Elsevier

Chemosphere

Volume 92, Issue 1, June 2013, Pages 31-37
Chemosphere

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

https://doi.org/10.1016/j.chemosphere.2013.03.012Get rights and content

Highlights

  • 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.

Abstracts

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.

Introduction

During the last 20 years, there has been considerable increase of interest in nanostructures. It obviously facilitates surge of new directions in the basic research and generates many novel experimental projects. New materials have been developed, tested, and fast forwarded into production lines. Nanomanufacturing becomes a substantial part of the 21st Century industry. However, this also might create adverse effects. In particular, some of nanomaterials can be harmful to the environment and humans.

Quantitative structure–property/activity relationships (QSPR/QSAR) are a tool for prediction of various endpoints (García et al., 2011, Garro Martinez et al., 2011, Ojha et al., 2011, Mullen et al., 2011, Ibezim et al., 2012) using molecular descriptors (Furtula and Gutman, 2011, Afantitis et al., 2011) calculated with molecular graph (Toropov and Roy, 2004, Castillo-Garit et al., 2007), quantum-chemical descriptors (Petrova et al., 2011) as well as with simplified molecular input-line entry system (SMILES) (Toropov et al., 2008, Toropov et al., 2012).

The understanding and predicting of the biological effects of the manufactured nanoparticles represents an important task of modern natural sciences. The experimental analysis of these substances is expensive. Theoretical investigations of such phenomena could provide an efficient approach to evaluation of nano-bio interactions (Leszczynski, 2010). Consequently, the development of the QSPR/QSAR for nanoparticles is useful from point of view the both praxis and theory. The recent review provides discussion of such challenges in developing Nano-QSAR methods (Puzyn et al., 2009).

The aim of the present study is the evaluation of the CORAL software as a possible tool of the QSAR analysis for cellular uptake of nanoparticles in PaCa2 cancer cells. This study was carried out for the nanoparticles involving the same metal core but various surfaces modified by different small molecules.

Section snippets

Data

We have examined 109 nanoparticles. They have the same nano-core, but various surfaces modifiers (small organic molecules). The cellular uptake in PaCa2 cancer cells of above-mentioned nanoparticles was studied. The selected endpoint (cellular uptake) is defined as minus decimal logarithm of the concentration (pM) of nanoparticles per cell (Fourches et al., 2010). The data for these 109 nanoparticles were randomly split into the sub-training, calibration, test, and validation sets. The roles of

Results and discussion

Table 3 contains the statistical quality of models which were built up with the CORAL software. The data were obtained according to the above-mentioned scheme (Fig. 2). Fig. 3 shows the models for five random splits graphically. One can see (Table 3) that all these models are statistically satisfactory, but each model contains good predictions i.e. dots near diagonal together with poor predictions i.e. groups of dots remote from the diagonal (Fig. 3).

If one carried out several runs of the Monte

Conclusions

The concept of QSAR as a random event is suggested as the alternative to build up QSAR with sole distribution of available data into the subset of the training and subset of validation. The CORAL software gives satisfactory and stable predictions of the cellular uptake of nanoparticles in PaCa2 cancer cells for five random splits (i.e. for five described random events).

Supplementary materials

(i) SMILES of examined substances together with the numerical data on the endpoint; (ii) details of the five splits into sub-training, calibration, test, and validation sets; and (iii) correlation weights (for three runs of the Monte Carlo optimization) of the most significant promoters of increase and decay of the endpoint for five splits which are examined in this work. It is to be noted that aforementioned data represented in the Supplementary materials section give possibility to reproduce

Acknowledgements

We thank the EC project NANOPUZZLES (Project Reference: 309837) and the National Science Foundation (NSF/CREST HRD-0833178, and EPSCoR Award #:362492-190200-01/NSFEPS-090378) for financial support.

References (23)

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