scholarly journals In silico drug repositioning of FDA-approved drugs to predict new inhibitors for alpha-synuclein aggregation

2020 ◽  
Vol 88 ◽  
pp. 107308
Author(s):  
Sedighe Sadat Jafaripour ◽  
Sajjad Gharaghani ◽  
Elmira Nazarshodeh ◽  
Shozeb Haider ◽  
Ali Akbar Saboury
Keyword(s):  
In Silico ◽  
Drug Repositioning ◽  
Alpha Synuclein ◽  
Approved Drugs ◽  
Alpha Synuclein Aggregation ◽  
Fda Approved Drugs

scholarly journals REPURPOSING FDA-APPROVED DRUGS AGAINST MULTIPLE PROTEINS OF SARS-CoV-2: AN in silico STUDY

2021 ◽  
pp. e00845
Author(s):  
Alfred Olaoluwa Akinlalu ◽  
Annapoorna Chamundi ◽  
Donald Terseer Yakumbur ◽  
Funmilayo I. Deborah Afolayan ◽  
Ijeoma Akunna Duru ◽  
...  
Keyword(s):  
In Silico ◽  
In Silico Study ◽  
Approved Drugs ◽  
Fda Approved Drugs

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.

In Silico Modeling of FDA-Approved Drugs for Discovery of Therapies Against Neglected Diseases: A Drug Repurposing Approach

2019 ◽  
pp. 625-648 ◽  
Author(s):  
Carolina L. Belllera ◽  
María L. Sbaraglini ◽  
Lucas N. Alberca ◽  
Juan I. Alice ◽  
Alan Talevi
Keyword(s):  
In Silico ◽  
Drug Repurposing ◽  
Neglected Diseases ◽  
In Silico Modeling ◽  
Approved Drugs ◽  
Fda Approved Drugs

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 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 A Drug Repositioning Approach Reveals that Streptococcus mutans Is Susceptible to a Diverse Range of Established Antimicrobials and Nonantibiotics

2017 ◽  
Vol 62 (1) ◽  
Author(s):  
S. Saputo ◽  
R. C. Faustoferri ◽  
R. G. Quivey
Keyword(s):  
Streptococcus Mutans ◽  
Biofilm Formation ◽  
Drug Repositioning ◽  
Drug Screen ◽  
Biofilm Inhibition ◽  
Diverse Range ◽  
Content Type ◽  
Multispecies Biofilm ◽  
Approved Drugs ◽  
Fda Approved Drugs

ABSTRACT Streptococcus mutans is the primary causative agent of dental caries and contributes to the multispecies biofilm known as dental plaque. An adenylate kinase-based assay was optimized for S. mutans to detect cell lysis when exposed to the Selleck library (Selleck Chemical, Houston, TX) of 853 FDA-approved drugs in, to our knowledge, the first high-throughput drug screen in S. mutans. We found 126 drugs with activity against S. mutans planktonic cultures, and they were classified into six categories: antibacterials (61), antineoplastics (23), ion channel effectors (9), other antimicrobials (7), antifungals (6), and other (20). These drugs were also tested for activity against S. mutans biofilm cultures, and 24 compounds were found to inhibit biofilm formation, 6 killed preexisting biofilms, 84 exhibited biofilm inhibition and killing activity, and 12 had no activity against biofilms. The activities of 9 selected compounds that exhibited antimicrobial activity were further characterized for their activity against S. mutans planktonic and biofilm cultures. Together, our results suggest that S. mutans exhibits a susceptibility profile to a diverse array of established and novel antibacterials.

scholarly journals Drug Repurposing Commonly Against Dengue Virus Capsid and SARS-CoV-2 Nucleocapsid: An in Silico Approach

2020 ◽  
Author(s):  
Debica Mukherjee ◽  
Rupesh Roy ◽  
UPASANA RAY
Keyword(s):  
Dengue Virus ◽  
Capsid Protein ◽  
In Silico ◽  
Rna Binding ◽  
Drug Repurposing ◽  
Virus Capsid ◽  
Approved Drugs ◽  
Full Swing ◽  
Fda Approved Drugs ◽  
Virus Capsid Protein

<p></p><p>In the middle of SARS-CoV-2 pandemic, dengue virus (DENV) is giving a silent warning as the season approaches nearer. There is no specific antiviral against DENV for use in the clinics. Thus, considering these facts we can potentially face both these viruses together increasing the clinical burden. The search for anti-viral drugs against SARS-CoV-2 is in full swing and repurposing of already ‘in-use’ drugs against other diseases or COVID-19 has drawn significant attention. Earlier we had reported few FDA approved anti-viral and anti-microbial drugs that could be tested for binding with SARS-CoV-2 nucleocapsid N terminal domain. We explored the possibility of interactions of the drugs screened for SARS-CoV2 with Dengue virus capsid protein. We report five FDA approved drugs that were seen to be docking onto the SARS-CoV-2 nucleocapsid RNA binding domain, also docking well with DENV capsid protein on the RNA binding site and/or the capsid’s membrane fusion domain. Thus, the present study proposes these five drugs as common antiviral candidates against both SARS-CoV-2 and DENV although the <i>in silico</i> study is subject to further validations.</p><br><p></p>

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