Optimal descriptor as a translator of eclectic data into endpoint prediction: mutagenicity of fullerene as a mathematical function of conditions.
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).
IRCCS - Istituto di Ricerche Farmacologiche Mario Negri, Via La Masa 19, Milano 20156, Italy. Electronic address: andrey.toropov@marionegri.it.
Andrey A
AA
Toropov
IRCCS - Istituto di Ricerche Farmacologiche Mario Negri, Via La Masa 19, Milano 20156, Italy.
Alla P
AP
Toropova
D002138:Calibration; D037741:Fullerenes / Q000737:chemistry / Q000633:toxicity*; D008027:Light; D008957:Models, Genetic; D009010:Monte Carlo Method; D009153:Mutagens / Q000737:chemistry / Q000633:toxicity*; D009154:Mutation; D053758:Nanoparticles / Q000737:chemistry / Q000633:toxicity*; D021281:Quantitative Structure-Activity Relationship; D012486:Salmonella typhimurium / Q000235:genetics*
D037741:Fullerenes; D009153:Mutagens
Bacterial reverse mutation test; Fullerene C60; Optimal descriptor; Quasi-QSAR