Physiologically based kinetic (PBK) modelling and human biomonitoring data for mixture risk assessment

Area of Science:

• Environmental Health
• Toxicology
• Computational Chemistry

Background:

• Human biomonitoring (HBM) data offers insights into complex chemical exposures.
• Assessing risks from combined chemical exposures is crucial for public health.
• Biomonitoring Equivalents (BE) and HBM health-based guidance values (HBM-HBGV) help interpret HBM data but are limited.

Purpose of the Study:

• To evaluate the utility and limitations of generic physiologically based kinetic (PBK) models for deriving BE values.
• To facilitate the use of HBM data in screening-level chemical mixture risk assessments.
• To test PBK modeling approaches for phenols and phthalates using real-world HBM data.

Main Methods:

• Utilized two generic PBK models: IndusChemFate and the High-Throughput Toxicokinetics package.
• Applied models to phenols and phthalates, using HBM data from Danish and Norwegian populations.
• Evaluated model prediction quality and demonstrated a case study for mixture risk assessment.

Main Results:

• Generic PBK models enhance the understanding and interpretation of HBM data.
• Deriving safety threshold levels in urine presents significant challenges and complexities.
• The approach is more feasible for persistent parent compounds in blood, but urine metabolite predictions carry high uncertainty.

Conclusions:

• Generic PBK models are valuable tools for HBM data interpretation and mixture risk assessment at a screening level.
• Further model refinement is necessary to reduce uncertainties, particularly for metabolite concentrations in urine.
• Future research should explore model performance for other chemical classes to improve simulation accuracy.