The importance of patient voices and Net Treatment Benefit

A Q&A with Sebastien Coppe, CEO of One2Treat, on One2Treat Voice

What motivated the development of the One2Treat Voice app, and how does it build upon One2Treat’s broader platform goals?

The One2Treat Voice app was developed to address a central challenge in clinical development: ensuring that the primary endpoint, or primary analysis, of a clinical trial truly reflects the priorities of patients and clinicians.

Traditionally, trial endpoints are selected by small expert groups. Each endpoint is typically analysed separately, each focusing on a single outcome measure, such as seizure frequency for the primary endpoint of a Phase III trial in rare diseases.

While important, this narrow approach can overlook other dimensions crucial to the patients such as their quality of life, tolerability, or daily functioning that matter deeply to patients and clinicians.

One2Treat Voice changes this by enabling patients, caregivers, investigators, and sponsors to directly integrate their multi-faceted perspectives, as well as to prioritise which outcomes are most relevant to them.

Through a structured, scalable, and anonymised process, participants compare simulated patient outcomes, allowing the app’s adaptive algorithm to quantify the relative importance of different outcomes and the thresholds that define meaningful clinical benefit.

These structured preferences then feed into the broader One2Treat platform, where multiple prioritised outcomes are integrated into a Net Treatment Benefit (NTB) analysis using rigorous statistical methodology.

The NTB assessment may be used as the primary analysis of a clinical trial. This leads to trial designs that are more aligned with real needs, scientifically robust and efficient. Indeed, this approach often leads to large sample size reduction.

As a consequence, if you need less patients to answer your clinical trial’s primary analysis, you can reduce study timelines and costs, ultimately delivering faster evidence that better demonstrates the overall medical value of a new treatment.

How are patient voices being integrated into the development and refinement of solutions? Why is this important?

Patient’s voices are central to the design of One2Treat Voice, which was built to bring together perspectives from patients, caregivers, advocates, clinicians, key opinion leaders, and pharma experts.

Each group contributes a different lens. Patients highlight the day-to-day realities of living with disease, caregivers and advocates bring broader community perspectives, while clinicians and KOLs ensure clinical relevance.

By capturing and balancing these diverse viewpoints, One2Treat Voice enables sponsors to identify the outcomes that truly matter across the spectrum of stakeholders.

This diversity is not only valuable for trial design, it also informs better decision-making throughout development.

Early engagement with regulatory bodies ensures that the prespecified primary endpoint meets both scientific standards and patient priorities, increasing the likelihood of regulatory acceptance, payer alignment, and clinical adoption.

Most importantly, integrating the patient voice is not just a methodological improvement, it is a moral imperative.

When treatments are evaluated using primary endpoints that reflect real needs and lived experiences, the therapies reaching the market are more likely to deliver meaningful benefit.

In the end, this approach brings better treatments to patients sooner, and in ways that truly match their expectations.

What makes the Net Treatment Benefit a compelling approach for multi-dimensional endpoint definition?

The Net Treatment Benefit (NTB) is a powerful tool for assessing multi-dimensional endpoints in randomised clinical trials.

Unlike traditional approaches that focus on a single primary outcome, NTB integrates multiple clinically relevant outcomes into one holistic measure of treatment effect.

To assess the Net Treatment Benefit, we use the Generalized Pairwise Comparisons (GPC) methodology. This method evaluates every possible patient pair between treatment and control arms in randomised clinical trials.

Each pair is compared first on the most important outcome, to see if one patient got a significantly larger clinical benefit.

If equal, the comparison moves to the second most important outcome, asking the same question; “was there a clinically significant benefit for one of the two patients?”.

As long as we cannot distinguish a clear benefit for one of the two patients, we keep looking to the next outcome in the list of prioritised outcomes.

The NTB then expresses the net probability that a random patient on the experimental treatment does better overall than a random patient on control.

For example, an NTB of 20 per cent means a patient has a 20 per cent greater chance of doing better with the new treatment compared to the control treatment.

Interestingly, because the NTB is a difference of probabilities, its inverse estimates the “number needed to treat” (NNT).

A NTB of 20 per cent corresponds to a NNT of five, meaning that, on average, five patients yields one additional patient that does better on the experimental treatment than on control.

This patient-centered, statistically robust approach offers several advantages. It leverages information from all meaningful outcomes, enhancing statistical power and often reducing the required sample size.

For instance, in oncology, it may capture trade-offs between overall survival, tumor response, quality of life, and tolerability that matter in real-world decision-making.

Importantly, NTB has been successfully applied in trials and regulatory submissions (for instance in cardiovascular disease and in rare disease trials), demonstrating its value as a robust, intuitive, and clinically relevant measure of treatment effect.

How can holistic data analysis improve outcomes?

Holistic data analysis has the power to transform how we understand the overall medical value of a treatment.

Traditional trial reporting often focuses on a single primary outcome, which can leave a wealth of clinically relevant data underutilised.

One2Treat Insights addresses this gap by analysing multiple prioritised outcomes together, creating a comprehensive view of a treatment’s net benefit.

For instance, in oncology, prioritising multiple outcomes such as overall survival, symptom relief, quality of life, and tolerability into a single treatment assessment, One2Treat Insights can help to understand not just whether a treatment works, but how it performs across dimensions that matter to patients and clinicians.

This approach can reveal meaningful trade-offs, for example, a therapy that modestly improves survival but significantly reduces toxicities and enhances quality of life may be the better choice for certain patients.

This type of analysis provides a quantitative way to estimate the balance of benefits and risks which is particularly useful for dose selection, a topic of growing importance for sponsors in light of the FDA’s Optimus Project.

These insights are valuable at every level: sponsors gain a clearer picture of treatment differentiation, regulators and payers see evidence aligned with real priorities, and clinicians and patients receive information that helps them choose the most suitable therapy based on individual needs.

Ultimately, holistic clinical data assessment ensures that trial results are not reduced to a single number but instead reflect the complexity of multi-faceted patient experience, improving decision-making and supporting better health outcomes.

Looking ahead, where do you see the greatest opportunity to disrupt traditional models in trial design?

The greatest opportunity to disrupt traditional trial design lies in moving beyond a single dimension as primary endpoint and embracing a more holistic view of treatment effects that includes efficacy, safety and quality of life measures.

Today, most trials rely on unidimensional measures. While essential, these endpoints often fail to capture the complexity that patients experience: symptom burden, functional ability, and quality of life.

This gap is especially pronounced in rare diseases, where patient populations are small and the range of symptoms is diverse.

Future trial models must adopt approaches that integrate these varied dimensions without sacrificing rigor.

The real disruption comes from systematically embedding the multi-faceted patient and clinician perspectives early in protocol development, ensuring that the primary analysis of a trial reflects the realities and priorities of daily living with a disease.

This evolution allows sponsors to generate evidence that resonates with regulators, payers, and, most importantly, patients.

Rare disease research highlights the urgency: when sample sizes are small, every data point matters and overlooking meaningful outcomes risks missing a treatment’s true value.

By designing trials that balance statistical power with clinical relevance, sponsors can accelerate development, reduce costs, and deliver therapies that are more readily adopted by patients and clinicians.