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. 2024 Sep 11;14(1):21258.
doi: 10.1038/s41598-024-71922-1.

Drug repurposing for Parkinson's disease by biological pathway based edge-weighted network proximity analysis

Manyoung Han  1 , Seunghwan Jung  1 , Doheon Lee  2
Affiliations

Drug repurposing for Parkinson's disease by biological pathway based edge-weighted network proximity analysis

Manyoung Han et al. Sci Rep. .

Abstract

Parkinson's disease is the second most frequent neurodegenerative disease, and its severity is increasing with extended life expectancy. Most of current treatments provide symptomatic relief; however, disease progression is not inhibited. There are multiple trials for treatments that target the causes of the disease but they were flawed. The mechanisms underlying neurodegenerative diseases are intricate, and understanding the interplay among the biological elements involved is crucial. These relationships can be effectively analyzed through biological networks, and the application of network-based analyses in the context of neurodegenerative disease treatment has gained considerable attention. Moreover, considering the significance differences in interactions between biological elements within the network is important. Therefore, we introduce a novel biological pathway based edge-weighted network construction method for drug repurposing in Parkinson's disease. The interaction found in multiple Parkinson's disease-related pathways is more significant than other interactions, and this significance is reflected in the network edge weights. Using the edge-weighted network construction method, we found a significant difference in the efficacy between known and unknown Parkinson's disease drugs. The method predicts drug-disease interactions more accurately than approaches that do not consider the significance differences among interactions, and the paths between the drug and disease within the network correspond to the drug's mechanism of action. In summary, we propose a network-based drug repurposing method using the biological pathway based edge-weighted network. Using this methodology, researchers can find novel drug candidates for the parkinson's disease and their mechanism of actions.

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Conflict of interest statement

The authors declare no competing interests.

Figures

Fig. 1
Fig. 1
Method overview for the biological pathway based edge-weighted human gene network construction 1. Data on gene interactions were obtained from CODA(Context-Oriented Directed Association) database. 2. Human gene network edge weights are modified based on the biological pathway information from KEGG(Kyoto Encyclopedia of Genes and Genomes) database and GSEA(Gene Set Enrichment Analysis). 3. Proximity between drug and disease is the sum of edge weights underlying the closest path between drug target and disease genes.
Fig. 2
Fig. 2
Proximity difference between known and unknown Parkinson’s disease drugs using (a) the non edge-weighted network(p-value = 0.13), (b) the edge-weighted network considering the number of pathways(p-value = 0.05), (c) the edge-weighted network considering the number of pathways and the pathways’ correlation with the Parkinson’s disease(p-value = 0.03).
Fig. 3
Fig. 3
(a) ROC curve using the non edge-weighted network, the edge-weighted network considering the number of pathways (STEP 1) and the edge-weighted network considering the number of pathways and the relationship of the pathways with the Parkinson’s disease (STEP 1, 2). (b) Permutation test showing significantly improved AUROC using weighted path length.
Fig. 4
Fig. 4
Recall with top 50 highest proximity drugs using the non edge-weighted network, the edge-weighted network considering the number of pathways (STEP 1) and the edge-weighted network considering the number of pathways and the relationship of the pathways with the Parkinson’s disease (STEP 1, 2).
Fig. 5
Fig. 5
AUROC and recall performance between PD and drugs using the biological pathway based edge-weighted network construction method(Weighted path length) and other prediction methods. The method demonstrated superior performance in predicting relationships between PD and drugs.
Fig. 6
Fig. 6
Shortest path between (a) trihexyphenidyl, (b) benzatropine, (c) bromocriptine and the Parkinson’s disease using the pathway-weighted network corresponded to the each drug mechanism of actions.
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