Computational models for the assessment of manufactured nanomaterials: Development of model reporting standards and mapping of the model landscape

Lamon, Lara; Asturiol, David; Vílchez, Alejandro; Ruperez-Illescas, R.; Cabellos, Joan; Richarz, Andrea-N.; Worth, Andrew

doi:10.1016/j.comtox.2018.12.002

Cited by 36 publications

(21 citation statements)

References 65 publications

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“…In the past decade, a variety of data driven computational approaches, such as Quantitative Structure-Activity Relationship (QSAR) models and read-across approaches that use diverse machine learning methods, have been used to predict NM-related toxicity [ 10 , 148 ]. The goal of these methods is to map the material description and intrinsic/extrinsic physicochemical properties to the biological outcomes to identify NM properties of concern, and facilitate design of NM that avoid, reduce or modulate these properties [ 150 , 151 ]. Computational techniques provide additional benefits where specific descriptors or properties are not readily measurable, as they can derive these using chemistry- and physics-based materials modelling [ 148 ].…”

Section: Resultsmentioning

confidence: 99%

Can an InChI for Nano Address the Need for a Simplified Representation of Complex Nanomaterials across Experimental and Nanoinformatics Studies?

et al. 2020

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Chemoinformatics has developed efficient ways of representing chemical structures for small molecules as simple text strings, simplified molecular-input line-entry system (SMILES) and the IUPAC International Chemical Identifier (InChI), which are machine-readable. In particular, InChIs have been extended to encode formalized representations of mixtures and reactions, and work is ongoing to represent polymers and other macromolecules in this way. The next frontier is encoding the multi-component structures of nanomaterials (NMs) in a machine-readable format to enable linking of datasets for nanoinformatics and regulatory applications. A workshop organized by the H2020 research infrastructure NanoCommons and the nanoinformatics project NanoSolveIT analyzed issues involved in developing an InChI for NMs (NInChI). The layers needed to capture NM structures include but are not limited to: core composition (possibly multi-layered); surface topography; surface coatings or functionalization; doping with other chemicals; and representation of impurities. NM distributions (size, shape, composition, surface properties, etc.), types of chemical linkages connecting surface functionalization and coating molecules to the core, and various crystallographic forms exhibited by NMs also need to be considered. Six case studies were conducted to elucidate requirements for unambiguous description of NMs. The suggested NInChI layers are intended to stimulate further analysis that will lead to the first version of a “nano” extension to the InChI standard.

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Section: Resultsmentioning

confidence: 99%

Can an InChI for Nano Address the Need for a Simplified Representation of Complex Nanomaterials across Experimental and Nanoinformatics Studies?

et al. 2020

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“…And as a matter of fact in silico models are the subject of intensive research and different computational models for nanomaterials have been reported (Lamon et al 2018). Among them, QSAR models (Fourches et al 2010;Puzyn et al 2011;Winkler et al 2013;Gajewicz et al 2015b;Pan et al 2016), read-across (Gajewicz et al 2015a(Gajewicz et al , 2017Gajewicz 2017a, b), neural network (Fjodorova et al 2017) or decision tree (Gajewicz et al 2018) classifications.…”

Section: Discussionmentioning

confidence: 99%

Towards an alternative to nano-QSAR for nanoparticle toxicity ranking in case of small datasets

et al. 2019

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Statistical analysis approaches have been developed to predict the biological response to nanoparticles, especially quantitative structure-activity relationship (QSAR) models. But one major limitation remains the quantitative lack of data to build accurate models. The aim of this study was to investigate if simple alternative mathematical models could rank nanoparticles in a very binary way (i.e. toxic or not) in case of small dataset. We synthesized and characterized 25 nanoparticles from 6 metal (hydr)oxide families with particle size and shape tuning. We assessed their toxicity on RAW 264.7 cells and investigated relationships with both physicochemical and dimensional descriptors. A simple partial least square (PLS) regression analysis allowed ranking nanoparticles with respect to their toxicity, without false-negative results. But this model was not predictive due to the specific response of each family to dimensional parameters variations. A classification tree extracted the same main bulk descriptor as PLS, but interestingly showed the relevance of dimensional descriptors for the second and third node. We thus recommend the development of family-specific models and propose the combination of these two simple methods as pre-screening tools, a compromise to bridge the gap between case-by-case studies (expensive and time-consuming) and sophisticated nano-QSAR models (not suitable for small datasets).

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“…As the rule of thumb, model validation is performed by increasing the number of involved variables and assessing performance [26,27]. The ratio of (count of NMs)/(count of descriptors) has a cut-off value of 5 (Topliss ratio), which is recommended while regulating to avoid needless complexity, according to the parsimony principle [16,28]. The final number of QSAR descriptors should not exceed six, but when knowledge of the relevance of properties to nanotoxicity is limited, a large number of initial descriptors should be sought [29].…”

Section: Feature Selectionmentioning

confidence: 99%

Practices and Trends of Machine Learning Application in Nanotoxicology

et al. 2020

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Machine Learning (ML) techniques have been applied in the field of nanotoxicology with very encouraging results. Adverse effects of nanoforms are affected by multiple features described by theoretical descriptors, nano-specific measured properties, and experimental conditions. ML has been proven very helpful in this field in order to gain an insight into features effecting toxicity, predicting possible adverse effects as part of proactive risk analysis, and informing safe design. At this juncture, it is important to document and categorize the work that has been carried out. This study investigates and bookmarks ML methodologies used to predict nano (eco)-toxicological outcomes in nanotoxicology during the last decade. It provides a review of the sequenced steps involved in implementing an ML model, from data pre-processing, to model implementation, model validation, and applicability domain. The review gathers and presents the step-wise information on techniques and procedures of existing models that can be used readily to assemble new nanotoxicological in silico studies and accelerates the regulation of in silico tools in nanotoxicology. ML applications in nanotoxicology comprise an active and diverse collection of ongoing efforts, although it is still in their early steps toward a scientific accord, subsequent guidelines, and regulation adoption. This study is an important bookend to a decade of ML applications to nanotoxicology and serves as a useful guide to further in silico applications.

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Computational models for the assessment of manufactured nanomaterials: Development of model reporting standards and mapping of the model landscape

Cited by 36 publications

References 65 publications

Can an InChI for Nano Address the Need for a Simplified Representation of Complex Nanomaterials across Experimental and Nanoinformatics Studies?

Can an InChI for Nano Address the Need for a Simplified Representation of Complex Nanomaterials across Experimental and Nanoinformatics Studies?

Towards an alternative to nano-QSAR for nanoparticle toxicity ranking in case of small datasets

Practices and Trends of Machine Learning Application in Nanotoxicology

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