Leveraging Extraction Testing to Predict Patient Exposure to Polymeric Medical Device Leachables Using Physics-based Models
P Turner and RM Elder and K Nahan and A Talley and S Shah and TV Duncan and EM Sussman and DM Saylor, TOXICOLOGICAL SCIENCES, 178, 201-211 (2020).
Toxicological risk assessment approaches are increasingly being used in lieu of animal testing to address toxicological concerns associated with release of chemical constituents from polymeric medical device components. These approaches currently rely on in vitro extraction testing in aggressive environments to estimate patient exposure to these constituents, but the clinical relevance of the test results is often ambiguous. Physics-based mass transport models can provide a framework to interpret extraction test results to provide more clinically relevant exposure estimates. However, the models require system-specific material properties, such as diffusion (D) and partition coefficients (K), to be established a priori for the extraction conditions. Using systems comprised high-density polyethylene and 4 different additives, we demonstrate that these properties can be quantified through standard extraction testing in hexane and isopropyl alcohol. The values of D and K derived in this manner were consistent with theoretical predictions for these quantities. Based on these results, we discuss both the challenges and benefits to leveraging extraction data to parameterize physics-based exposure models. Our observations suggest that clinically relevant, yet still conservative, exposure dose estimates provided by applying this approach to a single extraction measurement can be more than 100 times lower than would be measured under typical aggressive extraction conditions. However, to apply the framework on a routine basis, limiting values of D and K must be established for device- relevant systems either through the aggregation and analysis of more extensive extraction test data and/or advancements in theoretical and computational modeling efforts to predict these quantities.
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