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 Repurposing of medications for pulmonary arterial hypertension

2020 ◽  
Vol 10 (4) ◽  
pp. 204589402094149
Author(s):  
Mark Toshner ◽  
Edda Spiekerkoetter ◽  
Harm Bogaard ◽  
Georg Hansmann ◽  
Sylvia Nikkho ◽  
...  
Keyword(s):  
Clinical Trials ◽  
Pulmonary Arterial Hypertension ◽  
Arterial Hypertension ◽  
Unmet Need ◽  
Drug Repurposing ◽  
Innovative Drug ◽  
Future Design ◽  
Open Debate ◽  
Pulmonary Arterial ◽  
New Treatments

This manuscript on drug repurposing incorporates the broad experience of members of the Pulmonary Vascular Research Institute’s Innovative Drug Development Initiative as an open debate platform for academia, the pharmaceutical industry and regulatory experts surrounding the future design of clinical trials in pulmonary hypertension. Drug repurposing, use of a drug in a disease for which it was not originally developed, in pulmonary arterial hypertension has been a remarkable success story, as highlighted by positive large phase 3 clinical trials using epoprostenol, bosentan, iloprost, and sildenafil. Despite the availability of multiple therapies for pulmonary arterial hypertension, mortality rates have modestly changed. Moreover, pulmonary arterial hypertension patients are highly symptomatic and frequently end up on parental therapy and lung transplant waiting lists. Therefore, an unmet need for new treatments exists and drug repurposing may be an important avenue to address this problem.

A Role of Clinical Trial in Management of Hypertension and Medication of Hypertension

2021 ◽  
pp. 3215-3222
Author(s):  
Minesh Patel ◽  
G.S. Chakraborthy
Keyword(s):  
Heart Failure ◽  
Blood Pressure ◽  
Clinical Trials ◽  
Left Ventricle ◽  
New Drugs ◽  
Research Review ◽  
Incremental Growth ◽  
Indian Food ◽  
New Treatments

Clinical trials are essence for the progress of new treatments. Whether a person should engage confide in on their compassionate of the liability and gain for themselves and for society as an entity. Clinical trials are research review in which people volunteer to attempt major treatments, interventions or experiment as a means to forbid, detect, evaluate or manage assorted diseases or medical conditions. Some investigations glance at how people react to a new arbitration and what side effects valor occur. Every new medicine and treatment initiated with volunteers engage in clinical trials. We incur our present high ideal of medical care to studies that have been operate in the past under guidance of the INDIAN Food and Drug Administration (FDA). In addition to Research on new drugs and devices, clinical trials bring a scientific footing for urge and treating patients. Even when researchers do not achieve the conclusion they anticipate; trial results can help point scientists in the mend direction. Blood pressure is great because the larger than your blood pressure is, the larger than your risk of health problems in the future. If your blood pressure is higher than it is putting extra ache on your arteries and on your heart. High blood pressure clouts your heart to work higher to pump blood to the comfort of your body. This causes part of your heart (left ventricle) to congeal. A congeal left ventricle high your risk of heart attack, heart failure and sudden cardiac death. Heart failure. The arena for clinical trials of hypertension management is in transition. The stage of mega trials may not be bygone but is assuredly in decline. Incremental growth in the therapies assessable in the face of a high global disease hardship has imply that hypertension researchers have also attract on getting beat efficacy and value from the available treatments through arrangement improvement, combinations, and algorithms. There has been go on amuse in the role of nonpharmacological compute in cure and management of hypertension.

Success in Meeting the Primary Endpoint in Phase III Trials: A Comparison of Industry and Cooperative Group Trials

Blood ◽  
2011 ◽  
Vol 118 (21) ◽  
pp. 508-508
Author(s):  
Benjamin Djulbegovic ◽  
Ambuj Kumar ◽  
Branko Miladinovic ◽  
Asmita Mhaskar ◽  
Tea Reljic ◽  
...  
Keyword(s):  
Clinical Trials ◽  
Public Sector ◽  
Success Rate ◽  
Odds Ratio ◽  
Phase Iii ◽  
High Likelihood ◽  
Specific Cancer ◽  
Experimental Treatments ◽  
New Treatments ◽  
Design Bias

Abstract Abstract 508 Background: Evaluation of research effort, especially estimation of the proportion of treatment successes in randomized clinical trials (RCTs), has important ethical, scientific, and public policy implications. Whether commercial or public sector research programs generate higher discovery rate of new successful treatments when tested in cancer RCTs is not known. These research programs are postulated to be governed by two competing hypotheses. The “equipoise/uncertainty hypothesis” assumes that investigators cannot predict trial results in advance, and as a consequence, the rate of discovering new treatments is about 50%. In contrast, the “design bias hypothesis” assumes that researchers conduct only those RCTs which have high likelihood of success. We hypothesize that the public sector RCTs are governed by the equipoise hypothesis while the industry-sponsored (IS) RCTs are based on the design bias hypothesis. Here we conduct the comparative systematic assessment to investigate if IS RCTs are associated with higher success rates than publicly-sponsored trials (PS) according to design bias versus equipoise/uncertainty hypothesis, respectively. Methods: All consecutive, published and unpublished, phase III cancer RCTs assessing treatment superiority and conducted by Canada's NCIC Clinical Trials Group (NCIC CTG) and GlaxoSmithKline (GSK) from 1980 to June 2010 were included. All trial protocols from GSK and NCIC CTG were reviewed independently by two reviewers to determine their eligibility. Two reviewers independently extracted data from eligible study protocols and publications using a standardized form. Three metrics were extracted to determine treatment successes: (1) the proportion of statistically significant trials favoring new or standard treatments, (2) the proportion of the trials in which new treatments were considered superior according to the original investigators, and (3) quantitative synthesis of data for primary outcomes as defined in each trial. An experimental regimen (drug compound or combinations or procedures), which was not tested previously in an RCT involving a specific cancer population or for alleviation of symptoms was classified as a major innovation. If a drug or regimen was already tested in a specific cancer population and testing involved dose modifications or changes in route of administration, it was classified as a minor innovation. Results: Between1980 to 2010 NCIC CTG conducted 77 RCTs enrolling 33,260 patients while GSK conducted 40 cancer RCTs accruing 19,889 patients. Forty two percent (99%CI 24 to 60) of the results were statistically significant favoring experimental treatments in GSK versus 25% (99%CI 13 to 37) in the NCIC CTG cohort (p=0.04). Investigators concluded that new treatments were superior to standard treatments in 80% of GSK versus 44% of NCIC CTG RCTs (p<0.0001) The GSK investigators deemed 32% (99%CI 14 to 50; 14/44) of interventions as “breakthroughs” versus 10% (99%CI 1 to 18; 8/82) by NCIC CTG investigators (p=0.002). Pooled analysis for the primary outcome indicated higher success rate in GSK trials (odds ratio: 0.61 [99%CI 0.47–0.78]) versus NCIC trials (odds ratio: 0.86 [99%CI 0.74–1.00]) (p=0.003). Experimental treatments were considered as major innovations in 32% (99%CI 15 to 49; 16/50) of GSK vs. 93% (99%CI 86 to 100; 78/84) of NCIC CTG trials (p<0.0001). Increased success rate in IS RCTs was mainly due to testing of new palliative agents, while the research program of NCIC CTG largely focused on development of therapies to improve survival. Conclusions: This first study evaluating the treatment success and pattern of therapeutic discoveries in IS versus PS research showed that industry discovers more successful new treatments compared with public sector. However, industry appears to undertake RCTs with high likelihood of success. PS research had significantly high proportion of major innovations compared with IS research. Disclosures: No relevant conflicts of interest to declare.

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 Utilisation of the Prestwick Chemical Library®to identify drugs that inhibit the growth of Mycobacteria

2018 ◽  
Author(s):  
Panchali Kanvatirth ◽  
Rose E. Jeeves ◽  
Joanna Bacon ◽  
Gurdyal S. Besra ◽  
Luke J. Alderwick
Keyword(s):  
Drug Repurposing ◽  
Antimycobacterial Activity ◽  
Drug Regimen ◽  
New Drugs ◽  
Bacterial Disease ◽  
Drug Resistant ◽  
Chemical Library ◽  
Incidence And Mortality ◽  
Approved Drugs ◽  
New Treatments

AbstractTuberculosis (TB) is an infectious bacterial disease that kills approximately 1.3 million people every year. Despite global efforts to reduce both the incidence and mortality associated with TB, the emergence of drug resistant strains has slowed any progress made towards combating the spread of this deadly disease. The current TB drug regimen is inadequate, takes months to complete and poses significant challenges when administering to patients suffering from drug resistant TB. New treatments that are faster, simpler and more affordable are urgently required. Arguably, a good strategy to discover new drugs is to start with an old drug. Here, we have screened a library of 1200 FDA approved drugs from the Prestwick Chemical library®using a GFP microplate assay. Drugs were screened against GFP expressing strains ofMycobacterium smegmatisandMycobacterium bovisBCG as surrogates forMycobacterium tuberculosis,the causative agent of TB in humans. We identified several classes of drugs that displayed antimycobacterial activity against bothM. smegmatisandM. bovisBCG, however each organism also displayed some selectivity towards certain drug classes. Variant analysis of whole genomes sequenced for resistant mutants raised to florfenicol, vanoxerine and pentamidine highlight new pathways that could be exploited in drug repurposing programmes.

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 Drug Repurposing: A Paradigm Shift in Drug Discovery

2020 ◽  
Vol 5 (04) ◽  
pp. 60-68
Author(s):  
Saravanan Jayaram ◽  
Emdormi Rymbai ◽  
Deepa Sugumar ◽  
Divakar Selvaraj
Keyword(s):  
Drug Discovery ◽  
Success Rate ◽  
Chemical Structure ◽  
Target Identification ◽  
Drug Repurposing ◽  
New Drugs ◽  
Attractive Option ◽  
Traditional Drug ◽  
Repurposed Drugs

The traditional methods of drug discovery and drug development are a tedious, complex, and costly process. Target identification, target validation; lead identification; and lead optimization are a lengthy and unreliable process that further complicates the discovery of new drugs. A study of more than 15 years reports that the success rate in the discovery of new drugs in the fields of ophthalmology, cardiovascular, infectious disease, and oncology to be 32.6%, 25.5%, 25.2% and 3.4%, respectively. A tedious and costly process coupled with a very low success rate makes the traditional drug discovery a less attractive option. Therefore, an alternative to traditional drug discovery is drug repurposing, a process in which already existing drugs are repurposed for conditions other than which were originally intended. Typical examples of repurposed drugs are thalidomide, sildenafil, memantine, mirtazapine, mifepristone, etc. In recent times, several databases have been developed to hasten drug repurposing based on the side effect profile, the similarity of chemical structure, and target site. This work reviews the pivotal role of drug repurposing in drug discovery and the databases currently available for drug repurposing.

Drug repurposing in Oncology, an attractive opportunity for novel combinatorial regimens

2020 ◽  
Vol 27 ◽  
Author(s):  
Paolo Falvo ◽  
Stefania Orecchioni ◽  
Stefania Roma ◽  
Alessandro Raveane ◽  
Francesco Bertolini
Keyword(s):  
Clinical Trials ◽  
Therapeutic Approach ◽  
Drug Repurposing ◽  
Healthcare Systems ◽  
Cancer Genome ◽  
The Other ◽  
New Drugs ◽  
Preclinical Models ◽  
Computational Approaches ◽  
Innovative Therapies

The costs of developing, validating and buying new drugs are dramatically increasing. On the other hand, sobering economies have difficulties in sustaining their healthcare systems, particularly in countries with an eldering population requiring increasing welfare. This conundrum requires immediate action, and a possible option is to study the large, already present arsenal of drugs approved and to use them for innovative therapies. This possibility is particularly interesting in oncology, where the complexity of cancer genome dictates in most patients a multistep therapeutic approach. In this review we discuss a) Computational approaches; b) preclinical models; c) currently ongoing or already published clinical trials in the drug repurposing field in oncology; and d) drug repurposing to overcome resistance to previous therapies.

scholarly journals Task-Driven Knowledge Graph Filtering Improves Prioritizing Drugs for Repurposing

2021 ◽  
Author(s):  
Florin Ratajczak ◽  
Mitchell Joblin ◽  
Martin Ringsquandl ◽  
Marcel Hildebrandt
Keyword(s):  
Domain Knowledge ◽  
Drug Repurposing ◽  
New Drugs ◽  
Knowledge Graph ◽  
Biomedical Data ◽  
Biomedical Knowledge ◽  
Relation Type ◽  
Knowledge Graphs ◽  
Improved Performance ◽  
Efficient Learning

Abstract Background Drug repurposing aims at finding new targets for already developed drugs. It becomes more relevant as the cost of discovering new drugs steadily increases. To find new potential targets for a drug, an abundance of methods and existing biomedical knowledge from different domains can be leveraged. Recently, knowledge graphs have emerged in the biomedical domain that integrate information about genes, drugs, diseases and other biological domains. Knowledge graphs can be used to predict new connections between compounds and diseases, leveraging the interconnected biomedical data around them. While real world use cases such as drug repurposing are only interested in one specific relation type, widely used knowledge graph embedding models simultaneously optimize over all relation types in the graph. This can lead the models to underfit the data that is most relevant for the desired relation type. We propose a method that leverages domain knowledge in the form of metapaths and use them to filter two biomedical knowledge graphs (Hetionet and DRKG) for the purpose of improving performance on the prediction task of drug repurposing while simultaneously increasing computational efficiency. Results We find that our method reduces the number of entities by 60% on Hetionet and 26% on DRKG, while leading to an improvement in prediction performance of up to 40.8% on Hetionet and 12.4% on DRKG, with an average improvement of 17.5% on Hetionet and 5.1% on DRKG. Additionally, prioritization of antiviral compounds for SARS CoV-2 improves after task-driven filtering is applied. Conclusion Knowledge graphs contain facts that are counter productive for specific tasks, in our case drug repurposing. We also demonstrate that these facts can be removed, resulting in an improved performance in that task and a more efficient learning process.

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.

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