Nanotoxicology data for <i>in silico</i> tools: a literature review

Furxhi, Irini; Murphy, Finbarr; Mullins, Martin; Arvanitis, Athanasios; Poland, Craig A.

doi:10.1080/17435390.2020.1729439

Cited by 53 publications

(48 citation statements)

References 113 publications

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“…The first sequential part of Figure 1 such as dataset formation that addresses the endpoint and an in depth-analysis (mapping) of the most common endpoints predicted in the reviewed studies has been addressed in a separate, companion article. However, it is briefly discussed hereafter [18].…”

Section: Discussionmentioning

confidence: 99%

“…Lastly, the toxicological endpoint of the study is obtained to be used as the predicted output of the model. A detailed description of the datasets used can be found elsewhere [18].…”

Section: Dataset Formationmentioning

confidence: 99%

“…Fourches et al [76] resulted in binary classification, which transforms the features by splitting at their arithmetic mean. Furxhi I. et al [18] demonstrated that almost half of the cases derived from the studies in their literature review predicted the outcome in a binary format.…”

Section: Normalization and Discretizationmentioning

confidence: 99%

“…Regarding the number of descriptors in the models reviewed (Figure 4 left),~75% of the studies used less than 10 descriptors, which reflects computational limitations or a lack of data. In Furxhi I. et al [18], a thorough analysis of the data issue in computational nanotoxicology is provided, stretching from missing data to experimental protocols and concepts. There is a shift in ongoing research toward monitoring, identifying, and quantifying p-chem properties of nanoforms (Figure 4, right).…”

Section: Challenges and Perspectivesmentioning

confidence: 99%

“…Artificial Neural Networks have a great deal of potentiality but data paucity limits their use for the time being. This review is preceded by another analysis of the literature identified herein, focusing on data collection, curation, and utilization [18] as a precursor to data pre-processing and model implementation.…”

Section: Introductionmentioning

confidence: 99%

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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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Section: Discussionmentioning

confidence: 99%

“…Lastly, the toxicological endpoint of the study is obtained to be used as the predicted output of the model. A detailed description of the datasets used can be found elsewhere [18].…”

Section: Dataset Formationmentioning

confidence: 99%

Section: Normalization and Discretizationmentioning

confidence: 99%

Section: Challenges and Perspectivesmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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Practices and Trends of Machine Learning Application in Nanotoxicology

et al. 2020

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Nanoparticle delivery through the BBB in central nervous system tuberculosis

Griego

Scarpa

Matteis

et al. 2023

Ibrain

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Recent advances in Nanotechnology have revolutionized the production of materials for biomedical applications. Nowadays, there is a plethora of nanomaterials with potential for use towards improvement of human health.On the other hand, very little is known about how these materials interact with biological systems, especially at the nanoscale level, mainly because of the lack of specific methods to probe these interactions. In this review, we will analytically describe the journey of nanoparticles (NPs) through the brain, starting from the very first moment upon injection. We will preliminarily provide a brief overlook of the physicochemical properties of NPs. Then, we will discuss how these NPs interact with the body compartments and biological barriers, before reaching the blood-brain barrier (BBB), the last gate guarding the brain. Particular attention will be paid to the interaction with the biomolecular, the bio-mesoscopic, the (blood) cellular, and the tissue barriers, with a focus on the BBB. This will be framed in the context of brain infections, especially considering central nervous system tuberculosis (CNS-TB), which is one of the most devastating forms of human mycobacterial infections. The final aim of this review is not a collection, nor a list, of current literature data, as it provides the readers with the analytical tools and guidelines for the design of effective and rational NPs for delivery in the infected brain.

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Analysis of global and Latin‐American trends in nanotoxicology with a focus on carbon nanomaterials: a scientometric approach

Geracitano

Fagan

Monserrat

2021

J of Chemical Tech & Biotech

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BACKGROUND:The scientific trends and developments in the field of nanotoxicology were analyzed through a scientometric approach. This study aimed to describe and unravel the main topics on nanomaterials concerning their potential toxicity. It was generated four databases: two for global nanotoxicological papers (Ds1 and Ds2) and two with a Latin-American focus (LA1 and LA2). Ds2 and LA2 databases were constructed with studies dealing with the toxic effects of carbon nanomaterials. Scientometric analyses were performed using CiteSpace software. The following items were evaluated: frequencies, burst, centrality for co-citations of Web of Science categories, keywords, references, authors, and countries.RESULTS: Global analysis resulted in a total of 29 798 papers for Ds1 and 3835 for Ds2. Latin-American papers resulted in 1397 articles for LA1 and 148 for LA2. Scientometric analyses indicated a specialization of the topics covered over time, ranging from general categories (such as Chemistry) to more specialized ones (such as Genetics or Neurosciences). Nano-silver prevailed in the nanotoxicological studies and graphene dominated in the field of carbon nanomaterials. In the last 5 years, a prominent growth has been observed in the number of studies that focus on the potential impact of nanomaterials on the environment.CONCLUSION: The research efforts in nanotoxicology have been mainly concentrated on assays that use nano-silver; meanwhile, in carbon nanomaterials, the focus has been concentrated on toxicological tests with graphene. Overall, we stress the importance of nanotoxicology as a strategy to obtain scientific information that can aid in the environmental sustainability of nanotechnologies.

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Nanotoxicology data for in silico tools: a literature review

Cited by 53 publications

References 113 publications

Practices and Trends of Machine Learning Application in Nanotoxicology

Practices and Trends of Machine Learning Application in Nanotoxicology

Nanoparticle delivery through the BBB in central nervous system tuberculosis

Analysis of global and Latin‐American trends in nanotoxicology with a focus on carbon nanomaterials: a scientometric approach

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