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Computational approaches to chemical hazard assessment.

ABSTRACT: Computational prediction of toxicity has reached new heights as a result of decades of growth in the magnitude and diversity of biological data. Public packages for statistics and machine learning make model creation faster. New theory in machine learning and cheminformatics enables integration of chemical structure, toxicogenomics, simulated and physical data in the prediction of chemical health hazards, and other toxicological information. Our earlier publications have characterized a toxicological dataset of unprecedented scale resulting from the European REACH legislation (Registration Evaluation Authorisation and Restriction of Chemicals). These publications dove into potential use cases for regulatory data and some models for exploiting this data. This article analyzes the options for the identification and categorization of chemicals, moves on to the derivation of descriptive features for chemicals, discusses different kinds of targets modeled in computational toxicology, and ends with a high-level perspective of the algorithms used to create computational toxicology models.

SUBMITTER: Luechtefeld T

PROVIDER: S-EPMC5848496 | biostudies-literature | 2017

REPOSITORIES: biostudies-literature

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Computational approaches to chemical hazard assessment.

Luechtefeld Thomas T Hartung Thomas T

ALTEX 20170101 4

Computational prediction of toxicity has reached new heights as a result of decades of growth in the magnitude and diversity of biological data. Public packages for statistics and machine learning make model creation faster. New theory in machine learning and cheminformatics enables integration of chemical structure, toxicogenomics, simulated and physical data in the prediction of chemical health hazards, and other toxicological information. Our earlier publications have characterized a toxicolo ...[more]

PMID: 29101769

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Project description:Toxicology is traditionally divided between human and eco-toxicology. In the shared pursuit of environmental health, this separation does not account for discoveries made in the comparative studies of animal genomes. Here, we provide evidence on the feasibility of understanding the health impact of chemicals on all animals, including ecological keystone species and humans, based on a significant number of conserved genes and their functional associations to health-related outcomes across much of animal diversity. We test four conditions to understand the value of comparative genomics data to inform mechanism-based human and environmental hazard assessment: (1) genes that are most fundamental for health evolved early during animal evolution; (2) the molecular functions of pathways are better conserved among distantly related species than the individual genes that are members of these pathways; (3) the most conserved pathways among animals are those that cause adverse health outcomes when disrupted; (4) gene sets that serve as molecular signatures of biological processes or disease-states are largely enriched by evolutionarily conserved genes across the animal phylogeny. The concept of homology is applied in a comparative analysis of gene families and pathways among invertebrate and vertebrate species compared with humans. Results show that over 70% of gene families associated with disease are shared among the greatest variety of animal species through evolution. Pathway conservation between invertebrates and humans is based on the degree of conservation within vertebrates and the number of interacting genes within the human network. Human gene sets that already serve as biomarkers are enriched by evolutionarily conserved genes across the animal phylogeny. By implementing a comparative method for chemical hazard assessment, human and eco-toxicology converge towards a more holistic and mechanistic understanding of toxicity disrupting biological processes that are important for health and shared among animals (including humans).

Dataset Information

Computational approaches to chemical hazard assessment.

Publications

Computational approaches to chemical hazard assessment.

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