Making big sense from big data in toxicology by read-across

Thresholds of Toxicological Concern – Setting a threshold for testing below which there is little concern

“…Similarly, the use of (Q)SAR in REACH has not met expectations (Hartung, 2016) as the regular reports on the use of alternatives under REACH show 9 . This does not say that these methods could not be used more extensively, but obviously the process is not encouraging this enough at the current state of science.…”

“…With the availability of toxicological "big data", a fusion of read-across and (Q)SAR as more automated read-across by machine-learning is now possible (Hartung, 2016). The first commercial solution was recently released by Underwriters Laboratories (UL) 10 .…”

“…But, all together, the enormously increasing availability of toxicological data (Hartung, 2016;Zhu et al, 2016) fuels the opportunities for TTC. There is still a lot to be done as to general data-sharing, data curation, machine-readability, harmonized ontologies (Hardy et al, 2012a,b), etc.…”

“…The environmental part, especially when you subtract smoking (Hartung, 2016), adds small risks to the random chance and is difficult to prove and thus to prevent for any given chemical, except in extreme exposure situations, for example at certain workplaces. So, without reentering the argument of threshold versus linear extrapolation, there is a practical threshold where the risk exerted is so small that there is no longer really concern.…”

E-cigarettes and the need and opportunities for alternatives to animal testing

“…Many of these have been detailed in this series of articles as well as our workshop reports and shall not be reiterated here. The reader is referred to the respective papers for in vivo (Hartung, 2008a(Hartung, , 2013, in vitro (Hartung, 2007(Hartung, , 2013Hartung and Leist, 2008;Leist et al, 2008) and in silico approaches (Hartung and Hoffmann, 2009;Hartung, 2016c), in vitro work for testing cosmetics (Hartung, 2008b), chemicals (Hartung, 2010b), nanomaterials (Hartung, 2010a;Hartung and Sabbioni, 2011), pharmaceuticals (Rovida et al, 2015b) and food (Hartung and Koëter, 2008), organo-typic cultures (Alépée et al, 2014;Andersen et al, 2014;Hartung, 2014;Marx et al, 2016), refinement of animal testing (Zurlo and Hutchinson, 2014), Integrated Testing Strategies (Hartung et al, 2013a;Rovida et al, 2015a), pathways of toxicity (Hartung and McBride, 2011;Kleensang et al, 2014;Tollefsen et al, 2014), omics technologies (Bouhifd et al, 2013(Bouhifd et al, , 2015aRamirez et al, 2013), and high-content imaging (van Vliet et al, 2014). These approaches come with different advantages and disadvantages in general and in particular for testing e-cigarettes (Tab.…”