Predicting water solubility and octanol water partition coefficient for carbon nanotubes based on the chiral vector

Toropov, Andrey A.; Leszczyńska, Danuta; Leszczyński, Jerzy

doi:10.1016/j.compbiolchem.2007.02.002

Cited by 57 publications

(26 citation statements)

References 10 publications

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“…Quantitative Nano-structure Activity Relationships (QNAR) (Burello and Worth, 2011a, Burello and Worth, 2011b, Puzyn et al, 2011, Puzyn et al, 2009a, Puzyn et al, 2009b, Toropov et al, 2007, Toropov and Leszczynski, 2006, Liu et al, 2013b, Liu et al, 2014, Liu et al, 2013a, Gómez et al, 2013 In silico tools for hazard assessment (Liu et al, 2014, Liu et al, 2013a, Liu et al, 2013b In silico tools for hazard assessment Some of these tools are capable of assessing uncertainties. The Precautionary Matrix for Synthetic Nanomaterials uses a "specific framework conditions" criterion that represents uncertainties resulting from knowledge gaps with respect to the origin of the MNs, their characteristics and uses.…”

Section: Control Banding and Risk Screening Toolsmentioning

confidence: 99%

“…Statistical analysis and machine learning methods (e.g. principal component analysis, neural networks) have also been applied to model the properties and effects of MNs (Puzyn et al, 2009b, Puzyn et al, 2009a, Toropov et al, 2007, Toropov and Leszczynski, 2006, Sayes et al, 2013, Lynch et al, 2014. Such methods were used for example in the EU-funded MODERN project with the aim to establish in silico modelling of the effects of metal and metal oxide MNs (Liu et al, 2013b, Liu et al, 2014, Liu et al, 2013a, Gómez et al, 2013.…”

Section: Hazard Assessment Toolsmentioning

confidence: 99%

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Frameworks and tools for risk assessment of manufactured nanomaterials

Hristozov

Gottardo

Semenzin

et al. 2016

Environment International

101

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Commercialization of nanotechnologies entails a regulatory requirement for understanding their environmental, health and safety (EHS) risks. Today we face challenges to assess these risks, which emerge from uncertainties around the interactions of manufactured nanomaterials (MNs) with humans and the environment. In order to reduce these uncertainties, it is necessary to generate sound scientific data on hazard and exposure by means of relevant frameworks and tools. The development of such approaches to facilitate the risk assessment (RA) of MNs has become a dynamic area of research. The aim of this paper was to review and critically analyse these approaches against a set of relevant criteria. The analysis concluded that none of the reviewed frameworks were able to fulfill all evaluation criteria. Many of the existing modelling tools are designed to provide screeninglevel assessments rather than to support regulatory RA and risk management. Nevertheless, there is a tendency towards developing more quantitative, higher-tier models, capable of incorporating uncertainty into their analyses. There is also a trend towards developing validated experimental protocols for material identification and hazard testing, reproducible across laboratories. These tools could enable a shift from a costly case-by-case RA of MNs towards a targeted, flexible and efficient process, based on grouping and read-across strategies and compliant with the 3R (Replacement, Reduction, Refinement) principles. In order to facilitate this process, it is important to transform the current efforts on developing databases and computational models into creating an integrated data and tools infrastructure to support the risk assessment and management of MNs.

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Section: Control Banding and Risk Screening Toolsmentioning

confidence: 99%

Section: Hazard Assessment Toolsmentioning

confidence: 99%

Frameworks and tools for risk assessment of manufactured nanomaterials

Hristozov

Gottardo

Semenzin

et al. 2016

Environment International

101

View full text Add to dashboard Cite

show abstract

“…This data-driven approach brings many advantages in terms of cost, time-effectiveness and ethical concerns. Although it has been satisfactorily used to predict the physicochemical properties of NMs, such as solubility (Gajewicz, 2012;Sivaraman, Srinivasan, Vasudeva Rao, & Natarajan, 2001;Toropov, Leszczynska, & Leszczynski, 2007;Toropov, Toropova, Benfenati, Leszczynska, & Leszczynski, 2009) and elasticity (Mohammadpour, Awang, & Abdullah, 2011;Toropov & Leszczynski, 2006), development of reliable (Q)SAR models becomes more complicated when the actual processes and the endpoints of interest are biologically complex.…”

Section: Introductionmentioning

confidence: 99%

(Q)SAR modelling of nanomaterial toxicity: A critical review

et al. 2015

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There is an increasing recognition that nanomaterials pose a risk to human health, and that the novel engineered nanomaterials (ENMs) in the nanotechnology industry and their increasing industrial usage poses the most immediate problem for hazard assessment, as many of them remain untested. The large number of materials and their variants (different sizes and coatings for instance) that require testing and ethical pressure towards non-animal testing means that expensive animal bioassay is precluded, and the use of (quantitative) structure activity relationships ((Q)SAR) models as an alternative source of hazard information should be explored.(Q)SAR modelling can be applied to fill the critical knowledge gaps by making the best use of existing data, prioritize physicochemical parameters driving toxicity, and provide practical solutions to the risk assessment problems caused by the diversity of ENMs. This paper covers the core components required for successful application of (Q)SAR technologies to ENMs toxicity prediction, and summarizes the published nano-(Q)SAR studies and outlines the challenges ahead for nano-(Q)SAR modelling. It provides a critical review of (1) the present status of the availability of ENMs characterization/toxicity data, (2) the characterization of nanostructures that meets the need of (Q)SAR analysis, (3) the summary of published nano-(Q)SAR studies and their limitations, (4) the in silico tools for (Q)SAR screening of nanotoxicity and (5) the prospective directions for the development of nano-(Q)SAR models.

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“…Within this technique, the calculated optimal descriptor depends both on the molecular structure and the property under analysis (

K_{o c}

), but does not explicitly depend on the 3D-molecular geometry. We have shown the importance of optimal descriptors in previous QSPR studies [17,18,19,20,21]. …”

Section: Introductionmentioning

confidence: 90%

Conformation-Independent QSPR Approach for the Soil Sorption Coefficient of Heterogeneous Compounds

Aranda

Martínez

Castro

et al. 2016

IJMS

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We predict the soil sorption coefficient for a heterogeneous set of 643 organic non-ionic compounds by means of Quantitative Structure-Property Relationships (QSPR). A conformation-independent representation of the chemical structure is established. The 17,538 molecular descriptors derived with PaDEL and EPI Suite softwares are simultaneously analyzed through linear regressions obtained with the Replacement Method variable subset selection technique. The best predictive three-descriptors QSPR is developed on a reduced training set of 93 chemicals, having an acceptable predictive capability on 550 test set compounds. We also establish a model with a single optimal descriptor derived from CORAL freeware. The present approach compares fairly well with a previously reported one that uses Dragon descriptors.

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Predicting water solubility and octanol water partition coefficient for carbon nanotubes based on the chiral vector

Cited by 57 publications

References 10 publications

Frameworks and tools for risk assessment of manufactured nanomaterials

Frameworks and tools for risk assessment of manufactured nanomaterials

(Q)SAR modelling of nanomaterial toxicity: A critical review

Conformation-Independent QSPR Approach for the Soil Sorption Coefficient of Heterogeneous Compounds

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