A Literature-Based Knowledge Graph Embedding Method for Identifying Drug Repurposing Opportunities in Rare Diseases

Abstract

One in ten people are affected by rare diseases, and three out of ten children with rare diseases will not live past age five. However, the small market size of individual rare diseases, combined with the time and capital requirements of pharmaceutical R&D, have hindered the development of new drugs for these cases. A promising alternative is drug repurposing, whereby existing FDA-approved drugs might be used to treat diseases different from their original indications. In order to generate drug repurposing hypotheses in a systematic and comprehensive fashion, it is essential to integrate information from across the literature of pharmacology, genetics, and pathology. To this end, we leverage a newly developed knowledge graph, the Global Network of Biomedical Relationships (GNBR). GNBR is a large, heterogeneous knowledge graph comprising drug, disease, and gene (or protein) entities linked by a small set of semantic themes derived from the abstracts of biomedical literature. We apply a knowledge graph embedding method that explicitly models the uncertainty associated with literature-derived relationships and uses link prediction to generate drug repurposing hypotheses. This approach achieves high performance on a gold-standard test set of known drug indications (AUROC = 0.89) and is capable of generating novel repurposing hypotheses, which we independently validate using external literature sources and protein interaction networks. Finally, we demonstrate the ability of our model to produce explanations of its predictions.

Artificial intelligence in COVID-19 drug repurposing

“…Increasing interest exists in developing graph representation learning techniques for drug repurposing. Sosa and colleagues 32 constructed a medical knowledge graph of drugs, diseases, genes, and proteins from the biomedical literature and used graph embedding techniques for predicting the links between drugs and diseases. Gysi and colleagues 33 developed a method that was based on graph neural network and presented a case study on SARS-CoV-2 with 81 potential repurposing candidates identified.…”

Probabilistic Box Embeddings for Uncertain Knowledge Graph Reasoning

“…Probase (Wu et al, 2012) is a general probabilistic taxonomy obtained from syntactic extraction. Aforementioned UKGs have supported numerous knowledge-driven applications, such as literature-based drug repurposing (Sosa et al, 2020).…”

“…Similarly, while (The Beatles, genre, Rock) and (The Beatles, genre, Pop) are both true, the first one may receive a slightly higher confidence, since the Beatles is generally considered a rock band. Such confidence information is important when answering questions like Who is the main competitor of Honda?, or extracting confident knowledge for drug repurposing (Sosa et al, 2020).…”

Literature-Wide Association Studies (LWAS) for a Rare Disease: Drug Repurposing for Inflammatory Breast Cancer

“…The words are positioned in the vector space such that those with common linguistic context are found in close proximity. It has been employed for drug repurposing in a couple of cases [ 18 , 19 ].…”