The role of computational toxicology in the risk assessment of food products

Timothy E.H. Allen; Steve Gutsell; Ans Punt

doi:10.1016/B978-0-12-819470-6.00007-X

The role of computational toxicology in the risk assessment of food products

Timothy E.H. Allen, Steve Gutsell, Ans Punt

Team Toxicology

Research output: Chapter in Book/Report/Conference proceeding › Chapter › Academic › peer-review

2 Citations (Scopus)

Abstract

Computational toxicology is a growing field with the aim of answering a diverse set of toxicological questions using computational algorithms. This includes questions such as: Can we predict the toxicity of a new chemical based on existing data points? How can we interpret large amounts of biological data? How does a chemical distribute itself inside the body? In this chapter, we aim to introduce general ideas around modeling techniques and couple this to cutting-edge research examples. We discuss the strengths of in silico toxicology and why they are coming to the forefront amongst new approach methodologies. Finally, we consider what the future holds, the importance of high-quality data, and the hurdles that must be overcome to see further regulatory acceptance of in silico models.

Original language	English
Title of host publication	Present Knowledge in Food Safety
Subtitle of host publication	A Risk-Based Approach through the Food Chain
Editors	M.E. Knowles, L.E. Anelich, A.R. Boobis, B. Popping
Publisher	Elsevier
Chapter	44
Pages	643-659
Number of pages	17
ISBN (Electronic)	9780128194706
ISBN (Print)	9780128231548
DOIs	https://doi.org/10.1016/B978-0-12-819470-6.00007-X
Publication status	Published - 14 Oct 2022

Keywords

AOP
big data
Computational toxicology
in silico modeling
machine learning
PBK
QSAR

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10.1016/B978-0-12-819470-6.00007-X

Cite this

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title = "The role of computational toxicology in the risk assessment of food products",

abstract = "Computational toxicology is a growing field with the aim of answering a diverse set of toxicological questions using computational algorithms. This includes questions such as: Can we predict the toxicity of a new chemical based on existing data points? How can we interpret large amounts of biological data? How does a chemical distribute itself inside the body? In this chapter, we aim to introduce general ideas around modeling techniques and couple this to cutting-edge research examples. We discuss the strengths of in silico toxicology and why they are coming to the forefront amongst new approach methodologies. Finally, we consider what the future holds, the importance of high-quality data, and the hurdles that must be overcome to see further regulatory acceptance of in silico models.",

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The role of computational toxicology in the risk assessment of food products. / Allen, Timothy E.H.; Gutsell, Steve; Punt, Ans.
Present Knowledge in Food Safety: A Risk-Based Approach through the Food Chain. ed. / M.E. Knowles; L.E. Anelich; A.R. Boobis; B. Popping. Elsevier, 2022. p. 643-659.

Research output: Chapter in Book/Report/Conference proceeding › Chapter › Academic › peer-review

TY - CHAP

T1 - The role of computational toxicology in the risk assessment of food products

AU - Allen, Timothy E.H.

AU - Gutsell, Steve

AU - Punt, Ans

PY - 2022/10/14

Y1 - 2022/10/14

N2 - Computational toxicology is a growing field with the aim of answering a diverse set of toxicological questions using computational algorithms. This includes questions such as: Can we predict the toxicity of a new chemical based on existing data points? How can we interpret large amounts of biological data? How does a chemical distribute itself inside the body? In this chapter, we aim to introduce general ideas around modeling techniques and couple this to cutting-edge research examples. We discuss the strengths of in silico toxicology and why they are coming to the forefront amongst new approach methodologies. Finally, we consider what the future holds, the importance of high-quality data, and the hurdles that must be overcome to see further regulatory acceptance of in silico models.

AB - Computational toxicology is a growing field with the aim of answering a diverse set of toxicological questions using computational algorithms. This includes questions such as: Can we predict the toxicity of a new chemical based on existing data points? How can we interpret large amounts of biological data? How does a chemical distribute itself inside the body? In this chapter, we aim to introduce general ideas around modeling techniques and couple this to cutting-edge research examples. We discuss the strengths of in silico toxicology and why they are coming to the forefront amongst new approach methodologies. Finally, we consider what the future holds, the importance of high-quality data, and the hurdles that must be overcome to see further regulatory acceptance of in silico models.

KW - AOP

KW - big data

KW - Computational toxicology

KW - in silico modeling

KW - machine learning

KW - PBK

KW - QSAR

U2 - 10.1016/B978-0-12-819470-6.00007-X

DO - 10.1016/B978-0-12-819470-6.00007-X

M3 - Chapter

AN - SCOPUS:85142763681

SN - 9780128231548

SP - 643

EP - 659

BT - Present Knowledge in Food Safety

A2 - Knowles, M.E.

A2 - Anelich, L.E.

A2 - Boobis, A.R.

A2 - Popping, B.

PB - Elsevier

ER -

The role of computational toxicology in the risk assessment of food products

Abstract

Keywords

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