scholarly journals A review on drug repurposing applicable to COVID-19

2020 ◽  
Author(s):  
Serena Dotolo ◽  
Anna Marabotti ◽  
Angelo Facchiano ◽  
Roberto Tagliaferri
Keyword(s):  
Artificial Intelligence ◽  
Drug Target ◽  
Lower Cost ◽  
Drug Repositioning ◽  
Drug Repurposing ◽  
Chemical Compounds ◽  
The Moment ◽  
New Applications ◽  
Novel Drug ◽  
Small Chemical

Abstract Drug repurposing involves the identification of new applications for existing drugs at a lower cost and in a shorter time. There are different computational drug-repurposing strategies and some of these approaches have been applied to the coronavirus disease 2019 (COVID-19) pandemic. Computational drug-repositioning approaches applied to COVID-19 can be broadly categorized into (i) network-based models, (ii) structure-based approaches and (iii) artificial intelligence (AI) approaches. Network-based approaches are divided into two categories: network-based clustering approaches and network-based propagation approaches. Both of them allowed to annotate some important patterns, to identify proteins that are functionally associated with COVID-19 and to discover novel drug–disease or drug–target relationships useful for new therapies. Structure-based approaches allowed to identify small chemical compounds able to bind macromolecular targets to evaluate how a chemical compound can interact with the biological counterpart, trying to find new applications for existing drugs. AI-based networks appear, at the moment, less relevant since they need more data for their application.

scholarly journals Using systems medicine to identify a therapeutic agent with potential for repurposing in inflammatory bowel disease

2020 ◽  
Vol 13 (11) ◽  
pp. dmm044040 ◽  
Author(s):  
Katie Lloyd ◽  
Stamatia Papoutsopoulou ◽  
Emily Smith ◽  
Philip Stegmaier ◽  
Francois Bergey ◽  
...  
Keyword(s):  
Drug Discovery ◽  
In Silico ◽  
Nuclear Translocation ◽  
Drug Repositioning ◽  
Drug Repurposing ◽  
Peritoneal Macrophages ◽  
Bowel Diseases ◽  
Inflammatory Bowel ◽  
Aseptic Conditions ◽  
Novel Drug

ABSTRACTInflammatory bowel diseases (IBDs) cause significant morbidity and mortality. Aberrant NF-κB signalling is strongly associated with these conditions, and several established drugs influence the NF-κB signalling network to exert their effect. This study aimed to identify drugs that alter NF-κB signalling and could be repositioned for use in IBD. The SysmedIBD Consortium established a novel drug-repurposing pipeline based on a combination of in silico drug discovery and biological assays targeted at demonstrating an impact on NF-κB signalling, and a murine model of IBD. The drug discovery algorithm identified several drugs already established in IBD, including corticosteroids. The highest-ranked drug was the macrolide antibiotic clarithromycin, which has previously been reported to have anti-inflammatory effects in aseptic conditions. The effects of clarithromycin effects were validated in several experiments: it influenced NF-κB-mediated transcription in murine peritoneal macrophages and intestinal enteroids; it suppressed NF-κB protein shuttling in murine reporter enteroids; it suppressed NF-κB (p65) DNA binding in the small intestine of mice exposed to lipopolysaccharide; and it reduced the severity of dextran sulphate sodium-induced colitis in C57BL/6 mice. Clarithromycin also suppressed NF-κB (p65) nuclear translocation in human intestinal enteroids. These findings demonstrate that in silico drug repositioning algorithms can viably be allied to laboratory validation assays in the context of IBD, and that further clinical assessment of clarithromycin in the management of IBD is required.This article has an associated First Person interview with the joint first authors of the paper.

scholarly journals DTI-SNNFRA: Drug-target interaction prediction by shared nearest neighbors and fuzzy-rough approximation

PLoS ONE ◽  
2021 ◽  
Vol 16 (2) ◽  
pp. e0246920
Author(s):  
Sk Mazharul Islam ◽  
Sk Md Mosaddek Hossain ◽  
Sumanta Ray
Keyword(s):  
Drug Discovery ◽  
Drug Target ◽  
Drug Repositioning ◽  
Geometric Mean ◽  
Imbalanced Data ◽  
Drug Repurposing ◽  
Search Space ◽  
Classification Model ◽  
Target Interaction ◽  
Rough Approximation

In-silico prediction of repurposable drugs is an effective drug discovery strategy that supplements de-nevo drug discovery from scratch. Reduced development time, less cost and absence of severe side effects are significant advantages of using drug repositioning. Most recent and most advanced artificial intelligence (AI) approaches have boosted drug repurposing in terms of throughput and accuracy enormously. However, with the growing number of drugs, targets and their massive interactions produce imbalanced data which may not be suitable as input to the classification model directly. Here, we have proposed DTI-SNNFRA, a framework for predicting drug-target interaction (DTI), based on shared nearest neighbour (SNN) and fuzzy-rough approximation (FRA). It uses sampling techniques to collectively reduce the vast search space covering the available drugs, targets and millions of interactions between them. DTI-SNNFRA operates in two stages: first, it uses SNN followed by a partitioning clustering for sampling the search space. Next, it computes the degree of fuzzy-rough approximations and proper degree threshold selection for the negative samples’ undersampling from all possible interaction pairs between drugs and targets obtained in the first stage. Finally, classification is performed using the positive and selected negative samples. We have evaluated the efficacy of DTI-SNNFRA using AUC (Area under ROC Curve), Geometric Mean, and F1 Score. The model performs exceptionally well with a high prediction score of 0.95 for ROC-AUC. The predicted drug-target interactions are validated through an existing drug-target database (Connectivity Map (Cmap)).

scholarly journals Coupled matrix–matrix and coupled tensor–matrix completion methods for predicting drug–target interactions

2020 ◽  
Author(s):  
Maryam Bagherian ◽  
Renaid B Kim ◽  
Cheng Jiang ◽  
Maureen A Sartor ◽  
Harm Derksen ◽  
...  
Keyword(s):  
Matrix Factorization ◽  
Drug Target ◽  
Drug Repositioning ◽  
Matrix Completion ◽  
Area Under The Curve ◽  
Drug Repurposing ◽  
Computational Prediction ◽  
Comprehensive Information ◽  
The Matrix ◽  
Benchmark Datasets

Abstract Predicting the interactions between drugs and targets plays an important role in the process of new drug discovery, drug repurposing (also known as drug repositioning). There is a need to develop novel and efficient prediction approaches in order to avoid the costly and laborious process of determining drug–target interactions (DTIs) based on experiments alone. These computational prediction approaches should be capable of identifying the potential DTIs in a timely manner. Matrix factorization methods have been proven to be the most reliable group of methods. Here, we first propose a matrix factorization-based method termed ‘Coupled Matrix–Matrix Completion’ (CMMC). Next, in order to utilize more comprehensive information provided in different databases and incorporate multiple types of scores for drug–drug similarities and target–target relationship, we then extend CMMC to ‘Coupled Tensor–Matrix Completion’ (CTMC) by considering drug–drug and target–target similarity/interaction tensors. Results: Evaluation on two benchmark datasets, DrugBank and TTD, shows that CTMC outperforms the matrix-factorization-based methods: GRMF, $L_{2,1}$-GRMF, NRLMF and NRLMF$\beta $. Based on the evaluation, CMMC and CTMC outperform the above three methods in term of area under the curve, F1 score, sensitivity and specificity in a considerably shorter run time.

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.

Artificial Intelligence Effecting a Paradigm Shift in Drug Development

2020 ◽  
pp. 247263032095693
Author(s):  
Masturah Bte Mohd Abdul Rashid
Keyword(s):  
Artificial Intelligence ◽  
Clinical Trials ◽  
Drug Development ◽  
Paradigm Shift ◽  
Drug Repositioning ◽  
Target Identification ◽  
Success Rates ◽  
Drug Target Identification ◽  
The Cost ◽  
Novel Drug

The inverse relationship between the cost of drug development and the successful integration of drugs into the market has resulted in the need for innovative solutions to overcome this burgeoning problem. This problem could be attributed to several factors, including the premature termination of clinical trials, regulatory factors, or decisions made in the earlier drug development processes. The introduction of artificial intelligence (AI) to accelerate and assist drug development has resulted in cheaper and more efficient processes, ultimately improving the success rates of clinical trials. This review aims to showcase and compare the different applications of AI technology that aid automation and improve success in drug development, particularly in novel drug target identification and design, drug repositioning, biomarker identification, and effective patient stratification, through exploration of different disease landscapes. In addition, it will also highlight how these technologies are translated into the clinic. This paradigm shift will lead to even greater advancements in the integration of AI in automating processes within drug development and discovery, enabling the probability and reality of attaining future precision and personalized medicine.

scholarly journals Virtual Screening of Natural Metabolites as Candidate Drugs for Rheumatoid Arthritis

2020 ◽  
Vol 8 (4S4) ◽  
pp. 226-230
Keyword(s):  
Rheumatoid Arthritis ◽  
Side Effects ◽  
Autoimmune Disease ◽  
Virtual Screening ◽  
Membrane Protein ◽  
Drug Target ◽  
Biological Response ◽  
Chemical Compounds ◽  
Novel Drug ◽  
Autodock 4.2

Inflammation is a complex biological response to reject and heal any harmful stimuli created by pathogens, damaged cells or irritants. One of the more prevalent inflammatory disease found in 0.5-1.0% of the world’s population is Rheumatoid arthritis (RA). RA is an autoimmune disease affecting the synovia. The actual reason for this disease is still unknown and is more complex to study. So, the drugs which are commercialized acts only to reduce the outcome of the disease, pain, by inhibiting the vital enzymes responsible for the synthesis of inflammatory mediators called prostaglandins. Cyclooxygenase- I and Cyclooxygenase- II are the commonly targeted enzymes by the current drugs in market. These drugs are reported to affect the normal physiological functions of various organs leading to side effects. PGE2 is the major prostaglandin involved in this disorder and found abundant in the affected synovia. mPGES- I is a membrane protein involved in the biosynthesis of PGE2 which has been reported as a novel drug target to treat RA. Though synthesized chemical compounds have higher anti-inflammatory activity; they are reported to possess a number of side effects. Thus a library of natural compounds are collected and screened virtually as mPGES-1 inhibitors using Autodock 4.2.

scholarly journals Uncovering new drug properties in target-based drug-drug similarity networks

2020 ◽  
Author(s):  
Lucreţia Udrescu ◽  
Paul Bogdan ◽  
Aimée Chiş ◽  
Ioan Ovidiu Sîrbu ◽  
Alexandru Topîrceanu ◽  
...  
Keyword(s):  
Machine Learning ◽  
Azelaic Acid ◽  
Drug Target ◽  
Drug Repositioning ◽  
Traditional Approach ◽  
Test Procedure ◽  
Drug Repurposing ◽  
Biological Targets ◽  
Chemical Structures ◽  
Drug Similarity

ABSTRACTDespite recent advances in bioinformatics, systems biology, and machine learning, the accurate prediction of drug properties remains an open problem. Indeed, because the biological environment is a complex system, the traditional approach – based on knowledge about the chemical structures – cannot fully explain the nature of interactions between drugs and biological targets. Consequently, in this paper, we propose an unsupervised machine learning approach that uses the information we know about drug-target interactions to infer drug properties. To this end, we define drug similarity based on drug-target interactions and build a weighted Drug-Drug Similarity Network according to the drug-drug similarity relationships. Using an energy-model network layout, we generate drug communities that are associated with specific, dominant drug properties. DrugBank confirms the properties of 59.52% of the drugs in these communities, and 26.98% are existing drug repositioning hints we reconstruct with our DDSN approach. The remaining 13.49% of the drugs seem not to match the dominant pharmacologic property; thus, we consider them as drug repurposing hints. The resources required to test all these repurposing hints are considerable. Therefore we introduce a mechanism of prioritization based on the betweenness/degree node centrality. By using betweenness/degree as an indicator of drug repurposing potential, we select Azelaic acid and Meprobamate as a possible antineoplastic and antifungal, respectively. Finally, we use a test procedure, based on molecular docking, to further analyze the repurposing of Azelaic acid and Meprobamate.

The Significance of the Study of the Brain's Hippocampus for the Progress of Biologically-Inspired Computational Systems

2020 ◽  
pp. 63-79
Author(s):  
Konstantinos Domdouzis
Keyword(s):  
Artificial Intelligence ◽  
Biologically Inspired ◽  
The Moment ◽  
New Applications ◽  
Computational Systems ◽  
The Impact

The field of Artificial Intelligence faces unprecedented progress. It is expected that the use of Artificial Intelligence to different sectors of science and economy will be increased. This is also shown by the fact that at the moment, Artificial Intelligence is characterised by popularity which is proven through the constant presentations on the news. This chapter shows how the study of the brain's hippocampus can further progress the field of Artificial Intelligence. The chapter presents indicative examples of the literature that show how the study of the hippocampus can lead to the development of specific applications. It also shows the impact to the development of biologically-inspired systems through the analysis of specific capabilities of the hippocampus. A number of conclusions are drawn in relation to the significance of the study of the brain's hippocampus for the development of new applications.

scholarly journals Uncovering New Drug Properties in Target-Based Drug–Drug Similarity Networks

Pharmaceutics ◽  
2020 ◽  
Vol 12 (9) ◽  
pp. 879 ◽  
Author(s):  
Lucreţia Udrescu ◽  
Paul Bogdan ◽  
Aimée Chiş ◽  
Ioan Ovidiu Sîrbu ◽  
Alexandru Topîrceanu ◽  
...  
Keyword(s):  
Machine Learning ◽  
Azelaic Acid ◽  
Drug Target ◽  
Drug Repositioning ◽  
Traditional Approach ◽  
Test Procedure ◽  
Drug Repurposing ◽  
Biological Targets ◽  
Chemical Structures ◽  
Drug Similarity

Despite recent advances in bioinformatics, systems biology, and machine learning, the accurate prediction of drug properties remains an open problem. Indeed, because the biological environment is a complex system, the traditional approach—based on knowledge about the chemical structures—can not fully explain the nature of interactions between drugs and biological targets. Consequently, in this paper, we propose an unsupervised machine learning approach that uses the information we know about drug–target interactions to infer drug properties. To this end, we define drug similarity based on drug–target interactions and build a weighted Drug–Drug Similarity Network according to the drug–drug similarity relationships. Using an energy-model network layout, we generate drug communities associated with specific, dominant drug properties. DrugBank confirms the properties of 59.52% of the drugs in these communities, and 26.98% are existing drug repositioning hints we reconstruct with our DDSN approach. The remaining 13.49% of the drugs seem not to match the dominant pharmacologic property; thus, we consider them potential drug repurposing hints. The resources required to test all these repurposing hints are considerable. Therefore we introduce a mechanism of prioritization based on the betweenness/degree node centrality. Using betweenness/degree as an indicator of drug repurposing potential, we select Azelaic acid and Meprobamate as a possible antineoplastic and antifungal, respectively. Finally, we use a test procedure based on molecular docking to analyze Azelaic acid and Meprobamate’s repurposing.

scholarly journals Multiplex-Heterogeneous Network Embedding for Drug Repositioning

2021 ◽  
Author(s):  
Léo Pio-Lopez
Keyword(s):  
Heterogeneous Networks ◽  
Heterogeneous Network ◽  
Link Prediction ◽  
Drug Target ◽  
Drug Repositioning ◽  
Drug Repurposing ◽  
Network Embedding ◽  
Medical Treatments ◽  
Vector Representations

Drug repositioning (also called drug repurposing) is a strategy for identifying new therapeutic targets for existing drugs. This approach is of great importance in pharmacology as it is a faster and cheaper way to develop new medical treatments. In this paper, we present, to our knowledge, the first application of multiplex-heterogeneous network embedding to drug repositioning. Network embedding learns the vector representations of nodes, opening the whole machine learning toolbox for a wide variety of applications including link prediction, node labelling or clustering. So far, the application of network embedding for drug repositioning focused on heterogeneous networks. Our approach for drug repositioning is based on multiplex-heterogeneous network embedding. Such method allows the richness and complexity of multiplex and heterogeneous networks to be projected in the same vector space. In other words, multiplex-heterogeneous networks aggregate different multi-omics data in the same network representation. We validate the approach on a task of link prediction and on a case study for SARS-CoV2 drug repositioning. Experimental results show that our approach is highly robust and effective for finding new drug-target associations.

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