Computational identification of preservatives with potential neuronal cytotoxicity

Hung Lin Kan, Chia Chi Wang, Ying Chi Lin, Chun Wei Tung

Research output: Contribution to journalArticlepeer-review

Abstract

Preservatives play a vital role in cosmetics by preventing microbiological contamination for keeping products safe to use. However, a few commonly used preservatives have been suggested to be neurotoxic. Cytotoxicity to neuronal cells is commonly used as the first-tier assay for assessing chemical-induced neurotoxicity. Given the time and resources required for chemical screening, computational methods are attractive alternatives over experimental approaches in prioritizing chemicals prior to further experimental evaluations. In this study, we developed a Quantitative Structure-Activity Relationships (QSAR) model for the identification of potential neurotoxicants. A set of 681 chemicals was utilized to construct a robust prediction model using oversampling and Random Forest algorithms. Within a defined applicability domain, the independent test on 452 chemicals showed a high accuracy of 87.7%. The application of the model to 157 preservatives identified 15 chemicals potentially toxic to neuronal cells. Three of them were further validated by in vitro experiments. The results suggested that further experiments are desirable for assessing the neurotoxicity of the identified preservatives with potential neuronal cytotoxicity.

Original languageEnglish
Article number104815
JournalRegulatory Toxicology and Pharmacology
Volume119
DOIs
Publication statusPublished - Feb 2021

Keywords

  • Computational toxicology
  • Neuronal cytotoxicity
  • Preservatives
  • QSAR
  • SH-SY5Y

ASJC Scopus subject areas

  • Toxicology

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