Optimal descriptor as a translator of eclectic data into endpoint prediction: Mutagenicity of fullerene as a mathematical function of conditions

Andrey A. Toropov, Alla P. Toropova

Research output: Contribution to journalArticlepeer-review

Abstract

The experimental data on the bacterial reverse mutation test on C60 nanoparticles (TA100) is examined as an endpoint. By means of the optimal descriptors calculated with the Monte Carlo method a mathematical model of the endpoint has been built up. The model is the mathematical function of (i) dose (g/plate); (ii) metabolic activation (i.e. with S9 mix or without S9 mix); and (iii) illumination (i.e. dark or irradiation). The statistical quality of the model is the following: n=10, r2=0.7549, q2=0.5709, s=7.67, F=25 (Training set); n=5, r2=0.8987, s=18.4 (Calibration set); and n=5, r2=0.6968, s=10.9 (Validation set).

Original languageEnglish
Pages (from-to)262-264
Number of pages3
JournalChemosphere
Volume104
DOIs
Publication statusPublished - 2014

Keywords

  • Bacterial reverse mutation test
  • Fullerene C60
  • Optimal descriptor
  • Quasi-QSAR

ASJC Scopus subject areas

  • Environmental Chemistry
  • Chemistry(all)

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