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R. Williams et al.
can be used for prioritisation during drug development. It is important, however, to
understand the coverage of the chemical space of each model, to ensure that generated
predictions are reliable. To enable the prediction of idiosyncratic toxic effects, the
variability amongst individual protein/enzyme levels, expression and activity in the
target population should be considered [58].
3.7 Conclusion and Future Directions
Structural alerts provide much more than a simple binary prediction of toxicity hazard. Firstly, they can also be used to form mechanistic chemical categories within
which quantitative read across predictions can be made. Secondly, the additional
expert knowledge housed within an expert system can be used to prioritise in
chemico/ in vitro testing by considering the applicability of each individual information source. Thirdly, the generated data can then be combined with all of the in
silico information in a weight of evidence approach to arrive at a final conclusion
about the hazard potential and/or potency of a chemical of interest.
The use of in silico methodologies as decision support tools is now common
practice for certain toxicity endpoints. The relative success of these tools has unveiled
further challenges relating to interpreting and applying the results of models. The key
issues going forward for all these models, with respect to the regulatory context, are
how to guide the appropriate use of these techniques, and to provide an appropriate
level of interpretation for the results they produce to instil familiarity and confidence
in their use. The use of the AOP framework as a construct to arrange models and data
provides one way to tackle such challenges, whilst helping to focus the development
of new tests to support the mechanistic requirements.
References
1. Ashby J, Tennant RW (1988) Chemical structure, Salmonella mutagenicity and extent of carcinogenicity as indicators of genotoxic carcinogenesis among 222 chemicals tested in rodents
by the U.S. NCI/NTP. Mutat Res 204:17–115
2. Marchant C et al (2008) In silico tools for sharing data and knowledge on toxicity and
metabolism: derek for windows, meteor, and vitic. Toxicol Mech Methods 18:177–187
3. Barber C et al (2015) Establishing best practise in the application of expert review of mutagenicity under ICH M7. Regul Toxicol Pharmacol 73:367–377
4. Hanser T et al (2014) Self organising hypothesis networks: a new approach for representing
and structuring SAR knowledge. J Chemoinf 6:21
5. Barber C et al (2017) Distinguishing between expert and statistical systems for application
under ICH M7. Regul Toxicol Pharmacol 84:124–130
6. Ankley GT et al (2010) Adverse outcome pathways: a conceptual framework to support ecotoxicology research and risk assessment. Environ Toxicol Chem 29:730–741
7. OECD (2010) Test No. 429: skin sensitisation: local lymph node assay. https://doi.org/10.1787/
9789264071100-en. Accessed 28 Aug 2018
R. Williams et al.
can be used for prioritisation during drug development. It is important, however, to
understand the coverage of the chemical space of each model, to ensure that generated
predictions are reliable. To enable the prediction of idiosyncratic toxic effects, the
variability amongst individual protein/enzyme levels, expression and activity in the
target population should be considered [58].
3.7 Conclusion and Future Directions
Structural alerts provide much more than a simple binary prediction of toxicity hazard. Firstly, they can also be used to form mechanistic chemical categories within
which quantitative read across predictions can be made. Secondly, the additional
expert knowledge housed within an expert system can be used to prioritise in
chemico/ in vitro testing by considering the applicability of each individual information source. Thirdly, the generated data can then be combined with all of the in
silico information in a weight of evidence approach to arrive at a final conclusion
about the hazard potential and/or potency of a chemical of interest.
The use of in silico methodologies as decision support tools is now common
practice for certain toxicity endpoints. The relative success of these tools has unveiled
further challenges relating to interpreting and applying the results of models. The key
issues going forward for all these models, with respect to the regulatory context, are
how to guide the appropriate use of these techniques, and to provide an appropriate
level of interpretation for the results they produce to instil familiarity and confidence
in their use. The use of the AOP framework as a construct to arrange models and data
provides one way to tackle such challenges, whilst helping to focus the development
of new tests to support the mechanistic requirements.
References
1. Ashby J, Tennant RW (1988) Chemical structure, Salmonella mutagenicity and extent of carcinogenicity as indicators of genotoxic carcinogenesis among 222 chemicals tested in rodents
by the U.S. NCI/NTP. Mutat Res 204:17–115
2. Marchant C et al (2008) In silico tools for sharing data and knowledge on toxicity and
metabolism: derek for windows, meteor, and vitic. Toxicol Mech Methods 18:177–187
3. Barber C et al (2015) Establishing best practise in the application of expert review of mutagenicity under ICH M7. Regul Toxicol Pharmacol 73:367–377
4. Hanser T et al (2014) Self organising hypothesis networks: a new approach for representing
and structuring SAR knowledge. J Chemoinf 6:21
5. Barber C et al (2017) Distinguishing between expert and statistical systems for application
under ICH M7. Regul Toxicol Pharmacol 84:124–130
6. Ankley GT et al (2010) Adverse outcome pathways: a conceptual framework to support ecotoxicology research and risk assessment. Environ Toxicol Chem 29:730–741
7. OECD (2010) Test No. 429: skin sensitisation: local lymph node assay. https://doi.org/10.1787/
9789264071100-en. Accessed 28 Aug 2018