The Centers for Disease Control (CDC) estimates that healthcare acquired infections (HAIs) are responsible for 99,000 deaths in the US annually. Disinfectants are formulated to be stronger to combat against increasingly resistant pathogens causing HAIs. However, these disinfectants may potentially interact with the polymers used in medical devices and environment of care, causing cracking and premature failure. Many device manufacturers have cleaner recommendations based on active ingredient classes, neglecting the potentially deleterious effects of inactive ingredients. Here, we conducted design of experiments (DOE) to study the effect of inactive disinfectant ingredients on different polymer surfaces and utilized the data to evaluate predictive models, which can be used to rapidly screen future formulations. Commercial resins were tested against different formulations as shown in images. Visual scoring and tensile strength retention analysis were then used to identify general surface compatibility trends. Statistical analysis of the observed results indicate that pH and phenylmethanol are significant contributors to material compatibility. There also appears to be a synergistic effect between high pH and presence of phenylmethanol, suggesting that a combination of both may cause compatibility concerns. For “PC-2”, 5-Methyl-4,7-dioxadecane-2-ol also has a high inverse correlation. Neural network modeling of strength retention showed an overall root mean square error (RMSE) of 0.14. Gaussian process showed an RMSE of 0.14 overall. We then excluded three formulations during model building and used those for validation. Neural network showed an RMSE of 0.37 for the three formulations, while Gaussian process had an RMSE of 0.27. More optimization is required to reduce the RMSE. This study shows that generalizing material compatibility based solely on the active ingredient of disinfectants leads to grossly inaccurate results. It is our recommendation that inactive ingredients and pH also be considered to provide accurate instructions for use. Further work is needed to develop accurate predictive modeling, which will be the basis of research going forward.

Effect of inactive disinfectant ingredients on polymer surfaces and use of predictive modeling

ACS Fall 2021

The official archive and repository for all ACS Meetings & Expositions abstracts and select posters or presentations.