Literature-Based Discovery Predicts Antihistamines Are a Promising Repurposed Adjuvant Therapy for Parkinson's Disease

• Type of Material: Article
• Authors: Gabriella Tandra, Georgia Institute of Technology; Amy Yoone, Georgia Institute of Technology; Rhea Mathew, Georgia Institute of Technology; Minzhi Wang, Georgia Institute of Technology; Chadwick Hales, Emory University; Cassie Mitchell, Emory University
• Language: English
• Date: 2023-08-01
• Publisher: MDPI
• Publication Version: Final Published Version
• Copyright Statement: © 2023 by the authors.
• License: Creative Commons Attribution 4.0 International
• Final Published Version (URL): http://dx.doi.org/10.3390/ijms241512339
• Title of Journal or Parent Work: INTERNATIONAL JOURNAL OF MOLECULAR SCIENCES
• Volume: 24
• Issue: 15
• Grant/Funding Information: This research was funded by the McCamish Parkinson’s Disease Innovation Program at Georgia Institute of Technology and Emory University to C.S.M and C.M., National Science Foundation grant 1944247 to C.S.M., National Institute of Health grant U19-AG056169 sub-award to C.S.M., and the Chan Zuckerberg Initiative grant 253558 to C.S.M.
• Keywords: HEPATITIS; Chemistry; RISK; Chemistry, Multidisciplinary; Life Sciences & Biomedicine; movement disorders; artificial intelligence; machine learning; Physical Sciences; HISTAMINE; antihistamines; Science & Technology; Biochemistry & Molecular Biology; repurposed drugs; Parkinson's disease
• Research Categories: Biology, Molecular; Chemistry, Biochemistry; Biology, Neuroscience

Abstract

Parkinson’s disease (PD) is a movement disorder caused by a dopamine deficit in the brain. Current therapies primarily focus on dopamine modulators or replacements, such as levodopa. Although dopamine replacement can help alleviate PD symptoms, therapies targeting the underlying neurodegenerative process are limited. The study objective was to use artificial intelligence to rank the most promising repurposed drug candidates for PD. Natural language processing (NLP) techniques were used to extract text relationships from 33+ million biomedical journal articles from PubMed and map relationships between genes, proteins, drugs, diseases, etc., into a knowledge graph. Cross-domain text mining, hub network analysis, and unsupervised learning rank aggregation were performed in SemNet 2.0 to predict the most relevant drug candidates to levodopa and PD using relevance-based HeteSim scores. The top predicted adjuvant PD therapies included ebastine, an antihistamine for perennial allergic rhinitis; levocetirizine, another antihistamine; vancomycin, a powerful antibiotic; captopril, an angiotensin-converting enzyme (ACE) inhibitor; and neramexane, an N-methyl-D-aspartate (NMDA) receptor agonist. Cross-domain text mining predicted that antihistamines exhibit the capacity to synergistically alleviate Parkinsonian symptoms when used with dopamine modulators like levodopa or levodopa–carbidopa. The relationship patterns among the identified adjuvant candidates suggest that the likely therapeutic mechanism(s) of action of antihistamines for combatting the multi-factorial PD pathology include counteracting oxidative stress, amending the balance of neurotransmitters, and decreasing the proliferation of inflammatory mediators. Finally, cross-domain text mining interestingly predicted a strong relationship between PD and liver disease.