In silico toxicology protocols

Research output: Journal contributionsJournal articlesResearchpeer-review


  • Glenn J. Myatt
  • Ernst Ahlberg
  • Yumi Akahori
  • David Allen
  • Alexander Amberg
  • Lennart T. Anger
  • Aynur Aptula
  • Scott Auerbach
  • Lisa Beilke
  • Phillip Bellion
  • Romualdo Benigni
  • Joel Bercu
  • Ewan D. Booth
  • Dave Bower
  • Alessandro Brigo
  • Natalie Burden
  • Zoryana Cammerer
  • Mark T.D. Cronin
  • Kevin P. Cross
  • Laura Custer
  • Magdalena Dettwiler
  • Krista Dobo
  • Kevin A. Ford
  • Marie C. Fortin
  • Samantha E. Gad-McDonald
  • Nichola Gellatly
  • Véronique Gervais
  • Kyle P. Glover
  • Susanne Glowienke
  • Jacky Van Gompel
  • Steve Gutsell
  • Barry Hardy
  • James S. Harvey
  • Jedd Hillegass
  • Masamitsu Honma
  • Jui Hua Hsieh
  • Chia Wen Hsu
  • Kathy Hughes
  • Candice Johnson
  • Robert Jolly
  • David Jones
  • Ray Kemper
  • Michelle O. Kenyon
  • Marlene T. Kim
  • Naomi L. Kruhlak
  • Sunil A. Kulkarni
  • Penny Leavitt
  • Bernhard Majer
  • Scott Masten
  • Scott Miller
  • Janet Moser
  • Moiz Mumtaz
  • Wolfgang Muster
  • Louise Neilson
  • Tudor I. Oprea
  • Grace Patlewicz
  • Alexandre Paulino
  • Elena Lo Piparo
  • Mark Powley
  • Donald P. Quigley
  • M. Vijayaraj Reddy
  • Andrea Nicole Richarz
  • Patricia Ruiz
  • Benoit Schilter
  • Rositsa Serafimova
  • Wendy Simpson
  • Lidiya Stavitskaya
  • Reinhard Stidl
  • Diana Suarez-Rodriguez
  • David T. Szabo
  • Andrew Teasdale
  • Alejandra Trejo-Martin
  • Jean Pierre Valentin
  • Anna Vuorinen
  • Brian A. Wall
  • Pete Watts
  • Angela T. White
  • Joerg Wichard
  • Kristine L. Witt
  • Adam Woolley
  • David Woolley
  • Craig Zwickl
  • Catrin Hasselgren

The present publication surveys several applications of in silico (i.e., computational) toxicology approaches across different industries and institutions. It highlights the need to develop standardized protocols when conducting toxicity-related predictions. This contribution articulates the information needed for protocols to support in silico predictions for major toxicological endpoints of concern (e.g., genetic toxicity, carcinogenicity, acute toxicity, reproductive toxicity, developmental toxicity) across several industries and regulatory bodies. Such novel in silico toxicology (IST) protocols, when fully developed and implemented, will ensure in silico toxicological assessments are performed and evaluated in a consistent, reproducible, and well-documented manner across industries and regulatory bodies to support wider uptake and acceptance of the approaches. The development of IST protocols is an initiative developed through a collaboration among an international consortium to reflect the state-of-the-art in in silico toxicology for hazard identification and characterization. A general outline for describing the development of such protocols is included and it is based on in silico predictions and/or available experimental data for a defined series of relevant toxicological effects or mechanisms. The publication presents a novel approach for determining the reliability of in silico predictions alongside experimental data. In addition, we discuss how to determine the level of confidence in the assessment based on the relevance and reliability of the information.

Original languageEnglish
JournalRegulatory toxicology and pharmacology : RTP
Pages (from-to)1-17
Number of pages17
Publication statusPublished - 01.07.2018

Bibliographical note

Funding Information:
Research reported in this publication was supported by the National Institute of Environmental Health Sciences of the National Institutes of Health under Award Number R43ES026909 . The content is solely the responsibility of the authors and does not necessarily represent the official views of the National Institutes of Health. The authors would like to thank Sachin Bhusari and George Pugh from the Coca-Cola Company for their input into the manuscript.

Publisher Copyright:
© 2018 The Authors

    Research areas

  • Computational toxicology, Expert alert, Expert review, In silico, In silico toxicology, Predictive toxicology, QSAR
  • Chemistry