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references.bib
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@article{Gajewicz14,
author={A. Gajewicz and Mark T.D Cronin and Bakhtiyor Rasulev and Jerzy Leszczynski and Tomasz Puzyn},
title={Novel approach for efficient predictions properties of large pool of nanomaterials based on limited set of species: nano-read-across},
journal={Nanotechnology},
volume={26},
number={1},
pages={015701},
url={http://stacks.iop.org/0957-4484/26/i=1/a=015701},
year={2015},
abstract={Creating suitable chemical categories and developing read-across methods, supported by quantum mechanical calculations, can be an effective solution to solving key problems related to current scarcity of data on the toxicity of various nanoparticles. This study has demonstrated that by applying a nano-read-across, the cytotoxicity of nano-sized metal oxides could be estimated with a similar level of accuracy as provided by quantitative structure-activity relationship for nanomaterials (nano-QSAR model(s)). The method presented is a suitable computational tool for the preliminary hazard assessment of nanomaterials. It also could be used for the identification of nanomaterials that may pose potential negative impact to human health and the environment. Such approaches are especially necessary when there is paucity of relevant and reliable data points to develop and validate nano-QSAR models.}
}
@Article{Gajewicz17,
author ="Gajewicz, A. and Jagiello, K. and Cronin, M. T. D. and Leszczynski, J. and Puzyn, T.",
title ="Addressing a bottle neck for regulation of nanomaterials: quantitative read-across (Nano-QRA) algorithm for cases when only limited data is available",
journal ="Environ. Sci.: Nano",
year ="2017",
volume ="4",
issue ="2",
pages ="346-358",
publisher ="The Royal Society of Chemistry",
doi ="10.1039/C6EN00399K",
url ="http://dx.doi.org/10.1039/C6EN00399K",
abstract ="The number and variety of engineered nanoparticles have been growing exponentially. Since the experimental evaluation of nanoparticles causing public health concerns is expensive and time consuming{,} efficient computational tools are amongst the most suitable approaches to identifying potential negative impacts{,} to the human health and the environment{,} of new nanomaterials before their production. However{,} developing computational models complimentary to experiments is impossible without incorporating consistent and high quality experimental data. Although there are limited available data in the literature{,} one may apply read-across techniques that seem to be an attractive and pragmatic alternative way of predicting missing physico-chemical or toxicological data. Unfortunately{,} the existing methods of read-across are strongly dependent on the expert{'}s knowledge. In consequence{,} the results of estimations may vary dependently on personal experience of expert conducting the study and as such cannot guarantee the reproducibility of their results. Therefore{,} it is essential to develop novel read-across algorithm(s) that will provide reliable predictions of the missing data without the need to for additional experiments. We proposed a novel quantitative read-across approach for nanomaterials (Nano-QRA) that addresses and overcomes a basic limitation of existing methods. It is based on: one-point-slope{,} two-point formula{,} or the equation of a plane passing through three points. The proposed Nano-QRA approach is a simple and effective algorithm for filling data gaps in quantitative manner providing reliable predictions of the missing data."}
@article{Schultz15,
title = "A strategy for structuring and reporting a read-across prediction of toxicity ",
journal = "Regulatory Toxicology and Pharmacology ",
volume = "72",
number = "3",
pages = "586 - 601",
year = "2015",
note = "",
issn = "0273-2300",
doi = "http://dx.doi.org/10.1016/j.yrtph.2015.05.016",
url = "http://www.sciencedirect.com/science/article/pii/S0273230015001154",
author = "T.W. Schultz and P. Amcoff and E. Berggren and F. Gautier and M. Klaric and D.J. Knight and C. Mahony and M. Schwarz and A. White and M.T.D. Cronin",
keywords = "Read-across",
keywords = "Similarity",
keywords = "Uncertainty",
keywords = "Chemical analogue identification",
keywords = "Prediction",
keywords = "Toxicity",
keywords = "Regulatory acceptance",
keywords = "OECD",
keywords = "REACH "
}
@article{Dekkers16,
title = "Towards a nanospecific approach for risk assessment ",
journal = "Regulatory Toxicology and Pharmacology ",
volume = "80",
number = "",
pages = "46 - 59",
year = "2016",
note = "",
issn = "0273-2300",
doi = "http://dx.doi.org/10.1016/j.yrtph.2016.05.037",
url = "http://www.sciencedirect.com/science/article/pii/S0273230016301581",
author = "Susan Dekkers and Agnes G. Oomen and Eric A.J. Bleeker and Rob J. Vandebriel and Christian Micheletti and Joan Cabellos and Gemma Janer and Natalia Fuentes and Socorro Vázquez-Campos and Teresa Borges and Maria João Silva and Adriele Prina-Mello and Dania Movia and Fabrice Nesslany and Ana R. Ribeiro and Paulo Emílio Leite and Monique Groenewold and Flemming R. Cassee and Adrienne J.A.M. Sips and Aart Dijkzeul and Tom van Teunenbroek and Susan W.P. Wijnhoven",
keywords = "Nanomaterials",
keywords = "Risk assessment approach",
keywords = "Prioritisation",
keywords = "Grouping",
keywords = "Read-across",
keywords = "(Q)SARs",
keywords = "Testing strategy "
}
@article{Arts15,
title = "A decision-making framework for the grouping and testing of nanomaterials (DF4nanoGrouping) ",
journal = "Regulatory Toxicology and Pharmacology ",
volume = "71",
number = "2, Supplement",
pages = "S1 - S27",
year = "2015",
note = "A decision-making framework for the grouping and testing of nanomaterials (DF4nanoGrouping) ",
issn = "0273-2300",
doi = "http://dx.doi.org/10.1016/j.yrtph.2015.03.007",
url = "http://www.sciencedirect.com/science/article/pii/S0273230015000549",
author = "Josje H.E. Arts and Mackenzie Hadi and Muhammad-Adeel Irfan and Athena M. Keene and Reinhard Kreiling and Delina Lyon and Monika Maier and Karin Michel and Thomas Petry and Ursula G. Sauer and David Warheit and Karin Wiench and Wendel Wohlleben and Robert Landsiedel",
keywords = "Nanomaterials",
keywords = "Grouping",
keywords = "Read-across",
keywords = "Intrinsic material properties",
keywords = "System-dependent properties",
keywords = "Biopersistence",
keywords = "Biodistribution",
keywords = "Cellular effects",
keywords = "Apical toxic effects",
keywords = "Risk assessment "
}
@article{Arts14,
title = "A critical appraisal of existing concepts for the grouping of nanomaterials ",
journal = "Regulatory Toxicology and Pharmacology ",
volume = "70",
number = "2",
pages = "492 - 506",
year = "2014",
note = "",
issn = "0273-2300",
doi = "http://dx.doi.org/10.1016/j.yrtph.2014.07.025",
url = "http://www.sciencedirect.com/science/article/pii/S0273230014001809",
author = "Josje H.E. Arts and Mackenzie Hadi and Athena M. Keene and Reinhard Kreiling and Delina Lyon and Monika Maier and Karin Michel and Thomas Petry and Ursula G. Sauer and David Warheit and Karin Wiench and Robert Landsiedel",
keywords = "Nanomaterials",
keywords = "Risk assessment",
keywords = "REACH",
keywords = "Toxic Substances Control Act",
keywords = "Grouping of substances",
keywords = "Physico-chemical characterization",
keywords = "Exposure assessment",
keywords = "Hazard assessment",
keywords = "Biokinetics",
keywords = "Characterization "
}
@Article{Gütlein2013,
author = {Gütlein, Martin and Helma, Christoph and Karwath, Andreas and Kramer, Stefan},
title = {A Large-Scale Empirical Evaluation of Cross-Validation and External Test Set Validation in (Q)SAR},
journal = {Molecular Informatics},
volume = {32},
number = {5-6},
publisher = {WILEY-VCH Verlag},
issn = {1868-1751},
url = {http://dx.doi.org/10.1002/minf.201200134},
doi = {10.1002/minf.201200134},
pages = {516--528},
keywords = {Cheminformatics, Structure-activity relationship, Validation, Cross-validation, External validation},
year = {2013},
}
@article {Kamath15,
author = "Kamath, Padmaja and Fernandez, Alberto and Giralt, Francesc and Rallo, Robert",
title = "Predicting Cell Association of Surface-Modified Nanoparticles Using Protein Corona Structure - Activity Relationships (PCSAR)",
journal = "Current Topics in Medicinal Chemistry",
volume = "15",
number = "18",
year = "2015",
abstract = "Nanoparticles are likely to interact in real-case application scenarios with mixtures of proteins and biomolecules that will absorb onto their surface forming the so-called protein corona. Information related to the composition of the protein corona and net cell association
was collected from literature for a library of surface-modified gold and silver nanoparticles. For each protein in the corona, sequence information was extracted and used to calculate physicochemical properties and statistical descriptors. Data cleaning and preprocessing techniques including
statistical analysis and feature selection methods were applied to remove highly correlated, redundant and non-significant features. A weighting technique was applied to construct specific signatures that represent the corona composition for each nanoparticle. Using this basic set of protein
descriptors, a new Protein Corona Structure-Activity Relationship (PCSAR) that relates net cell association with the physicochemical descriptors of the proteins that form the corona was developed and validated. The features that resulted from the feature selection were in line with already
published literature, and the computational model constructed on these features had a good accuracy (R2LOO=0.76 and R2LMO(25%)=0.72) and stability, with the advantage that the fingerprints based on physicochemical descriptors were independent of the specific proteins that form the corona.",
pages = "1930-1937",
itemtype = "ARTICLE",
parent_itemid = "infobike://ben/ctmc",
issn = "1568-0266",
publishercode ="ben",
url = "http://www.ingentaconnect.com/content/ben/ctmc/2015/00000015/00000018/art00015",
keyword = "Fingerprint, Surface modification, Protein corona, Physicochemical properties, Multilinear regression, Cell association, Nanoparticles"
}
@article{Papa16,
author = {E. Papa and J. P. Doucet and A. Sangion and A. Doucet-Panaye},
title = {Investigation of the influence of protein corona composition on gold nanoparticle bioactivity using machine learning approaches},
journal = {SAR and QSAR in Environmental Research},
volume = {27},
number = {7},
pages = {521-538},
year = {2016},
doi = {10.1080/1062936X.2016.1197310},
note ={PMID: 27329717},
URL = {
http://dx.doi.org/10.1080/1062936X.2016.1197310
},
eprint = {
http://dx.doi.org/10.1080/1062936X.2016.1197310
}
}
@Book{Xie2015,
title = {Dynamic Documents with {R} and knitr},
author = {Yihui Xie},
publisher = {Chapman and Hall/CRC},
address = {Boca Raton, Florida},
year = {2015},
edition = {2nd},
note = {ISBN 978-1498716963},
url = {http://yihui.name/knitr/},
}
@Article{Gütlein2012,
AUTHOR = {Gütlein, Martin and Karwath, Andreas and Kramer, Stefan},
TITLE = {CheS-Mapper - Chemical Space Mapping and Visualization in 3D},
JOURNAL = {Journal of Cheminformatics},
VOLUME = {4},
YEAR = {2012},
NUMBER = {1},
PAGES = {7},
URL = {http://www.jcheminf.com/content/4/1/7},
DOI = {10.1186/1758-2946-4-7},
PubMedID = {22424447},
ISSN = {1758-2946},
ABSTRACT = {Analyzing chemical datasets is a challenging task for scientific researchers in the field of chemoinformatics. It is important, yet difficult to understand the relationship between the structure of chemical compounds, their physico-chemical properties, and biological or toxic effects. To that respect, visualization tools can help to better comprehend the underlying correlations. Our recently developed 3D molecular viewer CheS-Mapper (Chemical Space Mapper) divides large datasets into clusters of similar compounds and consequently arranges them in 3D space, such that their spatial proximity reflects their similarity. The user can indirectly determine similarity, by selecting which features to employ in the process. The tool can use and calculate different kind of features, like structural fragments as well as quantitative chemical descriptors. These features can be highlighted within CheS-Mapper, which aids the chemist to better understand patterns and regularities and relate the observations to established scientific knowledge. As a final function, the tool can also be used to select and export specific subsets of a given dataset for further analysis.},
}
@article{Bender04,
author = {Andreas Bender and Hamse Y. Mussa, and and Robert C. Glen and Stephan Reiling},
title = {Molecular Similarity Searching Using Atom Environments, Information-Based Feature Selection, and a Naïve Bayesian Classifier},
journal = {Journal of Chemical Information and Computer Sciences},
volume = {44},
number = {1},
pages = {170-178},
year = {2004},
doi = {10.1021/ci034207y},
note ={PMID: 14741025},
URL = {
http://dx.doi.org/10.1021/ci034207y
},
eprint = {
http://dx.doi.org/10.1021/ci034207y
}
}
@article{Maunz2013,
doi = {10.3389/fphar.2013.00038},
url = {http://dx.doi.org/10.3389/fphar.2013.00038},
year = {2013},
publisher = {Frontiers Media {SA}},
volume = {4},
author = {Andreas Maunz and Martin G\"{u}tlein and Micha Rautenberg and David Vorgrimmler and Denis Gebele and Christoph Helma},
title = {lazar: a modular predictive toxicology framework},
journal = {Frontiers in Pharmacology}
}
@article{doi:10.1021/ci00057a005,
author = {David Weininger},
title = {SMILES, a chemical language and information system. 1. Introduction to methodology and encoding rules},
journal = {Journal of Chemical Information and Computer Sciences},
volume = {28},
number = {1},
pages = {31-36},
year = {1988},
doi = {10.1021/ci00057a005},
URL = {
http://dx.doi.org/10.1021/ci00057a005
},
eprint = {
http://dx.doi.org/10.1021/ci00057a005
}
}
@article{OBoyle2011,
doi = {10.1186/1758-2946-3-33},
url = {http://dx.doi.org/10.1186/1758-2946-3-33},
year = {2011},
publisher = {Springer Science and Business Media},
volume = {3},
number = {1},
pages = {33},
author = {Noel M OBoyle and Michael Banck and Craig A James and Chris Morley and Tim Vandermeersch and Geoffrey R Hutchison},
title = {Open Babel: An open chemical toolbox},
journal = {Journal of Cheminformatics}
}
@article{mazzatorta08,
author = {Paolo Mazzatorta and Manuel Dominguez Estevez and Myriam Coulet and Benoit Schilter},
title = {Modeling Oral Rat Chronic Toxicity},
journal = {Journal of Chemical Information and Modeling},
volume = {48},
number = {10},
pages = {1949-1954},
year = {2008},
doi = {10.1021/ci8001974},
note ={PMID: 18803370},
URL = {
http://dx.doi.org/10.1021/ci8001974
},
eprint = {
http://dx.doi.org/10.1021/ci8001974
}
}
@Manual{pls,
title = {pls: Partial Least Squares and Principal Component Regression},
author = {Bjørn-Helge Mevik and Ron Wehrens and Kristian Hovde Liland},
year = {2015},
note = {R package version 2.5-0},
url = {https://CRAN.R-project.org/package=pls},
}
@ARTICLE{Kuhn08,
author = {Max Kuhn},
title = {Building predictive models in R using the caret package},
journal = {J. of Stat. Soft},
year = {2008}
}
@article{Jeliazkova15,
author = {Nina Jeliazkova and Charalampos Chomenidis and Philip Doganis and Bengt Fadeel and Roland Grafström and Barry Hardy and Janna Hastings and Markus Hegi and Vedrin Jeliazkov and Nikolay Kochev and Pekka Kohonen and Cristian R. Munteanu and Haralambos Sarimveis and Bart Smeets and Pantelis Sopasakis and Georgia Tsiliki and David Vorgrimmler and Egon Willighagen },
title = {The eNanoMapper database for nanomaterial safety information},
journal = {Beilstein J. Nanotechnol.},
pages = {1609–1634},
number = {6},
year = {2015},
doi = {doi:10.3762/bjnano.6.165}
}
@article{Walkey14,
author = {Carl D. Walkey and Jonathan B. Olsen and Fayi Song and Rong Liu and Hongbo Guo and D. Wesley H. Olsen and Yoram Cohen and Andrew Emili and Warren C. W. Chan},
title = {Protein Corona Fingerprinting Predicts the Cellular Interaction of Gold and Silver Nanoparticles},
journal = {ACS Nano},
volume = {8},
number = {3},
pages = {2439-2455},
year = {2014},
doi = {10.1021/nn406018q},
note ={PMID: 24517450},
URL = {
http://dx.doi.org/10.1021/nn406018q
},
eprint = {
http://dx.doi.org/10.1021/nn406018q
}
}
@Article{Liu15,
author ="Liu, Rong and Jiang, Wen and Walkey, Carl D. and Chan, Warren C. W. and Cohen, Yoram",
title ="Prediction of nanoparticles-cell association based on corona proteins and physicochemical properties",
journal ="Nanoscale",
year ="2015",
volume ="7",
issue ="21",
pages ="9664-9675",
publisher ="The Royal Society of Chemistry",
doi ="10.1039/C5NR01537E",
url ="http://dx.doi.org/10.1039/C5NR01537E",
abstract ="Cellular association of nanoparticles (NPs) in biological fluids is affected by proteins adsorbed onto the NP surface{,} forming a {"}protein corona{"}{,} thereby impacting cellular bioactivity. Here we investigate{,} based on an extensive gold NPs protein corona dataset{,} the relationships between NP-cell association and protein corona fingerprints (PCFs) as well as NP physicochemical properties. Accordingly{,} quantitative structure-activity relationships (QSARs) were developed based on both linear and non-linear support vector regression (SVR) models making use of a sequential forward floating selection of descriptors. The SVR model with only 6 serum proteins and zeta potential had higher accuracy (R2 = 0.895) relative to the linear model (R2 = 0.850) with 11 PCFs. Considering the initial pool of 148 descriptors{,} the APOB{,} A1AT{,} ANT3{,} and PLMN serum proteins along with NP zeta potential were identified as most significant to correlating NP-cell association. The present study suggests that QSARs exploration of NP-cell association data{,} considering the role of both NP protein corona and physicochemical properties{,} can support the planning and interpretation of toxicity studies and guide the design of NPs for biomedical applications."}