MECHANISTIC & AI-ENABLED TOXICOLOGY

Predicting how chemicals and nanomaterials shape internal dose and biological response.

We integrate mechanistic modeling, quantitative toxicology, and artificial intelligence to connect environmental exposure with internal dose, biological interactions, and health-relevant outcomes.

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Conceptual human-relevant toxicology illustration: environmental chemicals and smooth engineered nanomaterials connect through a neural network to a translucent human torso, organ compartments, dose curves and tissue response.

Research Areas

We develop mechanistic and AI-enabled approaches to connect environmental exposure and therapeutic delivery with internal dose, biological interactions, and health-relevant outcomes.

Conceptual blood and organ compartment network with internal-dose curves.

Mechanistic Biological Modeling for Next-Generation Risk Assessment

Translate environmental exposure into internal and target-tissue dose using PBPK/PBTK, IVIVE/QIVIVE, and human-relevant risk assessment.

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Conceptual molecular descriptors connected to a neural network and predictive modeling.

AI-Enabled Predictive Toxicology

Integrate machine learning with mechanistic and experimental evidence to predict toxicokinetics, toxicity, and chemical behavior when data are sparse.

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Conceptual smooth engineered sphere, nanorods and platelet interacting with a human cell membrane.

Nano-Bio Interactions & Nanomedicine

Determine how biological identity and nano-bio interactions control nanoparticle fate, tissue distribution, therapeutic delivery, and off-target exposure.

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Conceptual porous biomaterial releasing particles at a tissue interface.

Biomaterials & Regional Drug Delivery

Determine how material properties, release kinetics, and administration strategies shape target-tissue exposure and biological response.

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