Dosimetric anchoring of in vivo and in vitro studies for perfluorooctanoate and perfluorooctanesulfonate.

In order to compare between in vivo toxicity studies, dosimetry is needed to translate study-specific dose regimens into dose metrics such as tissue concentration. These tissue concentrations may then be compared with in vitro bioactivity assays to perhaps identify mechanisms relevant to the lowest observed effect level (LOEL) dose group and the onset of the observed in vivo toxicity. Here, we examine the perfluorinated compounds (PFCs) perfluorooctanoate (PFOA) and perfluorooctanesulfonate (PFOS). We analyzed 9 in vivo toxicity studies for PFOA and 13 in vivo toxicity studies for PFOS. Both PFCs caused multiple effects in various test species, strains, and genders. We used a Bayesian pharmacokinetic (PK) modeling framework to incorporate data from 6 PFOA PK studies and 2 PFOS PK studies (conducted in 3 species) to predict dose metrics for the in vivo LOELs and no observed effect levels (NOELs). We estimated PK parameters for 11 combinations of chemical, species, strain, and gender. Despite divergent study designs and species-specific PK, for a given effect, we found that the predicted dose metrics corresponding to the LOELs (and NOELs where available) occur at similar concentrations. In vitro assay results for PFOA and PFOS from EPA's ToxCast project were then examined. We found that most in vitro bioactivity occurs at concentrations lower than the predicted concentrations for the in vivo LOELs and higher than the predicted concentrations for the in vivo NOELs (where available), for a variety of nonimmunological effects. These results indicate that given sufficient PK data, the in vivo LOELs dose regimens, but not necessarily the effects, could have been predicted from in vitro studies for these 2 PFCs.

Temporal concordance between apical and transcriptional points of departure for chemical risk assessment.

The number of legacy chemicals without toxicity reference values combined with the rate of new chemical development is overwhelming the capacity of the traditional risk assessment paradigm. More efficient approaches are needed to quantitatively estimate chemical risks. In this study, rats were dosed orally with multiple doses of six chemicals for 5 days and 2, 4, and 13 weeks. Target organs were analyzed for traditional histological and organ weight changes and transcriptional changes using microarrays. Histological and organ weight changes in this study and the tumor incidences in the original cancer bioassays were analyzed using benchmark dose (BMD) methods to identify noncancer and cancer points of departure. The dose-response changes in gene expression were also analyzed using BMD methods and the responses grouped based on signaling pathways. A comparison of transcriptional BMD values for the most sensitive pathway with BMD values for the noncancer and cancer apical endpoints showed a high degree of correlation at all time points. When the analysis included data from an earlier study with eight additional chemicals, transcriptional BMD values for the most sensitive pathway were significantly correlated with noncancer (r = 0.827, p = 0.0031) and cancer-related (r = 0.940, p = 0.0002) BMD values at 13 weeks. The average ratio of apical-to-transcriptional BMD values was less than two, suggesting that for the current chemicals, transcriptional perturbation did not occur at significantly lower doses than apical responses. Based on our results, we propose a practical framework for application of transcriptomic data to chemical risk assessment.

Developmental toxicity prediction.

Developmental toxicity may be estimated using commercial and noncommercial software that is already available in the market and/or literature, or models may be built from scratch using both commercial and noncommercial software packages. In this chapter, commonly available software programs that can predict the developmental toxicity of chemicals are described. In addition, a method for developing qualitative structure-activity relationship (SAR) models to predict the developmental toxicity of chemicals qualitatively (yes/no prediction) and quantitative structure-activity relationship (QSAR) models to predict quantitative estimates (e.g., LOAEL) of developmental toxicants is also described in this chapter. Additional information described in this chapter include methods to predict physicochemical properties of chemicals that can be used as descriptor variables in the model building process, statistical methods that be used to build QSAR models as well as methods to validate the models that are developed. Most of the methods described in this chapter can be used to develop models for health endpoints other than developmental toxicity as well.

An in silico approach for evaluating a fraction-based, risk assessment method for total petroleum hydrocarbon mixtures.

Both the Massachusetts Department of Environmental Protection (MADEP) and the Total Petroleum Hydrocarbon Criteria Working Group (TPHCWG) developed fraction-based approaches for assessing human health risks posed by total petroleum hydrocarbon (TPH) mixtures in the environment. Both organizations defined TPH fractions based on their expected environmental fate and by analytical chemical methods. They derived toxicity values for selected compounds within each fraction and used these as surrogates to assess hazard or risk of exposure to the whole fractions. Membership in a TPH fraction is generally defined by the number of carbon atoms in a compound and by a compound's equivalent carbon (EC) number index, which can predict its environmental fate. Here, we systematically and objectively re-evaluate the assignment of TPH to specific fractions using comparative molecular field analysis and hierarchical clustering. The approach is transparent and reproducible, reducing inherent reliance on judgment when toxicity information is limited. Our evaluation of membership in these fractions is highly consistent (˜80% on average across various fractions) with the empirical approach of MADEP and TPHCWG. Furthermore, the results support the general methodology of mixture risk assessment to assess both cancer and noncancer risk values after the application of fractionation.

Application of transcriptional benchmark dose values in quantitative cancer and noncancer risk assessment.

The traditional approach for estimating noncancer and cancer reference values in quantitative chemical risk assessment is time and resource intensive. The extent and nature of the studies required under the traditional approach has limited the number of chemicals with published risk assessments. In this study, female mice were exposed for 13 weeks to multiple concentrations of five chemicals that were positive in a 2-year cancer bioassay. Traditional histological and organ weight changes were evaluated, and gene expression microarray analysis was performed on the target tissues. The histological, organ weight changes, and the original tumor incidences in the original cancer bioassay were analyzed using standard benchmark dose (BMD) methods to identify noncancer and cancer points of departure, respectively. The dose-related changes in gene expression were also analyzed using a BMD approach and the responses grouped based on cellular biological processes. A comparison of the transcriptional BMD values with those for the traditional noncancer and cancer apical endpoints showed a high degree of correlation for specific cellular biological processes. For chemicals with human exposure data, the transcriptional BMD values were also used to calculate a margin of exposure. The margins of exposure ranged from 1900 to 54,000. Both the correlation between the BMD values for the transcriptional and apical endpoints and the margin of exposure analysis suggest that transcriptional BMD values may be used as potential points of departure for noncancer and cancer risk assessment.