Machine Learning of Toxicological Big Data Enables Read-Across Structure Activity Relationships (RASAR) Outperforming Animal Test Reproducibility

Luechtefeld, Thomas; Marsh, Dan; Rowlands, Craig; Hartung, Thomas

Machine Learning of Toxicological Big Data Enables Read-Across Structure Activity Relationships (RASAR) Outperforming Animal Test Reproducibility

Dateien

Luechtefeld_2-149wcgv6cisgi6.pdfGröße: 1.45 MBDownloads: 371

Datum

2018

Autor:innen

Luechtefeld, Thomas

Marsh, Dan

Rowlands, Craig

Hartung, Thomas

Angaben zur Forschungsförderung

European Union (EU): 681002

Projekt

EUToxRisk21

Open Access-Veröffentlichung

Open Access Hybrid

Sammlungen

Biologie

Publikationstyp

Zeitschriftenartikel

Publikationsstatus

Published

Erschienen in

Toxicological Sciences. 2018, 165(1), pp. 198-212. ISSN 1096-6080. eISSN 1096-0929. Available under: doi: 10.1093/toxsci/kfy152

Zusammenfassung

Earlier we created a chemical hazard database via natural language processing of dossiers submitted to the European Chemical Agency with approximately 10 000 chemicals. We identified repeat OECD guideline tests to establish reproducibility of acute oral and dermal toxicity, eye and skin irritation, mutagenicity and skin sensitization. Based on 350–700+ chemicals each, the probability that an OECD guideline animal test would output the same result in a repeat test was 78%–96% (sensitivity 50%–87%). An expanded database with more than 866 000 chemical properties/hazards was used as training data and to model health hazards and chemical properties. The constructed models automate and extend the read-across method of chemical classification. The novel models called RASARs (read-across structure activity relationship) use binary fingerprints and Jaccard distance to define chemical similarity. A large chemical similarity adjacency matrix is constructed from this similarity metric and is used to derive feature vectors for supervised learning. We show results on 9 health hazards from 2 kinds of RASARs—“Simple” and “Data Fusion”. The “Simple” RASAR seeks to duplicate the traditional read-across method, predicting hazard from chemical analogs with known hazard data. The “Data Fusion” RASAR extends this concept by creating large feature vectors from all available property data rather than only the modeled hazard. Simple RASAR models tested in cross-validation achieve 70%–80% balanced accuracies with constraints on tested compounds. Cross validation of data fusion RASARs show balanced accuracies in the 80%–95% range across 9 health hazards with no constraints on tested compounds.

Fachgebiet (DDC)

570 Biowissenschaften, Biologie

Zitieren

ISO 690

LUECHTEFELD, Thomas, Dan MARSH, Craig ROWLANDS, Thomas HARTUNG, 2018. Machine Learning of Toxicological Big Data Enables Read-Across Structure Activity Relationships (RASAR) Outperforming Animal Test Reproducibility. In: Toxicological Sciences. 2018, 165(1), pp. 198-212. ISSN 1096-6080. eISSN 1096-0929. Available under: doi: 10.1093/toxsci/kfy152

BibTex

@article{Luechtefeld2018-09-01Machi-44087,
  year={2018},
  doi={10.1093/toxsci/kfy152},
  title={Machine Learning of Toxicological Big Data Enables Read-Across Structure Activity Relationships (RASAR) Outperforming Animal Test Reproducibility},
  number={1},
  volume={165},
  issn={1096-6080},
  journal={Toxicological Sciences},
  pages={198--212},
  author={Luechtefeld, Thomas and Marsh, Dan and Rowlands, Craig and Hartung, Thomas}
}

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