpha,beta-unsaturated derivatives of esters, aldehydes, and ketones, a series of six structure-toxicity relationships were evaluated. The structure feature of all assessed compounds, an acetylenic or olefinic moiety conjugated to a carbonyl group, is inherently electrophilic and conveys the capacity to exhibit enhanced toxicity. However, the toxic potency of alpha,beta-unsaturated carbonyl compounds is dependent on the specific molecular structure with several trends being observed. Specific observations include: (1) between homologues, the acetylenic-substituted derivative was more toxic than the corresponding olefinic-substituted one, respectively; (2) between olefinic-homologues, terminal vinyl-substituted derivative was more toxic than the internal vinylene-substituted one; (3) within alpha,beta-unsaturated ketones, methyl substitution on the vinyl carbon atoms reduces toxicity with methyl-substitution on the carbon atom farthest from the carbonyl group exhibiting the greater inhibition; (4) between alpha,beta-unsaturated carbonyl compounds with the carbon-carbon double bond on the end of the molecule (vinyl ketones) and those with carbon-oxygen double bonds on the end of the molecule (aldehydes), the ketones are more toxic than the aldehydes; (5) between homologues of alpha,beta-unsaturated esters, those with additional unsaturated moieties (allyl, propargyl, or vinyl groups) were more toxic than homologues having relevant unsaturated moieties (propyl or ethyl groups); (6) between alpha,beta-unsaturated carbonyl compounds with different shaped alkyl-groups (i.e. different degrees of branching), homologues with straight-chain hydrocarbon moieties were more toxic than those with branched groups.
45 citations
TL;DR: It has been demonstrated that consensus-type kNN QSAR models, derived from the arithmetic mean of individual QS AR models were statistically robust and provided more accurate predictions than the great majority of the individual Q SAR models.
Abstract: A novel method (in the context of quantitative structure-activity relationship (QSAR)) based on the k nearest neighbour (kNN) principle, has recently been introduced for the derivation of predictive structure-activity relationships. Its performance has been tested for estimating the estrogen binding affinity of a diverse set of 142 organic molecules. Highly predictive models have been obtained. Moreover, it has been demonstrated that consensus-type kNN QSAR models, derived from the arithmetic mean of individual QSAR models were statistically robust and provided more accurate predictions than the great majority of the individual QSAR models. Finally, the consensus QSAR method was tested with 3D QSAR and log P data from a widely used steroid benchmark data set.
43 citations
TL;DR: Quantitative structure-toxicity relationship (QSTR) models were derived for estimating the acute oral toxicity of organophosphorus pesticides to male and female rats and the best results were obtained with an 8/4/1 ANN model trained with the back-propagation and conjugate gradient descent algorithms.
Abstract: Quantitative structure-toxicity relationship (QSTR) models were derived for estimating the acute oral toxicity of organophosphorus pesticides to male and female rats. The 51 chemicals of the training set and the nine compounds of the external testing set were described by means of autocorrelation vectors encoding lipophilicity, molar refractivity, H-bonding acceptor ability (HBA) and H-bonding donor ability (HBD) of the molecules. A feature selection was employed for selecting the most relevant autocorrelation descriptors. A PLS regression analysis and an artificial neural network (ANN) were used for deriving models accounting for the sex of the organisms in the estimation of the toxicity of pesticides. The best results were obtained with an 8/4/1 ANN model trained with the back-propagation and conjugate gradient descent algorithms. The root mean square residual (RMSR) values for the training set and the external testing set equaled 0.29 and 0.26, respectively.
41 citations
TL;DR: Three common errors in QSAR research are discussed, which, if corrected, will place in silico methods fully complementary to the strategic use of in vitro and in vivo methods.
Abstract: The quantitative structure-activity relationship (QSAR) science agenda is being determined by its skeptics. Toxic substances control legislation over the past 30 years was born of a culture that tests animals and interprets the results of those tests in attempts to protect public health. Even with the current awareness that there are many more chemicals to assess than resources and test data permit, those skeptical of QSAR are predominant in the regulatory setting. Bureaucracies founded on laboratory testing, whether a private or governmental agency, will only begrudgingly accept QSAR as a strategic tool for designing chemicals and managing chemical risks. Every major milestone in QSAR accomplishments has been met with stronger skepticism that QSAR cannot replace animal testing. The QSAR research community needs to embrace the arguments of the skeptics and design research to overcome the perceived inadequacies of current QSAR methods. This paper will discuss three common errors in QSAR research, which, if...
36 citations
TL;DR: Quantitative structure–activity relationship (QSAR) models were derived from a structurally heterogeneous set of 200 phenol derivatives for which the 50% growth inhibition concentration values to the ciliated protozoan Tetrahymena pyriformis were available.
Abstract: Quantitative structure–activity relationship (QSAR) models were derived from a structurally heterogeneous set of 200 phenol derivatives for which the 50% growth inhibition concentration (IGC50) 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.
34 citations
TL;DR: Using a large heterogeneous data-set of 640 organic chemicals, predictive Quantitative Structure-Activity Relationship models for fish bioconcentration factor (BCF) are developed, offering good predictivity of this important environmental property.
Abstract: Using a large heterogeneous data-set of 640 organic chemicals, we have developed predictive Quantitative Structure-Activity Relationship models for fish bioconcentration factor (BCF). For 539 chemicals with a log Kow (octanol-water partition coefficient) range of -2.3 to 6.0, we developed a model with r2 = 0.664 and a standard error of 0.661; the primary descriptor was log Kow, and others were polarisability, number of amino groups, hydrogen bond acceptor ability and a molecular shape factor. For 101 chemicals with a log Kow range of 6.0-12.7, we developed a model with r2 = 0.710 and a standard error of 0.777; the descriptors were aqueous solubility (reflecting the importance of this property in governing uptake from aqueous solution), polarity, polarisability, hydrogen bond donor ability and molecular size. Bearing in mind the very great range of BCF values of highly hydrophobic chemicals, our model offers good predictivity of this important environmental property.
TL;DR: Progress made at an international level regarding the principles of validation is described, and the role of ECVAM regarding the practical validation of (Q)SARs is explained.
Abstract: Under the current chemicals legislation, the regulatory use of structure-activity relationships (SARs) and quantitative structure-activity relationships (QSARs), collectively referred to as (Q)SARs, for the assessment of chemicals is limited, partly due to concerns about the extent to which (Q)SAR estimates can be relied upon. On 29 October 2003, the European Commission adopted a legislative proposal that foresees the introduction of a new regulatory system for chemicals called REACH (Registration, Evaluation, and Authorisation of Chemicals), which will impose equivalent information requirements on both new and existing chemicals. For reasons of practicality, cost-effectiveness and animal welfare, it is envisaged that (Q)SARs will play an important role in the assessment of some 30,000 existing chemicals for which further information may be required under the REACH system. It will therefore be essential that the (Q)SAR models used will produce reliable estimates. To overcome the barriers in the acceptance of (Q)SARs for regulatory purposes, it is widely acknowledged that there needs to be international agreement on the principles of (Q)SAR validation, and that the process of (Q)SAR validation should be managed by independent organisations, with a view to providing independent advice to the regulators who make decisions on the acceptability of (Q)SARs. The European Centre for the Validation of Alternative Methods (ECVAM), which is part of the European Commission's Joint Research Centre (JRC), has a well-established role in providing independent scientific and technical advice to European policy makers. This paper describes progress made at an international level regarding the principles of validation, and explains the role of ECVAM regarding the practical validation of (Q)SARs.
TL;DR: The prediction of the toxicity of more reactive chemicals was found to require the use of additional descriptors, and the baseline effect was demonstrated by chemicals known to act by a narcotic mechanism of action, i.e., a relationship was observed between the toxicity and the logarithm of the octanol-water partition coefficient.
Abstract: A large data set containing values for fish, algae and Daphnia toxicity for more than 2000 chemicals and mixtures was investigated. The data set was taken from the New Chemicals Data Base of the European Union [hosted by the European Chemicals Bureau, Joint Research Centre, European Commission (http://ecb.jrc.it)]. The data are submitted by industry, according to the requirements of EU Council Directive 67/548/EEC as amended for the seventh time by EU Council Directive 92/32/EEC. The toxicities of neutral chemicals, salts, metal complexes, as well as chemical mixtures were extracted. A baseline effect was demonstrated by chemicals known to act by a narcotic mechanism of action, i.e., a relationship was observed between the toxicity and the logarithm of the octanol-water partition coefficient (log P). However, the prediction of the toxicity of more reactive chemicals was found to require the use of additional descriptors.
TL;DR: In the present study, partial least squares (PLS) regression together with 15 theoretical molecular structural descriptors was used to develop quantitative predictive models for vapor pressures of PAHs at different temperatures and it has been shown that the intermolecular dispersive interactions played a leading role in governing the values of log P L.
Abstract: Polycyclic aromatic hydrocarbons (PAHs) are typical and ubiquitous organic pollutants. Vapor pressures, which can be classified as solid vapor pressure (P S) and (subcooled) liquid vapor pressure (P L), are key physicochemical properties governing the environmental fate of organic pollutants. It is of great importance to develop predictive models of vapor pressures. In the present study, partial least squares (PLS) regression together with 15 theoretical molecular structural descriptors was used to develop quantitative predictive models for vapor pressures of PAHs at different temperatures. Two procedures were adopted to develop the optimal predictive models by eliminating redundant molecular structural descriptors. The cross-validated Q_{cum}^{2} values for the obtained models have been found higher than 0.975, indicating good predictive ability and robustness of the models. It has been shown that the intermolecular dispersive interactions played a leading role in governing the values of log P L. In addi...
TL;DR: It was shown that the multiple-database mutagenicity model showed a clear advantage over normally used single-database models and can be used to predict the mutagenic potential of new compounds.
Abstract: The Multiple Computer Automated Structure Evaluation (MCASE) program was used to evaluate the mutagenic potential of organic compounds. The experimental Ames test mutagenic activities for 2513 chemicals were collected from various literature sources. All chemicals have experimental results in one or more Salmonella tester strains. A general mutagenicity data set and fifteen individual Salmonella test strain data sets were compiled. Analysis of the learning sets by the MCASE program resulted in the derivation of good correlations between chemical structure and mutagenic activity. Significant improvement was obtained as more data was added to the learning databases when compared with the results of our previous reports. Several biophores were identified as being responsible for the mutagenic activity of the majority of active chemicals in each individual mutagenicity module. It was shown that the multiple-database mutagenicity model showed a clear advantage over normally used single-database models. The expertise produced by this analysis can be used to predict the mutagenic potential of new compounds.
TL;DR: The paper is illustrating how the general data mining methodology may be adapted to provide solutions to the problem of high throughput virtual screening of organic chemicals for possible acute toxicity to the fathead minnow fish.
Abstract: The paper is illustrating how the general data mining methodology may be adapted to provide solutions to the problem of high throughput virtual screening of organic chemicals for possible acute toxicity to the fathead minnow fish. The present approach involves mining fragment information from chemical structures and is using probabilistic neural networks to model the relationship between structure and toxicity. Probabilistic neural networks implement a special class of multivariate non-linear Bayesian statistical models. The mathematical principles supporting their use for value prediction purposes are clarified and their peculiarities discussed. As part of the research phase of the data mining process, a dataset consisting of 800 structures and associated fathead minnow (Pimephales promelas) 96-h LC50 acute toxicity endpoint information is used for both the purpose of identifying an advantageous combination of fragment descriptors and for training the neural networks. As a result, two powerful models are...
TL;DR: A SAR based carcinogenic toxicity prediction system, CISOC-PSCT, was developed and predicted the toxicity of compounds from a testing set of 304 carcinogenic compounds, 460 non-carcinogenic compounds and 94 compounds extracted from two traditional Chinese medicine herbs.
Abstract: A SAR based carcinogenic toxicity prediction system, CISOC-PSCT, was developed. It consisted of two principal phases: the construction of relationships between structural descriptors and carcinogenic toxicity indices, and prediction of the toxicity from the SAR model. The training set included 2738 carcinogenic and 4130 non-carcinogenic compounds. Three predefined topological types of substructures termed Star, Path and Ring were used to generate the descriptors for each structure in the training set. In this system, the defined carcinogenic toxicity index (CTI) was obtained from the probability of a structural descriptor to either belong to the carcinogenic or non-carcinogenic compounds. Based on these structural descriptors and their CTI, a SAR model was derived. Then the carcinogenic possibility (CP) and the carcinogenic impossibility (CIP) of compounds were predicted. The model was tested from a testing set of 304 carcinogenic compounds (MDL toxicity database), 460 non-carcinogenic compounds (CMC database) and 94 compounds extracted from two traditional Chinese medicine herbs.
TL;DR: This article describes the knowledge discovery process in predictive toxicology with a brief review of suitable algorithms and their advantages and disadvantages for each knowledge discovery step, followed by a more detailed description of a problem-specific implementation of the lazar prediction system.
Abstract: This article describes the knowledge discovery process in predictive toxicology. This process consists of five major steps (i) feature calculation, (ii) feature selection, (iii) model induction, (iv) model validation and (v) interpretation of predictions and models. Data mining is a part of the knowledge discovery process and consists of the application of data analysis and discovery algorithms, which can be useful in all of the above steps. A brief review of suitable algorithms and their advantages and disadvantages is given for each knowledge discovery step, followed by a more detailed description of a problem-specific implementation of the lazar prediction system.
TL;DR: To enable a quantitative assessment of quality and confidence in a QSAR, the terms deemed to be important were weighed and combined to create a Confidence Index (CI).
Abstract: Validation of a quantitative structure-activity relationship (QSAR) is now considered as an integral part of its development. Assessment of the quality of a QSAR and the confidence that may be placed in predictions from it are vital to any validation procedure. A number of terms associated with the quality of a QSAR, confidence in that QSAR, or both may be quantified. These terms include the: (1) goodness of fit of the model (r2); (2) predictivity of the model (Q2); (3) stability of the model described as the difference between fit and predictivity (Dfp); (4) number of compounds used in the training set (Nc); (5) number of descriptors used in the model (Nd); (6) range of toxicity values (Tr); (7) number of mechanisms of toxic action covered by the training set (Nm), as well as two factors associated with the biological data-confidence associated with, (8) reproducibility of the data (Rconf) and (9) confidence in the source of the data (Sconf). While all these factors may influence the quality of, and/or confidence in a particular QSAR, each varies within different limits. To enable a quantitative assessment of quality and confidence in a QSAR, the terms deemed to be important were weighed and combined to create a Confidence Index (CI): ((r2)4 x 6) x ((Q2)4 x 6) x (ln(Nc/10)) x (Tr) x (Sconf)0.5 (ln(N2d + 2)) x (ln(N2m + 2)) x ((r2)4 x 6) - ((Q2)4 x 6) + 1) x (Rconf)
TL;DR: From data submitted within the EU chemicals notification procedure, (Q)SARs for the prediction of local irritation/corrosion and/or sensitisation potential were developed and published and will be submitted for official validation and application within regulatory hazard assessment strategies.
Abstract: In 2001, the European Commission published a policy statement ("White Paper") on future chemicals regulation and risk reduction that proposed the use of non-animal test systems and tailor-made testing approaches, including (Q)SARs, to reduce financial costs and the number of test animals employed. The authors have compiled a database containing data submitted within the EU chemicals notification procedure. From these data, (Q)SARs for the prediction of local irritation/corrosion and/or sensitisation potential were developed and published. These (Q)SARs, together with an expert system supporting their use, will be submitted for official validation and application within regulatory hazard assessment strategies. The main features are: • two sets of structural alerts for the prediction of skin sensitisation hazard classification as defined by the European risk phrase R43, comprising 15 rules for chemical substructures deemed to be sensitising by direct action with cells or proteins, and three rules for substr...
TL;DR: Two models, one for Relative proliferative effect (RPE) and one for relative proliferative potency (RPP) for chemicals as compared to the effects and potency of 17β-estradiol are produced, showing potential usefulness in computational screening methods for environmental estrogens.
Abstract: A sizable number of environmental contaminants and natural products have been found to possess hormonal activity and have been termed endocrine-disrupting chemicals. Due to the vast number (estimated at about 58,000) of environmental contaminants, their potential to adversely affect the endocrine system, and the paucity of health effects data associated with them, the U.S. Congress was led to mandate testing of these compounds for endocrine-disrupting ability. Here we provide evidence that a computational structure–activity relationship (SAR) approach has the potential to rapidly and cost effectively screen and prioritize these compounds for further testing. Our models were based on data for 122 compounds assayed for estrogenicity in the ESCREEN assay. We produced two models, one for relative proliferative effect (RPE) and one for relative proliferative potency (RPP) for chemicals as compared to the effects and potency of 17β-estradiol. The RPE and RPP models achieved an 88 and 72% accurate prediction rat...
TL;DR: Quantitative structure-activity relationships (QSARs) based on the octanol/water partition coefficient were employed to predict acute toxicities of 36 substituted aromatic compounds and their mixtures and proved to be robust enough by using the leave-one-out test method.
Abstract: Quantitative structure-activity relationships (QSARs) based on the octanol/water partition coefficient were employed to predict acute toxicities of 36 substituted aromatic compounds and their mixtures. In this study, the model developed by Verhaar et al. was modified and used to calculate octanol/water partition coefficients of chemical mixtures. To validate the model, acute toxicities of these chemicals were measured to Vibrio fischeri in terms of EC50. The results indicated that the obtained QSAR models could be used to predict toxicities of samples consisting of these substituted aromatic compounds, individually or in combinations. The obtained equations were proved to be robust enough by using the leave-one-out test method. By classifying these chemicals into two groups, polar and non-polar, the toxicities of chemical mixtures within each group can be predicted accurately from their calculated partition coefficients.
TL;DR: This paper outlines an extension addressing a special case of the three-block (X/Y/Z) problem, where Z sits "under" Y, and views the X/Y relationship as the dominant problem, and seeks to use the additional information in Z in order to improve the interpretation of the Y-part of the Z/Y association.
Abstract: When X and Y are multivariate, the two-block partial least squares (PLS) method is often used. In this paper, we outline an extension addressing a special case of the three-block (X/Y/Z) problem, where Z sits "under" Y. We have called this approach three-block bi-focal PLS (3BIF-PLS). It views the X/Y relationship as the dominant problem, and seeks to use the additional information in Z in order to improve the interpretation of the Y-part of the X/Y association. Two data sets are used to illustrate 3BIF-PLS. Example I relates to single point mutants of haloalkane dehalogenase from Sphingomonas paucimobilis UT26 and their ability to transform halogenated hydrocarbons, some of which are found as organic pollutants in soil. Example II deals with soil remediation capability of bacteria. Whole bacterial communities are monitored over time using "DNA-fingerprinting" technology to see how pollution affects population composition. Since the data sets are large, hierarchical multivariate modelling is invoked to compress data prior to 3BIF-PLS analysis. It is concluded that the 3BIF-PLS approach works well. The paper contains a discussion of pros and cons of the method, and hints at further developmental opportunities.
TL;DR: Based on some fundamental quantum chemical descriptors computed by PM3 Hamiltonian, a quantitative structure-property relationship (QSPR) for specific activity of 17 polycyclic aromatic hydrocarbons of biocatalytic chlorination by chloroperoxidase (CPO) from Caldariomyces fumago was developed using partial least squares (PLS) regression.
Abstract: Based on some fundamental quantum chemical descriptors computed by PM3 Hamiltonian, a quantitative structure-property relationship (QSPR) for specific activity of 17 polycyclic aromatic hydrocarbons (PAHs) of biocatalytic chlorination by chloroperoxidase (CPO) from Caldariomyces fumago was developed using partial least squares (PLS) regression. The model can be used to estimate biocatalytic chlorination reaction rates of PAHs. The main factors affecting specific activity of PAHs of biocatalytic chlorination by CPO from Caldariomyces fumago are absolute hardness, dipole moment, absolute electronegativity, and molecular bulkness of the PAH molecules. The biocatalytic chlorination reaction rates of PAHs with large values of absolute hardness, absolute electronegativity, and molecular bulkness tend to be slow. Increasing dipole moment of PAHs leads to increase the specific activity.
TL;DR: The implementation of depth dependent soil concentrations might be a useful extension for steady state multimedia mass balance models and more field study has to be carried out to validate the model outcomes.
Abstract: In standard multimedia mass balance models, the soil compartment is modeled as a box with uniform concentrations, which often does not correspond with actual field situations. Therefore, the theoretically expected decrease of soil concentrations with depth was implemented in the multimedia model SimpleBox 3.0. The effects of this implementation on the model outcomes were explored for nine compounds in four environmental compartments. For compounds with a low penetration depth, the new model predicts substantially higher or lower concentrations in the vegetation compartment than the old model. For those compounds, predicted concentrations in surface water and air were higher in the new model, but the deviations from the old model were smaller than in the vegetation compartment. For compounds with a large penetration depth, the model adaptations show little effect. No field study was carried out to validate the results of the model calculations, but we did collect measured data on concentrations in vertical soil profiles from literature. According to those data, we concluded that the implementation of depth dependent soil concentrations might be a useful extension for steady state multimedia mass balance models. More field study has to be carried out to validate the model outcomes.
TL;DR: This work used an original combination of the similarity concept and physicochemical descriptors calculated by HYBOT, in order to construct stable QSAR models of guppy toxicity, which gave good results.
Abstract: Over half of known industrial pollutants have minimal toxic effect, in line with the concept of "baseline toxicity"; such toxicity usually correlates well with lipophilicity. The remainder require additional descriptors in order to model their toxicity by the QSAR approach. Hence, it has not been possible, to date, to develop common stable QSAR models for the toxicity of diverse chemicals with various modes of action on the basis of simple regression relationships. Any new methodology has to take such different modes of action into account. In our work, we used for this purpose an original combination of the similarity concept and physicochemical descriptors calculated by HYBOT, in order to construct stable QSAR models of guppy toxicity. The training set comprised 293 diverse chemicals. Experimental value(s) of one or more nearest related chemicals were used to take structural features and possible modes of toxic action into account. In addition, molecular polarisability and hydrogen bond descriptors for ...
TL;DR: The interest of QSAR models for estimating the potential pharmacological interest of the cyanobacterial secondary metabolites was discussed and the interest in isolating compounds from these organisms as source of active products with potential therapeutic applications was discussed.
Abstract: 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.
TL;DR: It appeared that the toxicity of compounds acting by more specific mechanisms of toxic action is difficult to predict, and the development of models for non-polar and polar narcosis had some success.
Abstract: In the present study, structure–activity relationship (QSAR) models for the prediction of the toxicity to the bacterium Sinorhizobium meliloti have been developed, based on a data set of 140 compounds. The data set is highly heterogeneous both in terms of chemistry and mechanisms of toxic action. For deriving QSARs, chemicals were divided into groups according to mechanism of action and chemical structure. The QSARs derived are considered to be of moderate statistical quality. A baseline effect (relationship between the toxicity and log P), which can be related to non-polar narcosis, was observed. To explain toxicity greater than the baseline toxicity, other structural descriptors were used. The development of models for non-polar and polar narcosis had some success. It appeared that the toxicity of compounds acting by more specific mechanisms of toxic action is difficult to predict. A global QSAR was also developed, which had square of the correlation coefficient r^{2} = 0.53. A QSAR with reasonable stat...
TL;DR: Fly ash samples containing polychlorinated dibenzo-p-dioxins and dibenzofurans (PCDD/Fs) were generated by combustion of polyvinyl chloride, wood, high-density polyethylene and styrene and the results showed that the stability of the PCDD/F molecules increased with the increase of chlorine atoms in the parent molecules.
Abstract: Fly ash samples containing polychlorinated dibenzo-p-dioxins and dibenzofurans (PCDD/Fs) were generated by combustion of polyvinyl chloride, wood, high-density polyethylene and styrene. By partial least-squares (PLS) regression, quantitative structure-property relationship (QSPR) models were developed for photolysis half-lives (t(1/2)) of PCDD/Fs adsorbed on fly ash surfaces and irradiated by UV-B of simulated sunlight. Quantum chemical descriptors computed by PM3 hamiltonian were used as predictor variables. The cross validated value for the optimal QSPR model was 0.678, indicating robustness and good predictive abilities of the model. The QSPR results showed that the stability of the PCDD/F molecules increased with the increase of chlorine atoms in the parent molecules. Increasing the energy of the highest occupied molecular orbital (E(HOMO)), the energy of the lowest unoccupied molecular orbital (E(LUMO)), E(LUMO)+E(HOMO) and E(LUMO)-E(HOMO) values of the PCDD/Fs led to decrease of log t(1/2) values. Increasing the most negative atomic charge on the oxygen atom of PCDD/Fs led to elevated log t(1/2) values. The log t(1/2) values of PCDD/Fs increased with the decrease of the largest negative atomic charge on a carbon atom.
TL;DR: A QSAR screening of 15 fish antibiotics and 132 xenobiotic molecules was performed with two aims: to develop a model for the estimation of octanol--water partition coefficient and to estimate the relative binding affinity to oestrogen receptor using a model constructed on the activities of 132 Xenobiotic compounds.
Abstract: The present study focuses on fish antibiotics which are an important group of pharmaceuticals used in fish farming to treat infections and, until recently, most of them have been exposed to the environment with very little attention. Information about the environmental behaviour and the description of the environmental fate of medical substances are difficult or expensive to obtain. The experimental information in terms of properties is reported when available, in other cases, it is estimated by standard tools as those provided by the United States Environmental Protection Agency EPISuite software and by custom quantitative structure-activity relationship (QSAR) applications. In this study, a QSAR screening of 15 fish antibiotics and 132 xenobiotic molecules was performed with two aims: (i) to develop a model for the estimation of octanol--water partition coefficient (logP) and (ii) to estimate the relative binding affinity to oestrogen receptor (log RBA) using a model constructed on the activities of 132 xenobiotic compounds. The custom models are based on constitutional, topological, electrostatic and quantum chemical descriptors computed by the CODESSA software. Kohonen neural networks (self organising maps) were used to study similarity between the considered chemicals while counter-propagation artificial neural networks were used to estimate the properties.