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 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.

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 Drug REpurposing using AI/ML tools - for Rare Diseases (DREAM-RD): A case study with Fragile X Syndrome (FXS)

2020 ◽  
Author(s):  
Kavitha Agastheeswaramoorthy ◽  
Aarti Sevilimedu
Keyword(s):  
Fragile X Syndrome ◽  
Drug Repositioning ◽  
Fragile X ◽  
Drug Repurposing ◽  
Machine Learning Algorithms ◽  
Specific Gene ◽  
Drug Induced ◽  
Approved Drugs ◽  
Disease Specific

AbstractDrug repositioning is emerging as an increasingly relevant option for rare disease therapy and management. Various methods for identifying suitable drug candidates have been tried and range from clinical symptomatic repurposing to data driven strategies which are based on the disease-specific gene or protein expression, modification, signalling and physiological perturbation profiles. The use of Artificial Intelligence (AI) and machine learning algorithms (ML) allows one to combine diverse data sets, and extract disease-specific data profiles which may not be intuitive or apparent from a subset of data. In this case study with Fragile X syndrome and autism, we have used multiple computational methodologies to extract profiles, which are then combined to arrive at a comprehensive signature (disease DEG). This DEG was then used to interrogate the large collection of drug-induced perturbation profiles present in public databases, to find appropriate small molecules to reverse or mimic the disease-profiles. We have labelled this pipeline Drug Repurposing using AI/ML tools - for Rare Diseases (DREAM-RD). We have shortlisted over 100 FDA approved drugs using the aforementioned pipeline, which may potentially be useful to ameliorate autistic phenotypes associated with FXS.

Synthesis and Anticancer Potentials of Quinoline Analogues: A Review of Literature

2020 ◽  
Vol 17 ◽  
Author(s):  
Ajay Kumar ◽  
Salahuddin ◽  
Avijit Mazumder ◽  
Mohammad Shahar Yar ◽  
Rajnish Kumar ◽  
...  
Keyword(s):  
Breast Cancer ◽  
Prostate Cancer ◽  
Colon Cancer ◽  
Pancreas Cancer ◽  
New Drugs ◽  
Cancer Breast ◽  
Privileged Structures ◽  
Approved Drugs ◽  
New Research ◽  
Cancer Côlon

Abstract: New drugs introduced on the market each year have privileged structures specifically for anticancer targets, of which quinoline-based analogues also play an important role. This review lit up quinoline and its derivatives, which have great potency against various cancer cells including prostate cancer, breast cancer, colon cancer, pancreas cancer and many more. This review describes the most likely process-scale synthetic approaches of quinoline and its derivatives having specific pharmacophore, for anticancer targets along. It is also described the undergoing development and recently approved drugs in tabular form. Quinoline moiety as privileged structural pharmacophore has most effective activity against different cancer cell lines like prostate cancer, breast cancer, stomach cancer, pancreas cancer, Colon cancer, CNS cancer and renal cancer. Because of this advantage, it has the potency to grow with new research works about the anticancer as well as enhancing the value of the investigative process in the field of medicinal chemistry by introducing new effective alignments of substituents. It can be used as lead compounds for further research in the subject of anticancer drug discovery.

scholarly journals In silico drug repositioning using deep learning and comprehensive similarity measures

2021 ◽  
Vol 22 (S3) ◽  
Author(s):  
Hai-Cheng Yi ◽  
Zhu-Hong You ◽  
Lei Wang ◽  
Xiao-Rui Su ◽  
Xi Zhou ◽  
...  
Keyword(s):  
Drug Repositioning ◽  
Similarity Measures ◽  
Predictive Ability ◽  
New Drugs ◽  
Potential Drug ◽  
Proposed Model ◽  
Disease Associations ◽  
Benchmark Datasets ◽  
Gated Recurrent Units ◽  
New Treatments

Abstract Background Drug repositioning, meanings finding new uses for existing drugs, which can accelerate the processing of new drugs research and development. Various computational methods have been presented to predict novel drug–disease associations for drug repositioning based on similarity measures among drugs and diseases. However, there are some known associations between drugs and diseases that previous studies not utilized. Methods In this work, we develop a deep gated recurrent units model to predict potential drug–disease interactions using comprehensive similarity measures and Gaussian interaction profile kernel. More specifically, the similarity measure is used to exploit discriminative feature for drugs based on their chemical fingerprints. Meanwhile, the Gaussian interactions profile kernel is employed to obtain efficient feature of diseases based on known disease-disease associations. Then, a deep gated recurrent units model is developed to predict potential drug–disease interactions. Results The performance of the proposed model is evaluated on two benchmark datasets under tenfold cross-validation. And to further verify the predictive ability, case studies for predicting new potential indications of drugs were carried out. Conclusion The experimental results proved the proposed model is a useful tool for predicting new indications for drugs or new treatments for diseases, and can accelerate drug repositioning and related drug research and discovery.

scholarly journals Current Trends in the Treatment of Human Epidermal Growth Factor Receptor-2 Negative Breast Cancer -

2020 ◽  
Vol 10 (1) ◽  
pp. 46-59
Author(s):  
Palanisamy Sivanandy ◽  
Suresh Shanmugam ◽  
Rui Ying Lau ◽  
Jonathan Yvong Syen Chin ◽  
Xiao Xiang Lee ◽  
...  
Keyword(s):  
Breast Cancer ◽  
Anticancer Drugs ◽  
Growth Factor Receptor ◽  
Progression Free Survival ◽  
New Drugs ◽  
Mortality And Morbidity ◽  
Risk Of Death ◽  
Review Period ◽  
Us Fda ◽  
Approved Drugs

Breast cancer affect almost 1 in 8 women and it is highest in developed and developing countries. There are many drugs exist for the treatment of breast cancer, but still the incidence of mortality and morbidity are high among all cancer types in most countries. Even though the conventional therapies play a major role in the management of breast cancer, its complications are obvious and unavoidable. The newer targeted drug therapy came in place to reduce complications in some extent, but not fully. Hence, a review was aimed to analyse the efficacy and safety of newer anticancer drugs that approved for the treatment of breast cancer by US-FDA from 2017 to 2019. The Olaparib, Talazoparib, and Ribociclib are the newly approved drugs for the treatment of breast cancer during this review period. Among these new drugs, Olaparib and Talazoparib alone or in combination with other anticancer drugs considered as safe and efficacious. Patients with Olaparib or Talazoparib as monotherapy have median progression-free survival of 2.8 to 8.6 months longer and has 42-46% lower risk of death (P

scholarly journals Target Based Computational Drug Repositioning for Astrocytoma

2016 ◽  
Vol 5 (8) ◽  
pp. 63-68
Author(s):  
Maria Manzoor ◽  
Irum Aslam ◽  
Shumaila Azam ◽  
Zanib Khan
Keyword(s):  
De Novo ◽  
Drug Repositioning ◽  
Tp53 Gene ◽  
Data Bank ◽  
Pharmaceutical Journal ◽  
New Drugs ◽  
Type Iv ◽  
Drug Databases ◽  
Drug Reposition ◽  
Approved Drugs

Drug reposition is innovative method as it provides new ways to measure drug kinetics, multiplexed assays and others. In drug repositioning already approved drugs are used due to which time is not wasted on initial clinical trials. Market attainment cost for repositioned drugs is far less than the market attainment cost for new drugs. The secondary indications of most of the approved drugs and the availability of approved drug databases can provide an efficient way of searching safer drugs for new indications. Drug repositioning can provide an alternative method to explore the safe anti-cancer agents. Astrocytoma is the one type of Brain tumor. There are four types of Astrocytoma which arise in different part of the brain. The type IV that is Glioblastoma is very aggressive form of it. Many genes are involved in spreading of this disease but it is normally caused by Tp53. Mutations in the Tp53 gene are identified in about 28% of de novo GBM and 65% of secondary , thus indicating that Tp53 abnormalities are common in the progression of disease. The structure for Tp53 protein was obtained from RCSB PDB: RCSB Protein Data Bank. For repositioning Drugs are randomly selected from Drug Bank. Those drugs which have fewer side effects as compared to the drugs for Glioblastoma are selected as a candidate compounds for docking. Patch Dock server was used to perform molecular docking. Then among all selected drugs, some drugs are reposition on the basis of significant binding interactions with target protein TP53.Manzoor et al., International Current Pharmaceutical Journal, July 2016, 5(8): 63-68http://www.icpjonline.com/documents/Vol5Issue8/01.pdf

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