Why patient-centric trial design is the future of oncology: Q&A with One2Treat’s chief medical officer Pascal Piedbois

Original content written for Discover Pharma, available here. Author: Pascal Piedbois.

As oncology research continues to evolve, the focus is increasingly shifting from traditional endpoints to patient-centered outcomes. Ahead of ASCO 2025, Pascal Piedbois, discusses how methodologies like Generalized Pairwise Comparisons (GPC) are transforming clinical trials to better reflect the realities of patient care. In this insightful Q&A, he shares his expectations for ASCO, explains the practical impact of GPC, and outlines how One2Treat is helping sponsors build more meaningful and efficient oncology studies.

What are you most looking forward to at ASCO 2025, and how do you see it shaping the future of oncology research?

I am most looking forward to seeing the latest advancements in personalized medicine and immunotherapy. But more than that, I expect ASCO 2025 to highlight a growing emphasis on comprehensive approaches that could revolutionize oncology research strategy. Approaches that not only aim to extend life but also respect patient priorities and preferences. The future of oncology lies in combining clinical efficacy with a deeper understanding of what matters most to patients. That mindset is starting to shape how we design trials, evaluate benefits, and ultimately guide treatment decisions.

As One2Treat attends ASCO, are there any particular scientific trends or sessions you’re especially excited about?

I’d like to attend sessions that go beyond the science of new treatments to address how we design research that truly reflects patient needs. There is growing interest in understanding patient preferences and in translating those insights into concrete changes in trial design, whether it’s how we define meaningful benefit, how we prioritize outcomes, or how we engage patients early in the protocol development process. ASCO is increasingly becoming a forum not just for drug innovation, but for rethinking how we include the patient voice in the way evidence is generated.

Traditional oncology trials often focus on tumor shrinkage and survival metrics. Why do you believe it’s time for a shift toward more patient-centric approaches like Generalized Pairwise Comparisons (GPC)?

As medical oncologists, we rely on endpoints like tumor response and overall survival for good reason. They’re critical indicators of efficacy. But patients experience treatment through a broader lens: side effects, daily functioning, and long-term quality of life are often just as impactful as how long they live. What’s needed isn’t a replacement of our traditional endpoints, but an evolution, one that allows us to assess treatment benefit more holistically.

Generalized Pairwise Comparisons (GPC) supports that evolution. It respects the hierarchy of what matters most in oncology — often survival first — while also making room for additional outcomes that reflect the reality of patient experience. It’s a way to enrich our evidence without compromising scientific rigor.

Can you explain, in simple terms, how GPC works and what makes it more reflective of patient priorities like safety and quality of life?

GPC, or Generalized Pairwise Comparisons is a statistical method that allows us to compare treatments across several outcomes, in a way that mirrors clinical decision-making.

At its core, GPC works by forming all possible pairs between patients in the treatment group and those in the control group. For example, if there are 10 patients in each group, we evaluate 100 unique comparisons. For each pair, we assess who had the better outcome based on a ranked list of priorities — typically starting with survival, followed by quality of life, side effects, and so on. If one patient clearly does better on the most important outcome, that pair is counted accordingly. If not, we move down the list.

What makes GPC powerful is that it doesn’t force us to choose a single primary endpoint, it allows us to integrate several outcomes, based on their clinical relevance. The result is a single, interpretable measure called Net Treatment Benefit (NTB), which is simply the net difference between the probability that a random patient in the treatment group has a better outcome than a random patient in the control group, and the probability of the opposite occurring. This kind of quantitative analysis gives a more complete picture of treatment impact, one that aligns with how patients and clinicians weigh trade-offs in real life.

How receptive are regulators and sponsors to using GPC in real-world trials — and what progress have you seen?

I think that regulators are increasingly receptive to methodologies like GPC, especially when they help capture clinical benefit in a way that reflects the totality of patient experience. What matters most to agencies like the EMA or FDA isn’t whether a method is novel, but whether it is clearly pre-specified, well-justified, and clinically interpretable. GPC has already been included in several regulatory submissions, often as a key secondary or exploratory analysis, and fits well within current initiatives focused on patient-centric evidence generation — such as FDA’s Project Patient Voice or Project Optimus.

On the sponsor side, we’ve seen growing adoption, particularly in trials where traditional endpoints alone may not tell the full story — for example, in rare diseases or complex oncology settings. What’s shifting is the mindset: the question is no longer whether we should consider patient-centered methodologies, but how we can implement them in a rigorous, operationally feasible way. GPC offers a structured framework to do just that.

What are some of the key challenges to adopting alternative trial methodologies in oncology?

One of the biggest challenges is cultural. As clinicians and researchers, we’re trained to think in terms of traditional endpoints — overall survival, progression-free survival, objective response — and those remain essential. But broadening our view to include prioritized, multi-dimensional outcomes requires a shift in mindset. It means rethinking what we consider “primary” and accepting that benefit can’t always be reduced to a single number or curve.

Another key challenge lies in the practical setup. For methods like GPC to work, we need to define a clear hierarchy of outcomes, and that’s not always straightforward. It requires early, structured input from both clinicians and patients to understand which outcomes truly matter and in what order. These preferences need to be captured before the trial begins, and doing so in a consistent, rigorous way across sites and stakeholders takes time, planning, and the right tools.

Finally, there’s an educational gap. Many clinicians are unfamiliar with these approaches, and sponsors may hesitate to deviate from conventional designs unless there’s a clear regulatory or strategic incentive. Overcoming that inertia takes time, but it starts with building confidence in methods that are both scientifically rigorous and clinically meaningful.

How does One2Treat help companies overcome those barriers — technically, scientifically, or culturally?

At One2Treat, we support sponsors in making early strategic decisions that place patient and clinician priorities at the center of trial design. A recurring challenge is identifying which outcomes to measure and how to prioritize them. To address this, we’ve developed One2Treat Voice, a software solution that facilitates the structured collection of preferences from key opinion leaders, investigators and patient advocates, enabling prioritization that’s evidence-based and operationally feasible.

This is part of a broader suite of tools we’re building to support modern trial design. One2Treat Insights helps sponsors to leverage the totality of evidence from prior trials to inform endpoint selection and data interpretation, while One2Treat Design enables simulation-based planning and sample size calculation to complement methodological guidance. Alongside these tools, we provide scientific and strategic advice to ensure that trial designs incorporate a more comprehensive and clinically relevant estimation of the Net Treatment Benefit, helping ensure that trial outcomes better reflect patient priorities, making the resulting evidence more clinically meaningful.

Beyond trial interpretation, NTB can also inform early R&D go/no go decisions by quantifying benefit-risk trade-offs across multiple outcomes, helping teams prioritize compounds with the most patient-relevant impact. In HTA contexts, NTB offers a transparent, interpretable metric that aligns with payer expectations for comprehensive value assessment.

Our goal is to make the adoption of patient-centered methodologies not just possible, but practical, by combining robust tools with the right scientific framework early in the trial design stage.

Could you share any case studies or examples where GPC made a significant impact on trial outcomes or interpretation?

GPC has already been used to provide a much richer understanding of treatment effects, particularly in complex clinical scenarios where relying on a single primary endpoint would have fallen short. In these situations, where multiple clinical outcomes may play a decisive role, GPC enables estimating a quantitative benefit risk balance to support decision-making.

This was exactly the challenge addressed in the SHAPERS trial for older patients with rectal cancer. The investigators needed to evaluate whether a shorter, less intensive treatment could offer a better overall balance of benefit and harm compared to total neoadjuvant therapy. Rather than using a traditional non-inferiority design, which would have required a large sample size to detect equivalence based on a single endpoint, the team leveraged GPC to estimate a Net Treatment Benefit based on multiple prioritized outcomes: survival, tumor progression, neuropathy, and toxicity. This approach allowed the trial to test for superiority, not just equivalence, while also ensuring that the primary analysis reflected trade-offs meaningful to patients. It offered a more efficient, clinically relevant framework that traditional endpoints alone could not provide

Another compelling use of GPC comes from re-analyses of existing trials. In a JAMA Oncology study, Péron et al. showed that the Net Treatment Benefit provided clearer, more intuitive insight than hazard ratios, especially when survival curves crossed or treatment effects emerged late. By reframing the question as “What’s my chance of living meaningfully longer with treatment?”, GPC helped uncover clinically relevant benefits that standard analyses often obscure. This approach allows us to extract more patient-relevant evidence from data we already have.

Finally, we’re now exploring how GPC can support dose selection in Phase II clinical trials, in alignment with FDA’s Project Optimus. By comparing multiple doses not just on efficacy but also on tolerability and quality of life, NTB offers quantitative evidence towards the most balanced treatment to support discussions with regulatory bodies.

Where do you see the biggest opportunities for improving oncology trials over the next five years?

We have a tremendous opportunity to rethink how we define and measure success in oncology trials. Over the next five years, I believe the biggest impact will come from integrating patient-centered methodologies into earlier stages of trial design, not just as an afterthought, but as a core design principle. By embedding structured patient and clinician input into endpoint selection and benefit-risk balance evaluation, we can generate evidence that is not only scientifically rigorous but also clinically meaningful.

Another key area is the smarter use of prior clinical trial data. Leveraging evidence through quantitative frameworks like NTB allow us to leverage prior evidence to inform endpoint prioritization, reduce sample size requirements, and design trials that are more focused and efficient. Taken together, these strategies can accelerate development while ensuring that trials answer the questions that matter most to patients and clinicians alike.

If you could change one thing about how oncology trials are currently designed or evaluated, what would it be?

I would change the way we think about what constitutes “meaningful benefit.” Too often, trial designs are anchored to endpoints that may not always reflect all that matters to patients. If I could change one thing, it would be to make the explicit prioritization of outcomes — guided by both clinical insight and patient perspective — a standard part of trial design.

This means going beyond survival curves and response rates to also consider factors like symptom burden, treatment-related toxicity, and quality of life outcomes. It also means valuing methods that allow for the integration of these outcomes in a structured, interpretable way, such as GPC. Redefining what constitutes benefit is essential if we want oncology clinical trials to keep pace with the growing emphasis on patient centricity. It’s not enough to demonstrate efficacy, we need to ensure that evidence reflects the outcomes patients value and the trade-offs they are actually willing to make.