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-close extrapolation methods for cancer and noncancer effects and assumes that all carcinogens arc mutagcnic 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), h) calculating essential doses that sequentially activate KQEs using the benchmark dose (BMD) methodology, and 0 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.