Browse
Submit Data
Databases
API
Help

Dataset Information

11 Views

0 Connections

0 Citations

0 Reanalyses

0 Downloads

Omics score: 0

Dataset for the quantitative structure-activity relationship (QSAR) modeling of the toxicity equivalency factors (TEFs) of PAHs and transformed PAH products.

ABSTRACT: Sixteen significant physicochemical predictor variables for thirty PAHs and transformed PAH products (TPPs) were retrieved individually prior to collation from ChemSpider.com [1] whilst their corresponding toxicity equivalency factor (TEF) end-point was obtained from published articles by Bortey-Sam, Ikenaka [2] and Wei, Bandowe [3]. In order to achieve a 5:1 ratio of the number of observations to predictors which is vital for an effective quantitative structure-activity relationship (QSAR) modelling, factor analysis was used to reduce the data. Four fundamental predictors were obtained whilst the observations were found to cluster into two main groups of nitro-PAHs and other analytes. It is anticipated that the data presented here is highly relevant for future studies on the toxicity and health effects of the analytes in the environment. Secondly, the fate and distribution patterns of PAHs and TPPs are influenced by the parameters in the dataset. In this regard, studies on the behaviour patterns of these environmental pollutants require this information for a comprehensive evaluation and interpretation of results. Researchers across varied fields of environmental science and toxicology will find this dataset very useful. This data currently serves as supplementary information for the research article in the Journal of Hazardous Materials by Gbeddy, Egodawatta [4].

SUBMITTER: Gbeddy G

PROVIDER: S-EPMC6909136 | biostudies-literature |

REPOSITORIES: biostudies-literature

ACCESS DATA

Json Xml

Similar Datasets

Identification and Toxicological Evaluation of Unsubstituted PAHs and Novel PAH Derivatives in Pavement Sealcoat Products.

Project description:Pavement sealcoat products contain high concentrations of unsubstituted polycyclic aromatic hydrocarbons (PAHs), but the assessment of the potential toxicological impact is limited without the inclusion of PAH derivatives. This study determined the concentrations of 23 unsubstituted PAHs, 11 high molecular weight-PAHs (MW302-PAHs), and 56 PAH derivatives, including 10 methyl-PAHs (MPAHs), 10 heterocyclic-PAHs (Hetero-PAHs), 26 nitrated-PAHs (NPAHs), and 10 oxygenated-PAHs (OPAHs) in coal-tar and asphalt based sealcoat products and time point scrapes. Inclusion of MW302-PAHs resulted in an increase of 4.1-38.7% in calculated benzo[a]pyrene-carcinogenic equivalent (B[a]Peq) concentrations for the coal-tar based products. Increases in some NPAH and OPAH concentrations were measured after application, suggesting the possibility of photochemical transformation of unsubstituted PAHs. The Ames assay indicated that the asphalt based product was not mutagenic, but the coal-tar based sealcoat products were. The zebrafish developmental toxicity tests suggested that fractions where NPAHs and OPAHs eluted have the most significant adverse effects.

| S-EPMC6075666 | biostudies-literature

Biodegradation of Mixed PAHs by PAH-Degrading Endophytic Bacteria.

Project description:Endophytic bacteria can promote plant growth, induce plant defence mechanisms, and increase plant resistance to organic contaminants. The aims of the present study were to isolate highly PAH-degrading endophytic bacteria from plants growing at PAH-contaminated sites and to evaluate the capabilities of these bacteria to degrade polycyclic aromatic hydrocarbons (PAHs) in vitro, which will be beneficial for re-colonizing target plants and reducing plant PAH residues through the inoculation of plants with endophytic bacteria. Two endophytic bacterial strains P₁ (Stenotrophomonas sp.) and P₃ (Pseudomonas sp.), which degraded more than 90% of phenanthrene (PHE) within 7 days, were isolated from Conyza canadensis and Trifolium pretense L., respectively. Both strains could use naphthalene (NAP), PHE, fluorene (FLR), pyrene (PYR), and benzo(a)pyrene (B(a)P) as the sole sources of carbon and energy. Moreover, these bacteria reduced the contamination of mixed PAHs at high levels after inoculation for 7 days; strain P₁ degraded 98.0% NAP, 83.1% FLR, 87.8% PHE, 14.4% PYR, and 1.6% B(a)P, and strain P₃ degraded 95.3% NAP, 87.9% FLR, 90.4% PHE, 6.9% PYR, and negligible B(a)P. Notably, the biodegradation of PAHs could be promoted through additional carbon and nitrogen nutrients; therein, beef extract was suggested as the optimal co-substrate for the degradation of PAHs by these two strains (99.1% PHE was degraded within 7 days). Compared with strain P₁, strain P₃ has more potential for the use in the removal of PAHs from plant tissues. These results provide a novel perspective in the reduction of plant PAH residues in PAH-contaminated sites through inoculating plants with highly PAH-degrading endophytic bacteria.

| S-EPMC4997491 | biostudies-literature

QSAR modeling for predicting reproductive toxicity of chemicals in rats for regulatory purposes.

Project description:The experimental determination of multi-generation reproductive toxicity of chemicals involves high costs and a large number of animal studies over a long period of time. Computational toxicology offers possibilities to overcome such difficulties. In this study, we have established ensemble machine learning (EML) based quantitative structure-activity relationship models for predicting the reproductive toxicity potential (LOAEL) of structurally diverse chemicals in accordance with the OECD guidelines. Accordingly, decision tree forest (DTF) and decision tree boost (DTB) QSAR models were developed using a novel dataset composed of the toxicity endpoints for 334 chemicals. Relevant structural features of chemicals responsible for toxicity potential were identified and used in QSAR modeling. The generalization and prediction abilities of the constructed QSAR models were evaluated by internal and external validation procedures and by deriving several stringent statistical criteria parameters. In the test set, the two models (DTF and DTB) yielded R2 of 0.856 and 0.945, between the experimental and predicted endpoint toxicity values. The models were also evaluated for predictive use through the most recent criteria based on root mean squared error (RMSE) and mean absolute error (MAE). The values of various statistical validation coefficients derived for the test data were above their respective threshold limits and thus put a high confidence in this analysis. The applicability domains of the constructed QSAR models were defined using the leverage and standardization approaches. The results suggest that the proposed QSAR models can reliably predict the reproductive toxicity potential of diverse chemicals and can be useful tools for screening new chemicals for safety assessment.

| S-EPMC6062388 | biostudies-literature

Dataset of curcumin derivatives for QSAR modeling of anti cancer against P388 cell line.

Project description:The dataset of curcumin derivatives consists of 45 compounds (Table 1) with their anti cancer biological activity (IC50) against P388 cell line. 45 curcumin derivatives were used in the model development where 30 of these compounds were in the training set and the remaining 15 compounds were in the test set. The development of the QSAR model involved the use of the multiple linear regression analysis (MLRA) method. Based on the method, r2 value, r2(CV) value of 0.81, 0.67 were obtained. The QSAR model was also employed to predict the biological activity of compounds in the test set. Predictive correlation coefficient r2 values of 0.88 were obtained for the test set.

| S-EPMC5061127 | biostudies-literature

Ensemble QSAR Modeling to Predict Multispecies Fish Toxicity Lethal Concentrations and Points of Departure.

Project description:QSAR modeling can be used to aid testing prioritization of the thousands of chemical substances for which no ecological toxicity data are available. We drew on the U.S. Environmental Protection Agency's ECOTOX database with additional data from ECHA to build a large data set containing in vivo test data on fish for thousands of chemical substances. This was used to create QSAR models to predict two types of end points: acute LC50 (median lethal concentration) and points of departure similar to the NOEC (no observed effect concentration) for any duration (named the "LC50" and "NOEC" models, respectively). These models used study covariates, such as species and exposure route, as features to facilitate the simultaneous use of varied data types. A novel method of substituting taxonomy groups for species dummy variables was introduced to maximize generalizability to different species. A stacked ensemble of three machine learning methods-random forest, gradient boosted trees, and support vector regression-was implemented to best make use of a large data set with many descriptors. The LC50 and NOEC models predicted end points within 1 order of magnitude 81% and 76% of the time, respectively, and had RMSEs of roughly 0.83 and 0.98 log10(mg/L), respectively. Benchmarks against the existing TEST and ECOSAR tools suggest improved prediction accuracy.

| S-EPMC7047609 | biostudies-literature

Biodegradation Potential of Bacillus sp. PAH-2 on PAHs for Oil-Contaminated Seawater.

Project description:Microbial degradation is a useful tool for inhibiting or preventing polycyclic aromatic hydrocarbons (PAHs) widely distributed in marine environment after oil spill accidents. This study aimed to evaluate the potential and diversity of bacteria Bacillus sp. PAH-2 on Benzo (a) anthracene (BaA), Pyrene (Pyr), and Benzo (a) pyrene (BaP), their composite system, aromatic components system, and crude oil. The seven-day degradation rates against BaA, Pyr, and BaP were 20.6%, 12.83%, and 17.49%, respectively. Further degradation study of aromatic components demonstrated PAH-2 had a high degradation rate of substances with poor stability of molecular structure. In addition, the degradation of PAHs in crude oil suggested PAH-2 not only made good use of PAHs in such a more complex structure of pollutants but the saturated hydrocarbons in the crude oil also showed a good application potential.

| S-EPMC8839208 | biostudies-literature

QSAR workbench: automating QSAR modeling to drive compound design.

Project description:We describe the QSAR Workbench, a system for the building and analysis of QSAR models. The system is built around the Pipeline Pilot workflow tool and provides access to a variety of model building algorithms for both continuous and categorical data. Traditionally models are built on a one by one basis and fully exploring the model space of algorithms and descriptor subsets is a time consuming basis. The QSAR Workbench provides a framework to allow for multiple models to be built over a number of modeling algorithms, descriptor combinations and data splits (training and test sets). Methods to analyze and compare models are provided, enabling the user to select the most appropriate model. The Workbench provides a consistent set of routines for data preparation and chemistry normalization that are also applied for predictions. The Workbench provides a large degree of automation with the ability to publish preconfigured model building workflows for a variety of problem domains, whilst providing experienced users full access to the underlying parameterization if required. Methods are provided to allow for publication of selected models as web services, thus providing integration with the chemistry desktop. We describe the design and implementation of the QSAR Workbench and demonstrate its utility through application to two public domain datasets.

| S-EPMC3657086 | biostudies-literature

Comparative developmental toxicity of environmentally relevant oxygenated PAHs.

Project description:Oxygenated polycyclic aromatic hydrocarbons (OPAHs) are byproducts of combustion and photo-oxidation of parent PAHs. OPAHs are widely present in the environment and pose an unknown hazard to human health. The developing zebrafish was used to evaluate a structurally diverse set of 38 OPAHs for malformation induction, gene expression changes and mitochondrial function. Zebrafish embryos were exposed from 6 to 120h post fertilization (hpf) to a dilution series of 38 different OPAHs and evaluated for 22 developmental endpoints. AHR activation was determined via CYP1A immunohistochemistry. Phenanthrenequinone (9,10-PHEQ), 1,9-benz-10-anthrone (BEZO), xanthone (XAN), benz(a)anthracene-7,12-dione (7,12-B[a]AQ), and 9,10-anthraquinone (9,10-ANTQ) were evaluated for transcriptional responses at 48hpf, prior to the onset of malformations. qRT-PCR was conducted for a number of oxidative stress genes, including the glutathione transferase(gst), glutathione peroxidase(gpx), and superoxide dismutase(sod) families. Bioenergetics was assayed to measure in vivo oxidative stress and mitochondrial function in 26hpf embryos exposed to OPAHs. Hierarchical clustering of the structure-activity outcomes indicated that the most toxic of the OPAHs contained adjacent diones on 6-carbon moieties or terminal, para-diones on multi-ring structures. 5-carbon moieties with adjacent diones were among the least toxic OPAHs while the toxicity of multi-ring structures with more centralized para-diones varied considerably. 9,10-PHEQ, BEZO, 7,12-B[a]AQ, and XAN exposures increased expression of several oxidative stress related genes and decreased oxygen consumption rate (OCR), a measurement of mitochondrial respiration. Comprehensive in vivo characterization of 38 structurally diverse OPAHs indicated differential AHR dependency and a prominent role for oxidative stress in the toxicity mechanisms.

| S-EPMC3976560 | biostudies-literature

Sigma-2 receptor ligands QSAR model dataset.

Project description:The data have been obtained from the Sigma-2 Receptor Selective Ligands Database (S2RSLDB) and refined according to the QSAR requirements. These data provide information about a set of 548 Sigma-2 (?2) receptor ligands selective over Sigma-1 (?1) receptor. The development of the QSAR model has been undertaken with the use of CORAL software using SMILES, molecular graphs and hybrid descriptors (SMILES and graph together). Data here reported include the regression for ?2 receptor pKi QSAR models. The QSAR model was also employed to predict the ?2 receptor pKi values of the FDA approved drugs that are herewith included.

| S-EPMC5491401 | biostudies-literature

Role of AHR in cardiac developmental toxicity of PAHs

Project description:Transcriptmic profiling of cardiac tissues in AHR2 knockdown and control zebrafish (Danio rerio) embryos exposed to BaP, FL amd BaP+FL at 2,6,12, and 18 hours post dosing

2014-11-24 | GSE57946 | GEO

OmicsDI is part of the ELIXIR infrastructure

OmicsDI is an Elixir interoperability service. Learn more ›

Tweets

OmicsDI Databases

PRIDE
PeptideAtlas
MassIVE
JPOST Repository
Physiome Model Repository

EGA
EVA
ENA
LINCS
PAXDB
Cell Collective

MetaboLights
Metabolomics Workbench
MetabolomeExpress
GNPS
BioModels
FAIRDOMHub

ArrayExpress
dbGaP
ExpressionAtlas
GEO
NODE

Information

Databases
Help
API
Contact us
Code on GitHub
Terms of use
Submit Data