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

2019 ◽  
Author(s):  
Daniel N. Sosa ◽  
Alexander Derry ◽  
Margaret Guo ◽  
Eric Wei ◽  
Connor Brinton ◽  
...  
Keyword(s):  
Rare Diseases ◽  
Drug Repurposing ◽  
Graph Embedding ◽  
Capital Requirements ◽  
Biomedical Literature ◽  
New Drugs ◽  
Global Network ◽  
Knowledge Graph ◽  
Promising Alternative ◽  
Embedding Method

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.

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

2019 ◽  
Author(s):  
Daniel N. Sosa ◽  
Alexander Derry ◽  
Margaret Guo ◽  
Eric Wei ◽  
Connor Brinton ◽  
...  
Keyword(s):  
Rare Diseases ◽  
Drug Repurposing ◽  
Graph Embedding ◽  
Knowledge Graph ◽  
Embedding Method

Repurposing Drugs to Combat Drug Resistance in Leprosy: A Review of Opportunities

2021 ◽  
Vol 25 ◽  
Author(s):  
Mukul Sharma ◽  
Pushpendra Singh
Keyword(s):  
Synergistic Effects ◽  
Drug Repurposing ◽  
New Drugs ◽  
Resistance Testing ◽  
Promising Alternative ◽  
Drug Molecules ◽  
Conventional Drug ◽  
Repurposed Drugs ◽  
Multiple Drugs

: Leprosy is caused by extremely slow-growing and uncultivated mycobacterial pathogens, namely Mycobacterium leprae and M. lepromatosis. Nearly 95% of the new cases of leprosy recorded globally are found in India, Brazil, and 20 other priority countries [WHO, 2019], of which nearly two-thirds of the cases are reported in India alone. Currently, leprosy is treated with dapsone, rifampicin, and clofazimine, also known as multi-drug therapy [MDT], as per the recommendations of WHO since 1981. Still, the number of new leprosy cases recorded globally has remained constant in the last one-decade ,and resistance to multiple drugs has been documented in various parts of the world, even though relapses are rare in patients treated with MDT. Antimicrobial resistance testing against M. leprae or the evaluation of the anti-leprosy activity of new drugs remains a challenge as leprosy bacilli do not grow in vitro. Besides, developing a new drug against leprosy through the conventional drug development process is not economically attractive or viable for pharma companies. Therefore, a promising alternative is the repurposing of existing drugs/approved medications or their derivatives for assessing their anti-leprosy potential. It is an efficient method to identify novel medicinal and therapeutic properties of approved drug molecules. Any combinatorial chemotherapy that combines these repurposed drugs with the existing first-line [MDT] and second-line drugs could improve the bactericidal and synergistic effects against these notorious bacteria and can help in achieving the much-cherished goal of “leprosy-free world”. This review highlights novel opportunities for drug repurposing to combat resistance to current therapeutic approaches.

scholarly journals From Off-Label to Repurposed Drug in Non-Oncological Rare Diseases: Definition and State of the Art in Selected EU Countries

2017 ◽  
Vol 1 ◽  
pp. maapoc.0000016 ◽  
Author(s):  
Paola Minghetti ◽  
Elena P. Lanati ◽  
Josie Godfrey ◽  
Oriol Solà-Morales ◽  
Olivier Wong ◽  
...  
Keyword(s):  
Rare Diseases ◽  
Drug Repurposing ◽  
Orphan Drugs ◽  
New Drugs ◽  
Success Rates ◽  
Great Opportunity ◽  
Off Label ◽  
Research Activities ◽  
Oncological Diseases ◽  
Repurposed Drugs

Introduction Almost 8,000 rare diseases exist worldwide, affecting approximately 350 million people. Nevertheless, only 5% receive a specific authorized or licensed treatment. The need for effective and rapidly available therapies is still unmet for many patients. Objective The objective is to define repurposing versus off-label drugs, and to evaluate pathways of repurposed drugs for rare non-oncological diseases in Italy, France, England, and Spain (the EU4 countries). Methods This original paper is based on 3 research activities: (i) a nonsystematic literature research; (ii) a questionnaire-based survey to regulatory experts; and (iii) research on approval timelines and therapy prices of repurposed non-oncology orphan drugs. Official approval dates in England are not available if the National Institute for Health and Care Excellence does not appraise the products. Results Only France provides a specific adaptive pathway from off-label to repurposed drugs. Pricing and reimbursement assessment for the drug samples varied across the EU4 countries: time-to-market for repurposed drugs versus new drugs is longer in all analyzed countries; that is, 979 days versus 462 days in Italy, 502 days versus 350 days in France, and 624 versus 378 days in Spain. Repurposed drugs have higher success rates from development to approval than novel drugs (30% vs. 11%). Small- and medium-sized enterprises owned 9 of 12 repurposed non-oncology orphan drugs, of which only 4 were reimbursed in all EU4 countries. Prices were more homogeneous across EU4 although the reimbursement rates were different. Conclusions Drug repurposing represents a great opportunity to treat rare non-oncological diseases. However, a more homogenous assessment across EU4 could ensure reimbursement and prices high enough to reward organizations investing in this field.

scholarly journals CTKG: A Knowledge Graph for Clinical Trials

2021 ◽  
Author(s):  
Ziqi Chen ◽  
Bo Peng ◽  
Vassilis N. Ioannidis ◽  
Mufei Li ◽  
George Karypis ◽  
...  
Keyword(s):  
Clinical Trials ◽  
Success Rate ◽  
Similarity Search ◽  
Drug Repurposing ◽  
New Drugs ◽  
Knowledge Graph ◽  
New Treatments

Effective and successful clinical trials are essential in developing new drugs and advancing new treatments. However, clinical trials are very expensive and easy to fail. The high cost and low success rate of clinical trials motivate research on inferring knowledge from existing clinical trials in innovative ways for designing future clinical trials. In this manuscript, we present our efforts on constructing the first publicly available Clinical Trials Knowledge Graph, denoted as CTKG. CTKG includes nodes representing medical entities in clinical trials (e.g., studies, drugs and conditions), and edges representing the relations among these entities (e.g., drugs used in studies). Our embedding analysis demonstrates the potential utilities of CTKG in various applications such as drug repurposing and similarity search, among others.

scholarly journals TransMS: Knowledge Graph Embedding for Complex Relations by Multidirectional Semantics

2019 ◽  
Author(s):  
Shihui Yang ◽  
Jidong Tian ◽  
Honglun Zhang ◽  
Junchi Yan ◽  
Hao He ◽  
...  
Keyword(s):  
Large Scale ◽  
State Of The Art ◽  
Graph Embedding ◽  
Knowledge Graph ◽  
Additional Parameter ◽  
Nonlinear Functions ◽  
Embedding Method ◽  
Low Dimension

Knowledge graph embedding, which projects the symbolic relations and entities onto low-dimension continuous spaces, is essential to knowledge graph completion. Recently, translation-based embedding models (e.g. TransE) have aroused increasing attention for their simplicity and effectiveness. These models attempt to translate semantics from head entities to tail entities with the relations and infer richer facts outside the knowledge graph. In this paper, we propose a novel knowledge graph embedding method named TransMS, which translates and transmits multidirectional semantics: i) the semantics of head/tail entities and relations to tail/head entities with nonlinear functions and ii) the semantics from entities to relations with linear bias vectors. Our model has merely one additional parameter α than TransE for each triplet, which results in its better scalability in large-scale knowledge graph. Experiments show that TransMS achieves substantial improvements against state-of-the-art baselines, especially the Hit@10s of head entity prediction for N-1 relations and tail entity prediction for 1-N relations improved by about 27.1% and 24.8% on FB15K database respectively.

Drug Repurposing for Parkinson’s Disease by Integrating Knowledge Graph Completion Model and Knowledge Fusion of Medical Literature

2021 ◽  
Vol 13 (1) ◽  
pp. 14
Author(s):  
Xiaolin Zhang ◽  
Chao Che
Keyword(s):  
Parkinson’S Disease ◽  
Parkinson's Disease ◽  
Knowledge Base ◽  
Medical Literature ◽  
Medical Knowledge ◽  
Drug Repurposing ◽  
New Drugs ◽  
Knowledge Graph ◽  
Knowledge Fusion ◽  
Effective Drugs

The prevalence of Parkinson’s disease increases a tremendous medical and economic burden to society. Therefore, the effective drugs are urgently required. However, the traditional development of effective drugs is costly and risky. Drug repurposing, which identifies new applications for existing drugs, is a feasible strategy for discovering new drugs for Parkinson’s disease. Drug repurposing is based on sufficient medical knowledge. The local medical knowledge base with manually labeled data contains a large number of accurate, but not novel, medical knowledge, while the medical literature containing the latest knowledge is difficult to utilize, because of unstructured data. This paper proposes a framework, named Drug Repurposing for Parkinson’s disease by integrating Knowledge Graph Completion method and Knowledge Fusion of medical literature data (DRKF) in order to make full use of a local medical knowledge base containing accurate knowledge and medical literature with novel knowledge. DRKF first extracts the relations that are related to Parkinson’s disease from medical literature and builds a medical literature knowledge graph. After that, the literature knowledge graph is fused with a local medical knowledge base that integrates several specific medical knowledge sources in order to construct a fused medical knowledge graph. Subsequently, knowledge graph completion methods are leveraged to predict the drug candidates for Parkinson’s disease by using the fused knowledge graph. Finally, we employ classic machine learning methods to repurpose the drug for Parkinson’s disease and compare the results with the method only using the literature-based knowledge graph in order to confirm the effectiveness of knowledge fusion. The experiment results demonstrate that our framework can achieve competitive performance, which confirms the effectiveness of our proposed DRKF for drug repurposing against Parkinson’s disease. It could be a supplement to traditional drug discovery methods.

scholarly journals An Integrative Network Science and Artificial Intelligence Drug Repurposing Approach for Muscle Atrophy in Spaceflight Microgravity

2021 ◽  
Vol 9 ◽  
Author(s):  
Vidya Manian ◽  
Jairo Orozco-Sandoval ◽  
Victor Diaz-Martinez
Keyword(s):  
Neural Network ◽  
Muscle Atrophy ◽  
Link Prediction ◽  
Preferential Attachment ◽  
Drug Repurposing ◽  
New Drugs ◽  
Gradient Boosting ◽  
Knowledge Graph ◽  
Drug Knowledge ◽  
Prediction Approach

Muscle atrophy is a side effect of several terrestrial diseases which also affects astronauts severely in space missions due to the reduced gravity in spaceflight. An integrative graph-theoretic network-based drug repurposing methodology quantifying the interplay of key gene regulations and protein–protein interactions in muscle atrophy conditions is presented. Transcriptomic datasets from mice in spaceflight from GeneLab have been extensively mined to extract the key genes that cause muscle atrophy in organ muscle tissues such as the thymus, liver, and spleen. Top muscle atrophy gene regulators are selected by Bayesian Markov blanket method and gene–disease knowledge graph is constructed using the scalable precision medicine knowledge engine. A deep graph neural network is trained for predicting links in the network. The top ranked diseases are identified and drugs are selected for repurposing using drug bank resource. A disease drug knowledge graph is constructed and the graph neural network is trained for predicting new drugs. The results are compared with machine learning methods such as random forest, and gradient boosting classifiers. Network measure based methods shows that preferential attachment has good performance for link prediction in both the gene–disease and disease–drug graphs. The receiver operating characteristic curves, and prediction accuracies for each method show that the random walk similarity measure and deep graph neural network outperforms the other methods. Several key target genes identified by the graph neural network are associated with diseases such as cancer, diabetes, and neural disorders. The novel link prediction approach applied to the disease drug knowledge graph identifies the Monoclonal Antibodies drug therapy as suitable candidate for drug repurposing for spaceflight induced microgravity. There are a total of 21 drugs identified as possible candidates for treating muscle atrophy. Graph neural network is a promising deep learning architecture for link prediction from gene–disease, and disease–drug networks.

scholarly journals Expert-Augmented Computational Drug Repurposing Identified Baricitinib as a Treatment for COVID-19

2021 ◽  
Vol 12 ◽  
Author(s):  
Daniel P. Smith ◽  
Olly Oechsle ◽  
Michael J. Rawling ◽  
Ed Savory ◽  
Alix M.B. Lacoste ◽  
...  
Keyword(s):  
Visual Analytics ◽  
Drug Repurposing ◽  
Standard Of Care ◽  
Biomedical Literature ◽  
Biological Research ◽  
Knowledge Graph ◽  
Neglected Diseases ◽  
Biomedical Knowledge ◽  
Computational Tools ◽  
Approved Drugs

The onset of the 2019 Severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) pandemic necessitated the identification of approved drugs to treat the disease, before the development, approval and widespread administration of suitable vaccines. To identify such a drug, we used a visual analytics workflow where computational tools applied over an AI-enhanced biomedical knowledge graph were combined with human expertise. The workflow comprised rapid augmentation of knowledge graph information from recent literature using machine learning (ML) based extraction, with human-guided iterative queries of the graph. Using this workflow, we identified the rheumatoid arthritis drug baricitinib as both an antiviral and anti-inflammatory therapy. The effectiveness of baricitinib was substantiated by the recent publication of the data from the ACTT-2 randomised Phase 3 trial, followed by emergency approval for use by the FDA, and a report from the CoV-BARRIER trial confirming significant reductions in mortality with baricitinib compared to standard of care. Such methods that iteratively combine computational tools with human expertise hold promise for the identification of treatments for rare and neglected diseases and, beyond drug repurposing, in areas of biological research where relevant data may be lacking or hidden in the mass of available biomedical literature.

scholarly journals Prediction of adverse drug reactions based on knowledge graph embedding

2021 ◽  
Vol 21 (1) ◽  
Author(s):  
Fei Zhang ◽  
Bo Sun ◽  
Xiaolin Diao ◽  
Wei Zhao ◽  
Ting Shu
Keyword(s):  
Logistic Regression ◽  
Adverse Drug Reactions ◽  
Language Processing ◽  
Graph Embedding ◽  
Classification Model ◽  
Knowledge Graph ◽  
Drug Reactions ◽  
Embedding Method ◽  
Medication Process ◽  
New Knowledge

Abstract Background Adverse drug reactions (ADRs) are an important concern in the medication process and can pose a substantial economic burden for patients and hospitals. Because of the limitations of clinical trials, it is difficult to identify all possible ADRs of a drug before it is marketed. We developed a new model based on data mining technology to predict potential ADRs based on available drug data. Method Based on the Word2Vec model in Nature Language Processing, we propose a new knowledge graph embedding method that embeds drugs and ADRs into their respective vectors and builds a logistic regression classification model to predict whether a given drug will have ADRs. Result First, a new knowledge graph embedding method was proposed, and comparison with similar studies showed that our model not only had high prediction accuracy but also was simpler in model structure. In our experiments, the AUC of the classification model reached a maximum of 0.87, and the mean AUC was 0.863. Conclusion In this paper, we introduce a new method to embed knowledge graph to vectorize drugs and ADRs, then use a logistic regression classification model to predict whether there is a causal relationship between them. The experiment showed that the use of knowledge graph embedding can effectively encode drugs and ADRs. And the proposed ADRs prediction system is also very effective.

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