Forum Summary

The Food and Drug Administration (FDA) seeks to ensure public trust in medical devices on the market.  Significant premarket testing must be performed before a device can be marketed, but even with this extensive testing, relatively small post-market signals can result in a device being removed from the market, undermining public trust and sending researchers back to the beginning of the arduous product development process.  Anticipating potential adverse effects is crucial, and failure mode analysis should be performed at the beginning of any new project.  Before one conducts a human trial, preclinical trials should be conducted in order to show that a device is probably safe and effective.  Choosing the right model system and the right endpoints for a preclinical trial is of the utmost importance.

The regulatory history of stents illustrates many of the issues encountered in the study of adverse events.  The first coronary artery stents were studied in pigs, and researchers correctly identified restenosis as a potential complication of stent implantation.  In subsequent clinical trials, physicians were able to monitor this adverse event because they anticipated it.  When drug-eluting stents were introduced, however, post-market surveillance revealed associations with delayed healing, inflammation, and thrombosis.  Combining a drug and a device raised unanticipated issues, and these problems negatively impacted the development of drug-eluting stents.  Translating stenting technology from coronary arteries to other arteries also proved to be challenging.  Animal studies showed that stent fracture rates in superficial femoral arteries are very high for stainless steel stents.  Stents with metal memory were significantly better at withstanding the motion of the femoral artery.  Based on these findings, the FDA recommends that studies of peripheral vascular devices involve analyses of changes in length, curvature, and other factors.  As the FDA seeks to facilitate translational research, it continually attempts to identify improved biomarkers and to predict potential modes of failure.     

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