The Human Toxome Project

Overview

Journal ALTEX

Date 2015 Mar 6

PMID 25742299

Citations 17

Authors

Mounir Bouhifd

Melvin E Andersen

Christina Baghdikian

Kim Boekelheide

Kevin M Crofton

Albert J Fornace Jr

Andre Kleensang

Henghong Li

Carolina Livi

Alexandra Maertens

Patrick D McMullen

Michael Rosenberg

Russell Thomas

Marguerite Vantangoli

James D Yager

Liang Zhao

Thomas Hartung

Affiliations

Soon will be listed here.

Abstract

The Human Toxome Project, funded as an NIH Transformative Research grant 2011-2016, is focused on developing the concepts and the means for deducing, validating and sharing molecular pathways of toxicity (PoT). Using the test case of estrogenic endocrine disruption, the responses of MCF-7 human breast cancer cells are being phenotyped by transcriptomics and mass-spectroscopy-based metabolomics. The bioinformatics tools for PoT deduction represent a core deliverable. A number of challenges for quality and standardization of cell systems, omics technologies and bioinformatics are being addressed. In parallel, concepts for annotation, validation and sharing of PoT information, as well as their link to adverse outcomes, are being developed. A reasonably comprehensive public database of PoT, the Human Toxome Knowledge-base, could become a point of reference for toxicological research and regulatory test strategies.

Citing Articles

Avoiding Regrettable Substitutions: Green Toxicology for Sustainable Chemistry.

Maertens A, Golden E, Hartung T ACS Sustain Chem Eng. 2022; 9(23):7749-7758.

PMID: 36051558 PMC: 9432817. DOI: 10.1021/acssuschemeng.0c09435.

Towards a biochemical approach to occupational stress management.

Shackleton G Heliyon. 2021; 7(6):e07175.

PMID: 34141933 PMC: 8187824. DOI: 10.1016/j.heliyon.2021.e07175.

Harnessing the power of novel animal-free test methods for the development of COVID-19 drugs and vaccines.

Busquet F, Hartung T, Pallocca G, Rovida C, Leist M Arch Toxicol. 2020; 94(6):2263-2272.

PMID: 32447523 PMC: 7245508. DOI: 10.1007/s00204-020-02787-2.

Advancing Computational Toxicology in the Big Data Era by Artificial Intelligence: Data-Driven and Mechanism-Driven Modeling for Chemical Toxicity.

Ciallella H, Zhu H Chem Res Toxicol. 2019; 32(4):536-547.

PMID: 30907586 PMC: 6688471. DOI: 10.1021/acs.chemrestox.8b00393.

A Human iPSC-derived 3D platform using primary brain cancer cells to study drug development and personalized medicine.

Plummer S, Wallace S, Ball G, Lloyd R, Schiapparelli P, Quinones-Hinojosa A Sci Rep. 2019; 9(1):1407.

PMID: 30723234 PMC: 6363784. DOI: 10.1038/s41598-018-38130-0.

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