Nanotoxicology data for in silico tools: a literature review

Abstract

The exercise of non-testing approaches in nanoparticles (NPs) hazard assessment is necessary for the risk assessment, considering cost and time efficiency, to identify, assess, and classify potential risks. One strategy for investigating the toxicological properties of a variety of NPs is by means of computational tools that decode how nano-specific features relate to toxicity and enable its prediction. This literature review records systematically the data used in published studies that predict nano (eco)-toxicological endpoints using machine learning models. Instead of seeking mechanistic interpretations this review maps the pathways followed, involving biological features in relation to NPs exposure, their physico-chemical characteristics and the most commonly predicted outcomes. The results, derived from published research of the last decade, are summarized visually, providing prior-based data mining paradigms to be readily used by the nanotoxicology community in computational studies.

Keywords:

• Nanoparticles
• nanotoxicology
• machine learning
• in silico

Notes

Disclosure statement

No potential conflict of interest was reported by the author(s).

Notes

1 http://portal.s2nano.org/ (Webpage accessed autumn 2019).

2 https://www.cdc.gov/niosh/data/default.html (Webpage accessed autumn 2019).

3 https://ochem.eu/home/show.do (Webpage accessed autumn 2019).

4 http://www.enanomapper.net/data (Webpage accessed autumn 2019).

5 http://nbi.oregonstate.edu/ (Webpage accessed autumn 2019).

6 https://www.mn-am.com/products/adrianacode. (Webpage accessed autumn 2019).

7 https://chm.kode-solutions.net/products_dragon.php. (Webpage accessed autumn 2019).

8 http://www-jmg.ch.cam.ac.uk/cil/SGTL/cerius2.html. (Webpage accessed autumn 2019).

9 https://www.chemcomp.com/. (Webpage accessed autumn 2019).

10 http://openmopac.net/. (Webpage accessed autumn 2019).

11 http://padel.nus.edu.sg/software/padeldescriptor/. (Webpage accessed autumn 2019).

12 https://www.chemicool.com/. (Webpage accessed autumn 2019).

13 https://www.chemcomp.com/index.htm. (Web address accessed in summer 2019).

14 https://www.h2020gracious.eu/ (Webpage accessed autumn 2019).

15 https://apps.ideaconsult.net/gracious/ui) (Webpage accessed autumn 2019).

16 http://www.nanofase.eu/ (Webpage accessed autumn 2019).

17 https://www.nanocommons.eu/ (Webpage accessed autumn 2019).

18 http://www.acenano-project.eu/ (Webpage accessed autumn 2019).

19 http://portal.s2nano.org/ (Webpage accessed autumn 2019).

20 https://cananolab.nci.nih.gov/ (Webpage accessed autumn 2019).

21 https://www.nanocommons.eu/ (Webpage accessed autumn 2019).

22 http://www.nanoinformatix.eu/ (Webpage accessed autumn 2019).

23 https://nanosolveit.eu/ (Webpage accessed autumn 2019).

Additional information

Funding

This work was supported by the European Union’s Horizon 2020 research and innovation in SMEs program under Grant Number [720851], project PROTECT. Craig A. Poland was supported by the Colt Foundation [project CF/01/17].