title: Quantitative nanostructure-activity relationship modeling.
abstract: Evaluation of biological effects, both desired and undesired, caused by manufactured nanoparticles (MNPs) is of critical importance for nanotechnology. Experimental studies, especially toxicological, are time-consuming, costly, and often impractical, calling for the development of efficient computational approaches capable of predicting biological effects of MNPs. To this end, we have investigated the potential of cheminformatics methods such as quantitative structure-activity relationship (QSAR) modeling to establish statistically significant relationships between measured biological activity profiles of MNPs and their physical, chemical, and geometrical properties, either measured experimentally or computed from the structure of MNPs. To reflect the context of the study, we termed our approach quantitative nanostructure-activity relationship (QNAR) modeling. We have employed two representative sets of MNPs studied recently using in vitro cell-based assays: (i) 51 various MNPs with diverse metal cores (Proc. Natl. Acad. Sci. 2008, 105, 7387-7392) and (ii) 109 MNPs with similar core but diverse surface modifiers (Nat. Biotechnol. 2005, 23, 1418-1423). We have generated QNAR models using machine learning approaches such as support vector machine (SVM)-based classification and k nearest neighbors (kNN)-based regression; their external prediction power was shown to be as high as 73% for classification modeling and having an R(2) of 0.72 for regression modeling. Our results suggest that QNAR models can be employed for: (i) predicting biological activity profiles of novel nanomaterials, and (ii) prioritizing the design and manufacturing of nanomaterials toward better and safer products.
authors.affiliation: Laboratory of Molecular Modeling, UNC Eshelman School of Pharmacy, University of North Carolina, Chapel Hill, North Carolina 27599, USA.
authors.forename: Denis
authors.initials: D
authors.lastname: Fourches
authors.forename: Dongqiuye
authors.initials: D
authors.lastname: Pu
authors.forename: Carlos
authors.initials: C
authors.lastname: Tassa
authors.forename: Ralph
authors.initials: R
authors.lastname: Weissleder
authors.forename: Stanley Y
authors.initials: SY
authors.lastname: Shaw
authors.forename: Russell J
authors.initials: RJ
authors.lastname: Mumper
authors.forename: Alexander
authors.initials: A
authors.lastname: Tropsha
mesh_terms: D000818:Animals; D045744:Cell Line, Tumor; D006801:Humans; D053768:Metal Nanoparticles / Q000737:chemistry*; D018625:Microscopy, Atomic Force / Q000379:methods; D015233:Models, Statistical; D008962:Models, Theoretical; D036103:Nanotechnology / Q000379:methods*; D010087:Oxides / Q000737:chemistry; D021281:Quantitative Structure-Activity Relationship; D051381:Rats; D012044:Regression Analysis; D013499:Surface Properties; D020298:U937 Cells
chemical_list: D010087:Oxides