A disease-specific screening-level modeling approach for assessing the cancer risks of pesticide mixtures

As humans are always exposed to multiple pesticides, it is necessary to conduct risk assessments for pesticide mixtures. Due to data limitations, in this study, we introduced a disease-specific screening-level modeling framework to simulate the cumulative cancer risk (CR) of carcinogenic pesticides, which was developed based on the lognormal dose-response (LDR) curve of chemicals with disease-specific modes of action (MOAs). The simulated results of a case study indicate that the cumulative CR can be at least two orders of magnitude higher than the simulated CRs of individual pesticides. The comparison between the LDR model and the linear extrapolation (or cancer slope factor, CSF) model indicates that the CSF model can greatly overestimate population cancer risks. In addition, we applied our model to evaluate current regulatory standards of carcinogenic pesticide mixtures, and the results indicate that current standards for the selected jurisdictions can control the cumulative cancer risks within the acceptable level. However, the CSF model suggests that all selected jurisdictions cannot protect population health against the carcinogenic pesticide mixture, which is due to the nature of the low-dose linear extrapolation that triggers an initial slope when the effect dose is close to zero. Thus, we concluded that although the MOAs of pesticides in human bodies must be evaluated in future studies, our disease-specific model can be a useful and practical tool for cancer risk assessment and regulatory management of pesticide mixtures.