Ryan Haumschild, PharmD, MS, MBA: I want to get some input from Josh because you lead a progressive cancer center with a lot of treatments going on. You brought up the pros and cons to real-world evidence earlier. Are you all using real-world evidence anyway within your cancer center or treatment order sets or pathways? If you’re not, what are some of those barriers and how do you see yourself overcoming those in the next couple of years? As Muhamed pointed out, there’s going to be a lot of reason to use real-world evidence as we start to select between therapies.

Joshua Richter, MD: One things you brought up twice is this 11;14 business. Muhamed and I are knee-deep in 11;14, which predicts response to venetoclax, a drug that’s not even approved in myeloma; it’s approved in leukemia and lymphoma. We’ve treated over 300 patients outside trial with venetoclax. And we’re digging into this with both hands, trying to understand who did well, who did poorly, and why, so that the next people we’re going to put on it are going to benefit from it.

The way we’re approaching it is, first we realized that toxicity data, that it’s hard to pull out. One thing we’re doing is what we call the multiomic approach. As much as possible, we gather all the demographics, all the routine stuff, but we’re trying to do next-generation sequencing, immune profile, proteomics, metabolomics—the whole shebang. Muhamed [Baljević] and Roy brought this up: this is never going to be 1 size fits all. It’s probably going to have to be a few sizes fit some. The question is, can we put people into buckets?

That’s how I view real-world data. We approach this almost like the UTI [urinary tract infection] modality. What I call the UTI modality is when you pee into a cup and they put some disks on it and then you get a call: “Ciprofloxacin will work, no problem.” We’re going to have to develop the throughput for genomics and proteomics and immunologics. People will come and get their marrow—hopefully in the future, peripheral blood—based not on randomized controlled data but real-world information. We can say, “You have this bucket that you fit in. This bucket would benefit from this type of approach.”

That’s our way of doing it: to cram as much data as possible and start to involve some computational computer people to help dig through it. If you look at some of these models, I don’t have the training for them. I joke that I wish I had better training in immunology. I went to fellowship when we were still giving classical chemotherapy. We don’t do that anymore. I wish I had immunologics training. But we’re getting the computer people involved to weed through this and make good sense of it.

Ryan Haumschild, PharmD, MS, MBA: By the way, that translocation 11;14 might have been intentional. I’m glad I teased that out of you because you’ve got a lot of expertise there. I really appreciate you touching on that. It’s a great example of how it’s affecting treatment in the current state.

Roy, I want to transition the question to you. We’ve talked about barriers and how we overcome them, and you brought up barriers originally. Do you see real-world evidence making its way into these payer pathways where if a provider like Dr Richter or Dr Baljević came forward with data and Muhamed had data that proved that would provide superior treatment, do you see payers adopting that? How do you see us getting through those barriers? If we start making treatment design and treatment pathway decisions as payers, do we accept some of these frontline data?

Roy Beveridge, MD: The answer is yes. The problem is that myeloma has become so complicated that when you’re talking to a medical reviewer at a good-size plan who wants to do the right thing, they’re overwhelmed with the complexity of the disease. Think about what we’ve been talking about for the past hour or so. We’re talking about transplant eligible or not transplant eligible. Is someone going to go on maintenance therapy, or are they not? Are they going to get a triplet, or are they going to get a quad? Then you start talking about genomics.

Most medical reviewers at plans are not myeloma experts. We have to come up with a system in which people begin to think about the increased complexity of this disease, primarily because of the cost. If you think about breast cancer, colon cancer, prostate cancer, and others—I don’t mean to minimize those diseases—the pathways are clear. You’ve got to get these tests done. You’ve got a whole bunch of choices in lung cancer. But the level of complexity and the costs associated in myeloma is just multiple of what we’re talking about with some of these other diseases.

Real-world evidence is extremely important, but so is having the right person understand what we’re talking about. It’s going to be easier when Muhamed says, “We can do this for less money.” They’re probably going to say yes. It’s going to be harder when Josh says, “We need to spend… This person is going to have a much higher chance if we do the CAR [chimeric antigen receptor] T-cell therapy. Yes, it’s going to cost you $500,000 or $600,000, but here are the data.” The natural evolution is the credibility of the person and the entity calling. That’s what it comes down to. Key opinion leaders are important to plans. When they’re making an approval for 1 person for 1 thing, they’re making an approval for not that 1 person but for everyone who has that same type of situation. They’re trying to understand if they can defend this position for not only the individual but the population as a whole.

This transcript has been edited for clarity.