Convenient quantification of myopathic change in muscle via electrical impedance myography

Project Summary Magnetic resonance imaging (MRI) is considered an excellent technology for quantifying muscle composition in a variety of disorders. MRI offers superb insights into muscle condition and pathology, including muscle size, the amount of fat deposition, and the presence of edema or inflammation. It is now often used as a tool in clinical trials to provide biomarkers for assessing disease progression and response to therapeutic intervention disorders ranging from Duchenne muscular dystrophy to atrophy related to aging or injury. However, MRI has major drawbacks as a muscle characterization tool, including inconvenience, high cost, the requirement for patients to lie flat (a major challenge in patients with impaired respiratory function), the need to standardize across systems, and the necessity for cumbersome image processing. A technology that could offer much of what of MRI has to offer but with greater convenience, lower cost, and simplified analysis could find wide application both for clinical trials but also ultimately for individual patient care. One technology that could achieve this is electrical impedance myography (EIM). EIM has independently been shown to correlate strongly with muscle pathology and to track disease progression and response to therapy. Myolex, Inc has made the development and application of EIM its focus. In this direct-to-Phase 2 SBIR application, Myolex proposes to establish EIM, via its new device, the mScan, as a valuable alternative to MRI for assessment of muscle condition in primary myopathic disorders. We propose to achieve this by performing EIM and MRI on patients with a variety of myopathies, including autoimmune and hereditary conditions, and using machine learning to develop predictive algorithms relating EIM to MRI. Our underlying hypothesis is that EIM data closely relates to muscle pathology as revealed by MRI and the simpler, more convenient technology of EIM can be trained to provide MRI-like assessments of muscle condition. In Specific Aim 1, in conjunction with physicians at Beth Israel Deaconess Medical Center, Harvard Medical School, we will collect MRI and EIM data on a cohort of healthy subjects and patients with primary myopathic conditions, including those with active muscle inflammation/ edema (secondary to myositis and toxic myopathy) and more chronic conditions (including hereditary myopathies and muscular dystrophies) of varying severity. Strength and functional data will also be collected. Using this data, in Specific Aim 2, we will develop predictive algorithms, via the penalized regression technique of Lasso (least absolute shrinkage and selection operator), leveraging EIM values to predict MRI findings. These findings will also be associated with functional measures. Successful algorithms will be incorporated into a cloud-based diagnostic engine to provide quantifiable data on muscle disease pathology. At the conclusion of this work, we will have developed an accurate, quantitative EIM system for predicting the type and severity of muscle disease pathology across a variety of conditions, helping to speed therapeutic drug development and to improve overall patient care.