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Using Computational Modeling for Perfluorinated Chemical Research

Computational models provide powerful tools to analyze large and complex sets of data generated from the laboratory to characterize and simulate the biological findings. There are multiple perfluorinated chemicals (PFCs) found in the environment (at least 14) and in humans (at least 4 commonly detected). EPA researchers use computational models to provide estimates to describe and predict the biological effects and characteristics of PFCs.

Several PFC research projects rely on computational modeling, especially pharmacokinetic simulations. Pharmacokinetic simulations demonstrate the fate of a chemical inside of the body, from initial exposure to elimination, and how the body’s absorption, distribution, etc. may alter the chemical compound. These simulations estimate how long different PFCs remain  in the body after exposure, what tissue compartments these chemicals are likely distributed into, and whether the body burden of these chemicals can be scaled by doses (extent of exposure).

These estimated parameters can be compared among species (for instance, rats vs. mice vs. humans) and genders to lend insights to the varying degrees of how long PFCs stay in the body. Computational models also determine how different PFCs may interact biologically, which is an important consideration because humans and wildlife are exposed to mixtures of PFCs in the real world.

Pharmacokinetic modeling of selected PFCs (carboxylates) indicates that chemical persistence in the body is proportional to the carbon-chain length (a characteristic of the chemical structure). The lower persistence and less toxic effects of short-chain PFCs (such as PFBA) may render these PFBA viable replacements for the long-chain PFCs (such as PFOS and PFOA) in commerce.