A toxicogenomic data space for system-level understanding and prediction of EDC-induced toxicity

A Sakhteman; M Failli; J Kublbeck; A L Levonen; V Fortino

doi:10.1016/j.envint.2021.106751

A toxicogenomic data space for system-level understanding and prediction of EDC-induced toxicity

Environ Int. 2021 Nov:156:106751. doi: 10.1016/j.envint.2021.106751. Epub 2021 Jul 13.

Authors

A Sakhteman¹, M Failli², J Kublbeck³, A L Levonen⁴, V Fortino⁵

Affiliations

¹ Institute of Biomedicine, University of Eastern Finland, Kuopio 70210, Finland.
² Department of Chemical, Materials and Industrial Engineering, University of Naples, 'Federico II', Naples 80125, Italy.
³ A.I. Virtanen Institute for Molecular Sciences, University of Eastern Finland, Kuopio 70210, Finland; School of Pharmacy, University of Eastern Finland, Kuopio 70210, Finland.
⁴ A.I. Virtanen Institute for Molecular Sciences, University of Eastern Finland, Kuopio 70210, Finland.
⁵ Institute of Biomedicine, University of Eastern Finland, Kuopio 70210, Finland. Electronic address: vittorio.fortino@uef.fi.

PMID: 34271427
DOI: 10.1016/j.envint.2021.106751

Abstract

Endocrine disrupting compounds (EDCs) are a persistent threat to humans and wildlife due to their ability to interfere with endocrine signaling pathways. Inspired by previous work to improve chemical hazard identification through the use of toxicogenomics data, we developed a genomic-oriented data space for profiling the molecular activity of EDCs in an in silico manner, and for creating predictive models that identify and prioritize EDCs. Predictive models of EDCs, derived from gene expression data from rats (in vivo and in vitro primary hepatocytes) and humans (in vitro primary hepatocytes and HepG2), achieve testing accuracy greater than 90%. Negative test sets indicate that known safer chemicals are not predicted as EDCs. The rat in vivo-based classifiers achieve accuracy greater than 75% when tested for invitro to in vivoextrapolation. This study reveals key metabolic pathways and genes affected by EDCs together with a set of predictive models that utilize these pathways to prioritize EDCs in dose/time dependent manner and to predict EDCevokedmetabolic diseases.

Keywords: Endocrine disrupting chemicals; In silico toxicity prediction; Machine learning; Metabolic diseases; Toxicogenomics.

Publication types

Research Support, Non-U.S. Gov't

MeSH terms

Animals
Computer Simulation
Endocrine Disruptors* / toxicity
Gene Expression
Rats
Toxicogenetics*

Substances

Endocrine Disruptors