How Basket-Trials are Reshaping Drug Development Timelines

Pharmaceutical Executive previously covered FDA’s efforts to expand the range of data allowed in clinical trials, including the use of the Bayesian method. This style of data collection allows for clinical trial data to be examined alongside existing, relevant data.

This expansion of data is especially important for clinical trials in any rare disease space that have limited patient populations. While clinical trials in this area still present unique difficulties, regulatory changes have provided solutions to issues related to limited data-sets.

Pharmaceutical Executive spoke with Lisa Bollinger, chief medical officer at Polaryx. The clinical-stage biotech focuses on treatments for rare, pediatric lysosomal storage disorders. Due to its focus, Polaryx has been able to update its trial design thanks to the previously mentioned regulatory updates.

Bollinger discusses the regulatory landscape in this area, along with the style of trial design that the company is utilizing and some of the unique challenges they still face.

Pharmaceutical Executive: How are basket trials reshaping development timelines?
Lisa Bollinger: Basket trials have been around for some time, especially in the oncology space. So, I would say in oncology, where you have large populations, basket trials really didn't change the landscape at all.

But, when you look at rare oncology indications (especially in pediatrics), that's where basket trials started being used to help support rare disease development.

There are a couple of things that that are really great about a basket trial. Number one, it helps with recruitment, because usually these rare diseases are treated at the same centers. If you have a study that's set up at a center that serves a lot of the CLNs, they're able to recruit for all of the different arms of the basket study at the same time, and you can imagine this is a lot more efficient than standing up individual trials for each of these rare diseases and trying to find out what hospitals treat these specific rare diseases.

It also enhances our ability to leverage the data that comes out of those trials. So, if you have diseases with commonality and you have them all in the same basket trial and you're getting results at the same time, there is this ability to use a Bayesian approach, which really borrows known prior information across in indications. This makes it easier to recruit, and it also makes it easier to share data across indications to then provide a more robust assessment of efficacy across different populations.

There's an operational efficiency as well. In our case, for Soteria, we have four arms in our basket trial (four different indications), but it allows us to stand up a single trial at these centers, so it's much easier from an operational standpoint to set up one single trial than it is to set up four individual trials at the same sites.