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.

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2,3,7, and the relatively non-toxic selective aryl hydrocarbon receptor (AhR) modulator 6-methyl-1,3,8-trichlorodibenzo-furan (MCDF) induced CYP1A1-dependent ethoxyresorufin O-deethylase activity and inhibited proliferation of seven estrogen receptor (ER) negative breast cancer cell lines. MCDF, TCDD and structurally related 2, 3,7,8-tetrachlorodibenzofuran, 1,2,3,7,8-pentachlorodibenzo-p-dioxin, 2,3,4,7,8-pentachlorodibenzofuran, and 3,3 0 ,4,4 0 ,5-pentachlorobiphenyl induced CYP1A1 and inhibited proliferation of BT-474 and MDA-MB-468 cells. In BT474 and MDA-MB-468 cells transfected with a small inhibitory RNA for the AhR, the antiproliferative activity of the chlorinated aromatic compounds was reversed, whereas for MCDF, only partial reversal was observed, suggesting that this compound acts through both AhR-dependent and AhR-independent pathways in these two cell lines. MCDF also inhibited tumor growth in athymic nude mice in which MDA-MB-468 cells were injected directly into the mammary fat pad. These results suggest that the AhR is a potential drug target for treatment of ER-negative breast cancer.

Other than possibly an increased risk of non-Hodgkin lymphoma, we find no other cause of death related to the mixture of the dioxin contaminants found in PCP.

The creation of large toxicological databases and advances in machine-learning techniques have empowered computational approaches in toxicology.