Cambridge, United Kingdom – 14th January, 2020 – Congenica, a digital health company enabling rapid analysis of complex genomic data to improve disease characterization and diagnosis, today announced the launch of Congenica Neuro™, a new application on its clinical decision support platform that facilitates faster, more accurate and comprehensive characterization and diagnosis of epilepsy and neurodevelopmental disorders.
Collectively, around 300 million people worldwide are estimated to have an epilepsy or neurodevelopmental disorder with thousands of newborn children being affected each year.1,2
In the absence of birth trauma or other environmental causes, understanding why a developmental disorder has occurred can be incredibly complex. However, recent studies have shown that many of these disorders have an underlying genetic cause.
Approximately 40% of children with neurodevelopmental disorders are found to have a molecular cause however, the molecular causes are extremely complicated and can be attributed to over 1,500 genetic loci.3,4 This extensive genetic heterogeneity presents a major challenge for even the most experienced physician.
To help overcome the diagnostic challenge, Congenica has developed Congenica Neuro, a proprietary application within its industry-leading clinical decision support platform to provide rapid, accurate and intuitive analysis and characterization of genetic epilepsy and neurodevelopmental disorders.
The software leverages expert-curated gene panels and pre-configured filters to automate DNA variant prioritization and streamline the identification, review and reporting of de novo variants in genes associated with neurodevelopmental disorders and disorders characterized by isolated or syndromic early onset epilepsy.
It is anticipated that Congenica Neuro will support healthcare professionals to characterize more epilepsy and neurodevelopmental disorder cases with greater efficiency in the same way that the Congenica platform has for rare disease cases.
Congenica software is used globally, throughout the UK NHS as the exclusive clinical decision support platform for the national Genomic Medicine Service and internationally by private and public hospitals and laboratories where the software reduces average interpretation costs by up to 95% and helps healthcare professionals to reach a decision 20-times faster.
Professor Norman Delanty, Consultant Neurologist and Associate Professor at the FutureNeuro Research Centre, Dublin: “In the evolving new era of genomic medicine, Congenica Neuro is an exciting and powerful tool to facilitate the rapid molecular diagnosis of young children with a variety of important neurodevelopment disorders including epilepsy and autism.
“The increasing volume of information related to molecular causation of brain disease is now beyond the scope of the majority of practicing clinicians. This automated timely diagnostic tool to determine the precise aetiology in an individual patient is becoming increasingly more relevant. An early precise diagnosis is of extreme importance to families and clinicians.”
Dr David Atkins, CEO, Congenica: “Congenica Neuro is a significant first-step for the company in focusing our platform’s extensive automation capabilities and our clinical expertise on a particular disease area to maximize the efficiency and opportunity for a diagnosis.
“Where once genomic analysis of these complex patient cases was incredibly challenging, even for specialists, the Congenica Neuro application provides healthcare professionals with simplified, accurate and reliable variant identification, clinical review and reporting – offering the potential to provide life-changing answers to millions of patients.”
Further information is available at www.congenica.com/neuro.
- Our World in Data. https://ourworldindata.org/neurodevelopmental-disorders
- World Health Organization (2019). https://www.who.int/news-room/fact-sheets/detail/epilepsy
- Shashi V, et al. Genet Med. 2014 Feb;16(2):176-82.
- The Development Disorder Genotype – Phenotype Database (DDG2P). https://decipher.sanger.ac.uk/ddd#ddgenes