December 14, 2023

Dr. Priyadip Ray, Ph.D., Lawrence Livermore National Laboratory

Amyotrophic lateral sclerosis, ALS, is a progressive neurodegenerative disease that leads to the loss of upper and lower motor neurons in the motor cortex, brain stem, and spinal cord. ALS is always fatal. For unknown reasons, Veterans are 1.5 times likelier than the general population to develop ALS.¹ The VA’s historical database is currently the largest longitudinal, comprehensive ALS dataset and has record of over 21,000 Veterans diagnosed with ALS. The pathophysiological mechanisms underlying ALS onset and progression are still largely unknown. Currently, there are no effective treatment strategies for ALS.

Priyadip Ray, Ph.D., at the Lawrence Livermore National Laboratory received a fiscal year 2020 Amyotrophic Lateral Sclerosis Research Program, ALSRP, Therapeutic Idea Award to identify drugs that could be repurposed to treat ALS. To do this, the team is testing the hypothesis that drugs prescribed for other indications can alter an individual’s risk for ALS and/or its progression. Throughout this project, the team, consisting of machine learning researchers from the Lawrence Livermore National Laboratory, clinicians from VA Palo Alto and pharmacology researchers from Stanford University and University of California Los Angeles, is using causal machine learning.

Causal machine learning is a method of analyzing large amounts of data to identify cause-and-effect relationships underlying the information within a dataset. This method can be used as a tool in medical research to find previously unknown drug interactions or disease outcome variables, among many other potential uses. The research team is applying causal machine learning techniques and leveraging de-identified electronic health record data.

The team curated a dataset of longitudinal electronic health records from Veterans with ALS and recently published a paper in the journal, Amyotrophic Lateral Sclerosis and Frontotemporal Degeneration. Although preliminary, they found a larger number of Veterans separated from the Army receiving a diagnosis with ALS than from other military branches. With the support of their ALSRP-funded award, the team identified several treatments that could be repurposed to enhance the lives of patients suffering from ALS. These treatments include statins, alpha-adrenergic blockers, and phosphodiesterase Type 5 inhibitors.

The team continues their efforts to identify further drugs that prolong survival for persons living with an ALS diagnosis. They also aim to identify which clinical and molecular mechanisms these drugs influence to better understand the development of ALS.

ALS is a devastating neurological disease. Despite decades of research, there are still minimal treatment options and no cure. Ray’s work identifying drugs that alter the risk of being diagnosed with ALS or its progression will have direct and immediate impact on clinical disease management and the patient community.

References:

¹Weisskopf, M. G., Cudkowicz, M. E., & Johnson, N. (2015). Military Service and Amyotrophic Lateral Sclerosis in a Population-based Cohort. Epidemiology (Cambridge, Mass.), 26(6), 831–838. https://doi.org/10.1097/EDE.0000000000000376

Links:

Public and Technical Abstracts: Causal Machine Learning for Drug Repurposing to Impact ALS Treatment

An Electronic Health Record Cohort of Veterans with Amyotrophic Lateral Sclerosis

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