QSAR as a random event: Modeling of nanoparticles uptake in PaCa2 cancer cells

• Published in: Chemosphere (Oxford) 2013-06, Vol.92 (1), p.31-37
• Main Authors: Toropov, Andrey A., Toropova, Alla P., Puzyn, Tomasz, Benfenati, Emilio, Gini, Giuseppina, Leszczynska, Danuta, Leszczynski, Jerzy
• Format: Article
• Language: English
• Subjects: Biological and medical sciences; Cell Line, Tumor; computer software; CORAL software; corals; data collection; Humans; Internet; Medical sciences; Miscellaneous; Models, Molecular; Monte Carlo Method; Nanoparticle; nanoparticles; Nanoparticles - chemistry; Optimal descriptor; pancreatic neoplasms; Pharmacology. Drug treatments; QSAR; Quantitative Structure-Activity Relationship; quantitative structure-activity relationships; Software
• ISSN: 0045-6535; 1879-1298
• DOI: 10.1016/j.chemosphere.2013.03.012

•QSAR analysis of cellular uptake in PaCa2 cancer cells of nanoparticles is carried out.•The concept of QSAR as a random event is suggested.•Five distributions 109 nanoparticles into the training and test sets are studied. Quantitative structure–property/activity relationships (QSPRs/QSARs) are a tool to predict various endpoints for various substances. The “classic” QSPR/QSAR analysis is based on the representation of the molecular structure by the molecular graph. However, simplified molecular input-line entry system (SMILES) gradually becomes most popular representation of the molecular structure in the databases available on the Internet. Under such circumstances, the development of molecular descriptors calculated directly from SMILES becomes attractive alternative to “classic” descriptors. The CORAL software (http://www.insilico.eu/coral) is provider of SMILES-based optimal molecular descriptors which are aimed to correlate with various endpoints. We analyzed data set on nanoparticles uptake in PaCa2 pancreatic cancer cells. The data set includes 109 nanoparticles with the same core but different surface modifiers (small organic molecules). The concept of a QSAR as a random event is suggested in opposition to “classic” QSARs which are based on the only one distribution of available data into the training and the validation sets. In other words, five random splits into the “visible” training set and the “invisible” validation set were examined. The SMILES-based optimal descriptors (obtained by the Monte Carlo technique) for these splits are calculated with the CORAL software. The statistical quality of all these models is good.