Advantages and limitations of classic and 3D QSAR approaches in nano-QSAR studies based on biological activity of fullerene derivatives

Jagiełło, Karolina; Grzonkowska, Monika; Swirog, Marta; Ahmed, Lucky; Rasulev, Bakhtiyor; Avramopoulos, Aggelos; Παπαδόπουλος, Μάνθος Γ.; Leszczyński, Jerzy; Puzyn, Tomasz

doi:10.1007/s11051-016-3564-1

Cited by 47 publications

(26 citation statements)

References 40 publications

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“…3) [47][48][49][50]. The leverage value, in this case, expresses similarity of a given compound, for which the prediction is made to the set of training compounds.…”

Section: Resultsmentioning

confidence: 99%

Molecular features of thymidine analogues governing the activity of human thymidine kinase

et al. 2018

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Modified uridines seem to be able to work as hypoxic tumour cell radiosensitisers. Before they sensitise cells to ionising radiation, they have to be incorporated into the genomic DNA and the latter process has to be preceded by the phosphorylation of the modified uridine, which in human cells is executed by human thymidine kinase 1 (hTK1). In the current study, we present the quantitative structure-activity relationship (QSAR) model allowing to identify and understand the molecular features of nucleoside derivatives governing the hTK1 kinase activity. The developed model meets all requirements of a reliable QSAR model and is based on only two molecular properties: the shape of the nucleoside determined by atom substitutions and the ability of the molecule to intermolecular interactions with the enzyme. These results have important implications for the rational designing of new hTK1 substrates and should significantly reduce the time and cost of studies on new radiosensitisers.

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“…3) [47][48][49][50]. The leverage value, in this case, expresses similarity of a given compound, for which the prediction is made to the set of training compounds.…”

Section: Resultsmentioning

confidence: 99%

Molecular features of thymidine analogues governing the activity of human thymidine kinase

et al. 2018

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“…Although multivariate linear model is still widely used for the prediction of biological effects, 168, 171–173 due to the significant nonlinearity in biological systems more often nonlinear algorithms 166 are used. Logistic regression, SVM, k-nearest neighbours (kNN) 166 and partial least squares (PLS) 174 are usually used for classification.…”

Section: Focusing On Biocorona – a Perspective From Statistical Modelmentioning

confidence: 99%

NanoEHS beyond toxicity – focusing on biocorona

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Mortimer

Chen

et al. 2017

Environ. Sci.: Nano

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The first phase of environmental health and safety of nanomaterials (nanoEHS) studies has been mainly focused on evidence-based investigations that probe the impact of nanoparticles, nanomaterials and nano-enabled products on biological and ecological systems. The integration of multiple disciplines, including colloidal science, nanomaterial science, chemistry, toxicology/immunology and environmental science, is necessary to understand the implications of nanotechnology for both human health and the environment. While strides have been made in connecting the physicochemical properties of nanomaterials with their hazard potential in tiered models, fundamental understanding of nano-biomolecular interactions and their implications for nanoEHS is largely absent from the literature. Research on nano-biomolecular interactions within the context of natural systems not only provides important clues for deciphering nanotoxicity and nanoparticle-induced pathology, but also presents vast new opportunities for screening beneficial material properties and designing greener products from bottom up. This review highlights new opportunities concerning nano-biomolecular interactions beyond the scope of toxicity.

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“…QSAR models do not provide insights into the toxicity pathways, but instead predict endpoints from in vivo or in vitro studies (e.g. carcinogenic or noncarcinogenic) (Jagiello et al 2016). Recently, novel models called RASARs (Read-Across Structure Activity Relationships) are being used to define chemical similarity (Luechtefeld et al 2018).…”

Section: Introductionmentioning

confidence: 99%

Application of Bayesian networks in determining nanoparticle-induced cellular outcomes using transcriptomics

et al. 2019

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Inroads have been made in our understanding of the risks posed to human health and the environment by nanoparticles (NPs) but this area requires continuous research and monitoring. Machine learning techniques have been applied to nanotoxicology with very encouraging results. This study deals with bridging physicochemical properties of NPs, experimental exposure conditions and in vitro characteristics with biological effects of NPs on a molecular cellular level from transcriptomics studies. The bridging is done by developing and implementing Bayesian Networks (BNs) with or without data preprocessing. The BN structures are derived either automatically or methodologically and compared. Early stage nanotoxicity measurements represent a challenge, not least when attempting to predict adverse outcomes and modeling is critical to understanding the biological effects of exposure to NPs. The preprocessed data-driven BN showed improved performance over automatically structured BN and the BN with unprocessed datasets. The prestructured BN captures inter relationships between NP properties, exposure condition and in vitro characteristics and links those with cellular effects based on statistic correlation findings. Information gain analysis showed that exposure dose, NP and cell line variables were the most influential attributes in predicting the biological effects. The BN methodology proposed in this study successfully predicts a number of toxicologically relevant cellular disrupted biological processes such as cell cycle and proliferation pathways, cell adhesion and extracellular matrix responses, DNA damage and repair mechanisms etc., with a success rate >80%. The model validation from independent data shows a robust and promising methodology for incorporating transcriptomics outcomes in a hazard and, by extension, risk assessment modeling framework by predicting affected cellular functions from experimental conditions.

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Advantages and limitations of classic and 3D QSAR approaches in nano-QSAR studies based on biological activity of fullerene derivatives

Cited by 47 publications

References 40 publications

Molecular features of thymidine analogues governing the activity of human thymidine kinase

Molecular features of thymidine analogues governing the activity of human thymidine kinase

NanoEHS beyond toxicity – focusing on biocorona

Application of Bayesian networks in determining nanoparticle-induced cellular outcomes using transcriptomics

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