title: Predictive toxicology of cobalt ferrite nanoparticles: comparative in-vitro study of different cellular models using methods of knowledge discovery from data.
abstract: BACKGROUND
  Cobalt-ferrite nanoparticles (Co-Fe NPs) are attractive for nanotechnology-based therapies. Thus, exploring their effect on viability of seven different cell lines representing different organs of the human body is highly important.
  
  
  METHODS
  The toxicological effects of Co-Fe NPs were studied by in-vitro exposure of A549 and NCIH441 cell-lines (lung), precision-cut lung slices from rat, HepG2 cell-line (liver), MDCK cell-line (kidney), Caco-2 TC7 cell-line (intestine), TK6 (lymphoblasts) and primary mouse dendritic-cells. Toxicity was examined following exposure to Co-Fe NPs in the concentration range of 0.05 -1.2 mM for 24 and 72 h, using Alamar blue, MTT and neutral red assays. Changes in oxidative stress were determined by a dichlorodihydrofluorescein diacetate based assay. Data analysis and predictive modeling of the obtained data sets were executed by employing methods of Knowledge Discovery from Data with emphasis on a decision tree model (J48).
  
  
  RESULTS
  Different dose-response curves of cell viability were obtained for each of the seven cell lines upon exposure to Co-Fe NPs. Increase of oxidative stress was induced by Co-Fe NPs and found to be dependent on the cell type. A high linear correlation (R2=0.97) was found between the toxicity of Co-Fe NPs and the extent of ROS generation following their exposure to Co-Fe NPs. The algorithm we applied to model the observed toxicity belongs to a type of supervised classifier. The decision tree model yielded the following order with decrease of the ranking parameter: NP concentrations (as the most influencing parameter), cell type (possessing the following hierarchy of cell sensitivity towards viability decrease: TK6 > Lung slices > NCIH441 > Caco-2 = MDCK > A549 > HepG2 = Dendritic) and time of exposure, where the highest-ranking parameter (NP concentration) provides the highest information gain with respect to toxicity. The validity of the chosen decision tree model J48 was established by yielding a higher accuracy than that of the well-known "naive bayes" classifier.
  
  
  CONCLUSIONS
  The observed correlation between the oxidative stress, caused by the presence of the Co-Fe NPs, with the hierarchy of sensitivity of the different cell types towards toxicity, suggests that oxidative stress is one possible mechanism for the toxicity of Co-Fe NPs.
authors.affiliation: Department of Physiology and Pharmacology, Faculty of Medicine and the Marian Gertner Institute for Medical Nanosystems, Tel Aviv University, Tel-Aviv 69978, Israel.
authors.forename: Limor
authors.initials: L
authors.lastname: Horev-Azaria
authors.forename: Giovanni
authors.initials: G
authors.lastname: Baldi
authors.forename: Delila
authors.initials: D
authors.lastname: Beno
authors.forename: Daniel
authors.initials: D
authors.lastname: Bonacchi
authors.forename: Ute
authors.initials: U
authors.lastname: Golla-Schindler
authors.forename: James C
authors.initials: JC
authors.lastname: Kirkpatrick
authors.forename: Susanne
authors.initials: S
authors.lastname: Kolle
authors.forename: Robert
authors.initials: R
authors.lastname: Landsiedel
authors.forename: Oded
authors.initials: O
authors.lastname: Maimon
authors.forename: Patrice N
authors.initials: PN
authors.lastname: Marche
authors.forename: Jessica
authors.initials: J
authors.lastname: Ponti
authors.forename: Roni
authors.initials: R
authors.lastname: Romano
authors.forename: François
authors.initials: F
authors.lastname: Rossi
authors.forename: Dieter
authors.initials: D
authors.lastname: Sommer
authors.forename: Chiara
authors.initials: C
authors.lastname: Uboldi
authors.forename: Ronald E
authors.initials: RE
authors.lastname: Unger
authors.forename: Christian
authors.initials: C
authors.lastname: Villiers
authors.forename: Rafi
authors.initials: R
authors.lastname: Korenstein
mesh_terms: D000465:Algorithms; D000818:Animals; D001185:Artificial Intelligence; D018938:Caco-2 Cells; D002470:Cell Survival / Q000187:drug effects; D003035:Cobalt / Q000633:toxicity*; D057225:Data Mining; D003661:Decision Support Techniques; D003663:Decision Trees; D004285:Dogs; D004305:Dose-Response Relationship, Drug; D005290:Ferric Compounds / Q000633:toxicity*; D056945:Hep G2 Cells; D006801:Humans; D016014:Linear Models; D061985:Madin Darby Canine Kidney Cells; D053768:Metal Nanoparticles; D051379:Mice; D018384:Oxidative Stress / Q000187:drug effects; D061251:Primary Cell Culture; D051381:Rats; D017382:Reactive Oxygen Species / Q000378:metabolism; D013997:Time Factors; D046509:Tissue Culture Techniques; D014116:Toxicology / Q000379:methods*
chemical_list: D005290:Ferric Compounds; D017382:Reactive Oxygen Species; C001215:ferrite; D003035:Cobalt
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