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Center for Global Research Data

in-silico DMD: Developing computational predictive models for the progression of Duchenne Muscular Dystrophy

Lead Investigator: Georgios Paliouras, National Centre for Scientific Research ‘Demokritos’ (NCSR-D)
Title of Proposal Research: in-silico DMD: Developing computational predictive models for the progression of Duchenne Muscular Dystrophy
Vivli Data Request: 6740
Funding Source: None
Potential Conflicts of Interest: None

Summary of the Proposed Research:

Duchenne muscular dystrophy (DMD) is a severe and fatal genetic disorder affecting around 1 in 5000 male births. Despite many years of research in DMD, we are still lacking a good understanding of the factors that influence the development of the disease. This lack has a significant effect on research progress and slows down the development of effective therapies. Recent clinical trials have produced encouraging results, but they have also demonstrated the variability of response between different patients. This variability remains largely unexplained, due to our limited understanding of disease progression.

The main purpose of the in-silico DMD project is to make the most of available data about DMD, in order to produce computational models of disease progression. These models will incorporate factors that explain the variability between different patients and will thus be useful in clinical trial design and therapy development.

Such computational predictive models can be generated with the use of Machine Learning (ML) methods on historical patient data. The ML methods are able to reveal intricate patterns in the data that explain the observed development of the disease for specific populations of patients.

Requested Studies:

A Prospective Natural History Study of Progression of Physical Impairment, Activity Limitation and Quality of Life in Duchenne Muscular Dystrophy.
Data Contributor: Cure Duchenne
Study ID: NCT01753804
Sponsor ID: PRO-DMD-01