Standard

New QSAR models to predict chromosome damaging potential based on the in vivo micronucleus test. / Van Bossuyt, Melissa; Raitano, Giuseppa; Honma, Masamitsu; Van Hoeck, Els; Vanhaecke, Tamara; Rogiers, Vera; Mertens, Birgit; Benfenati, Emilio.

In: Toxicology Letters, Vol. 329, 01.09.2020, p. 80-84.

Research output: Contribution to journalArticle

Harvard

APA

Vancouver

Author

Van Bossuyt, Melissa ; Raitano, Giuseppa ; Honma, Masamitsu ; Van Hoeck, Els ; Vanhaecke, Tamara ; Rogiers, Vera ; Mertens, Birgit ; Benfenati, Emilio. / New QSAR models to predict chromosome damaging potential based on the in vivo micronucleus test. In: Toxicology Letters. 2020 ; Vol. 329. pp. 80-84.

BibTeX

@article{0e90d5796748424f955a512d8ae017f1,
title = "New QSAR models to predict chromosome damaging potential based on the in vivo micronucleus test",
abstract = "A large number of computer-based prediction methods to determine the potential of chemicals to induce mutations at the gene level has been developed over the last decades. Conversely, only few such methods are currently available to predict potential structural and numerical chromosome aberrations. Even fewer of these are based on the preferred testing method for this endpoint, i.e. the micronucleus test. For the present work, in vivo micronucleus test results of 718 structurally diverse compounds were collected and applied for the construction of new models by means of the freely available SARpy in silico model building software. Multiple QSAR models were created using parameter variation and manual verification of (non-) alerting structures. To this extent, the original set of 718 compounds was split into a training (80 {\%}) and a test (20 {\%}) set. SARpy was applied on the training set to automatically extract sets of rules by generating and selecting substructures based on their prediction performance whereas the test set was used to evaluate model performance. Five different splits were made randomly, each of which had a similar balance between positive and negative substances compared to the full dataset. All generated models were characterised by an overall better performance than existing free and commercial models for the same endpoint, while demonstrating high coverage.",
keywords = "Chromosome damage, Genotoxicity, In silico model, In vivo micronucleus, QSAR",
author = "{Van Bossuyt}, Melissa and Giuseppa Raitano and Masamitsu Honma and {Van Hoeck}, Els and Tamara Vanhaecke and Vera Rogiers and Birgit Mertens and Emilio Benfenati",
note = "Copyright {\circledC} 2020 Elsevier B.V. All rights reserved.",
year = "2020",
month = "9",
day = "1",
doi = "10.1016/j.toxlet.2020.04.016",
language = "English",
volume = "329",
pages = "80--84",
journal = "Toxicology Letters",
issn = "0378-4274",
publisher = "Elsevier",

}

RIS

TY - JOUR

T1 - New QSAR models to predict chromosome damaging potential based on the in vivo micronucleus test

AU - Van Bossuyt, Melissa

AU - Raitano, Giuseppa

AU - Honma, Masamitsu

AU - Van Hoeck, Els

AU - Vanhaecke, Tamara

AU - Rogiers, Vera

AU - Mertens, Birgit

AU - Benfenati, Emilio

N1 - Copyright © 2020 Elsevier B.V. All rights reserved.

PY - 2020/9/1

Y1 - 2020/9/1

N2 - A large number of computer-based prediction methods to determine the potential of chemicals to induce mutations at the gene level has been developed over the last decades. Conversely, only few such methods are currently available to predict potential structural and numerical chromosome aberrations. Even fewer of these are based on the preferred testing method for this endpoint, i.e. the micronucleus test. For the present work, in vivo micronucleus test results of 718 structurally diverse compounds were collected and applied for the construction of new models by means of the freely available SARpy in silico model building software. Multiple QSAR models were created using parameter variation and manual verification of (non-) alerting structures. To this extent, the original set of 718 compounds was split into a training (80 %) and a test (20 %) set. SARpy was applied on the training set to automatically extract sets of rules by generating and selecting substructures based on their prediction performance whereas the test set was used to evaluate model performance. Five different splits were made randomly, each of which had a similar balance between positive and negative substances compared to the full dataset. All generated models were characterised by an overall better performance than existing free and commercial models for the same endpoint, while demonstrating high coverage.

AB - A large number of computer-based prediction methods to determine the potential of chemicals to induce mutations at the gene level has been developed over the last decades. Conversely, only few such methods are currently available to predict potential structural and numerical chromosome aberrations. Even fewer of these are based on the preferred testing method for this endpoint, i.e. the micronucleus test. For the present work, in vivo micronucleus test results of 718 structurally diverse compounds were collected and applied for the construction of new models by means of the freely available SARpy in silico model building software. Multiple QSAR models were created using parameter variation and manual verification of (non-) alerting structures. To this extent, the original set of 718 compounds was split into a training (80 %) and a test (20 %) set. SARpy was applied on the training set to automatically extract sets of rules by generating and selecting substructures based on their prediction performance whereas the test set was used to evaluate model performance. Five different splits were made randomly, each of which had a similar balance between positive and negative substances compared to the full dataset. All generated models were characterised by an overall better performance than existing free and commercial models for the same endpoint, while demonstrating high coverage.

KW - Chromosome damage

KW - Genotoxicity

KW - In silico model

KW - In vivo micronucleus

KW - QSAR

U2 - 10.1016/j.toxlet.2020.04.016

DO - 10.1016/j.toxlet.2020.04.016

M3 - Article

C2 - 32360788

VL - 329

SP - 80

EP - 84

JO - Toxicology Letters

JF - Toxicology Letters

SN - 0378-4274

ER -

ID: 52040911