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

  • Ping GongEmail author
  • Sundar Thangapandian
  • Yan Li
  • Gabriel Idakwo
  • Joseph Luttrell IV
  • Minjun Chen
  • Huixiao Hong
  • Chaoyang Zhang
Chapter
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) 

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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Copyright information

© Springer Nature Switzerland AG 2019

Authors and Affiliations

  • Ping Gong
    • 1
    Email author
  • Sundar Thangapandian
    • 1
  • Yan Li
    • 2
  • Gabriel Idakwo
    • 3
  • Joseph Luttrell IV
    • 3
  • Minjun Chen
    • 4
  • Huixiao Hong
    • 4
  • Chaoyang Zhang
    • 3
  1. 1.Environmental LaboratoryUS Army Engineer Research and Development CenterVicksburgUSA
  2. 2.Bennett Aerospace, Inc.CaryUSA
  3. 3.School of Computing Sciences and Computer EngineeringUniversity of Southern MississippiHattiesburgUSA
  4. 4.Division of Bioinformatics and BiostatisticsNational Center for Toxicological Research, U.S. Food and Drug AdministrationJeffersonUSA

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