Recon4IMD
Research type
Research Study
Full title
Reconstruction and Computational Modelling for Inherited Metabolic Diseases
IRAS ID
335410
Contact name
Shamima Rahman
Contact email
Sponsor organisation
National University of Galway
Duration of Study in the UK
2 years, 7 months, 31 days
Research summary
To date, according to the International Classification of Inherited Metabolic Disorders (ICIMD), over 1450 different Inherited Metabolic Diseases (IMDs) are known, and this number is rapidly increasing. Individually, each IMD is a rare disease with a prevalence of 0.1 to 15 per 100,000 newborns, but overall, 1 in 500 newborns is affected by this type of disorder. IMDs have a wide range of clinical presentation and severity, in which many patients may suffer from serious health problems leading to high morbidity, reduced life expectancy, and poor quality of life. Given the complexity of IMDs, diagnosis in the current standard of care is carried out in a sequential diagnostic process and can take several years.
The overall objectives of this study are to accelerate the diagnosis, and enable the personalised management of IMDs. There are established academic technologies for statistical genomic analysis, deep learning-based prediction of protein structure, and whole-body metabolic network modelling. These technologies shall be applied and integrated with patient-derived genomic, transcriptomic, proteomic and metabolomic data to generate personalised computational models. The personalised models will converge several advanced diagnostic technologies into a comprehensive systematic tool to accelerate diagnosis. To train, refine, and test these personalised computational models, the Recon4IMD clinical study will recruit the diagnosed patients with a variety of IMDs and undiagnosed patients suspected to have an IMD.
Patients shall be recruited from within established patient registries, the Unified European Registry for Inherited Metabolic Diseases (U-IMD), the Registry for Inherited Mitochondrial Diseases (GENOMIT) and established clinical cohorts (Solve-RD).
Personalised computational modelling will also be used to identify compensatory and aggravating mechanisms that are associated with clinical severity in a focussed subset of IMD patients. Furthermore, to maximise the potential impact, personalised modelling software will be developed to be generally applicable to a broad variety of IMDs.REC name
West Midlands - Coventry & Warwickshire Research Ethics Committee
REC reference
25/WM/0032
Date of REC Opinion
18 Feb 2025
REC opinion
Further Information Favourable Opinion