Metabolomics to Identify Targets in ALS
Abstract
Amyotrophic Lateral Sclerosis (ALS) a complex disease that is characterized by the death of neurons and the subsequent loss of control of muscles. The course of the disease varies among people with ALS. Most survive for less than 4 years with the disease, while some survive for more than a decade. This variability makes developing clinical trials challenging. It is difficult to tell whether the patients whose disease progresses slowly are benefiting from the drug, or would have progressed slowly without treatment. Improving treatment will require being able to tell apart the many different types of ALS and determining separately whether drugs are working for each group. We propose a new approach to discovering subtypes of ALS that will be of immediate use in improving clinical trials, and can have a long-term impact on the discovery of new ways to treat ALS. We aim to study the biological processes that vary among patients with faster or slower progression. We will take advantage of a large collection of blood samples from AnswerALS, a longitudinal study that followed 1,000 patients. We focus on two types of molecular measurements, i.e., metabolomics and neurofilaments, that have been shown to play a role in ALS. Metabolomics provide a snapshot of the biochemical state of a patient s cells. Neurofilament concentrations indicate the level of a patient s neuronal damage. With these methods, we will profile 150 people with ALS who have three or more visits. We will also profile 77 healthy controls and 47 people with other non-ALS motor neuron diseases. In contrast to previous work, we will focus on blood plasma samples from patients that have been monitored over time, through multiple clinical visits. We also introduce important innovations in our approach. First, we integrate these time-series data with other molecular markers that have already been collected by the Answer ALS consortium and with hundreds of thousands of data points from prior research about how molecules interact. We also use AI-based algorithms to identify clusters of patients with similar disease progression patterns. We expect that the results of our study can dramatically improve the design of clinical studies and, in the long term, can contribute to the development of new therapies for ALS.
Document Details
- Document Type
- DoD Grant Award
- Publication Date
- Dec 05, 2021
- Source ID
- W81XWH2110245
Entities
People
- Ernest Fraenkel
Organizations
- Massachusetts Institute of Technology
- United States Army