The CHemical RISk calculators (CHRIS) are a collection of modules to facilitate the use of physics-based models to estimate exposure as part of toxicological risk assessment (TRA) of medical device polymers. There are two different categories of modules, which include Medical Device Development Tools (MDDTs) and Regulatory Science Tools (RSTs). Please click on the information icon for more details about each category.

MDDT qualified modules ⓘ

A tool to conduct screening level risk assessments to aid in the biocompatibility evaluation of polymeric medical device components that contain color additives (CAs). These assessments can assist device manufacturers by providing instantaneous feedback on whether the presence of CAs or other additives and impurities associated with CAs in a device would require additional justification and/or testing to demonstrate acceptable biological risk.

RST qualified modules ⓘ

An updated version of the color additive module that contains additional features and more robust parameterization of the underlying transport model used to estimate patient exposure. Note that this version has not been qualified as an MDDT.

This module can be used similarly to the color additive tool, but can be applied to any non-color additive bulk additive or impurity (surface impurities / manufacturing residuals are excluded) that is present in a polymeric device component provided that the user can justify the chemical is a bulk species and that the total amount of the chemical is known, e.g. from a certificate of analysis.

This module can be used to estimate the fraction of the total pool or solvent concentration of a bulk chemical that will be extracted under different conditions, thereby providing feedback on “worst-case” device geometries and extraction conditions depending on the endpoint of interest.

Modules in development ⓘ

An updated version of the color additive module that can address additional polymers using a machine learning method.

An updated version of the bulk chemicals module that can address additional polymers using a machine learning method.