Dataset Information

Predictive Analysis Using Chemical-Gene Interaction Networks Consistent with Observed Endocrine Activity and Mutagenicity of U.S. Streams.

ABSTRACT: In a recent U.S. Geological Survey/U.S. Environmental Protection Agency study assessing more than 700 organic compounds in 38 streams, in vitro assays indicated generally low estrogen, androgen, and glucocorticoid receptor activities, with 13 surface waters with 17?-estradiol-equivalent (E2Eq) activities greater than a 1-ng/L estimated effects-based trigger value for estrogenic effects in male fish. Among the 36 samples assayed for mutagenicity in the Salmonella bioassay (reported here), 25% had low mutagenic activity and 75% were not mutagenic. Endocrine and mutagenic activities of the water samples were well correlated with each other and with the total number and cumulative concentrations of detected chemical contaminants. To test the predictive utility of knowledge-base-leveraging approaches, site-specific predicted chemical-gene (pCGA) and predicted analogous pathway-linked (pPLA) association networks identified in the Comparative Toxicogenomics Database were compared with observed endocrine/mutagenic bioactivities. We evaluated pCGA/pPLA patterns among sites by cluster analysis and principal component analysis and grouped the pPLA into broad mode-of-action classes. Measured E2eq and mutagenic activities correlated well with predicted pathways. The pPLA analysis also revealed correlations with signaling, metabolic, and regulatory groups, suggesting that other effects pathways may be associated with chemical contaminants in these waters and indicating the need for broader bioassay coverage to assess potential adverse impacts.

SUBMITTER: Berninger JP

PROVIDER: S-EPMC6770991 | biostudies-literature | 2019 Aug

REPOSITORIES: biostudies-literature

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Predictive Analysis Using Chemical-Gene Interaction Networks Consistent with Observed Endocrine Activity and Mutagenicity of U.S. Streams.

Berninger Jason P JP DeMarini David M DM Warren Sarah H SH Simmons Jane Ellen JE Wilson Vickie S VS Conley Justin M JM Armstrong Mikayla D MD Iwanowicz Luke R LR Kolpin Dana W DW Kuivila Kathryn M KM Reilly Timothy J TJ Romanok Kristin M KM Villeneuve Daniel L DL Bradley Paul M PM

Environmental science & technology 20190722 15

In a recent U.S. Geological Survey/U.S. Environmental Protection Agency study assessing more than 700 organic compounds in 38 streams, <i>in vitro</i> assays indicated generally low estrogen, androgen, and glucocorticoid receptor activities, with 13 surface waters with 17β-estradiol-equivalent (E2Eq) activities greater than a 1-ng/L estimated effects-based trigger value for estrogenic effects in male fish. Among the 36 samples assayed for mutagenicity in the <i>Salmonella</i> bioassay (reported ...[more]

PMID: 31287672

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Project description:UnlabelledBackgroundExperimental screening of chemical compounds for biological activity is a time consuming and expensive practice. In silico predictive models permit inexpensive, rapid "virtual screening" to prioritize selection of compounds for experimental testing. Both experimental and in silico screening can be used to test compounds for desirable or undesirable properties. Prior work on prediction of mutagenicity has primarily involved identification of toxicophores rather than whole-molecule predictive models. In this work, we examined a range of in silico predictive classification models for prediction of mutagenic properties of compounds, including methods such as J48 and SMO which have not previously been widely applied in cheminformatics.ResultsThe Bursi mutagenicity data set containing 4337 compounds (Set 1) and a Benchmark data set of 6512 compounds (Set 2) were taken as input data set in this work. A third data set (Set 3) was prepared by joining up the previous two sets. Classification algorithms including Naïve Bayes, Random Forest, J48 and SMO with 10 fold cross-validation and default parameters were used for model generation on these data sets. Models built using the combined performed better than those developed from the Benchmark data set. Significantly, Random Forest outperformed other classifiers for all the data sets, especially for Set 3 with 89.27% accuracy, 89% precision and ROC of 95.3%. To validate the developed models two external data sets, AID1189 and AID1194, with mutagenicity data were tested showing 62% accuracy with 67% precision and 65% ROC area and 91% accuracy, 91% precision with 96.3% ROC area respectively. A Random Forest model was used on approved drugs from DrugBank and metabolites from the Zinc Database with True Positives rate almost 85% showing the robustness of the model.ConclusionWe have created a new mutagenicity benchmark data set with around 8,000 compounds. Our work shows that highly accurate predictive mutagenicity models can be built using machine learning methods based on chemical descriptors and trained using this set, and these models provide a complement to toxicophores based methods. Further, our work supports other recent literature in showing that Random Forest models generally outperform other comparable machine learning methods for this kind of application.

Project description:BackgroundThe dynamics of biochemical reaction systems are constrained by the fundamental laws of thermodynamics, which impose well-defined relationships among the reaction rate constants characterizing these systems. Constructing biochemical reaction systems from experimental observations often leads to parameter values that do not satisfy the necessary thermodynamic constraints. This can result in models that are not physically realizable and may lead to inaccurate, or even erroneous, descriptions of cellular function.ResultsWe introduce a thermodynamically consistent model calibration (TCMC) method that can be effectively used to provide thermodynamically feasible values for the parameters of an open biochemical reaction system. The proposed method formulates the model calibration problem as a constrained optimization problem that takes thermodynamic constraints (and, if desired, additional non-thermodynamic constraints) into account. By calculating thermodynamically feasible values for the kinetic parameters of a well-known model of the EGF/ERK signaling cascade, we demonstrate the qualitative and quantitative significance of imposing thermodynamic constraints on these parameters and the effectiveness of our method for accomplishing this important task. MATLAB software, using the Systems Biology Toolbox 2.1, can be accessed from http://www.cis.jhu.edu/~goutsias/CSS lab/software.html. An SBML file containing the thermodynamically feasible EGF/ERK signaling cascade model can be found in the BioModels database.ConclusionsTCMC is a simple and flexible method for obtaining physically plausible values for the kinetic parameters of open biochemical reaction systems. It can be effectively used to recalculate a thermodynamically consistent set of parameter values for existing thermodynamically infeasible biochemical reaction models of cellular function as well as to estimate thermodynamically feasible values for the parameters of new models. Furthermore, TCMC can provide dimensionality reduction, better estimation performance, and lower computational complexity, and can help to alleviate the problem of data overfitting.

Project description:BackgroundIn drug discovery, a positive Ames test for bacterial mutation presents a significant hurdle to advancing a drug to clinical trials. In a previous paper, we discussed success in predicting the genotoxicity of reagent-sized aryl-amines (ArNH2), a structure frequently found in marketed drugs and in drug discovery, using quantum mechanics calculations of the energy required to generate the DNA-reactive nitrenium intermediate (ArNH:+). In this paper we approach the question of what molecular descriptors could improve these predictions and whether external data sets are appropriate for further training.ResultsIn trying to extend and improve this model beyond this quantum mechanical reaction energy, we faced considerable difficulty, which was surprising considering the long history and success of QSAR model development for this test. Other quantum mechanics descriptors were compared to this reaction energy including AM1 semi-empirical orbital energies, nitrenium formation with alternative leaving groups, nitrenium charge, and aryl-amine anion formation energy. Nitrenium formation energy, regardless of the starting species, was found to be the most useful single descriptor. External sets used in other QSAR investigations did not present the same difficulty using the same methods and descriptors. When considering all substructures rather than just aryl-amines, we also noted a significantly lower performance for the Novartis set. The performance gap between Novartis and external sets persists across different descriptors and learning methods. The profiles of the Novartis and external data are significantly different both in aryl-amines and considering all substructures. The Novartis and external data sets are easily separated in an unsupervised clustering using chemical fingerprints. The chemical differences are discussed and visualized using Kohonen Self-Organizing Maps trained on chemical fingerprints, mutagenic substructure prevalence, and molecular weight.ConclusionsDespite extensive work in the area of predicting this particular toxicity, work in designing and publishing more relevant test sets for compounds relevant to drug discovery is still necessary. This work also shows that great care must be taken in using QSAR models to replace experimental evidence. When considering all substructures, a random forest model, which can inherently cover distinct neighborhoods, built on Novartis data and previously reported external data provided a suitable model.

Dataset Information

Predictive Analysis Using Chemical-Gene Interaction Networks Consistent with Observed Endocrine Activity and Mutagenicity of U.S. Streams.

Publications

Predictive Analysis Using Chemical-Gene Interaction Networks Consistent with Observed Endocrine Activity and Mutagenicity of U.S. Streams.

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