Toward a systematic exploration of nano-bio interactions

Abstract

Many studies of nanomaterials make non-systematic alterations of nanoparticle physicochemical properties. Given the immense size of the property space for nanomaterials, such approaches are not very useful in elucidating fundamental relationships between inherent physicochemical properties of these materials and their interactions with, and effects on, biological systems. Data driven artificial intelligence methods such as machine learning algorithms have proven highly effective in generating models with good predictivity and some degree of interpretability. They can provide a viable method of reducing or eliminating animal testing. However, careful experimental design with the modelling of the results in mind is a proven and efficient way of exploring large materials spaces. This approach, coupled with high speed automated experimental synthesis and characterization technologies now appearing, is the fastest route to developing models that regulatory bodies may find useful. We advocate greatly increased focus on systematic modification of physicochemical properties of nanoparticles combined with comprehensive biological evaluation and computational analysis. This is essential to obtain better mechanistic understanding of nano-bio interactions, and to derive quantitatively predictive and robust models for the properties of nanomaterials that have useful domains of applicability.

The Current Understanding of Autophagy in Nanomaterial Toxicity and Its Implementation in Safety Assessment-Related Alternative Testing Strategies

“…How nanomaterials interact with biological systems has become an important and complex issue in terms of both the research and regulatory options. When nanomaterials encounter biomolecules or cells, their physicochemical properties have a major impact on the degree to which the material adversely perturbs biological systems [32,33]. Nano-bio interactions may also be affected by the properties of different cell types, the biological environment, and the assay methods applied, making the issues more complicated.…”

“…Nano-bio interactions may also be affected by the properties of different cell types, the biological environment, and the assay methods applied, making the issues more complicated. A thorough understanding of the mechanisms regarding nanomaterials-induced perturbation in biological systems such as autophagy induction is critical for a more comprehensive elucidation of nanotoxicity [33,34].…”

Application of Bayesian networks in determining nanoparticle-induced cellular outcomes using transcriptomics

“…Integration of omics with the toxicity of a substance is called toxicogenomics and bridges molecular and cellular effects (NRC 2005;Sahu et al 2015;George et al 2010). Capturing dysfunctions at a molecular level allows omics technologies to trace adverse effects at low doses including early and subclinical effects that traditional in vitro and in vivo studies may overlook (Kawata, Osawa and Okabe 2009;Bai et al 2017). Toxicokinetics models (Figure 1, center) such as Physiologically Based Pharmaco-Kinetic models (PBPK) and Physiologically Based Dose Response (PBDR) models enable in vitro-to-in vivo and in vivo-tohuman extrapolation of observations (Judson et al 2011;Li and Reineke 2012;Raies and Bajic 2016;Carlander et al 2016).…”

“…Although toxicity has been observed with a variety of NPs to variable extents and outcomes, how the specific physicochemical properties of different NPs and cellular properties of exposed systems relate to adverse effects is not comprehensively understood. The availability of data surrounding the interaction between biological systems and NPs is still relatively limited as compared to other chemical compounds (Bai et al 2017). Dealing with the variety of NPs physicochemical properties, routes of exposures, dosimetry, and their multiple effects on biological systems means that Machine Learning (ML) tools are particularly well suited toward the prediction of biological effects based on NPs properties (Winkler et al 2013;Marvin et al 2017;Sizochenko et al 2014).…”

Multi-hierarchical Profiling the Structure-Activity Relationships of Engineered Nanomaterials at Nano-Bio Interfaces

“…Recently, some progresses have been made using omics to investigate protein corona on ENM surfaces 11 , examine ENM-induced cell signaling changes 12, 13 , define the routes of ENM trafficking 14 , decipher cytotoxicity mechanisms 15 , etc . However, so far, no attempts have been made for nano-SAR assessments 16 . Since proteins and metabolites are the executors or end products of signaling pathways and multi-omics analyses offer a better view of the global biological changes 17 , we hypothesized that multi-hierarchical nano-SAR assessments could be achieved via coupling of proteomics and metabolomics analyses.…”