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Background, Tasks, Modeling Methods, and Challenges for Computational Toxicology

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Advances in Computational Toxicology

Part of the book series: Challenges and Advances in Computational Chemistry and Physics ((COCH,volume 30))

Abstract

Sound chemicals management requires scientific risk assessment schemes capable of predicting physical–chemical properties, environmental behavior, and toxicological effects of vast number of chemicals. However, the current experimental system cannot meet the need for risk assessment of the large and ever-increasing number of chemicals. Meanwhile, current experimental approaches are not sufficient for toxicology to thrive in the era of information. Thus, an auxiliary yet critical field for complementing the experimental sector of chemicals risk assessment has emerged: computational toxicology . Computational toxicology is an interdisciplinary field based especially on environmental chemistry, computational chemistry, chemo-bioinformatics, and systems biology, etc., and it aims at facilitating efficient simulation and prediction of environmental exposure, hazard, and risk of chemicals through various in silico models. Computational toxicology has profoundly changed the way people view and interpret basic concepts of toxicology. Meanwhile, this field is continuously borrowing ideas from exterior fields, which greatly promotes innovative development of toxicology. In this chapter, backgrounds and tasks of computational toxicology are firstly introduced. Then, a variety of in silico models linking key information of chemicals involved in the continuum of source to adverse outcome , such as source emission, concentrations in environmental compartments, exposure concentrations at biological target sites, and adverse efficacy or thresholds are described and discussed. Finally, challenges in computational toxicology such as parameterization for the proposed models, representation of complexity of living systems, and modeling of interlinked chemicals as mixtures are also discussed.

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Abbreviations

3R:

Replacement, reduction, and refinement

ABMs:

Agent/individual-based models

AM1:

Austin model 1

AMBER:

Assisted Model Building with Energy Refinement

AO:

Adverse outcome

AOP:

Adverse outcome pathway

CAS:

Chemical abstract service

CC:

Coupled-cluster

CGenFF:

CHARMM General Force Field

CHARMM:

Chemistry at HARvard Molecular Mechanics

CI:

Configuration interaction

CSBP:

Computational systems biology pathway

DFT:

Density functional theory

DNA:

Deoxyribonucleic acid

EPA:

Environmental Protection Agency

ESD:

Emission scenario documents

EU:

European Union

FF:

Force field

GAFF:

General AMBER Force Field

HF:

Hartree–Fock

HTS:

High-throughput screening

IVIVE:

In vitro–in vivo extrapolation

KE:

Key event

MD:

Molecular dynamics

MIE:

Molecular initiating event

MM:

Molecular mechanics

MNDO:

Modified neglect of diatomic overlap

MP:

Many-body perturbation

Nrf2:

Nuclear factor erythroid 2-related factor 2

OECD:

Organization of Economic Cooperation and Development

OSIRIS:

Optimized Strategies for Risk Assessment of Industrial Chemicals through Integration of Non-test and Test Information

PBDE:

Polybrominated diphenyl ester

PBTK:

Physiologically based toxicokinetics

QM:

Quantum mechanics

QSAR:

Quantitative structure–activity relationship

REACH:

Registration, evaluation, authorization and restriction of chemicals

RNA:

Ribonucleic acid

SE:

Semi-empirical

SEURAT:

Safety Evaluation Ultimately Replacing Animal Testing

SMILES:

Simplified molecular-input line-entry system

SOC:

Semi-volatile organic compound

US:

United States

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  1. Key Laboratory of Industrial Ecology and Environmental Engineering (Ministry of Education), School of Environmental Science and Technology, Dalian University of Technology, Dalian, China

    Zhongyu Wang & Jingwen Chen

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  1. National Center for Toxicological Research, US Food and Drug Administration, Jefferson, AR, USA

    Huixiao Hong

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Wang, Z., Chen, J. (2019). Background, Tasks, Modeling Methods, and Challenges for Computational Toxicology. In: Hong, H. (eds) Advances in Computational Toxicology. Challenges and Advances in Computational Chemistry and Physics, vol 30. Springer, Cham. https://doi.org/10.1007/978-3-030-16443-0_2

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