Original Research ARTICLE
Modeling Chronic Toxicity: A comparison of experimental variability with (Q)SAR/read-across predictions
- 1In Silico Toxicology (Switzerland), Switzerland
- 2Inst. f. Computer Science, Johannes Gutenberg-Universität Mainz, Germany
- 3Risk Assessment Division, Federal Food Safety and Veterinary Office, Switzerland
- 4Nestle Research Center, Nestle (Switzerland), Switzerland
This study compares the accuracy of (Q)SAR/read-across predictions with the
experimental variability of chronic LOAEL values from *in vivo* experiments.
We could demonstrate that predictions of the `lazar` algrorithm 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.
Keywords: (Q)SAR, Read-across, LOAEL, Experimental variability, lazar
Received: 29 Jan 2018;
Accepted: 10 Apr 2018.
Edited by:Sebastian Hoffmann, seh consulting + services, Germany
Reviewed by:Jan W. Van Der Laan, Medicines Evaluation Board, Netherlands
Zhichao Liu, National Center for Toxicological Research (FDA), United States
Copyright: © 2018 Helma, Vorgrimmler, Gebele, Gütlein, Engeli, Zarn, Schilter and LoPiparo. This is an open-access article distributed under the terms of the Creative Commons Attribution License (CC BY). The use, distribution or reproduction in other forums is permitted, provided the original author(s) and the copyright owner are credited and that the original publication in this journal is cited, in accordance with accepted academic practice. No use, distribution or reproduction is permitted which does not comply with these terms.
* Correspondence: Dr. Elena LoPiparo, Nestle (Switzerland), Nestle Research Center, Vevey, Switzerland, Elena.LoPiparo@rdls.nestle.com