In silico nanosafety assessment tools and their ecosystem-level integration prospect

Abstract

Engineered nanomaterials (ENMs) have tremendous potential in many fields, but their applications and commercialization are difficult to be widely implemented due to their safety concerns. Recently, in silico nanosafety assessment...

(Re)Conceptualizing decision-making tools in a risk governance framework for emerging technologies—the case of nanomaterials

“…There is a need to establish an interactive system by unifying various tools that allow answers to be given to specific questions. This is what the current nanoinformatics projects are aiming to accomplish, at least for the technical part of the nanosafety assessment (Afantitis et al 2020 ; Yu et al 2021 ).…”

Equation learning to identify nano-engineered particle–cell interactions: an interpretable machine learning approach

“…Here the framework included 11 potential model components, =which are the 0th to 10th order polynomial terms. As each model component can either be included or excluded, the framework selected from 2 11 = 2048 potential models. A different number of model components could be included; however, we found that 11 components struck a balance between the descriptive ability of the span of potential models, and the computational effort required to determine the learned model.…”

“…Mathematical and computational approaches play a crucial role in elucidating the impact of chemical, biological, and physical processes on particle–cell interactions. 6–11 Models that represent particle internalisation as a multistage reaction process have been used to examine the relative number of particles that are bound to the cell surface or internalised by the cells. 9,12–15 Such models suggest that particle saturation at the internalisation stage can significantly vary between cell lines, even if the different cell lines bind to particles at the same rate.…”

“…5 Mathematical and computational approaches play a crucial role in elucidating the impact of chemical, biological, and physical processes on particle-cell interactions. [6][7][8][9][10][11] Models that represent particle internalisation as a multistage reaction process have been used to examine the relative number of particles that are bound to the cell surface or internalised by the cells. 9,[12][13][14][15] Such models suggest that particle saturation at the internalisation stage can significantly vary between cell lines, even if the different cell lines bind to particles at the same rate.…”

Interpretable machine learning for investigating complex nanomaterial–plant–soil interactions

“…20 Furthermore, the in silico approach is recognized as a great tool to accelerate the nanomaterial discovery and nanosafety assessment 21,22 because it can be rapidly performed and offer more reproducible results for safety testing as well as enabling high-throughput screening. 23 Machine learning has been used to predict the uptake and translocation of nanomaterials and other contaminants in plants. For instance, artificial neural networks were used to predict the plant uptake of cooccurring CeO 2 nanoparticles and Cd by Brassica napus plants, and the key physiological factors were identified by the forward selection analysis.…”