Finding the right patients: The case for stratification in rare disease

The word diagnosis carries a lot of weight in the rare disease space. And for good reason. Patients often face months – if not years – of waiting, worrying, and questioning whether they will ever arrive at an answer to the question, ‘What disease am I living with?’

But while it is an important part of the journey, arriving at a diagnosis is just one chapter of a much longer story for both patients and clinicians.

Rare diseases are often heterogeneous, which means that two patients can share the same diagnosis, but have very different symptomology, disease progression, and therefore very different needs. As such, clinicians need to understand how the disease will progress for different patients and, perhaps most importantly, which treatment options are likely to have a meaningful benefit.

Traditionally, clinicians have used an empirical approach to match patients with the most effective treatments, however, advancements in molecular diagnostics have opened the door for an alternative approach: patient stratification.

“Patient stratification is basically matching the right patient with the right treatment – however, it is above the level of personalised medicine,” explains Aishwarya Ambhire, a principal in the insights and analytics team at Acumetis.

By grouping patients according to characteristics such as disease severity, clinical presentation, disease progression, or likely treatment response, researchers can build a more detailed picture of the population they are trying to treat. The goal, at least for Acumetis, is to look beyond the question of who has a particular disease, to focus on how a disease will progress, and who will benefit most from what type of intervention.

The limits of diagnosis

Rare diseases present a wide array of diagnostic challenges. While some have relatively well-established pathways, others can manifest in symptoms that overlap with more common conditions and affect several parts of the body at the same time. For clinicians, identifying the root cause is akin to finding a diagnostic needle in a haystack.

For Ambhire, von Willebrand disease (vWD) – an inherited bleeding disorder – perfectly illustrates how this can present in practice.

“In this disease, a female has heavy menstrual bleeding or when they just have a minor dental procedure, they bleed a lot,” she explains. “There could be many factors that could lead to heavy menstrual bleeding, hence you will not assume that a patient has von Willebrand right away.”

Even when patients arrived at a confirmed diagnosis, they may not have the same clinical needs. As Ambhire notes, some patients with vWD will only experience infrequent heavy menstrual bleed symptoms, and may never need routine treatment, while others may face substantial bleeding risks with continuous bleed intervention being necessary – both patients suffer from vWD – potentially with the same genotype, but presenting with varying bleeding phenotypes. The condition is the same, but the consequences are not. This is where patient stratification can act as a funnel, helping clinicians to identify those most in need of treatment.

“Diagnosis in rare disease is just a first step,” says Ambhire. “You actually have to go down the very specific route of patient stratification and what type of patients you are actually looking at because a patient can, for example, have the same vWD genotype, but their bleeding manifestations (phenotype) could be different. That is a challenge in the rare disease space.

“Patient stratification goes beyond a patient population’s prevalence and certain diagnosis rate” she continues. “The true prevalence differentiates between patients who have some type of symptomology, and patients with symptoms that actually manifest in disease progression that actually need treatment.”

A longer view of rare diseases

Alongside patient stratification, Ambhire points to newborn screening as a tool that can help to bridge the diagnosis gap by identifying conditions long before symptoms appear. While this can’t definitively say how the disease will manifest as the patient ages, it does create an opportunity to follow individuals from an early point in their lives, potentially revealing how the disease develops over time and can prevent or intervene early to slow or stop disease progression in its tracks.

Ambhire highlights lysosomal storage disease screening as a prominent example.

“In the US, for lysosomal storage diseases, such as Pompe Disease, is included in a newborn screening protocol even without confirmed family history,” she says. “You don't have a confirmed disease since you might have no symptom onset, but at least treaters/HCPs get a certain pool of patients that they can monitor over time since they now know that those have genetic underlying factors.

“If there was no newborn screening and you have Pompe – you can have a movement issue, you have a muscle issue, or you can have a cardiac issue – and you will bounce between general practitioners, neuro-muscular, and cardiac specialists based on your organ malfunction until you reach the correct Pompe specialist. It's a long journey for a patient to get to the correct diagnosis and subsequent treatment. You might even get treated for the wrong disease for a while.”

Ambhire’s example demonstrates an ongoing predicament for rare disease care. But earlier identification may give clinicians more time to understand a patient’s condition, and lets them monitor patients early on whose future clinical might remain uncertain. As such, ensuring quality clinical data is crucial.

The same data limitations, particularly longitudinal data limitations, are often cited as a prominent barriers in modern drug development as disease progression and manifestation is difficult to study in small patient populations with varying presentations; and due to the very small absolute number of patients it becomes even harder for researchers to establish who needs what treatment, and what an effective treatment can achieve within those few patients that present a certain way.

“There is often no robust data on these patients available,” says Ambhire. “Monitoring the journey of these patients is very important in rare diseases. Timeline issues, such as when did the patient get diagnosed, when did that patient receive their first treatment? What product type, what brand, and other treatments did the patient receive? If they switched products, when did that happen and why did they switch to the treatment?

“That type of detailed patient stratification data is scarce. You will have high level papers on prevalence rates with a general population, but then entering what we call “the patient funnel” of diagnosed patients, leading to actually addressable patients that could actually benefit from a certain medication or treatment, that funnel is incredibly difficult to define in rare diseases but even more important to address since it often becomes the deciding factor between product approval or reimbursement and leaving patients without a proper treatment option.”

Making the case for treatment

For drug developers, patient stratification data contributes to an important area of life sciences.

Developing any new drug is a long and costly affair. But in rare disease, where patient populations may be sparse and heterogeneity is common, justifying the investment needed to produce specific treatments is challenging, and convincing payers to back access for patients can be even tougher.

Hence stratification data is needed to generate evidence about the benefits of a certain treatment for a definable patient demographic justifying that a new therapeutic should be developed.

Ambhire describes this as a funnel. The patient population narrows at each stage. Beginning with the theoretical/mathematical number of people estimated to carry a rare disease, before reducing to those individuals who received an actual diagnosis, and then to those who develop symptoms and ultimately after many steps those who meet the criteria to actually benefit from a particular treatment. Ultimately, the final group of eligible individuals may be considerably smaller than what the headline prevalence number suggests.

There are, of course, limits to what stratification alone can achieve. It cannot compensate for weak clinical evidence of a drug, but it can point in what patient sub-population that drug might work more so; it cannot guarantee reimbursement, but it does provide payers and clinicians with a better understanding for what type of patient reimbursement should not be held back.

And that can make the difference between identifying a potential treatment population and demonstrating why a therapy should be made available to it and when not. However, patient stratification should not be used as the ultimate deciding factor, rather as guidance – because ultimately, in an imperfect medical environment the final judgement call needs to be “who heals is right” and that should be paid for – no matter what.

Besides that, for patients, stratification can also be quite helpful to clarify individual arising needs as their condition and time progresses, which allows them to prepare for what’s to come, and buckle up for the ride to remain a valuable member of our global society – no matter what that looks like, or how rare of a condition they will live with.

About Acumetis

Acumetis Blue

Acumetis is a global expert commercialisation partner purposefully built to help life sciences companies win in the age of connectivity. With an integrated model spanning the full asset lifecycle, deep functional and therapeutic-area expertise, and rigorous, outcome-driven thinking, Acumetis reframes complex problems and shapes solutions that are both imaginative and achievable.

With more than 350 experts globally and experience across 50+ geographic markets, Acumetis helps clients unlock value, accelerate performance and deliver better outcomes for patients.

About the author

Aishwarya Ambhire is principal in insights and analytics at Acumetis. With over six years of experience at Acumetis, Ambhire specialises in rare diseases; her focus areas include haemophilia, lysosomal storage diseases, and plasma-derived products like albumin, where she has contributed to shaping market strategies and providing valuable insights for clients. She holds an MSc in Bioengineering from the University of Illinois at Chicago and a BE in Biomedical Engineering from the University of Mumbai.

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