Combining a microphysiological system of three organ equivalents and transcriptomics to assess toxicological endpoints for cosmetic ingredients-Reference-Cited by-同舟云学术

Combining a microphysiological system of three organ equivalents and transcriptomics to assess toxicological endpoints for cosmetic ingredients

Published:2023 Issue:24 Volume:23 Page:5092-5106
ISSN:1473-0197
Container-title:Lab on a Chip
language:en
Short-container-title:Lab Chip

Author:

Indolfo Nathalia de Carvalho¹²^ORCID,Ganzerla Melissa Dibbernn³⁴,Doratioto Tábata Renée¹^ORCID,Avelino Thayná Mendonça⁴,Tofani Larissa Bueno⁴^ORCID,Peroni Luis Antonio⁴,Rabelo Renata Santos⁵,Arroteia Kelen Fabiola¹,Figueira Ana Carolina Migliorini⁴^ORCID

Affiliation:

1. Natura Cosméticos S.A., Cajamar, São Paulo, Brazil

2. Graduate Program in Pharmaceutical Sciences, Faculty of Pharmaceutical Sciences, State University of Campinas, Brazil

3. Graduate Program in Molecular and Morphofunctional Biology, Institute of Biology, State University of Campinas, Brazil

4. Brazilian Biosciences National Laboratory (LNBio), Center for Research in Energy and Materials (CNPEM), Campinas, Brazil

5. Brazilian Synchrotron Light Laboratory (LNLS), Center for Research in Energy and Materials (CNPEM), Campinas, Brazil

Abstract

A NAM, comprising a microphysiological system of three organ equivalents (skin, liver and intestine), combined with toxicological gene signature analysis, is a promising tool to assess chemical safety.

Funder

Laboratório Nacional de Biociências

Empresa Brasileira de Pesquisa e Inovação Industrial

Centro Nacional de Pesquisa em Energia e Materiais

Publisher

Royal Society of Chemistry (RSC)

Subject

Biomedical Engineering,General Chemistry,Biochemistry,Bioengineering

Link

http://pubs.rsc.org/en/content/articlepdf/2023/LC/D3LC00546A

Reference93 articles.

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3. SCCS (Scientific Committee on Consumer Safety) , SCCS Notes of Guidance for the Testing of Cosmetic Ingredients and their Safety Evaluation 12th revision , 2023

4. In silico toxicology: From structure–activity relationships towards deep learning and adverse outcome pathways