Workshop Report on the Evaluation of the Updated and Expanded Carcinogen Database to Support Derivation of Threshold of Toxicological Concern Values for DNA-Reactive Carcinogens

Abstract

The workshop concluded that the updated and curated database improved substantially on the existing CPDB with regard to data quality, study selection, and derivation of modern reference values. The focus on carcinogens avoids the derived threshold being “diluted” by irrelevant data. Categorization according to mode of action was seen as an added value as well as resolving the question of the contribution of non-DNA-reactive compounds. Sustainability is one open issue for the TTC database, as a constant update with newly published high-quality data would be an advantage. The release of the database to the public was strongly recommended by the workshop participants. They also pointed out that search and analysis functions, such as on structural similarity in the context of grouping approaches, would be of great benefit. The data set was found to contain several sources of uncertainty, e.g., regarding chemical selection, data variability of the BM D(L) values, etc. In this context, statistical analyses like bootstrapping were proposed to derive a good estimate of the robustness of, e.g., the 5th percent level. It was also discussed whether hierarchical or weight-of-evidence strategies could be used as an alternative to the min value approach to select the most scientifically defensible value instead of the lowest. Such analyses need to be further defined in terms of feasibility and relevance. Depending on the selection of the PoD, preferably BMD(L) values, a new assessment concept for the derivation of threshold values has to be developed and will need assessment factors other than 1/106 to derive a threshold. The final choice of an assessment factor was seen as a policy rather than a scientific decision. Nevertheless, the analysis of different options and the illustration of residual uncertainties and the robustness of such thresholds could help to increase confidence in the derived values/ approaches. The updated database will allow exploration of some subclasses for which specific thresholds can be derived comparable to, e.g., the newly developed thresholds for nitrosamines. Machine learning approaches may be helpful to cluster compounds according to their mechanistic features and their observed potency into categories or broader compound classes comparable to the Cramer Classes for non-DNA-reactive compounds. A recommendation on which approaches and tools should be used to make the call “DNA-reactive” would be a very valuable outcome of the project, outlining a “best set of rules”. The classification of compounds as non-DNA-reactive carcinogens was also seen as desirable, although this is currently not possible based on structural properties. One perspective could be to consider the risks of other mode of action groups such as genotoxicity, endocrine disruption, direct or cytotoxic mitogens. Future work could aim at a better understanding of properties/ mechanisms leading to differences in potency of DNA-reactive compound classes, e.g., by considering differences in toxicokinetic (absorption, metabolism, distribution, and excretion (ADME)) as well as in DNA adduct and repair processes. This could lead to a different basis for establishing acceptable thresholds.