A Perspective and a New Integrated Computational Strategy for Skin Sensitization Assessment

Vinicius M. Alves, Stephen J. Capuzzi, Rodolpho C. Braga, Joyce V.B. Borba, Arthur C. Silva, Thomas Luechtefeld, Thomas Hartung, Carolina Horta Andrade, Eugene N. Muratov, Alexander Tropsha

Research output: Contribution to journalReview article

Abstract

Traditionally, the skin sensitization potential of chemicals has been assessed using animal models. Due to growing ethical, political, and financial concerns, sustainable alternatives to animal testing need to be developed. As publicly available skin sensitization data continues to grow, computational approaches, such as alert-based systems, read-across, and QSAR models, are expected to reduce or replace animal testing for the prediction of human skin sensitization potential. Herein, we discuss current computational approaches to predicting skin sensitization and provide future perspectives of the field. As a proof-of-concept study, we have compiled the largest skin sensitization data set in the public domain and benchmarked several methods for building skin sensitization models. We propose a new comprehensive approach, which integrates multiple QSAR models developed with in vitro, in chemico, animal, and human data, and a Naive Bayes model for predicting human skin sensitization. Both the data sets and the KNIME implementation of the model allowing skin sensitization prediction for molecules of interest have been made freely available.

Original languageEnglish (US)
Pages (from-to)2845-2859
Number of pages15
JournalACS Sustainable Chemistry and Engineering
Volume6
Issue number3
DOIs
StatePublished - Mar 5 2018

Fingerprint

skin
Skin
Animals
animal
Testing
prediction
Molecules

Keywords

  • Alternative methods
  • Naïve Bayes
  • QSAR
  • Skin sensitization

ASJC Scopus subject areas

  • Chemistry(all)
  • Environmental Chemistry
  • Chemical Engineering(all)
  • Renewable Energy, Sustainability and the Environment

Cite this

Alves, V. M., Capuzzi, S. J., Braga, R. C., Borba, J. V. B., Silva, A. C., Luechtefeld, T., ... Tropsha, A. (2018). A Perspective and a New Integrated Computational Strategy for Skin Sensitization Assessment. ACS Sustainable Chemistry and Engineering, 6(3), 2845-2859. https://doi.org/10.1021/acssuschemeng.7b04220

A Perspective and a New Integrated Computational Strategy for Skin Sensitization Assessment. / Alves, Vinicius M.; Capuzzi, Stephen J.; Braga, Rodolpho C.; Borba, Joyce V.B.; Silva, Arthur C.; Luechtefeld, Thomas; Hartung, Thomas; Andrade, Carolina Horta; Muratov, Eugene N.; Tropsha, Alexander.

In: ACS Sustainable Chemistry and Engineering, Vol. 6, No. 3, 05.03.2018, p. 2845-2859.

Research output: Contribution to journalReview article

Alves, VM, Capuzzi, SJ, Braga, RC, Borba, JVB, Silva, AC, Luechtefeld, T, Hartung, T, Andrade, CH, Muratov, EN & Tropsha, A 2018, 'A Perspective and a New Integrated Computational Strategy for Skin Sensitization Assessment', ACS Sustainable Chemistry and Engineering, vol. 6, no. 3, pp. 2845-2859. https://doi.org/10.1021/acssuschemeng.7b04220
Alves, Vinicius M. ; Capuzzi, Stephen J. ; Braga, Rodolpho C. ; Borba, Joyce V.B. ; Silva, Arthur C. ; Luechtefeld, Thomas ; Hartung, Thomas ; Andrade, Carolina Horta ; Muratov, Eugene N. ; Tropsha, Alexander. / A Perspective and a New Integrated Computational Strategy for Skin Sensitization Assessment. In: ACS Sustainable Chemistry and Engineering. 2018 ; Vol. 6, No. 3. pp. 2845-2859.
@article{fb1451dfc41c4852830783c1191f6b67,
title = "A Perspective and a New Integrated Computational Strategy for Skin Sensitization Assessment",
abstract = "Traditionally, the skin sensitization potential of chemicals has been assessed using animal models. Due to growing ethical, political, and financial concerns, sustainable alternatives to animal testing need to be developed. As publicly available skin sensitization data continues to grow, computational approaches, such as alert-based systems, read-across, and QSAR models, are expected to reduce or replace animal testing for the prediction of human skin sensitization potential. Herein, we discuss current computational approaches to predicting skin sensitization and provide future perspectives of the field. As a proof-of-concept study, we have compiled the largest skin sensitization data set in the public domain and benchmarked several methods for building skin sensitization models. We propose a new comprehensive approach, which integrates multiple QSAR models developed with in vitro, in chemico, animal, and human data, and a Naive Bayes model for predicting human skin sensitization. Both the data sets and the KNIME implementation of the model allowing skin sensitization prediction for molecules of interest have been made freely available.",
keywords = "Alternative methods, Na{\"i}ve Bayes, QSAR, Skin sensitization",
author = "Alves, {Vinicius M.} and Capuzzi, {Stephen J.} and Braga, {Rodolpho C.} and Borba, {Joyce V.B.} and Silva, {Arthur C.} and Thomas Luechtefeld and Thomas Hartung and Andrade, {Carolina Horta} and Muratov, {Eugene N.} and Alexander Tropsha",
year = "2018",
month = "3",
day = "5",
doi = "10.1021/acssuschemeng.7b04220",
language = "English (US)",
volume = "6",
pages = "2845--2859",
journal = "ACS Sustainable Chemistry and Engineering",
issn = "2168-0485",
publisher = "American Chemical Society",
number = "3",

}

TY - JOUR

T1 - A Perspective and a New Integrated Computational Strategy for Skin Sensitization Assessment

AU - Alves, Vinicius M.

AU - Capuzzi, Stephen J.

AU - Braga, Rodolpho C.

AU - Borba, Joyce V.B.

AU - Silva, Arthur C.

AU - Luechtefeld, Thomas

AU - Hartung, Thomas

AU - Andrade, Carolina Horta

AU - Muratov, Eugene N.

AU - Tropsha, Alexander

PY - 2018/3/5

Y1 - 2018/3/5

N2 - Traditionally, the skin sensitization potential of chemicals has been assessed using animal models. Due to growing ethical, political, and financial concerns, sustainable alternatives to animal testing need to be developed. As publicly available skin sensitization data continues to grow, computational approaches, such as alert-based systems, read-across, and QSAR models, are expected to reduce or replace animal testing for the prediction of human skin sensitization potential. Herein, we discuss current computational approaches to predicting skin sensitization and provide future perspectives of the field. As a proof-of-concept study, we have compiled the largest skin sensitization data set in the public domain and benchmarked several methods for building skin sensitization models. We propose a new comprehensive approach, which integrates multiple QSAR models developed with in vitro, in chemico, animal, and human data, and a Naive Bayes model for predicting human skin sensitization. Both the data sets and the KNIME implementation of the model allowing skin sensitization prediction for molecules of interest have been made freely available.

AB - Traditionally, the skin sensitization potential of chemicals has been assessed using animal models. Due to growing ethical, political, and financial concerns, sustainable alternatives to animal testing need to be developed. As publicly available skin sensitization data continues to grow, computational approaches, such as alert-based systems, read-across, and QSAR models, are expected to reduce or replace animal testing for the prediction of human skin sensitization potential. Herein, we discuss current computational approaches to predicting skin sensitization and provide future perspectives of the field. As a proof-of-concept study, we have compiled the largest skin sensitization data set in the public domain and benchmarked several methods for building skin sensitization models. We propose a new comprehensive approach, which integrates multiple QSAR models developed with in vitro, in chemico, animal, and human data, and a Naive Bayes model for predicting human skin sensitization. Both the data sets and the KNIME implementation of the model allowing skin sensitization prediction for molecules of interest have been made freely available.

KW - Alternative methods

KW - Naïve Bayes

KW - QSAR

KW - Skin sensitization

UR - http://www.scopus.com/inward/record.url?scp=85043249013&partnerID=8YFLogxK

UR - http://www.scopus.com/inward/citedby.url?scp=85043249013&partnerID=8YFLogxK

U2 - 10.1021/acssuschemeng.7b04220

DO - 10.1021/acssuschemeng.7b04220

M3 - Review article

VL - 6

SP - 2845

EP - 2859

JO - ACS Sustainable Chemistry and Engineering

JF - ACS Sustainable Chemistry and Engineering

SN - 2168-0485

IS - 3

ER -