Quantitative Integration of Mode of Action Information in Dose-Response Modeling and POD Estimation for Nonmutagenic Carcinogens: A Case Study of TCDD.

BACKGROUND

Traditional dose-response assessment applies different low-dose extrapolation methods for cancer and noncancer effects and assumes that all carcinogens are mutagenic unless strong evidence suggests otherwise. Additionally, primarily focusing on one critical effect, dose-response modeling utilizes limited mode of action (MOA) data to inform low-dose risk.

OBJECTIVE

We aimed to build a dose-response modeling framework that continuously extends the curve into the low-dose region via a quantitative integration of MOA information and to estimate MOA-based points of departure (PODs) for nonmutagenic carcinogens.

METHODS

2,3,7,8-Tetrachlorodibenzo-p-dioxin (TCDD) was used as an example to demonstrate the new dose-response modeling framework. There were three major steps included: a) identifying and extracting key quantifiable events (KQEs), b) calculating essential doses that sequentially activate KQEs using the benchmark dose (BMD) methodology, and c) characterizing pathway dose-response relationship for MOA-based POD estimation.

RESULTS

We identified and extracted six KQEs and corresponding essential events composing the MOA of TCDD-induced liver tumors. With the essential doses estimated from the BMD method using various settings, three link functions were applied to model the pathway dose-response relationship. Given a toxicologically plausible definition of adversity, an MOA-based POD was derived from the pathway dose-response curve. The estimated MOA-based PODs were generally comparable with traditional PODs and can be further used to calculate reference doses (RfDs).

CONCLUSIONS

The proposed framework quantitatively integrated mechanistic information in the modeling process and provided a promising strategy to harmonize cancer and noncancer dose-response assessment through pathway dose-response modeling. However, the framework can also be limited by data availability and the understanding of the underlying mechanism. https://doi.org/10.1289/EHP12677.