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From Mendel to genetic rare disease diagnosis: Two centuries of innovation.

Credit Photo by John Towner on Unsplash

Enlightened breakthroughs...

More than 350 million people live with rare disorders, that is, a condition with which fewer than 200,000 have been diagnosed. About 80 percent of these rare disorders are genetic in origin, and 95 percent have no treatment approved by the FDA.

Our ability to better understand, diagnose and potentially find treatments for these conditions was significantly advanced by the Human Genome Project (HGP). From October 1990 until its completion in April 2003, the HGP brought together a team of international researchers who collectively defined over 90% of the human genome [i].

However, it would be fair to argue that the journey to understanding the human genome began with Gregor Johann Mendel, who is now recognised as the father of genetics. Born 200 years ago, in 1822, Mendel put his interest in plant genetics into practice with pea plant experiments conducted between 1856 and 1863, which established many of the rules of heredity, now referred to as the laws of Mendelian inheritance [ii].

His work was published in 1866 but was then forgotten about until 1900, when three researchers, Hugo de Vries, Carl Correns, and Erich Tschermak, independently realised the meaning and importance of Mendel`s groundbreaking work.

Mendel’s theories were finally associated with human disease by Sir Archibald Edward Garrod, who published the first findings from a study on recessive inheritance in human beings in 1902 [iii]. In 1909, the term “gene” was introduced for Mendel’s “traits” [iv].

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Accurate patient cohort identification

2022 07 WODC USA Presentation

Deploying AI in the real world to find rare disease patients: challenges and opportunities

Presentation - Tuesday 12th July 2022 at 16:15 to 16:35

  • Recap on what inTrigue is and what it has delivered to date.
  • An introduction to the challenges of deploying AI methodologies in real-world settings.
  • In order to help clinicians diagnose earlier, we need to factor many things into our deployment planning.
  • Earlier diagnosis means working in primary care or identifying the relevant treating physicians.
  • Using two rare diseases we will discuss some of the challenges that inTrigue overcomes.
  • Anyone interested in preparing their US or EU markets for an increase in the successful diagnosis of rare disease patients will be interested in this session.


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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.

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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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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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Determining patient prevalence with rare and ultra-rare diseases will help to build your gene therapy value story

Building Your Gene Therapy Value Story From Day One

Cell and gene therapies, also known as advanced therapy medicinal products (ATMPs), are potentially life changing. For patients with rare diseases, they extend the hope of a longer, healthier life and even of a cure. But these therapies are exceptionally expensive with up-front costs ranging from $500,000 to $2 million. Additionally, some incur heavy ongoing costs throughout the life of the patient.

For health technology assessment (HTA) organisations – which must balance clinical effectiveness, safety and efficacy with cost effectiveness, social outcomes and ethical considerations – the decision to support market access for ATMPs is a complex one. Budget constraints mean HTAs and insurers often must make tough decisions balancing the ATMP reimbursement with a reduction in spending elsewhere in the healthcare system. Consequently, therapies that are not viewed as compelling, face rejection. Moreover, the decision-making process can vary from region to region: vastly different decision criteria, for example, are adopted in the UK, the USA and China.

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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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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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