Relaxivity Contrast Imaging as Biomarker of Muscle Degeneration in ALS

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ABSTRACT Amyotrophic Lateral Sclerosis (ALS) is characterized by loss of spinal and cortical motor neurons, resulting in progressive muscle atrophy and eventually, death. The clinical heterogeneity and rapid progression of ALS continues to confound the identification of treatment response biomarkers. Currently, clinical trials (and practice) are forced to rely upon downstream indicators of disease status such as muscle strength, respiratory function and functional rating scales. Such measures, although validated, have a number of limitations. First, they have significant inter-rater variability. Second, the measures usually have relatively slow rates of change and thus, require months and even years to detect a treatment effect. These challenges underscore the unmet need for sensitive, reproducible, and non-invasive biomarkers of therapy response. To overcome these limitations, we propose to develop a non-invasive imaging approach, termed relaxivity contrast imaging (RCI). Unlike existing image-based biomarkers that reflect downstream changes in pathophysiology (e.g. T2 - edema, fat fraction), RCI is uniquely sensitive to myofiber architectural features (e.g. reduced fiber diameter and density, fiber atypia) exhibited by ALS patients. We hypothesize that RCI could serve as a pharmacodynamic/response biomarker to show efficacy of and biological response to therapeutic interventions in Phase 2/3 clinical trials of agents designed to slow or reverse neurodegeneration in ALS patients. To develop RCI, we will use a validated computational framework to systematically characterize the biophysical basis of RCI in the context of muscle degeneration and treatment response and use it to identify optimal acquisition and analysis strategies for applying RCI in a clinical trial. In preclinical rodent models of ALS, we will verify the association between RCI-based biomarkers and pathologic markers of muscle architecture and establish the utility of RCI-based biomarkers to detect response to therapy. In humans, we will characterize performance characteristics and repeatability of RCI protocols in healthy controls and ALS patients. To further refine RCI, we will establish quality control measures for RCI data acquisition and analysis and characterize age- and sex- dependent reference intervals of RCI-based biomarkers in healthy controls. Finally, we will establish the sensitivity of RCI-based biomarkers to disease progression in ALS patients and compare to other advanced image-based biomarkers and routine clinical markers of disease status. Ultimately, RCI has the potential to serve as a quantitative, myofiber-specific, image-based biomarker of early therapeutic response for ALS, potentially enabling smaller sample sizes, earlier Go/No Go decisions, more cost- effective clinical trials and, ultimately, improved patient care.