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Advances in Computational Toxicology pp 99–118Cite as

Mode-of-Action-Guided, Molecular Modeling-Based Toxicity Prediction: A Novel Approach for In Silico Predictive Toxicology

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

Abstract

Computational toxicology is a sub-discipline of toxicology concerned with the development and use of computer-based models and methodology to understand and predict chemical toxicity in a biological system (e.g., cells and organisms). Quantitative structure–activity relationship (QSAR) has been the predominant approach in computational toxicology. However, classical QSAR methodology has often suffered from low prediction accuracy, largely owing to the lack or non-integration of toxicological mechanisms. To address this lingering problem, we have developed a novel in silico toxicology approach that is based on molecular modeling and guided by mode of action (MoA). Our approach is implemented through a target-specific toxicity knowledgebase (TsTKb), consisting of a pre-categorized database of chemical MoA (ChemMoA) and a series of pre-built, category-specific classification and quantification models. ChemMoA serves as the depository of chemicals with known MoAs or molecular initiating events (i.e., known target biomacromolecules) and quantitative information for measured toxicity endpoints (if available). The models allow a user to qualitatively classify an uncharacterized chemical by MoA and quantitatively predict its toxicity potency. This approach is currently under development and will evolve to incorporate physiologically based pharmacokinetic (PBPK) modeling to address absorption, distribution, metabolism and excretion (ADME) processes in a biological system. The fully developed approach is believed to significantly advance in silico -based predictive toxicology and provide a new powerful toolbox for regulators, the chemical industry and the relevant academic communities.

Keywords

  • Mode of action (MoA)
  • Molecular dynamics (MD) simulation
  • Molecular docking
  • Deep learning
  • Predictive toxicology
  • Target-specific toxicity knowledgebase (TsTKb)
  • Chemical mode of action database (ChemMoA)
  • Qualitative classification
  • Quantitative prediction
  • Quantitative structure–activity relationship (QSAR)

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Fig. 6.1
Fig. 6.2
Fig. 6.3

Abbreviations

3D:

Three-dimensional

3Rs:

Refine, reduce, and replace

ACToR:

Aggregated Computational Toxicology Online Resource

ADME:

Absorption, distribution, metabolism, and excretion

AOP:

Adverse outcome pathway

BLAST:

Basic local alignment search tool

BPA:

Bisphenol A

ChemMoA:

Chemical MoA

DSSTox:

Distributed Structure-Searchable Toxicity

dyPLID:

Dynamic protein–ligand interaction descriptors

EADB:

Estrogenic Activity Database

EDKB:

Endocrine Disruptor Knowledge Base

EDSP:

Endocrine Disruptor Screening Program

EPA:

Environmental Protection Agency

EU:

European Union

FDA:

Food and Drug Administration

iPSC:

Induced Pluripotent Stem Cell

LTKB:

Liver Toxicity Knowledge Base

MD:

Molecular Dynamics

MIE:

Molecular Initiating Event

MMDB:

Molecular Modeling DataBase

MoA:

Mode of Action

NCBI:

National Center for Biotechnology Information

NRC:

National Research Council

OECD:

Organization for Economic Cooperation and Development

PBPK:

Physiologically based pharmacokinetic

PDB:

Protein Data Bank

QSAR:

Quantitative structure–activity relationship

RC:

Reference chemical

REACH:

Registration, Evaluation, Authorization, and Restriction of Chemicals

Risk21:

Risk Assessment in the twenty-first century

SEURAT:

Safety Evaluation Ultimately Replacing Animal Testing

SPLIF:

Structural protein–ligand interaction fingerprints

T3DB:

Toxin and Toxin Target Database

Tox21:

Toxicology in the twenty-first century

ToxCast:

Toxicity Forecaster

TsTKb:

Target-specific toxicity knowledgebase

USDA:

US Department of Agriculture

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Authors and Affiliations

  1. Environmental Laboratory, US Army Engineer Research and Development Center, Vicksburg, MS, 39180, USA

    Ping Gong & Sundar Thangapandian

  2. Bennett Aerospace, Inc., Cary, NC, 27518, USA

    Yan Li

  3. School of Computing Sciences and Computer Engineering, University of Southern Mississippi, Hattiesburg, MS, 39406, USA

    Gabriel Idakwo, Joseph Luttrell IV & Chaoyang Zhang

  4. Division of Bioinformatics and Biostatistics, National Center for Toxicological Research, U.S. Food and Drug Administration, Jefferson, AR, 72079, USA

    Minjun Chen & Huixiao Hong

Authors
  1. Ping Gong
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  2. Sundar Thangapandian
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  3. Yan Li
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  4. Gabriel Idakwo
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  5. Joseph Luttrell IV
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  6. Minjun Chen
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  7. Huixiao Hong
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  8. Chaoyang Zhang
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Correspondence to Ping Gong .

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Editors and Affiliations

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

    Dr. Huixiao Hong

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Gong, P. et al. (2019). Mode-of-Action-Guided, Molecular Modeling-Based Toxicity Prediction: A Novel Approach for In Silico Predictive 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_6

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