Risk-based principles and incompleteness theorems for linear dose-response extrapolation for carcinogenic chemicals

A dynamic inventory database for assessing age-, gender-, and route-specific chronic internal exposure to chemicals in support of human exposome research

In this study, we proposed a dynamic inventory database to evaluate chronic internal exposure to chemicals at a population level, which enables users to perform modeling exercises specific to a particular chemical, route of exposure, age group, and gender. The database was built based on the steady-state solution of physiologically based kinetic (PBK) models. The biotransfer factors [BTF, the steady-state ratio between the chemical concentration in human tissues and the average daily dose (ADD) of the chemical] of 931 organic chemicals in major organs and tissues were simulated for a total of 14 population age groups for males and females. The results indicated that infants and children had the highest simulated BTFs of chemicals, and middle-aged adults had the lowest simulated BTFs. The route-specific analysis of the simulated BTFs indicated that the biotransformation half-life and octanol-water partition coefficient of chemicals had a profound impact on the BTFs. Organ- and chemical-specific results indicated that the biotransfer potential of chemicals in human bodies was primarily determined by bio-thermodynamic variables (e.g., lipid contents). In conclusion, the proposed inventory database can be conveniently used to access chronic internal exposure doses of chemicals by multiplying the route-specific ADD values for different population groups. In future studies, we recommend incorporating human biotransformation data, partition coefficients of ionizable chemicals, age-specific vulnerable indicators (e.g., the degree of maturation of immune systems), physiological variations within the same age group (e.g., intensity of daily physical activities), growth rates (i.e., the dilution effect on chemical biotransfer), and all possible target organs of carcinogenicity (e.g., bladder) into the proposed dynamic inventory database to help promote human exposome research.

Beyond the cancer slope factor: Broad application of Bayesian and probabilistic approaches for cancer dose-response assessment

Traditional cancer slope factors derived from linear low-dose extrapolation give little consideration to uncertainties in dose-response model choice, interspecies extrapolation, and human variability. As noted previously by the National Academies, probabilistic methods can address these limitations, but have only been demonstrated in a few case studies. Here, we applied probabilistic approaches for Bayesian Model Averaging (BMA), interspecies extrapolation, and human variability distributions to 255 animal cancer bioassay datasets previously used by governmental agencies. We then derived predictions for both population cancer incidence and individual cancer risk. For model uncertainty, we found that lower confidence limits from BMA and from U.S. Environmental Protection Agency (EPA)'s Benchmark Dose Software (BMDS) correlated highly, with 86% differing by <10-fold. Incorporating other uncertainties and human variability, the lower confidence limits of the probabilistic risk-specific dose (RSD) at 10-6 population incidence were typically 3- to 30-fold lower than traditional slope factors. However, in a small (<7%) number of cases of highly non-linear experimental dose-response, the probabilistic RSDs were >10-fold less stringent. Probabilistic RSDs were also protective of individual risks of 10-4 in >99% of the population. We conclude that implementing Bayesian and probabilistic methods provides a more scientifically rigorous basis for cancer dose-response assessment and thereby improves overall cancer risk characterization.

Systematic review and meta-analysis of cancer risks in relation to environmental waste incinerator emissions: a meta-analysis of case-control and cohort studies

OBJECTIVES

Various toxic substances can be generated from incinerators, exposing nearby residents, and epidemiological studies have shown wide variations in risk estimates for cancer risk in populations living close to incinerators.

METHODS

Following the Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA) guidelines, a literature search and systematic review were conducted to identify studies conducted on general populations exposed to environmental incinerator emissions and cancer outcomes. Meta-analysis was performed according to the cancer types for which 2 or more studies were reported. Subgroup analysis was done for sex, the exposure estimation method, the study period, and the type of outcome.

RESULTS

Eleven studies were found for the qualitative review and meta-analysis. Seven studies had a case-control design, and 4 had a cohort design. The pooled effect size was not significant for breast, colorectal, liver, lung, lymphohematopoietic, stomach, bladder, central nervous system, and laryngeal cancers, non-Hodgkin lymphoma, sarcoma, leukemia, and all cancers. In the subgroup analysis, the pooled effect size of laryngeal cancer in females was 1.82 (95% confidence interval, 1.10 to 3.01), although only 2 studies were identified.

CONCLUSIONS

The meta-analysis did not provide evidence of an increased risk for any cancer among populations living near waste incinerators, except for laryngeal cancer in females. However, since relatively few studies were reviewed and some cancer types showed significant increases in individual studies, this evidence needs to be updated regularly.

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.

An equivalency iterative algorithm for cancer risk assessment of chemical mixtures with additive effects

To better estimate cumulative cancer risks and avoid the overestimated risk from the linear extrapolation, an equivalency iterative algorithm associated with a carcinogenesis hypothesis was introduced for a mixture of chemicals with the same mode of action (MOA). A lognormal dose-response function was applied for carcinogenic chemicals. Under some circumstances, the repetitive random iterative algorithm could be transformed into the nonrepetitive one. It was also demonstrated that the equivalent value for a lognormal-based equivalency iterative algorithm with the same shape parameter was independent of the operation order. Based on the theorems of the algorithm and Plackett and Hewlett's minimum effective dose assumption, the sum of toxicity-weighted dose for a mixture of chemicals was mathematically derived. Compared to the estimation of risk by the linear extrapolation method (e.g., cancer slope factors), the equivalency iterative algorithm for lognormal functions can avoid overestimated risk significantly, which can help better estimate the cumulative cancer risk for a mixture of chemicals with the same MOA.

A theorem on a product of lognormal variables and hybrid models for children's exposure to soil contaminants

This study developed hybrid Bayesian models to investigate the modeling process for children's exposure to soil contaminants, which involves the intrinsic uncertainty of the exposure model, people's judgments regarding random variables, and limited data resources. A hybrid Bayesian p-box was constructed, which was facilitated by a multiple integral dimensionality reduction (MIDR) theorem. The results indicated that exposure frequency (EF) dominated the exposure dose. The hybrid Bayesian p-box for the Frequentist-Bayesian (F-B) model at the 95th percentile of the simulated average daily dose (ADD) values corresponded to a 4.40 order-of-magnitude difference between the upper and lower bounds of the p-box. This considerable uncertainty was magnified by the combination of the highest posterior density (HPD) regions for three groups of the distribution parameters. For the Interior-Bayesian (I-B) hybrid model, the uncertainty of the outcomes, namely, [1.75 × 10^-8, 2.18 × 10^-8] mg kg^-1d^-1, was limited by the HPD regions for only one parameter unless the hyperparameters for the variables' distributions were further evaluated. It was concluded that the hybrid models could provide a novel understanding of the complexity of the exposure modeling process compared to the traditional modeling method.