Prediction of the effect of formulation on the toxicity of chemicals

Pritesh Mistry; Daniel Neagu; Antonio Sanchez-Ruiz; Paul R. Trundle; Jonathan D. Vessey; John Paul Gosling

doi:10.1039/C6TX00303F

Prediction of the effect of formulation on the toxicity of chemicals

Pritesh Mistry, Daniel Neagu, Antonio Sanchez-Ruiz, Paul R. Trundle, Jonathan D. Vessey, John Paul Gosling

Research output: Contribution to journal › Article › peer-review

6 Citations (Scopus)

Abstract

Two approaches for the prediction of which of two vehicles will result in lower toxicity for anticancer agents are presented. Machine-learning models are developed using decision tree, random forest and partial least squares methodologies and statistical evidence is presented to demonstrate that they represent valid models. Separately, a clustering method is presented that allows the ordering of vehicles by the toxicity they show for chemically-related compounds.

Original language	English
Pages (from-to)	42-53
Number of pages	12
Journal	Toxicology Research
Volume	6
Issue number	1
Early online date	1 Nov 2016
DOIs	https://doi.org/10.1039/C6TX00303F
Publication status	Published - 1 Jan 2017
Externally published	Yes

UN SDGs

This output contributes to the following UN Sustainable Development Goals (SDGs)

SDG 3 Good Health and Well-being

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Cite this

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title = "Prediction of the effect of formulation on the toxicity of chemicals",

abstract = "Two approaches for the prediction of which of two vehicles will result in lower toxicity for anticancer agents are presented. Machine-learning models are developed using decision tree, random forest and partial least squares methodologies and statistical evidence is presented to demonstrate that they represent valid models. Separately, a clustering method is presented that allows the ordering of vehicles by the toxicity they show for chemically-related compounds.",

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AU - Mistry, Pritesh

AU - Neagu, Daniel

AU - Sanchez-Ruiz, Antonio

AU - Trundle, Paul R.

AU - Vessey, Jonathan D.

AU - Gosling, John Paul

PY - 2017/1/1

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N2 - Two approaches for the prediction of which of two vehicles will result in lower toxicity for anticancer agents are presented. Machine-learning models are developed using decision tree, random forest and partial least squares methodologies and statistical evidence is presented to demonstrate that they represent valid models. Separately, a clustering method is presented that allows the ordering of vehicles by the toxicity they show for chemically-related compounds.

AB - Two approaches for the prediction of which of two vehicles will result in lower toxicity for anticancer agents are presented. Machine-learning models are developed using decision tree, random forest and partial least squares methodologies and statistical evidence is presented to demonstrate that they represent valid models. Separately, a clustering method is presented that allows the ordering of vehicles by the toxicity they show for chemically-related compounds.

KW - Machine-learning models

KW - Toxicity levels

KW - Drug compounds

UR - https://www.scopus.com/pages/publications/85008373479

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DO - 10.1039/C6TX00303F

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VL - 6

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JO - Toxicology Research

JF - Toxicology Research

IS - 1

ER -

Prediction of the effect of formulation on the toxicity of chemicals

Abstract

UN SDGs

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