Evaluation of a Novel Short‐Term Algal Toxicity Assay by the Development of QSARs and Inter‐Species Relationships for Narcotic Chemicals

Worgan, Andrew D. P.; Dearden, John C.; Edwards, R. W.; Netzeva, Tatiana I.; Cronin, Mark T.D.

doi:10.1002/qsar.200390013

Cited by 22 publications

(26 citation statements)

References 21 publications

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“…As expected, there is a strong trend between toxicity and hydrophobicity, although log K ow alone fails to describe the toxicity of all chemicals. Figure 2 also illustrates the previously developed QSAR for nonpolar narcosis (13), which appears as the characteristic baseline. The analysis of Figure 2 revealed that many of the compounds with toxicity above that predicted by the baseline effect are bioreactive in nature (26).…”

Section: Resultsmentioning

confidence: 99%

“…Fewer, if any, consistent data are available for algal endpoints, and hence, fewer QSARs are available for algal toxicity. To help address the shortfall in algal toxicity data, the authors have developed a short-term algal toxicity assay using Chlorella vulgaris (13). The test is based upon the premise that all living organisms, including algae, contain nonspecific esterases, the activity of which can be assessed by the measurement of the disappearance of an ester or the appearance of the product.…”

Section: Introductionmentioning

confidence: 99%

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Assessment and Modeling of the Toxicity of Organic Chemicals to Chlorella vulgaris: Development of a Novel Database

Cronin

Netzeva

Dearden

et al. 2004

Chem. Res. Toxicol.

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This study reports a database of toxicity values for 91 compounds assessed in a novel, rapid, and economical 15 min algal toxicity test. The toxicity data were measured using the unicellular green alga Chlorella vulgaris in an assay that determined the disappearance of fluorescein diacetate. The chemicals tested covered a wide range of physicochemical properties and mechanisms of action. Quantitative activity-activity relationships with the toxicity of the chemicals to other species (Tetrahymena pyriformis, Vibrio fischeri, and Pimephales promelas) showed strong relationships, although some differences resulting from different protocols were established. Quantitative structure-activity relationships (QSARs) were determined using linear [multiple linear regression (MLR)] and nonlinear [k-nearest neighbors (KNN)] methods. Three descriptors, accounting for hydrophobicity, electrophilicity, and a function of molecular size corrected for the presence of heteroatoms, were found to be important to model toxicity. The predictivity of MLR was compared to KNN using leave-one-out cross-validation and the simulation of an external test set. MLR demonstrated greater stability in validation. The results of this study showed that method selection in QSAR is task-dependent and it is inappropriate to resort to more complicated but less transparent methods, unless there are clear indications (e.g., inability of MLR to deal with the data set) for the need of such methods.

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Section: Resultsmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Assessment and Modeling of the Toxicity of Organic Chemicals to Chlorella vulgaris: Development of a Novel Database

Cronin

Netzeva

Dearden

et al. 2004

Chem. Res. Toxicol.

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“…The rest were put into the test set. The following 71 compounds were assigned to the training set: 11 182,187,190,191,194,196,198 Selection of the Descriptors. The method for selection of the descriptors used in this study was a modified method for selecting objects for the training and the test set described in the previous paragraph.…”

Section: Resultsmentioning

confidence: 99%

Classification of Potential Endocrine Disrupters on the Basis of Molecular Structure Using a Nonlinear Modeling Method

Roncaglioni

Novič

Vračko

et al. 2004

J. Chem. Inf. Comput. Sci.

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A methodology for the classification of endocrine disruption chemicals is proposed. It is based on a data set of 106 substances extracted from the list of 553 chemicals that were inspected by the European Union Commission for the scientific evidence of their endocrine disruption activity. The substances belong to different categories defined in the EU Commission report: (i) literature evidence for certainly active as endocrine disrupters, (ii) for potentially active, (iii) for less probable active--lacking evidence, and (iv) for certainty nonactive. 3D molecular coordinates were calculated using the AM1or the PM3 optimization method. From 3D coordinates an extensive set of molecular descriptors was calculated. The classification model based on the counterpropagation neural network was constructed and evaluated. This is the first time that the counterpropagation neural network is applied for the classification of compounds regarding their literature evidence for the endocrine disruption activity. The developed classification model is proposed as a tool for a preliminary assessment of potential endocrine disrupters, which would help the assessors to make the priority list for a large amount of chemicals that have to be tested with more expensive in vitro and in vivo methods.

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“…Parameters like dipole moment, LUMO energy, hardness and electrophilicity were considered to predict model, since these parameters are widely used to describe the stability, toxicity and reactivity of the chemical compounds (Kapur et al, 2000). We also considered the hydrophobicity, as many authors reported the quantitative correlation of toxicity, activity and metabolism of the compounds with the octanol-water distribution coefficient descriptor (Bundy et al, 2001, Ren and Frymier, 2002, Worgan et al, 2003. In order to establish a better predictability, PLS based QSAR study was performed by considering experimentally observed values (pIC 50 ), along with various descriptors, are presented in Tables 1.…”

Section: Qsar Models For Inhibitor Activitymentioning

confidence: 99%

In silico ADME/T and 3D QSAR analysis of KDR inhibitors

Hosen¹,

Dash²,

Khatun³

et al. 2017

J App Pharm Sci

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Tyrosine kinases (KDR) have been considered as a potential targets for the design of new anticancer agents. Recently, a series of N-4-chlorophenylnaphthamides has been reported with KDR inhibitory activity. In order to demonstrate the pharmacokinetics and the relationship between the structures and their inhibition of KDR, 3D-QSAR and in silico ADME/T analysis were performed on a dataset of 13 compounds. Quantum chemical parameters such as LUMO energy, HOMO energy, ionization energy (I), electron affinity (A), chemical potential (μ), hardness (η) and electrophilicity (χ) of the compounds are calculated by using semi-empirical SCF-MO method at PM3 level of theory and various SlogP descriptors from MOE software. In silico ADME/T analysis was performed by using different software's Discovery Studio 2.5, TOPKAT and QikProp 4.3. From in silico ADME and Toxicity studies, it was revealed that selected derivatives have good oral absorption rate and metabolism with no BBB penetration. QSTR (Quantitative Structure Toxicity Relationship) studies by using TOPAK in various computational animal models, showed high LD50 values and the compounds are found to be noncarcenogenic. Moreover, three different QSAR models were generated by Partial Least Squares (PLS) Regression method having correlation coefficient (Q 2 ) of 0.86382, 0.84372, and 0.82629, respectively. These models conclude a significant relationship of KDR inhibition with dipole moment (D), LUMO energy (ELUMO), hardness (η) and electrophilicity (χ), and hydrophobicity of compounds, regarding N-4-chlorophenylnaphthamides.

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Evaluation of a Novel Short‐Term Algal Toxicity Assay by the Development of QSARs and Inter‐Species Relationships for Narcotic Chemicals

Cited by 22 publications

References 21 publications

Assessment and Modeling of the Toxicity of Organic Chemicals to Chlorella vulgaris: Development of a Novel Database

Assessment and Modeling of the Toxicity of Organic Chemicals to Chlorella vulgaris: Development of a Novel Database

Classification of Potential Endocrine Disrupters on the Basis of Molecular Structure Using a Nonlinear Modeling Method

In silico ADME/T and 3D QSAR analysis of KDR inhibitors

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