Getting the Foundation Right: Q&A with Sam Hinsley, Phastar

Clinical Trials Day is a moment to reflect on the full breadth of work that goes into bringing new treatments to patients—including the statistical rigor that underpins every decision along the way.

To explore this further, Applied Clinical Trials spoke with Sam Hinsley, statistics manager at Phastar, about what responsible data use looks like across the development timeline, why cross-stakeholder collaboration has never mattered more, and what she hopes to achieve through the newly launched Early Phase ESIG.

ACT: What does Clinical Trials Day mean to you as a statistician?

Hinsley: I've had quite a lot of Clinical Trials Days through my working life, and for me it's really a time to step back and remember why we do what we do. As a statistician, we could work in many different fields, but I choose to work in clinical trials because I really love to think that what I'm doing is making a difference. It's a great time to celebrate everybody involved—the patients going into the studies, but everybody behind the scenes too.

I've got type one diabetes, and I myself have seen the impact of clinical trials through my life—new treatments, new devices, things like that. It's just nice to think that you're doing something useful.

ACT: How can statisticians help raise standards across the clinical development timeline?

Hinsley: There's a lot we could do. We're really there to make sure the data is used properly. Patients are coming into the study—it's a really precious resource, they're giving up their time—and we need to use that data well. Really the design and the analysis of clinical trials, at each stage, each time point, phase one, phase two, wherever we are, we need to make sure we're getting the right answer and being responsible with the data that we have.

We need to innovate, but we don't need to innovate for the sake of it. We do that to get a more accurate answer, or a more efficient way of getting to that answer. Medicine is complicated at the best of times, and it's more complicated every year—personalized medicine, things like that. The stats need to keep up.

In rare disease, for example, there's not much data. You can't just use the normal designs. You've got to think about Bayesian approaches, you've got to think about adaptive designs. And the future—what's AI going to bring? I think statisticians have a really key role in making sure that our answers are coming out in the best way possible.

ACT: Why is cross-stakeholder collaboration so important to innovation in clinical development?

Hinsley: I think it's always been important, but it's even more important now that things are changing and getting more complicated. As a statistician, you can come up with a trial design, and it just doesn't work because it's not fitting the situation. If you think about immunotherapy for treating cancer—it doesn't act the same as cytotoxic agents. Everything we knew before didn't necessarily work, and so the methods have to keep up.

But we really have to do that with the right knowledge. As a statistician, I could sit here and think, oh, we'll do this or that, but without input from the experts, I don't know if that's going to be suitable. We really need everybody coming together. We're all there for the same aim—we all want to improve health outcomes—so it would be pretty irresponsible not to collaborate. And as designs are becoming more complicated to match the complicated medicine aspect, it's even more important.

Personally, I think it's one of the best parts of the job—getting to understand the different sides of it.

ACT: You recently launched a new special interest group, the Early Phase ESIG. What are you hoping to achieve through that?

Hinsley: I'm super excited about this group. We started earlier in the year and we've now got just under 30 members, which is brilliant. We're bringing together statisticians from academia, pharmaceutical research organizations, all sorts of different places—anybody with expertise in early phase—with the big goal of raising and improving standards across the community.

We're going to do that through a lot of knowledge sharing. We all run studies, but isn't it great if we can know what everybody's doing, share what's gone well and what hasn't, and then help that knowledge reach beyond just statisticians?

Things change constantly in early phase. Project Optimus came out from the FDA fairly recently, talking about using more data, so the methods are changing there. If you've only worked on a couple of early phase studies in the last year, you might not have gotten there yet. So it's great to be able to share that information and move things forward with the right people in the room.

ACT: Why is it so important to raise standards specifically in early phase trials?

Hinsley: For me, early phase is the first stepping stone—the foundation of the whole pathway. Any decisions we make at that early stage can ultimately determine the whole success.

On average, it takes about 10 to 15 years for a drug to be approved from first going into humans to being approved. The average cost is about one to two billion dollars, and only around 10% of drugs that make it into humans actually get approved at the end. A lot of that is the drug not being efficacious enough, or being too toxic to tolerate. But what if we've made the wrong decision early on? We've said this is the dose to go with, but maybe it was too high, people can't tolerate it, and it doesn't make it through. What if a slightly lower dose was better, more people tolerate it, and ultimately that drug works?

It's absolutely vital that we get it right. That's exactly the reason Project Optimus came out—to make sure that this critical point is done right. Early phase studies are challenging. They don't have many subjects, and you're making decisions on not a lot of data. The statistical approaches are really, really important there. But again, coming back to collaboration—everybody needs to understand what this means. I've told you here's a design, but there needs to be that shared understanding. It's ultimately about maximizing our chance of getting good treatments through to patients at the end of the day.