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.