No abstract

Patients may be exposed to potentially carcinogenic color additives released from polymers used to manufacture medical devices; therefore, the need exists to adequately assess the safety of these compounds. The US FDA Center for Devices and Radiological Health (CDRH) recently issued draft guidance that, when final, will include FDA’s recommendations for the safety evaluation of color additives and other potentially toxic chemical entities that may be released from device materials. Specifically, the draft guidance outlines an approach that calls for evaluating the potential for the color additive to be released from the device in concert with available toxicity information about the additive to determine what types of toxicity information, if any, are necessary. However, when toxicity data are not available from the literature for the compounds of interest, a scientific rationale can sometimes be provided for omission of these tests. Although the FDA has issued draft guidance on this topic, the Agency continues to explore alternative approaches to understand when additional toxicity testing is needed to assure the safety of medical devices that contain color additives. An emerging approach that may be useful for determining the need for further testing of compounds released from device materials is Quantitative Structure Activity Relationship (QSAR) modeling. In this paper, we have shown how three publically available QSAR models (OpenTox/Lazar, Toxtree, and the OECD Toolbox) are able to successfully predict the carcinogenic potential of a set of color additives with a wide range of structures. As a result, this computational modeling approach may serve as a useful tool for determining the need to conduct carcinogenicity testing of color additives intended for use in medical devices.

Intravascular stents and endovascular stent-grafts provide a minimally invasive option for treating vascular disease and injury. Medical device manufacturers typically conduct radial pulsatile fatigue testing of intravascular stents and endovascular grafts to demonstrate that these devices will maintain their durability for ten years of implant life. While they are useful indicators of device performance, these test regimens do not always predict device durability in the clinical setting with perfect accuracy. In this paper, we address some of the common issues that should be considered in the design of fatigue tests, including appropriate sample sizes for fatigue testing, sample selection, loading conditions, and test setup issues. We also discuss finite element analysis of long-term cyclic fatigue. In addition, we describe appropriate methods for reporting the incidence of stent fractures after implantation. Our goals are to assist manufacturers and test laboratories in refining their in vitro fatigue testing methods to allow more accurate prediction of clinical device fractures, and to maximize the amount of useful data contained in clinical fracture reports.