A Closer Look at Pricing for Cell and Gene Therapy
Amar Kelkar, MD, a stem cell transplantation physician at the Dana-Farber Cancer Institute, discussed his research on the value of advanced therapeutics.
Amar Kelkar, MD
Credit: Dana-Farber
In recent years, chimeric antigen receptor T-cell products and gene therapies have provided transformative new treatments options for patients with blood cancers and other hematological diseases. Although these treatments have the potential to provide a substantial disease-modifying or even curative effect, they also often come at very high prices in the realm of millions of dollars.
Amar Kelkar, MD, a stem cell transplantation physician at the Dana-Farber Cancer Institute, and his colleagues are interested in evaluating the value of these treatments and improving their cost-effectiveness. Following a presentation on this topic Kelkar gave at the 2024 Tandem Meetings |Transplantation & Cellular Therapy Meetings of ASTCT and CIBMTR, held in San Antonio, Texas, February 21-24, 2024, CGTLive® sat down with him to learn more.
CGTLive: Can you give some background context about your presentation at Tandem 2024?
Amar Kelkar, MD: I was asked to come speak about how we value medical therapies, and specifically cell and gene therapies, in the current landscape. I started out with an overview of the current landscape of what therapies had been approved. There have been about 12 cell and gene therapies in the hematology space—1 of which was a modified cord blood product—but the other 11 are mostly cell and gene therapies for lymphoma, myeloma, and hemoglobinopathies, like transfusion-dependent thalassemia, sickle cell disease, and hemophiliaA and B.
What were the key points discussed during the session?
The first point we started on was ways that we define value. The first way we look at it is the way that clinicians and a lot of health systems look at it, which we call comparative effectiveness analysis. These are things like clinical trials, where we just look at outcomes and see which treatment has better outcomes based on whatever our predefined endpoints are. That's probably the most familiar to a clinical audience. We then went over other types, like cost minimization, where it's the opposite and you're only looking at costs, and these can be financial or nonfinancial, and trying to reduce them between 2 treatments that are considered to be roughly equivalent. Then we covered things like cost effectiveness and cost benefit analysis and then focused in more on cost effectiveness and cost minimization as tools for both the health system and for individual centers to look at to try to maximize how many therapies we could get to patients without overloading the system.
What would you say are the main takeaways from the session for the healthcare community?
There have been 5 randomized trials in the space of cell therapy so far: One, which was negative, and 4 which were positive. Of those 4 studies, they show us that we should be using these newer cell therapies as second-line or third-line therapies, depending on the lymphoma or myeloma space. That's very exciting and that part should not be missed in the context of discussing how we define value, which is often affected by how we price these therapies. The takeaway is that a lot of these therapies are overpriced. There's a lot of factors that go into that: There's scarce supply along with high demand, as well as manufacturing delays, and, of course, these are novel therapies on patent and so there's natural investment recoupment from these companies, as well. All these things factor into these higher prices. Our goal in doing this type of research is to try and push back on these higher prices to get them to a level that we consider to be cost-effective so that more patients can get these treatments—because one of the things that we went over was what a budget impact analysis would look like. What would it mean if every patient who was eligible—meaning patients who actually were in the clinical situations where they should get those cell or gene therapies—what would that cost be? We used an example of large B-cell lymphoma in the second-line and looked at axicabtagene ciloleucel and lisocabtagene maraleucel and saw that just at the current prices, added spending for those along with all the treatments and supportive care and things like that that went along with that, would add about $6.5 billion to the United States’ healthcare spending over just 5 years. That's in addition to what was already being spent in those areas. Even when you make these therapies cost-effective by our model, it's still an increase of about 4.5 billion. But the idea is that even that marginal difference allows a lot more room for us to spend that on other treatments and get more patients in for these, investments and infrastructure—so many other things that we can do to make sure patients can actually get these better therapies.
Can you speak about challenges and areas of interest for further exploration in this space?
Some of the challenges are going to be about how can we find ways to make sure that patients that actually need these treatments are getting them? One of the areas that we're looking at is things like specialized diagnostic testing, like minimal residual disease or circulating tumor DNA testing, to try to see if that can better direct who needs these newer, more expensive therapies and to optimize who will best benefit from them.
We're also looking at other disease areas and timings because there's more and more of these treatments. We’re also looking at things like equity adjustment for diseases like sickle cell disease for historically underrepresented patients in clinical trials and in disease research. While we think about value in the contexts that are used currently we also want to evolve those standards as we go forward. There's already been some great research in this from places like Yale, but we're hoping to incorporate that into our own research as well.
Is there anything else you would like to share?
Probably the only thing I didn't talk about was just the concept of how we define effectiveness or benefit. As I mentioned, we use comparative effectiveness analysis in defining clinical outcome benefit for patients, but that often doesn't incorporate quality of life, which is a major factor. Usually, when we think about why treatments are better, we think of things that either extend life or improve quality of life. If we can combine those in some way that would allow us to better capture how patients are benefiting. Sometimes treatments don't improve the length of life that much, but if they live much better, I think most people would take that as a better treatment and think of that as valuable.
One way that's done in economic analysis is combining these into a metric called quality-adjusted life-year (QALY). It's basically the equivalent of a life year where you have perfect quality of life for 1 year. By calculating that using a combination of patient-reported outcomes and overall survival data, you can actually create a metric that might be useful for clinicians in explaining to patients why a treatment is better—not just that they'll live longer, which is important in and of itself. Part of the goal in the talk was to educate patients about what that unit is. We have some interest in exploring using QALYs as a replacement for tools like Griffith’s or other measures that try to create a composite of quality of life and survival.
This transcript has been edited for clarity.
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