Predictive Dosimetry and Metabolism Core

What We Do

The Predictive Dosimetry and Metabolism Core (PDMC) provides Superfund Research Program (SRP) investigators with tools to predict how polycyclic aromatic hydrocarbons (PAHs), especially alkylated PAHs (a-PAHs), are metabolized and distributed in biological systems. Our mission is to translate laboratory findings into real-world human exposure scenarios by integrating experimental data with advanced computational models.

We support the OSU SRC Center by developing physiologically based pharmacokinetic (PBPK) models, identifying PAH metabolites using a reference-free approach, and predicting toxicity and receptor interactions of PAHs and their mixtures. These tools enable researchers to compare results across test systems and assess human health risks from environmental exposures.

Services We Offer

Computational Prediction of PAH Metabolites and Toxicity
We use tools like BioTransformer, ISiCLE, and DarkChem to predict chemical properties, mutagenicity, aryl hydrocarbon receptor (AhR) binding, and synergistic toxicity of a-PAHs and their metabolites.

Reference-Free Metabolite Identification
Using LC-IMS-MS/MS and the DEIMoS platform, we identify PAH metabolites in human, zebrafish, lung cell, and microbial systems without relying on chemical standards.

Dosimetry Modeling Across Systems
We develop PBPK models to predict internal concentrations of PAHs in humans, zebrafish, and lung cell cultures. These models support cross-system comparisons and translation to human exposure scenarios.

Risk Assessment Tools
We are developing an Excel-based risk calculator to support community and regulatory decision-making, including dietary risk assessments for Indigenous communities.

Collaborative Support
We work closely with SRP projects to provide metabolism rate data, metabolite identification, and dosimetry predictions tailored to specific experimental designs and exposure scenarios.

Our Current Research

1. Metabolite Prediction and Toxicity Modeling—We are building a comprehensive database of predicted a-PAH metabolites and using machine learning to model mutagenicity and AhR binding. These predictions support toxicity classification of novel PAH mixtures.

2. Metabolism Rate Measurement—We quantify metabolism rates of a-PAHs using pooled human hepatic microsomes and samples from zebrafish, lung cells, and microbial systems. This work supports the identification of key enzymes and metabolic pathways involved in PAH bioactivation and detoxification.

3. Cross-System Dosimetry Models—We are developing generalized PBPK models for a-PAHs, building on validated models for benzo[a]pyrene and dibenzo[def,p]chrysene. These models are being adapted for use in zebrafish and lung cell systems to support translational research across the Center.

4. Collaborative Integration—We support the Predicting Toxicity of PAH Mixtures, PAH Health Outcomes, and PAH Remediation and Transformations projects by providing metabolite predictions, dosimetry modeling, and metabolite identification. Our work enables cross-system comparisons and enhances the translational impact of SRC research.

5. Community Engagement—In collaboration with the Community Engagement Core, we are applying our risk assessment tools to support environmental health decisions in Indigenous communities, including dietary risk assessments for traditional foods.