Biomedical Engineering Reference
In-Depth Information
Regulatory agencies can take advantage of these models in the regulation of new and existing chemi-
cals. To date, a number of
in silico
techniques have been applied to detect and predict the toxicity
and fate of chemicals.
10.5.1 h
Igh
-t
hroughput
s
creeNINg
High-throughput screening (HTS) enables the rapid generation of large data sets for the assess-
ment of nanoparticles toxicity via using single-cell lines and simple organisms, laboratory automa-
tion, and robotic equipment. In HTS, simple assays, for example, the receptor binding assay, are
performed in multiwell-plate formats to test thousands of chemicals and their effects on a single
biological response at once (Houck and Kavlock, 2008). Unfortunately, the ultimate results are the
lack of activity for key toxicity targets, which means that it is inappropriate to use HTS as an indica-
tor of hazard or to define the mode of action. Recently, the emergence of multiparametric assays,
such as multiple concentrations, exposure times, target cell lines, and toxicity end points, facilitates
nanotoxicity screenings by HTS. However, this technique is vulnerable due to both systematic and
random errors. Random errors can be minimized by replicate measurements and procedural quality
controls, while systematic errors are more difficult to reduce. The latter ones are usually induced by
across-plate and within-plate row and column biases, and require the use of control wells to assess
plate-to-plate variabilities in multiplate assays and establish proper assay background levels. The
normalization of HTS raw data can remove systematic plate-to-plate variability and requires the
identification and elimination of data outliers. Moreover, it should be accomplished with the clear
interpretation of statistical parameters.
The exploratory analysis of HTS data can extract important information about possible toxicity
mechanisms and relationships among different cell responses. Heat maps combined with hierarchical
clustering are basic level analyses, providing ordered representations of data. Topology-preserving
mapping techniques are more comprehensive. For example, the self-organizing map (SOM) pro-
vides an ordered, 2D projection of data vectors containing the cell response information. It is both
used to identify similar nanoparticles and analyze nanoparticle biological activity profiles, includ-
ing multiple assay conditions, cell types, and cell responses (Cohen et al., 2013).
Although HTS data is far away from clearly identifying nanoparticles' effects on biological sys-
tems, they are indispensable for the development of other
in silico
toxicity models, such as quantita-
tive structure-activity relationships (QSAR).
10.5.2 q
uaNtItatIve
s
tructure
a
ctIvIty
r
elatIoNshIp
QSAR models are regression or classification models that relate a set of structural parameters to the
potency of biological activities. Figure 10.5 represents the workflow of a QSAR paradigm (Burello
and Worth, 2011). The application of QSARs encompasses both the human health effects and the
environmental impact of chemicals.
The first QSARs were based on the premise that toxicity could be related to certain molecular
properties of chemicals by mathematical methods. Early attempts were not successful, partly due to
the limited number of parameters that could be modeled, especially investigating complex toxicities
encompassing many different mechanisms of action. Although there have been progresses in mod-
eling chemical-biological interactions which could improve QSAR models, it is still an optimistic
idea that QSAR models will be able to play a major role in prediction of chemical toxicity. To date,
only a few nano-QSARs have been published, which are limited to small data sets and mainly focus
on metal- and metal oxide-nanoparticles (Cohen et al. 2013).
Puzyn et al. (2011) developed a model to describe the cytotoxicity of 17 different types of metal
oxide nanoparticles to
Escherichia coli
, which is expressed as the EC50. This report suggests
that the properties of nanoparticles are responsible for their activity and adverse effects on living
