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

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, r(2)=0.7549, q(2)=0.5709, s=7.67, F=25 (Training set); n=5, r(2)=0.8987, s=18.4 (Calibration set); and n=5, r(2)=0.6968, s=10.9 (Validation set).

journal_name

Chemosphere

journal_title

Chemosphere

authors

Toropov AA,Toropova AP

doi

10.1016/j.chemosphere.2013.10.079

subject

Has Abstract

pub_date

2014-06-01 00:00:00

pages

262-4

eissn

0045-6535

issn

1879-1298

pii

S0045-6535(13)01513-0

journal_volume

104

pub_type

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