Why may real world data not always reflect what was learned in a clinical trial?

Why may real-world data not always reflect what was learned in a clinical trial? Patients who enrolled on clinical trials, I am tremendously appreciative of their time and effort. You know, I joke to patients, they're putting their blood, soil, and tears into, you know, this clinical trial for the betterment of myeloma care for all future patients. But they're not always representative of some of the other patients that I see. So I think that's the biggest difference. So two things can happen. One, you know, patients who have the wherewithal to be at an academic center, at a major trial center where they're able to consider a clinical trial, are often healthier, have more resources, have more health literacy. And you know, clinical trials, even though we reimburse patients, they often cost money for the logistics of getting to the academic center and are not always reimbursed properly. So it's tricky. Being on clinical trial is not easy. As I alluded to earlier, clinical trials involve a lot more time of the patient, unfortunately. Many more tests like ultrasounds and echoes and bone marrow biopsies, often more time in clinic, as I alluded to, clinical trials often use older styles. For example, requiring a 24-hour urine assessment every three months. That's extra time stuck urinating into a jug and bringing it back to clinic. That's not for everybody. So I think that's one part of it, is the patients whom we approach for clinical trials or even approach our academic center for clinical trials are different. And the other part of it, and you know, again, that's not, I can't easily fix that. I wish that I could. The other part of it that we are trying to fix as a field is that clinical trial protocols are often restrictive. Clinical trial sponsors, meaning whoever is paying for the trial to be run, sometimes often drug companies, not always, sometimes we have IITs, which are investigator-initiated trials, meaning there's my idea, I'm using my research funding to pay for this. In all of those cases, however, we want to choose patients. We want to be able to see whether a drug is safe, whether a drug is effective. And unfortunately, patients who don't have the lab parameters to easily be able to prove either or have issues with either lab parameters, their blood work to improve safety or prove efficacy, often are not allowed onto those trials. What do I mean by that? For example, we want to make sure that this new drug doesn't make people's blood counts get too low. And so we have good ways to assess for that, but that requires the patient going onto the trial have good blood work to begin with. So patients who have cytopenia, meaning low blood counts, often are not eligible for clinical trials, which is unfortunate. Or efficacy, we want to make sure the drug is actually working, making the M-spike go down, making the plasma cytomas shrink, making the kappa and lambda ratio more normal or something along those lines. Patients who have oligosacretory or non-secretory myeloma, meaning they don't have those blood parameters that we can easily track, are all of a sudden excluded from these clinical trials and it's unfortunate. So I think that between those two, that explains a lot of why trials and real world data often reach different conclusions just because clinical trials to patients are healthier in a lot of ways. And so a good example of this would be with Teclistomab or L-Renatumab. So Teclistomab is a good example. It's a BCMA bispecific antibody. And the Majestic 1 study, we saw very impressive durations of responses, a ratio response of 18 months. So patients where that drug worked, it worked for a good solid year and a half on average. And it worked in over 60, 70% of patients. Several real world data sets have emerged since then. I've looked at this in real world patients receiving Teclistomab, often with Ticavelli commercially. And we actually, it's encouraging actually to see that the depth of response is the same. The response rate is the same. Still, about two thirds of patients, maybe 50 to 60% of patients can expect to see a clinical benefit from Teclistomab. But for whatever reason, compared to the trials, we have not really seen the same duration of response in the real world setting where the drug doesn't work for as long. Why is that difficult to say? My guess is that real world patients, you know, in the real world on these clinical trials, their T cells aren't as healthy. T cells need to work for Teclistomab to work. In real life, Teclistomab doses are often held for infections or complications or the patient might miss a dose because of this or another reason. So maybe in the real world, the actual dose intensity compared to the very regiment of clinical trial is not as rigid. And so doses get missed or unable to be given and therefore the drug doesn't work as well for as long. Which set of data are right? That's impossible to say. And the clinical trials are very important to show that the drug works to get approved. Real world data are what I look to, right? Because real world data reflects how I treat my patients typically and how my patients are able to be treated. And so I do look to real world data very importantly in that regard. Even though the data aren't as rich as a clinical trial, those patients are more likely to be the patient walking into my clinic. And so I think that's, you know, if there's a discrepancy between trial data and real world data, it's the real world data that I think is more applicable.