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In your own words, what is a “next gen” clinical trial?

Scheuren: The main point here is that we wanted to highlight trials that have been using newer approaches or focusing on different areas in terms of, for example, trial design. We wanted to highlight novel, innovative approaches that are being integrated into SCI clinical trials.

Kramer: We were really trying to emphasize that these aren’t your grandparent’s clinical trials anymore. These are the evolution of those trials and what we’ve learned from them, or what we’ve learned from past “failures” in the field. What we wanted to communicate is that they’re really not failures in many ways.

Why is it important to understand the “failures” in SCI clinical trials?

Kramer: There are a lot of reasons why clinical trials fail. It’s possible to say, ‘It failed because the science is bad’ or ‘The drugs or treatments didn’t work because SCI is complicated,’ but we also need to be ready to capture the subtlest of changes—because these initial changes are what will eventually lead to major breakthroughs. For example, we can’t expect to make people with SCI walk (as was often the case in early clinical trials), but we can try to achieve small effects on things like bowel and bladder function, or sensory and motor function. That’s why understanding the history of clinical trials and adapting them moving forward becomes really important, because if we keep trying to “hit a home run” with the outcomes we choose, we’re likely to miss the treatments that might actually be effective.

Scheuren: And even though clinical trials might not always show positive effects of the treatments they’re investigating, they add value in terms of understanding what doesn’t work or uncovering new possibilities for future research. Understanding how the trial was designed, which participants were included, and why certain findings weren’t achieved is important so we don’t make the same “mistakes” again. This is why we can’t only publish positive findings and transparency is encouraged. We need to report on and understand what didn’t work too.

What are some of the key challenges that SCI researchers face when conducting clinical trials?

Scheuren: One of the biggest challenges relates to the nature of SCI and the heterogeneity, or diversity, of injuries and responses to treatment. You might have two people that have similar injuries at the beginning, but their recovery is completely different. This ultimately affects recruitment for clinical trials and whether or not the resulting sample size is large enough to accurately detect a treatment effect.

Kramer: All of it comes down to the fact that most SCI research is done with small sample sizes. This is the challenge we’re trying to overcome. That’s where I think these next gen clinical trials are trying to tackle some of these issues.

What solutions are next gen clinical trials using to tackle issues related to the small size and diversity of samples in SCI research?

Kramer: Biomarkers can be used in clinical trials to understand how well the body responds to a treatment for a disease or condition, or why one person responds to an intervention when someone else didn’t. A biomarker is a biological molecule found in blood, other body fluids, or tissues that is a sign of a normal or abnormal process, or of a condition or disease. We know from other fields that a validated biomarker—one that has required significant energy, time, and money—results in greater clinical trial success. This is partly because in the early phases of the clinical trial you have a more reliable and robust assessment of whether there was a treatment effect, which provides confidence going into larger and more resource-intensive phases of the clinical trial. We’re starting to learn more about biomarkers that tell us about SCI, but it really hasn’t taken off yet.

There’s also been a lot of effort put into understanding how people recover from SCI, and we can actually deploy that knowledge in clinical trials to inform whether or not we have a treatment effect. Historically, treatment effects are determined on the basis of a comparison to a placebo-controlled condition. And that’s nice, but when there’s a lot of diversity in the sample, sometimes the placebo group does far worse or far better than you thought they were going to do. And it’s this movement of the placebo group that can actually dictate whether or not a therapy is beneficial or not. For example, if a therapy does nothing but the placebo group does worse, you now have the emergence of what looks like a treatment effect. And this is where you can bring in large, historical datasets to look at how well your treatment group did compared to other groups from previous research.

Scheuren: Other examples include using so called “adaptive designs” that allow researchers to make adjustments as the study progresses. These types of approaches can help researchers to figure out which participant is more likely to benefit from a specific intervention compared to another, or tailor trials to specific subgroups of people with SCI instead of applying the same approach to everyone.

Another problem when doing research on a small population of people, like people with SCI, is participant burden—which can include things like the time commitment, travel, discomfort from procedures, or psychological strain associated with participating in the research. How can adaptive designs be used to relieve the burden placed on people with SCI in clinical trials?

Scheuren: Some clinical trials are using what’s called a seamless design. The researchers enroll participants in the first phase of the trial and then use the same group of participants for the second phase of the trial. While this approach comes with some limitations, it ultimately reduces participant burden.

Kramer: Another simple example is interim analysis, which is when you analyze the data before the planned data collection is complete. Let’s say you wanted to collect data from 300 people, but you stop at 150 and take a look at the data. If you realize that the treatment is having no effect at that point, then you can stop the trial. It’s not a big scientific advancement but it’s about relieving the burden on the folks who are participating in the trial.

Clinical trials eat up a significant number of resources, including time, money, and personnel. What are some ways that next gen clinical trials are making SCI research more efficient?

Kramer: One thing that the COVID-19 pandemic taught us about clinical trials is that we need to be fast and flexible and try new things. We need to be able to anticipate when a treatment is or isn’t working so that if we need to, we can change gears and try something else. We are at a moment in time where a lot of time and energy goes into setting up infrastructure for clinical trials, which can be redundant and expensive. These adaptive designs are helping to overcome some of those resource issues.

Scheuren: A lot of time goes into it for the people who are participating in the trial as well. So, figuring out where to best allocate resources and when to apply which resources is important. Instead of enrolling participants into a trial where you already know that the treatment might not work based on your interim analysis, they can then have the opportunity to participate in a different trial, for example, accelerating the whole process.

Kramer: But there are still challenges with a more fast-paced approach. Safety should always be the number one consideration—you don’t want to be whipping in and out of treatments that are not safe. If it’s too high risk or there are too many unknowns, these adaptive designs might not be the best approach.

Are there any other key learnings from the next gen clinical trials that you’d like to highlight?

Kramer: Historically what we’ve done is study the biology of SCI in animals, usually rodents (like mice or rats), and then transfer that knowledge into treatments that we administer in humans. And there are a number of issues with that approach. Specifically, in the case of SCI, we know that there are some major differences in terms of the biology and response to injury between humans and rodents. But now there’s an opportunity to learn in humans and adjust what we do based on our understanding of the human experience. And with this knowledge, we can go back to the animal studies knowing which processes or links we need to understand and target to best understand how the treatment will work in humans.

Scheuren: Standardizing rehabilitation is something else that we’re starting to see more and more. As your readers will know, rehabilitation is a big factor after SCI, so trying to standardize the approach to rehabilitation at least within a given trial could be very beneficial in terms of reducing the heterogeneity that we’re already dealing with just based on SCI itself.

What role might artificial intelligence (AI) play in the future of SCI clinical trials?

Kramer: Hard to say—the opportunities are pretty much endless at the moment. There is little doubt that in my lifetime, we will be testing a drug that was initially conceived by AI in people with SCI. This is an emerging field of research and investment, and it will evolve really quickly. Beyond this exciting capacity, AI will play a role in all facets of clinical trials from recruitment to assessment of participants and analysis. Eventually, it will also likely replace some amount of decision making. For example, AI will tell us what intervention or treatment has the most evidence to move forward into clinical trials. I think this is really important because we need to coordinate our clinical trials. It makes very little sense to do one trial here and another there, both too small to have a meaningful conclusion. The historical problem has always been that we have humans deciding and this creates bias. With AI, we can just ask it, based on the available evidence, how should we prioritize our investment in clinical trials. For me, this is very exciting and resolves a longstanding problem.

What’s one piece of advice or key message that you’d like to leave with our readers?

Kramer: I think it has to be one of patience and optimism. There will unquestionably be more failures ahead. There will be things that we think work now that won’t work when we go onto the clinical trials, and those will be viewed as big disappointments. But I do think that as we learn more and more about SCI, more opportunities will come. We are making progress even though sometimes it doesn’t necessarily feel that way.

Scheuren: I fully agree with what Kip said, and I would add that increased collaboration across different disciplines is needed to better understand SCI in humans and move the field of SCI research forward. Every trial, every study, and every effort teaches us something valuable, and your involvement is a vital part of that journey.