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Rare Diseases

Cast the net

2022 07 WODC USA Workshop - How can AI inform better Clinical Development strategy, design, and patient stratification?

Credit Photo by Pietro Jeng Selfors on Pexels

Rare disease drug developers face significant challenges during clinical development, from finding patients to conduct their trials to addressing heterogeneity in the target patient population. To help rare disease innovators establish a better clinical development strategy, Volv is co-conducting an in-depth Workshop at the World Orphan Drug Congress (WODC) Boston, USA, which is being held between the 11th to 13th July 2022. Come and meet us at Booth #318.

Join us at our Workshop on Monday 11th July 15:00 to discuss:
Putting AI to work for rare diseases: How can AI inform better Clinical Development strategy, design, and patient stratification?

The Workshop will consider AI’s potential with inClude, for revolutionising how companies operate in the rare disease space. Areas that will be explored include:

  • novel approaches to obtaining new insights,
  • uncovering new information from claims data,
  • ways to better define target patient populations and novel endpoints, and
  • gaining new insights into the disease earlier in its progression.

Download the Workshop Agenda here:

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Creating ripples

It’s time to rethink clinical trial protocols, and ensure inclusive designs through democratising our health data, in a privacy-preserving way

Credit Photo by Marc Zimmer on Unsplash

It’s time to rethink...

Throughout my career in the pharmaceutical industry managing clinical trials and study programs, I have been confronted with the same recurring problems. Trials struggle to recruit and retain enough patients, they fail to meet target timelines and the vast majority don’t conclude on time.

There are some staggering statistics in the industry, for example, 86% of clinical trials don’t reach recruitment targets in the specified time and 90% of clinical drug development fails.

One obvious reason for these shocking figures is that the pharmaceutical industry overestimates its ability to recruit. But, more troublingly, study design and protocol development seemingly fail to truly reflect patients’ lives, or account for the reality in the clinic.

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Luggage Photo by Caroline Selfor on Unsplash

2021 11 WODC EU Workshop - Identifying rare: The patient journey vs the patient experience

Credit Photo by Caroline Selfors on Unsplash

Over the past year, many of you have joined us on a voyage of rare disease discovery. We have shared the huge challenges facing the rare disease community – from patients and their carers, clinicians and the companies developing life-saving treatments for patients in need.

How those challenges are framed varies depending on the stakeholder, but they boil down to the same issue: searching for, accurately identifying, diagnosing, and treating patients with rare and ultra-rare diseases. The difficulty is compounded by the fact that there are as many as 9,332 unique rare diseases and 21,582 synonyms, according to data from Orphanet.

We have described the ability to pick out patterns to identify patients with rare diseases as being a bit like identifying thousands of constellations of stars: Neither is within the scope of the human eye and both require extremely advanced technologies to even begin to decipher and separate patterns.

Yet to research and develop innovative products for rare diseases, companies must find ways to identify populations of patients for study purposes. And as the millions of patients with rare diseases and their families know all-too-well, the wait for a diagnosis, much less treatment, can be painfully slow, with symptoms often missed or not well-understood by the doctors that they see. Over the summer, we have shared real-world stories with you from patients with rare diseases and conditions or from the parents of children with debilitating rare diseases.

We shared Paul’s battle to get his narcolepsy recognised, diagnosed, and treated and the huge battle he faced professionally and medically. Tamsyn shared her difficult journey with getting the recognition, treatment and support she needed for her rare condition, Poland Syndrome, and how the poor understanding of her condition by healthcare professionals led her to studying biological sciences at university and to her passion for work in the rare disease space. And Bernd shared his story as a parent of a child with Alström syndrome and his battle to get his son, Ben, the support he needs.

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Picture of the World Orphan Drug Congress hosted a webinar titled, “How can AI impact industry?”

Driving patient-centricity through real-world evidence

Executive Summary

To acknowledge Rare Disease Day and the struggles facing patients with rare diseases, the World Orphan Drug Congress hosted a webinar titled, “How can AI impact industry?” The session, which explores the role of real-world evidence (RWE) to improve orphan drug development and access, brought together leaders in their field with deep knowledge of rare diseases and the importance of RWE in helping to identify the right solutions for the right patient.

During the discussion, several important and often overlooked themes were brought to the fore. The purpose of this paper is to explore these in greater depth and share the unique insights from the panel. These include the integral role of the patient in owning, managing, and deciding when, how and where to share their data. Certainly, a patient-focused approach that safeguards the individual’s privacy and ensures consent, is paramount if researchers and drug developers are to make full use of RWE to find therapeutic approaches and cures for rare diseases. In addition, the industry will need to address data sharing issues and legislative barriers and ensure they fully engage the regulators to bring therapies to patients in need. The paper provides a thoughtful and balanced discussion of these topics and aims to seed further insights as drug developers, clinical research experts, regulators, artificial intelligence specialists and, crucially, patients and their carers, seek to leverage tools such as RWE to help the millions of rare disease patients in need worldwide.

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Improving the Rare Disease Diagnostic Journey with using AI. Using Volv's methodology inTrigue and Scailyte's single-cell data analysis platform ScaiVision

Improving the Rare Disease Diagnostic Journey with Advanced AI

The journey from first symptoms to diagnosis is a long one for most patients with rare diseases. According to a survey from EURODIS, 25 per cent of patients with among the most common rare diseases waited between 5 and 30 years for a diagnosis and 40 per cent were misdiagnosed during that time.

There are many reasons why diagnosis is so challenging. One is that most physicians have limited knowledge about rare or ultra-rare diseases. Another is that as many as 60% of rare diseases present with significant heterogeneity of symptoms, making it extremely difficult to diagnose patients early enough in their disease progression.

A third barrier to diagnosis can often be attributed to the complexity of the diagnostic test currently available. For some rare conditions, deep muscle biopsies are still used for diagnosis and for some rare heart conditions, stents are often the only commercial diagnostic tool available to accurately identify a condition.

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Why can’t we find the 50% of people with rare diseases who remain undiagnosed?

Why can’t we find the 50% of people with rare diseases?

It might be said that picking out patterns to identify patients with rare diseases is a bit like distinguishing thousands of constellations of stars. Neither is within the scope of the human eye and both require extremely advanced technologies to even begin to decipher and separate patterns. Yet finding the 50 percent of undiagnosed patients with one of the approximately 7,000 rare diseases is a medical and clinical imperative.

Typically, the way clinicians diagnose patients is by taking what the broader healthcare industry knows about a disease – generally as described by key opinion leaders (KOL) – and correlating a patient’s symptoms to those definitions. The problem with this approach when it comes to rare and ultra-rare diseases is that it is subject to experiential bias. If the KOL has not observed a pattern of symptoms or the order in which those symptoms emerge differs significantly, the patient will likely remain undiagnosed.

There is so much we don’t know about rare disease, but what we do know is that there is enormous heterogeneity of symptoms – so much so that as many as 60% of rare diseases present with significant heterogeneity, according to genomics experts. Understanding this 60% variation in symptoms with rare diseases is undoubtedly the greatest challenge facing both healthcare professionals as well as the companies seeking to find and develop new treatment options. Even for those rare diseases where there are already treatments, the difficulty can be diagnosing patients early enough to limit the worst effects of the disease. For example, some symptoms may not be flagged as significant from a clinical perspective, despite the challenges they present to the patient on their journey to diagnosis, and by the time the patient’s symptoms escalate to correlate with recognised patterns, it’s often much later in the disease’s progression, on average, six years from the onset of symptoms.

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Quantifying Predictive Power of Features in Electronic Health Record Models – the Volv inTrigue way

From predictive to interpretable models

At Volv we provide the insights on our approaches and methodology 

Quantifying Predictive Power of Features in Electronic Health Record Models – the Volv inTrigue way


When we work on complex prediction models with our inTrigue methodology, we are often asked to help clinicians and others to interpret these models by listing the patient features (attributes) which are used by the model to form its predictions. And indeed, generating a list of predictors ranked by their ‘importance’ in a model can translate to improved interpretability and clinical impact. However, there is some work that needs to be carefully considered in order to produce tooling that is derived from complex models that can provide real benefit in a clinical situation. So Rich Colbaugh and I decided to discuss this for you, our audience.

Interpretability is a theme that often surfaces when considering data science model outputs, and the issue of a 'black-box' system is often cited 

as a challenge for customers and is discussed at many conferences. As it is important to deliver real world results, we discuss here what Volv does to create interpretable models of true utility. The journey from complex modelling to interpretable models is however not necessarily simple when dealing with real-world solutions involving highly dimensional messy data.

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This case study derives from a pharmaceutical company that approached Volv to develop a prediction model to find patients suffering from a rare disease

Finding Patients for Rare Diseases: A Case Study

The Case Study Problem

This case study derives from an ongoing Volv engagement, which started in July 2017. A pharmaceutical company approached Volv Global to develop a prediction model to identify additional patients suffering from the rare disease treatable by its specialist medicine.

This pharmaceutical company faced four difficulties: the disease prevalence was one in a million of population; specialist clinicians were able to diagnose the disease with no more than 76% accuracy; only one in four patients were ever identified; and those that were identified, were done so generally after six years of misdiagnoses. To compound these four difficulties, the pharmaceutical company was unable to provide medical records for any already diagnosed patients.

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