FDA deals Delcath a blow on cancer device

Delcath Systems ($DCTH) is seeking FDA approval for a drug-device combo to treat cancer, but the agency is pushing back its decision date, pointing out that the therapy carries serious risks for patients that likely outweigh its benefits.

As Bloomberg reports, FDA staff cites a high rate of treatment-related mortality in Delcath's clinical trials, with about 7% of patients dying of adverse events like liver failure and gastrointestinal bleeding after using the Melblez device. As a result, the agency is delaying its decision until Sept. 13.

The device is designed to treat eye cancer that has spread to the liver, using filters and pumps to cordon off the organ and then douse it with the chemotherapy melphalan. The FDA's issue is, in part, with those filters: The severe adverse reactions seem to stem from melphalan leaking out into the bloodstream, the agency points out.

As The Street reports, Delcath wants to switch to a new filter it believes will perform better, but the FDA says any new Melblez component must go through a randomized clinical trial to be considered, and the company doesn't have the proper data for a replacement filter.

That could well be a death sentence for Delcath's short-term FDA hopes, as the agency seems to think the device is unapprovable as-is, and the company's best bet to change the FDA's mind involves a time-consuming clinical trial that would indefinitely postpone a re-application.

Wall Street seems just as wary, as Delcath's share price tanked about 20% after the announcement, sitting at around 83 cents per share at 11:30 a.m. ET.

- read the FDA staff review (PDF)
- check out Bloomberg's take
- here's The Street's story

Suggested Articles

BD will begin working with Babson Diagnostics to help bring its lab-quality device for collecting blood from capillaries into retail pharmacies.

The former CEO of the molecular testing company Foundation Medicine, Troy Cox, has been named chairman of the Swiss big data firm Sophia Genetics.

Researchers at MIT used a machine-learning algorithm to uncover the potent antibiotic properties hiding within an old small-molecule candidate.