Origin of the TTC values for compounds that are genotoxic and/or carcinogenic and an approach for their re-evaluation

Figures & data

Figure 1. Tiers of the TTC concept as described in the WHO EFSA Report 2016 (EFSA 2016). For explanation of the “exclusionary categories” see section on “Outline of current TTC scheme”. The dashed boxes (green in online version) indicate where a conclusion on the acceptability of estimated human exposure can be reached. The longdash boxes (red in online version) indicate where safety at estimated human exposure cannot be assured and further information would be necessary to enable completion of the assessment.

Table 1. Genotoxicity profiles of Ames-positive carcinogens (taken from the approaches used in compiling the database of Kirkland et al. (2014) that are considered the strongest candidates to exert a genotoxic mode of action for carcinogenicity in descending order of confidence (Group1 strongest, group 6 weakest evidence). In vivo effects in the target tissue (and target species) for carcinogenicity would carry particular weight.

Group	In vivo mammalian mutation	In vivo mammalian cytogenicity	In vivo mammalian UDS^a or comet	In vitro mammalian cell mutation	In vitro mammalian cell cytogenicity
1	+	+	+ or NA^a	+	+
	+	+ or NA	+	+	+
	+ or NA	+	+	+	+
2	+	+	+ or NA	+	NA
	+	+ or NA	+	+	NA
	+ or NA	+	+	+	NA
	+	+	+ or NA	NA	+
	+	+ or NA	+	NA	+
	+ or NA	+	+	NA	+
3	+	+	+ or NA	NA	NA
	+	+ or NA	+	NA	NA
	+ or NA	+	+	NA	NA
4	+	NA	NA	+	+
	NA	+	NA	+	+
	NA	NA	+	+	+
5	+	NA	NA	+	NA
	+	NA	NA	NA	+
	NA	+	NA	+	NA
	NA	+	NA	NA	+
	NA	NA	+^c	+	NA
	NA	NA	+^c	NA	+
6	+	NA	NA	NA	NA
	NA	+	NA	NA	NA
	NA	NA	+^c	NA	NA

+ Evidence of genotoxicity using accepted criteria for the test.

a Some chemicals that gave negative results in liver UDS tests but were positive in the in vivo comet assay were included since the liver UDS test has been shown to be insensitive to a number of carcinogens (Kirkland & Speit 2008).

b NA: result not available, i.e. test was not performed.

c Care should be taken to evaluate the quality and source of the data if the only in vivo positive result is in a comet assay.

Table 2. A summary of freely available sets of rules, or profilers, which may be used to identify genotoxic carcinogens from the OECD QSAR toolbox.

Profiler/rule base	Details	Reference, if applicable
Protein binding alerts for chromosomal aberration by OASIS v1.1	28 structural alerts accounting for interactions of chemicals with specific proteins, such as topoisomerases, cellular protein adducts, etc. The scope of this profiler is to investigate the ability of target molecules to elicit clastogenicity and aneugenicity	Mekenyan et al. (2007)
DNA binding by OASIS v.1.3	Ames mutagenicity model (part of the OASIS TIMES system – see Table 3) with 78 structural alerts for interaction with DNA	Mekenyan et al. (2004) and Serafimova et al. (2007)
DNA binding by OECD	60 mechanistic organic chemistry fragments (in the form of structural alerts) for the binding of organic compounds to DNA	Enoch and Cronin (2010, 2012)
Carcinogenicity (genotox and non-genotox)alerts by ISS	A decision tree for estimating carcinogenicity, based on a list of 55 structural alerts (SAs). Of the alerts, 35 derive from the Toxtree module and 20 were derived separately. Most of the new SAs are relative to non- genotoxic carcinogenicity, whereas the SAs in the initial list mainly coded genotoxic carcinogenicity
In vitro mutagenicity (Ames test) alerts by ISS	Mutagenicity/Carcinogenicity module from Toxtree comprising a list of 30 structural alerts (SAs)
in vivo mutagenicity (micronucleus) alerts by ISS	ToxMicrulebase from the Toxtree software comprising 35 structural alerts (SAs) for a preliminary screening of potentially in vivo mutagens
OncoLogic Primary Classifier	Molecular definitions developed to mimic the structuralcriteria of chemical classes of potential carcinogenscovered by the U.S. Environmental Protection Agency’s OncoLogic™ Cancer Expert System for Predicting the Carcinogenicity Potential
ChemoTyper (Altamira/Molecular Networks)	Ashby Tennant genotoxic carcinogenic rules implemented in ChemTyper. This tool was implemented by a contract from US FDA CFSAN	Ashby and Tennant (1991), Ashby (1994), and Yang et al. (2015)

Table 3. A summary of commercially available expert systems based on structural alerts for the identification of genotoxic carcinogens.

System and supplier	Endpoint	Reference/link
DEREK Nexus from Lhasa Ltd	Various rule bases for mutagenicity including bacterial …. in vitro cytogenicity in mammalian cells, in vitro micronucleus studies	https://www.lhasalimited.org/derek_nexus/
ToxAlert, HazardExpert (CompuDrug)	Mutagenicity	http://www.compudrug.com/
Case Ultra MultiCASE	Mutagenicity	http://www.multicase.com/
Genetox Expert Alerts Suite (Leadscope)	Genetic Toxicity	http://www.leadscope.com/genetox_expert_alerts/
ChemTunesToxGPS (Molecular Networks/Altamira)	Rule-base for mutagenicity, in vitro chromosome aberration, and micronucleus (WOE prediction)	https://www.mn-am.com/products/chemtunes_studio

Table 4. A summary of computational systems to predict mutagenicity and related endpoints based on SAR, QSAR, or hybrid systems.

System and supplier	Endpoint(s)	Type of systems	Reference/link
ToxRead (Mario Negri Institute, Milan, Italy)	Ames mutagenicity	QSAR (chemical similarity) – freely available	http://www.toxgate.eu/
VEGA (Mario Negri Institute, Milan, Italy)	Ames mutagenicity	Hybrid (ToxTree) rules and QSAR – freely available	http://www.vega-qsar.eu/
Lazar	Ames mutagenicity	QSAR – freely available	http://lazar.in-silico.de
Toxicity Estimation Software Tool (TEST) (US EPA)	Ames mutagenicity	QSAR – freely available	https://www.epa.gov/chemical-research/toxicity-estimation-software-tool-test
Tox Suite (ACD Labs)	Ames mutagenicity	QSAR – commercial	http://www.acdlabs.com/products/pc_admet/tox/tox/
Non-human Genetic Toxicity Suite (Leadscope)	Salmonella and Escherichia coli mutagenicity, mouse lymphoma, in vitro chromosome aberrations, in vivo micronucleus	QSAR – commercial	http://www.leadscope.com/genetic_toxicity_suite/
Multicase MCASE/MC4PC (MultiCASEInc)	Many endpoints relating to mutagenicity and genotoxicity	QSAR – commercial	http://www.multicase.com/
OASIS-TIMES (Laboratory of Mathematical Chemistry, Bourgas University)	Mutagenicity, chromosomal aberrations and micronucleus formation including metabolism prediction	Hybrid rules and QSAR – commercial	http://oasis-lmc.org/?section=software&swid =4
PASS (Institute of Biomedical Chemistry of the Russian Academy of Medical Sciences)	Various mutagenicity	Hybrid rules and QSAR – commercial	http://www.pharmaexpert.ru/PASSOnline/
MolCode Toolbox	Mutagenicity	QSAR – commercial	http://molcode.com/
OpenTox	Micronucleus	QSAR – freely available	http://apps.ideaconsult.net:8080/ToxPredict
TOPKAT (Accelrys)	Ames mutagenicity	QSAR – commercial	http://accelrys.com/products/collaborative-science/biovia-discovery-studio/qsar-admet-and-predictive-toxicology.html
ChemTunesToxGPS (Molecular Networks/Altamira)	Ames Mutagenicity; in vitro chromosome aberration; in vivo micronucleus	QSAR (WOE prediction) – commercial	https://www.mn-am.com/products/chemtunes_studio
Sarah Nexus (Lhasa Ltd)	Mutagenicity	(Q)SAR – commercial	Hanser et al. (2014)

European Food Safety Authority (EFSA). 2016. Review of the Threshold of Toxicological Concern (TTC) approach and development of new TTC decision tree. EFSA Support. Publ. 13. Available from: http://onlinelibrary.wiley.com/doi/10.2903/sp.efsa.2016.EN-1006/pdf

 Google Scholar

Kirkland D, Zeiger E, Madia F, Corvi R. 2014. Can in vitro mammalian cell genotoxicity test results be used to complement positive results in the Ames test and help predict carcinogenic or in vivo genotoxic activity? II. Construction and analysis of a consolidated database. Mutat Res. 775:69–80.

 Web of Science ®Google Scholar

Kirkland D, Speit G. 2008. Evaluation of the ability of a battery of three in vitro genotoxicity tests to discriminate rodent carcinogens and non-carcinogens: III. Appropriate follow-up testing in vivo. Mutat Res. 654:114–132.

 PubMed Web of Science ®Google Scholar

Mekenyan O, Todorov M, Serafimova R, Stoeva S, Aptula A, Finking R, Jacob E. 2007. Identifying the structural requirements for chromosomal aberration by incorporating molecular flexibility and metabolic activation of chemicals. Chem Res Toxicol. 20:1927–1941.

 PubMed Web of Science ®Google Scholar

Mekenyan O, Dimitrov S, Serafimova R, Thompson ED, Kotov S, Dimitrova N, Walker JD. 2004. Identification of the structural requirements for mutagenicity by incorporating molecular flexibility and metabolic activation of chemicals I: TA100 model. Chem Res Toxicol. 17:753–766.

 PubMed Web of Science ®Google Scholar

Serafimova R, Todorov M, Pavlov T, Kotov S, Jacob E, Aptula A, Mekenyan O. 2007. Identification of the structural requirements for mutagencitiy, by incorporating molecular flexibility and metabolic activation of chemicals. II. General Ames mutagenicity model. Chem Res Toxicol. 20:662–676.

 PubMed Web of Science ®Google Scholar

Enoch SJ, Cronin MTD. 2010. A review of the electrophilic reaction chemistry involved in covalent DNA binding. Crit Rev Toxicol. 40:728–748.

 PubMed Web of Science ®Google Scholar

Enoch SJ, Cronin MTD. 2012. Development of new structural alerts suitable for chemical category formation for assigning covalent and non-covalent mechanisms relevant to DNA binding. Mutat Res Toxicol Environ Mutagen. 743:10–19.

 PubMed Web of Science ®Google Scholar

Ashby J, Tennant RW. 1991. Definitive relationships among chemical structure, carcinogenicity and mutagenicity for 301 chemicals tested by the U.S. NTP. Mutat Res. 257:229–306.

 PubMed Web of Science ®Google Scholar

Ashby J. 1994. International Commission for Protection Against Environmental Mutagens and Carcinogens. Two million rodent carcinogens? The role of SAR and QSAR in their detection. Mutat Res. 305:3–12.

 PubMed Web of Science ®Google Scholar

Yang C, Tarkhov A, Marusczyk J, Bienfait B, Gasteiger J, Kleinoeder T, Magdziarz T, Sacher O, Schwab CH, Schwoebel J, et al. 2015. New publicly available chemical query language, CSRML, to support chemotype representations for application to data mining and modeling. J Chem Inf Model. 55:510–528.

 PubMed Web of Science ®Google Scholar

Hanser T, Barber C, Rosser E, Vessey JD, Webb SJ, Werner S. 2014. Self organising hypothesis networks: a new approach for representing and structuring SAR knowledge. J Cheminf. 6:1–21.

 PubMed Web of Science ®Google Scholar