EIM Via the Myolex mScan as an ALS Biomarker

Amyotrophic lateral sclerosis (ALS) has been traditionally considered incurable and untreatable. But starting in the 1990s with the introduction of Riluzole, therapies are being discovered and ultimately approved for slowing disease progression. Given ALS's uniquely devastating nature, the fact that it is considered an "orphan disease", and uncertainties about its complex pathogenesis, many pharmaceutical companies continue to seek new therapeutic approaches. In fact, despite a relatively poor track record of success, there are a plethora of new studies starting up or planned in the near future. One critical aspect of all clinical trials is the need track to progression sensitively to identify the impact of therapy. Tools to track ALS progression must be convenient, objective (i.e. not influenced by patient or evaluator mood or engagement), require minimal training, be easily standardized, and be cost-efficient. Moreover, ideally, such measures, also called biomarkers, could also be used flexibly for improving individual patient care and could be applied effectively at home. Most ALS studies over the past two decades have relied on the ALS functional rating scale-revised (ALSFRS-R). Yet, it is relatively insensitive to change, altering, on average, less than 1 point per month, and requiring a large sample size to detect a drug effect, as demonstrated by several recent trials that have used it. There has been a push to identify accurate, objective biomarkers of ALS progression. In this study, the investigators propose to use Electrical impedance myography (EIM) to evaluate the progression of the disease. Work has shown that the EIM 50 kHz phase value from one or more muscles, followed sequentially, can serve as an effective overall biomarker for assessing the rate of ALS progression for a single person. Aim 1: To evaluate the sensitivity of EIM 50 kHz phase values to ALS progression, as measured by the Myolex mScan device, such that it may be able to serve as a monitoring, prognostic, or pharmacodynamic biomarker in future studies of ALS. Aim 2: To evaluate the potential for additional at-home assessments to improve sensitivity to change/deterioration in ALS and to assess general acceptability to patients/caregivers of doing these measurements at home. Aim 3: To utilize the full set of multifrequency parameters to assess disease progression overtime via the application of machine learning analytics to increase the power of EIM as an ALS biomarker.