scholarly journals RepCOOL: computational drug repositioning via integrating heterogeneous biological networks

2020 ◽  
Vol 18 (1) ◽  
Author(s):  
Ghazale Fahimian ◽  
Javad Zahiri ◽  
Seyed Shahriar Arab ◽  
Reza H. Sajedi
Keyword(s):  
Breast Cancer ◽  
Biological Networks ◽  
Drug Repositioning ◽  
Machine Learning Algorithms ◽  
New Drugs ◽  
Stage Ii ◽  
Cancer Stage ◽  
New Drug ◽  
Approved Drugs ◽  
Fda Approved Drugs

Abstract Background It often takes more than 10 years and costs more than 1 billion dollars to develop a new drug for a particular disease and bring it to the market. Drug repositioning can significantly reduce costs and time in drug development. Recently, computational drug repositioning attracted a considerable amount of attention among researchers, and a plethora of computational drug repositioning methods have been proposed. This methodology has widely been used in order to address various medical challenges, including cancer treatment. The most common cancers are lung and breast cancers. Thus, suggesting FDA-approved drugs via drug repositioning for breast cancer would help us to circumvent the approval process and subsequently save money as well as time. Methods In this study, we propose a novel network-based method, named RepCOOL, for drug repositioning. RepCOOL integrates various heterogeneous biological networks to suggest new drug candidates for a given disease. Results The proposed method showed a promising performance on benchmark datasets via rigorous cross-validation. The final drug repositioning model has been built based on a random forest classifier after examining various machine learning algorithms. Finally, in a case study, four FDA approved drugs were suggested for breast cancer stage II. Conclusion Results show the potency of the proposed method in detecting true drug-disease relationships. RepCOOL suggested four new drugs for breast cancer stage II namely Doxorubicin, Paclitaxel, Trastuzumab, and Tamoxifen.

scholarly journals RepCOOL: Computational Drug Repositioning Via Integrating Heterogeneous Biological Networks

2019 ◽  
Author(s):  
Ghazale Fahimian ◽  
Javad Zahiri ◽  
Seyed Sh. Arab ◽  
Reza H. Sajedi
Keyword(s):  
Breast Cancer ◽  
Biological Networks ◽  
Drug Repositioning ◽  
Machine Learning Algorithms ◽  
New Drugs ◽  
Stage Ii ◽  
Cancer Stage ◽  
New Drug ◽  
Benchmark Datasets ◽  
Approved Drugs

AbstractBackgroundIt often takes more than 10 years and costs more than one billion dollars to develop a new drug for a disease and bring it to the market. Drug repositioning can significantly reduce costs and times in drug development. Recently, computational drug repositioning attracted a considerable amount of attention among researchers, and a plethora of computational drug repositioning methods have been proposed.MethodsIn this study, we propose a novel network-based method, named RepCOOL, for drug repositioning. RepCOOL integrates various heterogeneous biological networks to suggest new drug candidates for a given disease.ResultsThe proposed method showed a promising performance on benchmark datasets via rigorous cross-validation. Final drug repositioning model has been built based on random forest classifier, after examining various machine learning algorithms. Finally, in a case study, four FDA approved drugs were suggested for breast cancer stage II.ConclusionResults show the strength of the proposed method in detecting true drug-disease relationships. RepCOOL suggested four new drugs for breast cancer stage II namely Doxorubicin, Paclitaxel, Trastuzumab and Tamoxifen.

scholarly journals PS4DR: A multimodal workflow for identification and prioritization of drugs based on pathway signatures

2020 ◽  
Author(s):  
Mhammad Asif Emon ◽  
Daniel Domingo-Fernández ◽  
Charles Tapley Hoyt ◽  
Martin Hofmann-Apitius
Keyword(s):  
Gene Expression ◽  
Genome Wide Association Study ◽  
Drug Repositioning ◽  
Heterogeneous Data ◽  
New Drugs ◽  
Gwas Data ◽  
Drug Candidates ◽  
Gene Expression Signatures ◽  
Approved Drugs ◽  
Fda Approved Drugs

Abstract Background: During the last decade, there has been a surge towards computational drug repositioning owing to constantly increasing -omics data in the biomedical research field. While numerous existing methods focus on the integration of heterogeneous data to propose candidate drugs, it is still challenging to substantiate their results with mechanistic insights of these candidate drugs. Therefore, there is a need for more innovative and efficient methods which can enable better integration of data and knowledge for drug repositioning. Results: Here, we present a customizable workflow ( PS4DR) which not only integrates high-throughput data such as genome-wide association study (GWAS) data and gene expression signatures from disease and drug perturbations but also takes pathway knowledge into consideration to predict drug candidates for repositioning. We have collected and integrated publicly available GWAS data and gene expression signatures for several diseases and hundreds of FDA-approved drugs or those under clinical trial in this study. Additionally, different pathway databases were used for mechanistic knowledge integration in the workflow. Using this systematic consolidation of data and knowledge, the workflow computes pathway signatures that assist in the prediction of new indications for approved and investigational drugs. Conclusion: We showcase PS4DR with applications demonstrating how this tool can be used for repositioning and identifying new drugs as well as proposing drugs that can simulate disease dysregulations. We were able to validate our workflow by demonstrating its capability to predict FDA-approved drugs for their known indications for several diseases. Further, PS4DR returned many potential drug candidates for repositioning that were backed up by epidemiological evidence extracted from scientific literature. Source code is freely available at https://github.com/ps4dr/ps4dr .

scholarly journals PS4DR: A multimodal workflow for identification and prioritization of drugs based on pathway signatures

2020 ◽  
Author(s):  
Mhammad Asif Emon ◽  
Daniel Domingo-Fernández ◽  
Charles Tapley Hoyt ◽  
Martin Hofmann-Apitius
Keyword(s):  
Gene Expression ◽  
Genome Wide Association Study ◽  
Drug Repositioning ◽  
Heterogeneous Data ◽  
New Drugs ◽  
Gwas Data ◽  
Drug Candidates ◽  
Gene Expression Signatures ◽  
Approved Drugs ◽  
Fda Approved Drugs

Abstract Background: During the last decade, there has been a surge towards computational drug repositioning owing to constantly increasing -omics data in the biomedical research field. While numerous existing methods focus on the integration of heterogeneous data to propose candidate drugs, it is still challenging to substantiate their results with mechanistic insights of these candidate drugs. Therefore, there is a need for more innovative and efficient methods which can enable better integration of data and knowledge for drug repositioning. Results: Here, we present a customizable workflow ( PS4DR) which not only integrates high-throughput data such as genome-wide association study (GWAS) data and gene expression signatures from disease and drug perturbations but also takes pathway knowledge into consideration to predict drug candidates for repositioning. We have collected and integrated publicly available GWAS data and gene expression signatures for several diseases and hundreds of FDA-approved drugs or those under clinical trial in this study. Additionally, different pathway databases were used for mechanistic knowledge integration in the workflow. Using this systematic consolidation of data and knowledge, the workflow computes pathway signatures that assist in the prediction of new indications for approved and investigational drugs. Conclusion: We showcase PS4DR with applications demonstrating how this tool can be used for repositioning and identifying new drugs as well as proposing drugs that can simulate disease dysregulations. We were able to validate our workflow by demonstrating its capability to predict FDA-approved drugs for their known indications for several diseases. Further, PS4DR returned many potential drug candidates for repositioning that were backed up by epidemiological evidence extracted from scientific literature. Source code is freely available at https://github.com/ps4dr/ps4dr .

QSICPM: A Novel Quantitative Semantic Interaction Calculation Prediction Method for Drug Repositioning Based on Biomedical Literature Semantic Data (Preprint)

2019 ◽  
Author(s):  
Guozheng Rao ◽  
Jinhe Gao ◽  
Zhang Li ◽  
Qing Cong ◽  
Zhiyong Feng
Keyword(s):  
Breast Cancer ◽  
Alzheimer’S Disease ◽  
Parkinson’S Disease ◽  
Alzheimer's Disease ◽  
Drug Repositioning ◽  
Gene Prioritization ◽  
New Drugs ◽  
Sorting Method ◽  
New Drug ◽  
Quantitative Calculation

BACKGROUND The process of developing new drugs is very tortuous. Bringing new drugs to the market requires billions of dollars in investment, which takes an average of about 13-15 years. In order to overcome these difficulties, more and more companies and pharmaceutical companies have begun to adopt the strategy of “repositioning drugs” instead of new drug development. OBJECTIVE Traditional drug repositioning methods often focus on relationships between entities, ignoring the semantic component of relationships. Therefore, we propose a new drug repositioning method to calculate the impact of pathogenic entities on disease mechanisms by quantifying semantic interactions. METHODS The QSICPM proposed in this paper divides the relevant interactions into three quantitative calculation layers based on the cause of disease. Representing interactions between the same type of pathogenic entities, interactions between different types of pathogenic entities, and pathogenic entity – drug interactions, respectively. QSICPM calculates the influence of drugs on the disease mechanism by utilizing the positive semantic relationships in each layer of the quantitative calculation process. And the gene prioritization sorting method and protein prioritization sorting method are proposed to sort the calculation results. The higher the ranking of the drugs in the results, the more likely the drug becomes an effective drug for the disease. RESULTS We used QSICPM to perform drug repositioning experiments on Parkinson's disease, breast cancer, and Alzheimer's disease. The experimental results predicted 881 potential drugs or pharmacological substances for PD disease, 830 potential drugs or pharmacological substances for BC disease, and 1180 potential drugs or pharmacological substances for AD disease. What’s more, the result set was sorted according to gene prioritization sorting and protein prioritization sorting. In the top 25 parts of the ranking, the average precision of the three results reached 68%, 75%, and 64%, respectively. The accuracy and applicability of QSICPM were verified. CONCLUSIONS This paper proposes a new research method for drug repositioning based on semantic relationship quantitative calculation. The performance of the QSICPM method was verified by drug repositioning experiments for Parkinson's disease, Breast cancer, and Alzheimer's disease. The results prove that QSICPM is a drug repositioning method with strong prediction precision and wide applicability.

scholarly journals PS4DR: A multimodal workflow for identification and prioritization of drugs based on pathway signatures

2020 ◽  
Author(s):  
Mhammad Asif Emon ◽  
Daniel Domingo-Fernández ◽  
Charles Tapley Hoyt ◽  
Martin Hofmann-Apitius
Keyword(s):  
Gene Expression ◽  
Genome Wide Association Study ◽  
Drug Repositioning ◽  
Heterogeneous Data ◽  
New Drugs ◽  
Gwas Data ◽  
Drug Candidates ◽  
Gene Expression Signatures ◽  
Approved Drugs ◽  
Fda Approved Drugs

Abstract Background: During the last decade, there has been a surge towards computational drug repositioning owing to constantly increasing -omics data in the biomedical research field. While numerous existing methods focus on the integration of heterogeneous data to propose candidate drugs, it is still challenging to substantiate their results with mechanistic insights of these candidate drugs. Therefore, there is a need for more innovative and efficient methods which can enable better integration of data and knowledge for drug repositioning.Results: Here, we present a customizable workflow (PS4DR) which not only integrates high-throughput data such as genome-wide association study (GWAS) data and gene expression signatures from disease and drug perturbations but also takes pathway knowledge into consideration to predict drug candidates for repositioning. We have collected and integrated publicly available GWAS data and gene expression signatures for several diseases and hundreds of FDA-approved drugs or those under clinical trial in this study. Additionally, different pathway databases were used for mechanistic knowledge integration in the workflow. Using this systematic consolidation of data and knowledge, the workflow computes pathway signatures that assist in the prediction of new indications for approved and investigational drugs. Conclusion: We showcase PS4DR with applications demonstrating how this tool can be used for repositioning and identifying new drugs as well as proposing drugs that can simulate disease dysregulations. We were able to validate our workflow by demonstrating its capability to predict FDA-approved drugs for their known indications for several diseases. Further, PS4DR returned many potential drug candidates for repositioning that were backed up by epidemiological evidence extracted from scientific literature. Source code is freely available at https://github.com/ps4dr/ps4dr.

scholarly journals PS4DR: A multimodal workflow for identification and prioritization of drugs based on pathway signatures

2020 ◽  
Author(s):  
Mhammad Asif Emon ◽  
Daniel Domingo-Fernández ◽  
Charles Tapley Hoyt ◽  
Martin Hofmann-Apitius
Keyword(s):  
Gene Expression ◽  
Genome Wide Association Study ◽  
Drug Repositioning ◽  
Heterogeneous Data ◽  
New Drugs ◽  
Gwas Data ◽  
Drug Candidates ◽  
Gene Expression Signatures ◽  
Approved Drugs ◽  
Fda Approved Drugs

Abstract Background: During the last decade, there has been a surge towards computational drug repositioning owing to constantly increasing -omics data in the biomedical research field. While numerous existing methods focus on the integration of heterogeneous data to propose candidate drugs, it is still challenging to substantiate their results with mechanistic insights of these candidate drugs. Therefore, there is a need for more innovative and efficient methods which can enable better integration of data and knowledge for drug repositioning.Results: Here, we present a customizable workflow (PS4DR) which not only integrates high-throughput data such as genome-wide association study (GWAS) data and gene expression signatures from disease and drug perturbations but also takes pathway knowledge into consideration to predict drug candidates for repositioning. We have collected and integrated publicly available GWAS data and gene expression signatures for several diseases and hundreds of FDA-approved drugs or those under clinical trial in this study. Additionally, different pathway databases were used for mechanistic knowledge integration in the workflow. Using this systematic consolidation of data and knowledge, the workflow computes pathway signatures that assist in the prediction of new indications for approved and investigational drugs. Conclusion: We showcase PS4DR with applications demonstrating how this tool can be used for repositioning and identifying new drugs as well as proposing drugs that can simulate disease dysregulations. We were able to validate our workflow by demonstrating its capability to predict FDA-approved drugs for their known indications for several diseases. Further, PS4DR returned many potential drug candidates for repositioning that were backed up by epidemiological evidence extracted from scientific literature. Source code is freely available at https://github.com/ps4dr/ps4dr.

scholarly journals Targeting SARS-CoV-2 Polymerase with New Nucleoside Analogues

Molecules ◽  
2021 ◽  
Vol 26 (11) ◽  
pp. 3461
Author(s):  
Vasiliki Daikopoulou ◽  
Panagiotis Apostolou ◽  
Sofia Mourati ◽  
Ioanna Vlachou ◽  
Maria Gougousi ◽  
...  
Keyword(s):  
Inhibitory Activity ◽  
Drug Repositioning ◽  
Nucleoside Analogues ◽  
In Vitro Assays ◽  
Rna Dependent Rna Polymerase ◽  
Sugar Moiety ◽  
The Family ◽  
Approved Drugs ◽  
Fda Approved Drugs

Despite the fact that COVID-19 vaccines are already available on the market, there have not been any effective FDA-approved drugs to treat this disease. There are several already known drugs that through drug repositioning have shown an inhibitory activity against SARS-CoV-2 RNA-dependent RNA polymerase. These drugs are included in the family of nucleoside analogues. In our efforts, we synthesized a group of new nucleoside analogues, which are modified at the sugar moiety that is replaced by a quinazoline entity. Different nucleobase derivatives are used in order to increase the inhibition. Five new nucleoside analogues were evaluated with in vitro assays for targeting polymerase of SARS-CoV-2.

scholarly journals Screening of FDA Approved Drugs Against COVID-19 Main Protease: Coronavirus Disease

2020 ◽  
Author(s):  
Vijayakumar Balakrishnan ◽  
Karthik Lakshminarayanan
Keyword(s):  
Vaccine Development ◽  
New Drugs ◽  
World Health ◽  
Wuhan City ◽  
Drug Candidates ◽  
Human Contact ◽  
Main Protease ◽  
Potent Drug ◽  
Approved Drugs ◽  
Fda Approved Drugs

In the end of December 2019, a new strain of coronavirus was identified in the Wuhan city of Hubei province in China. Within a shorter period of time, an unprecedented outbreak of this strain was witnessed over the entire Wuhan city. This novel coronavirus strain was later officially renamed as COVID-19 (Coronavirus disease 2019) by the World Health Organization. The mode of transmission had been found to be human-to-human contact and hence resulted in a rapid surge across the globe where more than 1,100,000 people have been infected with COVID-19. In the current scenario, finding potent drug candidates for the treatment of COVID-19 has emerged as the most challenging task for clinicians and researchers worldwide. Identification of new drugs and vaccine development may take from a few months to years based on the clinical trial processes. To overcome the several limitations involved in identifying and bringing out potent drug candidates for treating COVID-19, in the present study attempts were made to screen the FDA approved drugs using High Throughput Virtual Screening (HTVS). The COVID-19 main protease (COVID-19 Mpro) was chosen as the drug target for which the FDA approved drugs were initially screened with HTVS. The drug candidates that exhibited favorable docking score, energy and emodel calculations were further taken for performing Induced Fit Docking (IFD) using Schrodinger’s GLIDE. From the flexible docking results, the following four FDA approved drugs Sincalide, Pentagastrin, Ritonavir and Phytonadione were identified. In particular, Sincalide and Pentagastrin can be considered potential key players for the treatment of COVID-19 disease.

scholarly journals Palbociclib and Ribociclib Analogs Targeting CDK4 and CDK6 for Breast Cancer Treatment: A Molecular Modeling Study

2020 ◽  
Author(s):  
Dhoha TRIKI ◽  
sravya Kuchibhotla ◽  
Denis Fourches
Keyword(s):  
Breast Cancer ◽  
Molecular Modeling ◽  
Breast Cancer Treatment ◽  
Large Set ◽  
Dynamic Interactions ◽  
Cyclin Dependent Kinases ◽  
Highly Active ◽  
Approved Drugs ◽  
Fda Approved Drugs ◽  
Treat Breast Cancer

<div>Palbociclib and Ribociclib are FDA approved drugs that target cyclin dependent kinases CDK4 and CDK6 to treat breast cancer. Herein, we conducted a cheminformatics analysis of a large set of their analogs. The study highlights (i) several clusters of similar compounds with excellent inhibitory profiles, (ii) their shared CDK-ligand intermolecular interactions as predicted by 3D docking, and (iii) key dynamic interactions shared by highly active CDK4/6 inhibitors.<br></div>

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