The round-robin approach applied to nanoinformatics: consensus prediction of nanomaterials zeta potential

• Dimitra-Danai Varsou,
• Arkaprava Banerjee,
• Joyita Roy,
• Kunal Roy,
• Giannis Savvas,
• Haralambos Sarimveis,
• Ewelina Wyrzykowska,
• Mateusz Balicki,
• Tomasz Puzyn,
• Georgia Melagraki,
• Iseult Lynch and
• Antreas Afantitis

Beilstein J. Nanotechnol. 2024, 15, 1536–1553, doi:10.3762/bjnano.15.121

• ; read-across; QSPR; round-robin test; zeta potential; Introduction Nanotechnology, defined as the ability to manipulate matter at the nanoscale, has opened an array of possibilities for multiple applications that take advantage of the unique properties of nanomaterials (NMs). From targeted drug

• a broad range of computational and data-driven methodologies for the exposure, hazard, and risk assessment of NMs, such as quantitative structure–activity relationship models adapted to the specificities of NMs (nanoQSAR) and grouping/read-across models, specifically developed to accurately predict

• relationship (QSPR/QSFR) modelling, read-across, and deep learning models. Mikolajczyk et al. [16] implemented a consensus nano-QSPR scheme for the prediction of the ZP of metal oxide nanoparticles (NPs) based on the size and a quantum mechanical descriptor encoding the energy of the highest occupied molecular

Introducing third-generation periodic table descriptors for nano-qRASTR modeling of zebrafish toxicity of metal oxide nanoparticles

• Supratik Kar and
• Siyun Yang

Beilstein J. Nanotechnol. 2024, 15, 1142–1152, doi:10.3762/bjnano.15.93

• model, a nano-quantitative read across structure–toxicity relationship (nano-qRASTR) model was created. This model integrated read-across descriptors with modeled descriptors from the nano-QSTR approach. The nano-qRASTR model, featuring three attributes, outperformed the previously reported simple QSTR

• the years, QSAR/QSPR/QSTR techniques have been employed to establish correlations between various characteristics of nanomaterials and their toxicity [19][20][21][22][23]. Nano-quantitative read-across structure–toxicity relationship (nano-qRASTR) models are an advanced approach that builds upon the

• principles of nano-quantitative structure–toxicity relationship (nano-QSTR) models. These models integrate read-across techniques with traditional quantitative structure–activity relationship (QSAR) methods to enhance the predictive capabilities, particularly in datasets with limited data points [19]. Using

A review on the structural characterization of nanomaterials for nano-QSAR models

• Salvador Moncho,
• Eva Serrano-Candelas,
• Jesús Vicente de Julián-Ortiz and
• Rafael Gozalbes

Beilstein J. Nanotechnol. 2024, 15, 854–866, doi:10.3762/bjnano.15.71

• precursors. Similarly, Gul et al. compiled a dataset of nanoforms in cell viability tests to perform an association rule mining analysis in which the synthesis method was included among the identifiers of the nanoparticles [86]. In another example, in the read-across models developed by Varsou et al. [77

Exploring the relationships between physiochemical properties of nanoparticles and cell damage to combat cancer growth using simple periodic table-based descriptors

• Joyita Roy and
• Kunal Roy

Beilstein J. Nanotechnol. 2024, 15, 297–309, doi:10.3762/bjnano.15.27

• read-across approach for the developed MLR model with eight descriptors. Model validation The validation procedure is the prerequisite for the application of nano-QSPR models. Rigorous validation of the developed models was performed following principles of the Organization for Economic Cooperation and

• from the DTC lab software tools (http://teqip.jdvu.ac.in/QSAR_Tools/). To further validate model 2 for the similarity-based prediction, we have performed chemical read-across analysis. Prediction reliability indicator (PRI) tool Ensuring the reliability of predictions for a new set of data is a vital

• present work, highlighting our confident approach to the study. Read-across analysis The read-across technique is a reliable and scientifically proven method to predict the endpoint of a new compound, also known as the target compound. This technique involves utilizing data from similar substances that

Prediction of cytotoxicity of heavy metals adsorbed on nano-TiO₂ with periodic table descriptors using machine learning approaches

• Joyita Roy,
• Souvik Pore and
• Kunal Roy

Beilstein J. Nanotechnol. 2023, 14, 939–950, doi:10.3762/bjnano.14.77

• derivatives [15], 51 manufactured nanoparticles with varying core metals, coatings, and surface attachments [16], and 80 surface-modified multiwall carbon nanotubes have been reported. Another approach, namely nano-read-across (nano-RA) [17], has been used to determine the cytotoxicity of unknown

Needs and challenges for assessing the environmental impacts of engineered nanomaterials (ENMs)

• Michelle Romero-Franco,
• Hilary A. Godwin,
• Muhammad Bilal and
• Yoram Cohen

Beilstein J. Nanotechnol. 2017, 8, 989–1014, doi:10.3762/bjnano.8.101

• ) “Nano Task Force” as a regulatory framework to guide the users on grouping ENMs to make human health hazard assessment and identify information needs/research priorities for inhaled ENMs [36]. This framework leverages the concept of “read-across”, which allows data gaps to be filled assuming that ENMs

• if the ENM has a pulmonary half-life of less than 40 days. For soluble ENMs, no further nano-specific sub-grouping is specified and read-across of the properties of the dissolved materials to the corresponding bulk materials is applied. Biopersistent high aspect ratio (HAR) ENMs are defined as ENMs

An ISA-TAB-Nano based data collection framework to support data-driven modelling of nanotoxicology

• Richard L. Marchese Robinson,
• Mark T. D. Cronin,
• Andrea-Nicole Richarz and
• Robert Rallo

Beilstein J. Nanotechnol. 2015, 6, 1978–1999, doi:10.3762/bjnano.6.202

• ) for nanomaterials – so-called quantitative nanostructure–activity relationships (“QNARs”) [10] or “nano-QSARs” [13] – as well as “category formation” and “read-across” predictions [9][14][15]. In order to make most effective use of these data, experimental datasets should be made available via a

The eNanoMapper database for nanomaterial safety information

• Nina Jeliazkova,
• Charalampos Chomenidis,
• Philip Doganis,
• Bengt Fadeel,
• Roland Grafström,
• Barry Hardy,
• Janna Hastings,
• Markus Hegi,
• Vedrin Jeliazkov,
• Nikolay Kochev,
• Pekka Kohonen,
• Cristian R. Munteanu,
• Haralambos Sarimveis,
• Bart Smeets,
• Pantelis Sopasakis,
• Georgia Tsiliki,
• David Vorgrimmler and
• Egon Willighagen

Beilstein J. Nanotechnol. 2015, 6, 1609–1634, doi:10.3762/bjnano.6.165

• similarity assessment based on structure analogy is the basis of read across and chemical grouping. However, there is a common understanding that the most difficult part in read across is “rationalising the similarity”. Violations of the “similarity property principle” exist due to a variety of reasons [38

• medicinal chemistry. In the context of nanosafety assessment there is not yet a standardized approach for NM similarity, however a number of attempts for NM grouping and read across have been published recently [41][42]. Apart from enabling searching by well-defined chemical structures, the chemical