Aggregated data access for rare disease therapeutics development: Building RARe-SOURCE for the α-Dystroglycanopathies

Topic:

Pre-Clinical Research

Poster Number: 281

Author(s):

Matthew Lefkowitz, NNDCS, A. Reghan Foley, MD, Neuromuscular and Neurogenetic Disorders of Childhood Section, NINDS, NIH, Elizabeth Ottinger, PhD, National Center for Advancing Translational Sciences, National Institutes of Health, Sharie Haugabook, PhD, National Center for Advancing Translational Sciences, National Institutes of Health, Ann Knebel, PhD, RN, Division of Preclinical Innovation National Center for Advancing Translational Sciences NIH, Uma Mudunuri, Advanced Biomedical Computational Science, Frederick National Laboratory for Cancer Research, Daniel Watson, Advanced Biomedical Computational Science, Frederick National Laboratory for Cancer Research, Mohammad Alodadi, PhD, Advanced Biomedical Computational Science, Frederick National Laboratory for Cancer Research, Donald Lo, PhD, EATRIS, Carsten Bönnemann, MD, Neuromuscular and Neurogenetic Disorders of Childhood Section, NINDS, NIH

With the explosion of big data in the field of rare disease, there is a growing need for bridging preclinical/clinical genetic and mechanistic data across rare diseases to identify opportunities for therapeutic translation. RARe-SOURCE is a tool currently under development that uses artificial intelligence algorithms to employ natural language processing (NLP) techniques to identify commonalities between rare disease pathomechanisms under the hypothesis that there are many diseases among the 7,000 known rare disease groups that may not appear to be related by disease presentation or by disease cause/mechanism but could be treatable by common (drug) therapies. Here, we are building a module within RARe-SOURCE using the α-dystroglycanopathies (αDGs) as a test case for this approach, beginning with Fukutin-related αDG. We hypothesize that there are commonalities to be identified within the genotype/phenotype correlations as well as mechanistic pathways within the dystroglycanopathies (DGs) that could be exploited therapeutically. We are using a combination of extraction of the scientific literature, large multi-omic databases, and clinical data aggregated via the computational methods behind RARe-SOURCE to test the capabilities of the system to effectively harmonize information from disparate data sources to become available via the RARe-SOURCE interface. This should then allow for the recognition of connections and commonalities between separate rare disease entities, highlighting therapeutic opportunities. To test this paradigm, this study will: 1. complete a comprehensive curated literature search of the αDGs, using Fukutin as an initial target, 2. teach RARe-SOURCE to complete automated literature extraction with consistent data-basing, and 3. use RARe-SOURCE to pull in larger data sets from genomic and variant databases to combine with the literature extraction. This pilot study will thereby aide in developing the necessary automatization of the RARe-SOURCE process to expand to the 7,000 known rare disease groups currently without treatment options.