There is a strong demand for early stage identification of potential Substances of Very High Concern (SVHC). SVHCs are substances that are classified as carcinogenic, mutagenic or reprotoxic (CMR);... Show moreThere is a strong demand for early stage identification of potential Substances of Very High Concern (SVHC). SVHCs are substances that are classified as carcinogenic, mutagenic or reprotoxic (CMR); persistent, bioaccumulative and toxic (PBT) or very persistent and very bioaccumulative (vPvB); or as substances with an equivalent level of concern, like endocrine disruption (ED). The endeavor to improve the identification of potential SVHCs is also acknowledged by the European Commission, in their long-term vision towards a non-toxic environment. However, it has been shown difficult to identify substances as potentially harmful.With this goal in mind, we have developed a methodology that predicts whether a substance is a potential SVHC based on chemical similarity to chemicals already identified as SVHC. The approach is based on the structural property principle, which states that structurally similar chemicals are likely to have similar properties.We systematically analyzed the predictive performance of 112 similarity measures (i.e. all different combinations of 16 binary fingerprints and 7 similarity coefficients) classifying the substances in the dataset as (potential) SVHC or non-SVHC. The outcomes were analyzed for 546 substances that we collected within the Dutch SVHC database – with identified CMR, PBT/vPvB and/or ED properties – and 411 substances that lack these hazardous properties. The best similarity measures showed a high predictive performance with a balanced accuracy of 85% correct identifications for the whole dataset of SVHC substances, and 80% for CMR, 95% for PBT/vPvB and 99% for ED subgroups.This effective screening methodology showed great potential for early stage identification of potential SVHCs. This model can be applied within regulatory frameworks and safe-by-design trajectories, and hence can contribute to the EU goal of achieving a non-toxic environment. Show less
While hazard assessment of chemicals can make direct use of descriptive adverse outcome pathways (AOPs), risk assessment requires quantitative relationships from exposure to effect timing and... Show moreWhile hazard assessment of chemicals can make direct use of descriptive adverse outcome pathways (AOPs), risk assessment requires quantitative relationships from exposure to effect timing and magnitude. To seamlessly integrate the data generated by alternative methods or in vivo testing, quantitative AOPs (qAOPs) providing dose-time-response predictions are more valuable than qualitative AOPs. Here, we compare three approaches to qAOP building: empirical dose-response modeling, Bayesian network (BN) calibration, and systems biology (SB) modeling. These methods were applied to the quantification of a simplified oxidative stress induced chronic kidney disease AOP, on the basis of in vitro data obtained on RPTEC/TERT1 cells exposed to potassium bromate. Effectopedia was used to store the experimental data and the developed models in a unified representation so they can be compared and further analyzed. We argue that despite the fact that dose-response models give adequate fits to the data they should be accompanied by mechanistic SB modeling to gain a proper perspective on the quantification. BNs can be both more precise than dose-response models and simpler than SB models, but more experience with their usage is needed. Show less
