Title |
Modeling Chronic Toxicity: A Comparison of Experimental Variability With (Q)SAR/Read-Across Predictions
|
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Published in |
Frontiers in Pharmacology, April 2018
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DOI | 10.3389/fphar.2018.00413 |
Pubmed ID | |
Authors |
Christoph Helma, David Vorgrimmler, Denis Gebele, Martin Gütlein, Barbara Engeli, Jürg Zarn, Benoit Schilter, Elena Lo Piparo |
Abstract |
This study compares the accuracy of (Q)SAR/read-across predictions with the experimental variability of chronic lowest-observed-adverse-effect levels (LOAELs) from in vivo experiments. We could demonstrate that predictions of the lazy structure-activity relationships (lazar) algorithm within the applicability domain of the training data have the same variability as the experimental training data. Predictions with a lower similarity threshold (i.e., a larger distance from the applicability domain) are also significantly better than random guessing, but the errors to be expected are higher and a manual inspection of prediction results is highly recommended. |
X Demographics
Geographical breakdown
Country | Count | As % |
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Switzerland | 1 | 50% |
Spain | 1 | 50% |
Demographic breakdown
Type | Count | As % |
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Members of the public | 2 | 100% |
Mendeley readers
Geographical breakdown
Country | Count | As % |
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Unknown | 19 | 100% |
Demographic breakdown
Readers by professional status | Count | As % |
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Other | 3 | 16% |
Unspecified | 2 | 11% |
Student > Doctoral Student | 2 | 11% |
Student > Ph. D. Student | 2 | 11% |
Student > Master | 2 | 11% |
Other | 2 | 11% |
Unknown | 6 | 32% |
Readers by discipline | Count | As % |
---|---|---|
Biochemistry, Genetics and Molecular Biology | 3 | 16% |
Chemistry | 3 | 16% |
Unspecified | 2 | 11% |
Pharmacology, Toxicology and Pharmaceutical Science | 2 | 11% |
Agricultural and Biological Sciences | 1 | 5% |
Other | 2 | 11% |
Unknown | 6 | 32% |