scholarly journals Leveraging Distributed Biomedical Knowledge Sources to Discover Novel Uses for Known Drugs

2019 ◽  
Author(s):  
Finn Womack ◽  
Jason McClelland ◽  
David Koslicki
Keyword(s):  
Structural Information ◽  
Drug Repositioning ◽  
Low Cost ◽  
Drug Repurposing ◽  
Biological Knowledge ◽  
Biomedical Data ◽  
Learning Approaches ◽  
Data Types ◽  
Biomedical Knowledge ◽  
Promising Tool

AbstractComputational drug repurposing, also called drug repositioning, is a low cost, promising tool for finding new uses for existing drugs. With the continued growth of repositories of biomedical data and knowledge, increasingly varied kinds of information are available to train machine learning approaches to drug repurposing. However, existing efforts to integrate a diversity of data sources have been limited to only a small selection of data types, typically gene expression data, drug structural information, and protein interaction networks. In this study, we leverage a graph-based approach to integrate biological knowledge from 20 publicly accessible repositories to represent information involving 11 distinct bioentity types. We then employ a graph node embedding scheme and use utilize a random forest model to make novel predictions about which drugs can be used to treat certain diseases. Utilizing this approach, we find a performance improvement over existing computational drug repurposing approaches and find promising drug repositioning targets, including drug and disease pairs currently in clinical trials.

scholarly journals Overcoming sparseness of biomedical networks to identify drug repositioning candidates

2020 ◽  
Author(s):  
Aleksandar Poleksic
Keyword(s):  
Biological Networks ◽  
Drug Targets ◽  
Drug Repositioning ◽  
Drug Repurposing ◽  
Drug Efficacy ◽  
Computational Procedure ◽  
Computational Method ◽  
Biomedical Data ◽  
Disease Associations ◽  
Disease Associated Genes

AbstractModeling complex biological systems is necessary to understand biochemical interactions behind pharmacological effects of drugs. Successful in silico drug repurposing requires a thorough exploration of diverse biochemical concepts and their relationships, including drug’s adverse reactions, drug targets, disease symptoms, as well as disease associated genes and their pathways, to name a few. We present a computational method for inferring drug-disease associations from complex but incomplete and biased biological networks. Our method employs the compressed sensing technique to overcome the sparseness of biomedical data and, in turn, to enrich the set of verified relationships between different biomedical entities. We present a strategy for identifying network paths supportive of drug efficacy as well as a computational procedure capable of combining different network patterns to better distinguish treatments from non-treatments. The data and programs are freely available at http://bioinfo.cs.uni.edu/AEONET.html.

Application of Artificial Intelligence in Drug Repurposing: A mini-review

2021 ◽  
Vol 01 ◽  
Author(s):  
Gurudeeban Selvaraj ◽  
Satyavani Kaliamurthi ◽  
Gilles H. Peslherbe ◽  
Dong-Qing Wei
Keyword(s):  
Artificial Intelligence ◽  
Drug Repositioning ◽  
Target Identification ◽  
Drug Repurposing ◽  
Search Term ◽  
Biomedical Data ◽  
Drug Candidates ◽  
Drug Target Identification ◽  
Speed Up ◽  
Tools And Techniques

Background and aim: Advancement of extra-ordinary biomedical data (genomics, proteomics, metabolomics, drug libraries, and patient care data), evolution of super-computers, and continuous development of new algorithms that lead to a generous revolution in artificial intelligence (AI). Currently, many biotech and pharmaceutical companies made reasonable investments in and have co-operation with AI companies and increasing the chance of better healthcare tools development, includes biomarker and drug target identification, designing a new class of drugs and drug repurposing. Thus, the study is intended to project the pros and cons of AI in the application of drug repositioning. Methods: Using the search term “AI” and “drug repurposing” the relevant literatures retrieved and reviewed from different sources includes PubMed, Google Scholar, and Scopus. Results: Drug discovery is a lengthy process, however, leveraging the AI approaches in drug repurposing via quick virtual screening may enhance and speed-up the identification of potential drug candidates against communicable and non-communicable diseases. Therefore, in this mini-review, we have discussed different algorithms, tools and techniques, advantages, limitations on predicting the target in repurposing a drug. Conclusions: AI technology in drug repurposing with the association of pharmacology can efficiently identify drug candidates against pandemic diseases.

scholarly journals Drug Repurposing Using Biological Networks

Processes ◽  
2021 ◽  
Vol 9 (6) ◽  
pp. 1057
Author(s):  
Francisco Javier Somolinos ◽  
Carlos León ◽  
Sara Guerrero-Aspizua
Keyword(s):  
Computational Methods ◽  
Biological Networks ◽  
High Performance ◽  
Biological Network ◽  
Drug Repositioning ◽  
Drug Repurposing ◽  
Network Models ◽  
Biomedical Data ◽  
Multiple Targets ◽  
Medical Indication

Drug repositioning is a strategy to identify new uses for existing, approved, or research drugs that are outside the scope of its original medical indication. Drug repurposing is based on the fact that one drug can act on multiple targets or that two diseases can have molecular similarities, among others. Currently, thanks to the rapid advancement of high-performance technologies, a massive amount of biological and biomedical data is being generated. This allows the use of computational methods and models based on biological networks to develop new possibilities for drug repurposing. Therefore, here, we provide an in-depth review of the main applications of drug repositioning that have been carried out using biological network models. The goal of this review is to show the usefulness of these computational methods to predict associations and to find candidate drugs for repositioning in new indications of certain diseases.

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 Predicting Drug-Induced Liver Injury Using Machine Learning on a Diverse Set of Predictors

2021 ◽  
Vol 12 ◽  
Author(s):  
Temidayo Adeluwa ◽  
Brett A. McGregor ◽  
Kai Guo ◽  
Junguk Hur
Keyword(s):  
Machine Learning ◽  
Liver Injury ◽  
Cell Lines ◽  
Prediction Models ◽  
Structural Information ◽  
Current Data ◽  
Learning Approaches ◽  
Data Types ◽  
Drug Induced ◽  
Drug Induced Liver Injury

A major challenge in drug development is safety and toxicity concerns due to drug side effects. One such side effect, drug-induced liver injury (DILI), is considered a primary factor in regulatory clearance. The Critical Assessment of Massive Data Analysis (CAMDA) 2020 CMap Drug Safety Challenge goal was to develop prediction models based on gene perturbation of six preselected cell-lines (CMap L1000), extended structural information (MOLD2), toxicity data (TOX21), and FDA reporting of adverse events (FAERS). Four types of DILI classes were targeted, including two clinically relevant scores and two control classifications, designed by the CAMDA organizers. The L1000 gene expression data had variable drug coverage across cell lines with only 247 out of 617 drugs in the study measured in all six cell types. We addressed this coverage issue by using Kru-Bor ranked merging to generate a singular drug expression signature across all six cell lines. These merged signatures were then narrowed down to the top and bottom 100, 250, 500, or 1,000 genes most perturbed by drug treatment. These signatures were subject to feature selection using Fisher’s exact test to identify genes predictive of DILI status. Models based solely on expression signatures had varying results for clinical DILI subtypes with an accuracy ranging from 0.49 to 0.67 and Matthews Correlation Coefficient (MCC) values ranging from -0.03 to 0.1. Models built using FAERS, MOLD2, and TOX21 also had similar results in predicting clinical DILI scores with accuracy ranging from 0.56 to 0.67 with MCC scores ranging from 0.12 to 0.36. To incorporate these various data types with expression-based models, we utilized soft, hard, and weighted ensemble voting methods using the top three performing models for each DILI classification. These voting models achieved a balanced accuracy up to 0.54 and 0.60 for the clinically relevant DILI subtypes. Overall, from our experiment, traditional machine learning approaches may not be optimal as a classification method for the current data.

scholarly journals It is time to apply biclustering: a comprehensive review of biclustering applications in biological and biomedical data

2018 ◽  
Vol 20 (4) ◽  
pp. 1450-1465 ◽  
Author(s):  
Juan Xie ◽  
Anjun Ma ◽  
Anne Fennell ◽  
Qin Ma ◽  
Jing Zhao
Keyword(s):  
Large Data ◽  
Data Interpretation ◽  
Biological Data ◽  
Computational Techniques ◽  
Biological Knowledge ◽  
Biomedical Data ◽  
Data Types ◽  
Data Mining Technique ◽  
Data Set ◽  
Methodology Development

Abstract Biclustering is a powerful data mining technique that allows clustering of rows and columns, simultaneously, in a matrix-format data set. It was first applied to gene expression data in 2000, aiming to identify co-expressed genes under a subset of all the conditions/samples. During the past 17 years, tens of biclustering algorithms and tools have been developed to enhance the ability to make sense out of large data sets generated in the wake of high-throughput omics technologies. These algorithms and tools have been applied to a wide variety of data types, including but not limited to, genomes, transcriptomes, exomes, epigenomes, phenomes and pharmacogenomes. However, there is still a considerable gap between biclustering methodology development and comprehensive data interpretation, mainly because of the lack of knowledge for the selection of appropriate biclustering tools and further supporting computational techniques in specific studies. Here, we first deliver a brief introduction to the existing biclustering algorithms and tools in public domain, and then systematically summarize the basic applications of biclustering for biological data and more advanced applications of biclustering for biomedical data. This review will assist researchers to effectively analyze their big data and generate valuable biological knowledge and novel insights with higher efficiency.

scholarly journals Drug Repurposing and Orphan Disease Therapeutics

2020 ◽  
Author(s):  
Neha Dhir ◽  
Ashish Jain ◽  
Dhruv Mahendru ◽  
Ajay Prakash ◽  
Bikash Medhi
Keyword(s):  
Drug Repositioning ◽  
Low Cost ◽  
Drug Repurposing ◽  
Orphan Disease ◽  
Current Status ◽  
Regulatory Guidelines ◽  
Repurposed Drugs ◽  
Approved Drugs ◽  
Clinical Conditions ◽  
Fda Approved Drugs

Drug repurposing (or drug repositioning) is an innovative way to find out the new indications of a drug that already exists in the market with known therapeutic indications. It offers an effective way to drug developers or the pharmaceutical companies to identify new targets for FDA-approved drugs. Less time consumption, low cost and low risk of failure are some of the advantages being offered with drug repurposing. Sildenafil (Viagra), a landmark example of a repurposed drug, was introduced into the market as an antianginal drug. But at present, its use is repurposed as drug for erectile dysfunction. In a similar way, numerous drugs are there that have been successfully repurposed in managing the clinical conditions. The chapter would be highlighting the various drug repurposing strategies, drugs repurposed in the past and the current status of repurposed drugs in the orphan disease therapeutics along with regulatory guidelines for drug repurposing.

Recent Advances in Drug Repurposing for Parkinson’s Disease

2019 ◽  
Vol 26 (28) ◽  
pp. 5340-5362 ◽  
Author(s):  
Xin Chen ◽  
Giuseppe Gumina ◽  
Kristopher G. Virga
Keyword(s):  
Parkinson’S Disease ◽  
Parkinson's Disease ◽  
Drug Discovery ◽  
De Novo ◽  
Drug Repositioning ◽  
Drug Repurposing ◽  
Cost Effective ◽  
New Drugs ◽  
Recent Advances ◽  
Clinical Drugs

:As a long-term degenerative disorder of the central nervous system that mostly affects older people, Parkinson’s disease is a growing health threat to our ever-aging population. Despite remarkable advances in our understanding of this disease, all therapeutics currently available only act to improve symptoms but cannot stop the disease progression. Therefore, it is essential that more effective drug discovery methods and approaches are developed, validated, and used for the discovery of disease-modifying treatments for Parkinson’s disease. Drug repurposing, also known as drug repositioning, or the process of finding new uses for existing or abandoned pharmaceuticals, has been recognized as a cost-effective and timeefficient way to develop new drugs, being equally promising as de novo drug discovery in the field of neurodegeneration and, more specifically for Parkinson’s disease. The availability of several established libraries of clinical drugs and fast evolvement in disease biology, genomics and bioinformatics has stimulated the momentums of both in silico and activity-based drug repurposing. With the successful clinical introduction of several repurposed drugs for Parkinson’s disease, drug repurposing has now become a robust alternative approach to the discovery and development of novel drugs for this disease. In this review, recent advances in drug repurposing for Parkinson’s disease will be discussed.

Molecular Topology and Other Promiscuity Determinants as Predictors of Therapeutic Class - A Theoretical Framework to Guide Drug Repositioning?

2018 ◽  
Vol 18 (13) ◽  
pp. 1110-1122 ◽  
Author(s):  
Juan F. Morales ◽  
Lucas N. Alberca ◽  
Sara Chuguransky ◽  
Mauricio E. Di Ianni ◽  
Alan Talevi ◽  
...  
Keyword(s):  
Molecular Descriptors ◽  
Drug Repositioning ◽  
Drug Repurposing ◽  
Topological Descriptors ◽  
Log P ◽  
Acidity Constant ◽  
Molecular Topology ◽  
Clustering Methods ◽  
Mean Values ◽  
Qsar Models

Much interest has been paid in the last decade on molecular predictors of promiscuity, including molecular weight, log P, molecular complexity, acidity constant and molecular topology, with correlations between promiscuity and those descriptors seemingly being context-dependent. It has been observed that certain therapeutic categories (e.g. mood disorders therapies) display a tendency to include multi-target agents (i.e. selective non-selectivity). Numerous QSAR models based on topological descriptors suggest that the topology of a given drug could be used to infer its therapeutic applications. Here, we have used descriptive statistics to explore the distribution of molecular topology descriptors and other promiscuity predictors across different therapeutic categories. Working with the publicly available ChEMBL database and 14 molecular descriptors, both hierarchical and non-hierchical clustering methods were applied to the descriptors mean values of the therapeutic categories after the refinement of the database (770 drugs grouped into 34 therapeutic categories). On the other hand, another publicly available database (repoDB) was used to retrieve cases of clinically-approved drug repositioning examples that could be classified into the therapeutic categories considered by the aforementioned clusters (111 cases), and the correspondence between the two studies was evaluated. Interestingly, a 3- cluster hierarchical clustering scheme based on only 14 molecular descriptors linked to promiscuity seem to explain up to 82.9% of approved cases of drug repurposing retrieved of repoDB. Therapeutic categories seem to display distinctive molecular patterns, which could be used as a basis for drug screening and drug design campaigns, and to unveil drug repurposing opportunities between particular therapeutic categories.

scholarly journals MNBDR: A Module Network Based Method for Drug Repositioning

Genes ◽  
2020 ◽  
Vol 12 (1) ◽  
pp. 25
Author(s):  
He-Gang Chen ◽  
Xiong-Hui Zhou
Keyword(s):  
Gene Expression ◽  
Protein Interactions ◽  
Drug Response ◽  
Drug Repositioning ◽  
Expression Profiles ◽  
Drug Repurposing ◽  
Recent Decade ◽  
Data Set ◽  
Module Network ◽  
The Cross

Drug repurposing/repositioning, which aims to find novel indications for existing drugs, contributes to reducing the time and cost for drug development. For the recent decade, gene expression profiles of drug stimulating samples have been successfully used in drug repurposing. However, most of the existing methods neglect the gene modules and the interactions among the modules, although the cross-talks among pathways are common in drug response. It is essential to develop a method that utilizes the cross-talks information to predict the reliable candidate associations. In this study, we developed MNBDR (Module Network Based Drug Repositioning), a novel method that based on module network to screen drugs. It integrated protein–protein interactions and gene expression profile of human, to predict drug candidates for diseases. Specifically, the MNBDR mined dense modules through protein–protein interaction (PPI) network and constructed a module network to reveal cross-talks among modules. Then, together with the module network, based on existing gene expression data set of drug stimulation samples and disease samples, we used random walk algorithms to capture essential modules in disease development and proposed a new indicator to screen potential drugs for a given disease. Results showed MNBDR could provide better performance than popular methods. Moreover, functional analysis of the essential modules in the network indicated our method could reveal biological mechanism in drug response.

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