Public Funding for Mendelian Ltd

40,000 people in the UK (c.200,000 in the US) are affected by a neuroendocrine tumour (NET). NETs are rare cancers that are hard to detect, and are often mistaken for other conditions. As a result patients face significant delays in reaching a diagnosis (median \>4 years from symptomatic onset). Late diagnosis affects patient prognosis (survival rate), costs the NHS (while patients go through many tests) and diminishes the effectiveness of treatments. Mendelian, a UK digital health company, has developed MendelScan, a technology that can scan electronic health records to detect patients who likely have a rare disease but have been missed by standard care. It is a Class 1 Medical device that uses pseudonymised data to protect patient privacy, while supporting NHS clinicians to offer world-leading care. Mendelian already works with the NHS and life sciences organisations to detect potentially undiagnosed patients with 30 rare diseases, and aims to find them paths to treatment, diagnosis or clinical trials. This grant will fund Mendelian's industrial research into NETs, with the purpose of expanding the MendelScan platform. If successful, this would lead to an ever greater value of Mendelian's technology: for healthcare providers and payers across the world (including the NHS); for patients who are sick but who lack a diagnosis; and for life sciences companies who seek to get patients early access to therapeutic technologies. Mendelian will aim to generate sufficient clinical and technology validation for MendelScan in NETs, and remains committed to the ethical, collaborative development of digital health technologies, to benefit patients around the world.

Individually, rare diseases impact small numbers of people. Collectively, they impact 1 in 17 people and are a public health priority. A significant portion of rare diseases are, by their nature, difficult to diagnose. Non-specific symptoms may present slowly over time as the diseases impact multiple organ systems leading to repeated mis-diagnoses. Average time to diagnosis exceeds 5 years and the likelihood of positive outcomes frequently declines over this time. Mendelian's goal is to shorten the long 'diagnostic odyssey' rare disease patients endure. By helping to identify rare diseases faster, Mendelian is also contributing to significant costs reduction in the NHS and reducing health inequity in rare disease diagnosis, by providing the right information to the right clinician at the right time. The Company has been working in the field of rare diseases for the last 5 years and has been a strong advocate for the use of digital health and data in Rare Disease diagnosis. Mendelian has a strong team of clinicians, computer scientists and patient advocacy executives who are all passionate about providing answers faster to patients who need it the most. Mendelian's technology has the ability to positively impact the daily lives of patients, by allowing doctors to diagnose patients in a shorter time frame. With an early detection of a rare disease, it allows patients to put an end to prolonged diagnostic odyssey and have a much better quality of life, being able to adapt to their disease and begin treatment where required. At the same time, it is important to protect patient privacy and confidentiality. Mendelian employs best practices with regards to technical architecture and patterns to ensure data integration, access and processing is secure. This project will allow the Company to develop scalable ways to safely grow its footprint in the NHS.

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"**VISION:** The diagnosis of rare disease is complex, challenging and costly - with missed and misdiagnosis having a significant health and economic consequences for both patients and the NHS. We intend to redress this and make diagnosis easy, quick and most importantly accurate - overcoming some the key technical and clinical issues associated with the current diagnosis process. **OBJECTIVES**: Building on our prototype rare disease database, which suggests rare disease diagnosis with 78% accuracy based upon the input of of 5 defined symptoms, we want to develop the world's first automated rare disease screening tool. Designed to sit alongside and integrate directly with medical record software at a GP clinic or hospital, utilizing SnoMed code classifiers, the tool is capable of automatically scanning patient's medical record for symptoms, past and present, and then cross referencing these against our live database of 8000+ rare diseases, flagging potential, undiagnosed rare disease patients. These are then ranked in order to give a weighting, enabling a doctor to make an informed decision regarding likelihood, need and urgency of treatment. Many of these hard to diagnose diseases have effective treatments that can drastically improve patient outcomes and reduce costs and burden on medical systems. **FOCUS:** The project focuses on the development of four key elements; 1. Integration into electronic health systems through API and with SnoMed code classifiers. 2. Development of the patient screening system which will plug onto the current Mendelian diagnosis suggestion tool 3. Working with selected specialist physicians and clinical experts to determine the patterns, weighting and recommendations for each disease group as well as the health economics behind these. 4. Development of the personalised alert system **INNOVATION**: The first real-time patient screening tool that will flag potential undiagnosed rare disease patients within health systems. **OUTCOME**: There are over 3 million rare disease patients in the UK (350m+ worldwide) making this a sizeable commercial opportunity, as well as directly addressing a defined UK NHS and global need. The benefits of this are sizable and immediate; * Patients: Quicker access to the correct treatment, improving health impacts and enable preventative treatments through earlier diagnosis. * Clinicians: Reduce diagnosis time;, reduce misdiagnosis; reducing research needed for diagnosis and number of appointment. * NHS: Potentially save NHS £2.6bn per year in rare disease cost (£15bn NHS spending X 25% diagnosis spend x 70% saving)."

Unfortunately, a lot of the information we need is stored online in complex and unorganised databases. Understanding DNA's mutations is difficult and only an external expert with deep knowledge and experience in the field could help us make sense of them and their phenotypic importance (i.e which ones to keep vs ones to discard). Moreover, the field being so dynamic, we found it would be useless for us to start learning these expert skills as they would have to be constantly updated.