scholarly journals A Screening Cascade to Identify ERβ Ligands

2014 ◽  
Vol 12 (1) ◽  
pp. nrs.12003 ◽  
Author(s):  
Carly S. Filgueira ◽  
Cindy Benod ◽  
Xiaohua Lou ◽  
Prem S. Gunamalai ◽  
Rosa A. Villagomez ◽  
...  
Keyword(s):  
High Throughput Screening ◽  
De Novo ◽  
Drug Repurposing ◽  
Cross Reactivity ◽  
Cancer Cell Proliferation ◽  
De Novo Drug Design ◽  
Binding Domains ◽  
Differential Scanning Fluorimetry ◽  
Approved Drugs ◽  
Screening Cascade

The establishment of effective high throughput screening cascades to identify nuclear receptor (NR) ligands that will trigger defined, therapeutically useful sets of NR activities is of considerable importance. Repositioning of existing approved drugs with known side effect profiles can provide advantages because de novo drug design suffers from high developmental failure rates and undesirable side effects which have dramatically increased costs. Ligands that target estrogen receptor β (ERβ) could be useful in a variety of diseases ranging from cancer to neurological to cardiovascular disorders. In this context, it is important to minimize cross-reactivity with ERα, which has been shown to trigger increased rates of several types of cancer. Because of high sequence similarities between the ligand binding domains of ERα and ERβ, preferentially targeting one subtype can prove challenging. Here, we describe a sequential ligand screening approach comprised of complementary in-house assays to identify small molecules that are selective for ERβ. Methods include differential scanning fluorimetry, fluorescence polarization and a GAL4 transactivation assay. We used this strategy to screen several commercially-available chemical libraries, identifying thirty ERβ binders that were examined for their selectivity for ERβ versus ERα, and tested the effects of selected ligands in a prostate cancer cell proliferation assay. We suggest that this approach could be used to rapidly identify candidates for drug repurposing.

scholarly journals Data-Driven Drug Repurposing for COVID-19

2020 ◽  
Author(s):  
Raghvendra Mall ◽  
Abdurrahman Elbasir ◽  
Hossam Al Meer ◽  
Sanjay Chawla ◽  
Ehsan Ullah
Keyword(s):  
Clinical Trials ◽  
De Novo ◽  
Drug Repurposing ◽  
Binding Energies ◽  
Protein Activity ◽  
De Novo Drug Design ◽  
Docking Simulations ◽  
Antibiotic Drugs ◽  
Approved Drugs ◽  
Ranked List

<div>Motivation: A global effort is underway to identify drugs for the treatment of COVID-19. Since de novo drug design is an extremely long, time-consuming, and expensive process, efforts are underway to discover existing drugs that can be</div><div>repurposed for COVID-19.</div><div><br></div><div>Model: We propose a machine learning representation framework that uses deep learning induced vector embeddings of drugs and viral proteins as features to predict drug-viral protein activity. The prediction model in-turn is used to build an ensemble framework to rank approved drugs based on their ability to inhibit the three main proteases (enzymes) of the SARS-COV-2 virus.</div><div><br></div><div>Results: We identify a ranked list of 19 drugs as potential targets including 7 antivirals, 6 anticancer, 3 antibiotics, 2 antimalarial, and 1 antifungal. Several drugs, such as Remdesivir, Lopinavir, Ritonavir, and Hydroxychloroquine, in our ranked list, are currently in clinical trials. Moreover, through molecular docking simulations, we demonstrate that majority of the anticancer and antibiotic drugs in our ranked list have low binding energies and thus high binding affinity with the 3CL-pro protease of SARS-COV-2 virus.</div><div><br></div><div>Disclaimer: Our models are computational and the drugs suggested should not be taken for treating COVID-19 without a doctor's advice, as further wet-lab research and clinical trials are essential to elucidate their efficacy for this purpose.</div>

scholarly journals Data-Driven Drug Repurposing for COVID-19

2020 ◽  
Author(s):  
Raghvendra Mall ◽  
Abdurrahman Elbasir ◽  
Hossam Al Meer ◽  
Sanjay Chawla ◽  
Ehsan Ullah
Keyword(s):  
Clinical Trials ◽  
De Novo ◽  
Drug Repurposing ◽  
Binding Energies ◽  
Protein Activity ◽  
De Novo Drug Design ◽  
Docking Simulations ◽  
Antibiotic Drugs ◽  
Approved Drugs ◽  
Ranked List

<div>Motivation: A global effort is underway to identify drugs for the treatment of COVID-19. Since de novo drug design is an extremely long, time-consuming, and expensive process, efforts are underway to discover existing drugs that can be</div><div>repurposed for COVID-19.</div><div><br></div><div>Model: We propose a machine learning representation framework that uses deep learning induced vector embeddings of drugs and viral proteins as features to predict drug-viral protein activity. The prediction model in-turn is used to build an ensemble framework to rank approved drugs based on their ability to inhibit the three main proteases (enzymes) of the SARS-COV-2 virus.</div><div><br></div><div>Results: We identify a ranked list of 19 drugs as potential targets including 7 antivirals, 6 anticancer, 3 antibiotics, 2 antimalarial, and 1 antifungal. Several drugs, such as Remdesivir, Lopinavir, Ritonavir, and Hydroxychloroquine, in our ranked list, are currently in clinical trials. Moreover, through molecular docking simulations, we demonstrate that majority of the anticancer and antibiotic drugs in our ranked list have low binding energies and thus high binding affinity with the 3CL-pro protease of SARS-COV-2 virus.</div><div><br></div><div>Disclaimer: Our models are computational and the drugs suggested should not be taken for treating COVID-19 without a doctor's advice, as further wet-lab research and clinical trials are essential to elucidate their efficacy for this purpose.</div>

Recent Drug-Repurposing-Driven Advances in the Discovery of Novel Antibiotics

2019 ◽  
Vol 26 (28) ◽  
pp. 5363-5388 ◽  
Author(s):  
Ananda Kumar Konreddy ◽  
Grandhe Usha Rani ◽  
Kyeong Lee ◽  
Yongseok Choi
Keyword(s):  
Drug Discovery ◽  
Bacterial Infections ◽  
De Novo ◽  
Genetic Disorder ◽  
Antibacterial Properties ◽  
Drug Repurposing ◽  
Global Public Health ◽  
Approved Drugs ◽  
Fda Approved Drugs ◽  
Novel Antibiotics

: Drug repurposing is a safe and successful pathway to speed up the novel drug discovery and development processes compared with de novo drug discovery approaches. Drug repurposing uses FDA-approved drugs and drugs that failed in clinical trials, which have detailed information on potential toxicity, formulation, and pharmacology. Technical advancements in the informatics, genomics, and biological sciences account for the major success of drug repurposing in identifying secondary indications of existing drugs. Drug repurposing is playing a vital role in filling the gap in the discovery of potential antibiotics. Bacterial infections emerged as an ever-increasing global public health threat by dint of multidrug resistance to existing drugs. This raises the urgent need of development of new antibiotics that can effectively fight multidrug-resistant bacterial infections (MDRBIs). The present review describes the key role of drug repurposing in the development of antibiotics during 2016–2017 and of the details of recently FDA-approved antibiotics, pipeline antibiotics, and antibacterial properties of various FDA-approved drugs of anti-cancer, anti-fungal, anti-hyperlipidemia, antiinflammatory, anti-malarial, anti-parasitic, anti-viral, genetic disorder, immune modulator, etc. Further, in view of combination therapies with the existing antibiotics, their potential for new implications for MDRBIs is discussed. The current review may provide essential data for the development of quick, safe, effective, and novel antibiotics for current needs and suggest acuity in its effective implications for inhibiting MDRBIs by repurposing existing drugs.

scholarly journals Indicator Regularized Non-Negative Matrix Factorization Method-Based Drug Repurposing for COVID-19

2021 ◽  
Vol 11 ◽  
Author(s):  
Xianfang Tang ◽  
Lijun Cai ◽  
Yajie Meng ◽  
JunLin Xu ◽  
Changcheng Lu ◽  
...  
Keyword(s):  
Matrix Factorization ◽  
De Novo ◽  
Characteristic Curve ◽  
Drug Repurposing ◽  
Factorization Method ◽  
Therapeutic Drugs ◽  
Indicator Matrix ◽  
Novel Coronavirus ◽  
Approved Drugs ◽  
Non Negative Matrix Factorization

A novel coronavirus, named COVID-19, has become one of the most prevalent and severe infectious diseases in human history. Currently, there are only very few vaccines and therapeutic drugs against COVID-19, and their efficacies are yet to be tested. Drug repurposing aims to explore new applications of approved drugs, which can significantly reduce time and cost compared with de novo drug discovery. In this study, we built a virus-drug dataset, which included 34 viruses, 210 drugs, and 437 confirmed related virus-drug pairs from existing literature. Besides, we developed an Indicator Regularized non-negative Matrix Factorization (IRNMF) method, which introduced the indicator matrix and Karush-Kuhn-Tucker condition into the non-negative matrix factorization algorithm. According to the 5-fold cross-validation on the virus-drug dataset, the performance of IRNMF was better than other methods, and its Area Under receiver operating characteristic Curve (AUC) value was 0.8127. Additionally, we analyzed the case on COVID-19 infection, and our results suggested that the IRNMF algorithm could prioritize unknown virus-drug associations.

scholarly journals Computational guided approach for drug repurposing against SARS-CoV-2

2021 ◽  
Author(s):  
Jigisha Anand ◽  
Tanmay Ghildiyal ◽  
Aakanksha Madhwal ◽  
Rishabh Bhatt ◽  
Devvret Verma ◽  
...  
Keyword(s):  
Drug Targets ◽  
De Novo ◽  
Drug Repositioning ◽  
Drug Repurposing ◽  
Computational Docking ◽  
Drug Candidates ◽  
Docking Tool ◽  
Inhibitory Potential ◽  
Approved Drugs ◽  
Tract Infections

Background: In the current SARS-CoV-2 outbreak, drug repositioning emerges as a promising approach to develop efficient therapeutics in comparison to de novo drug development. The present investigation screened 130 US FDA-approved drugs including hypertension, cardiovascular diseases, respiratory tract infections (RTI), antibiotics and antiviral drugs for their inhibitory potential against SARS-CoV-2. Materials & methods: The molecular drug targets against SARS-CoV-2 proteins were determined by the iGEMDOCK computational docking tool. The protein homology models were generated through SWISS Model workspace. The pharmacokinetics of all the ligands was determined by ADMET analysis. Results: The study identified 15 potent drugs exhibiting significant inhibitory potential against SARS-CoV-2. Conclusion: Our investigation has identified possible repurposed drug candidates to improve the current modus operandi of the treatment given to COVID-19 patients.

scholarly journals Gliptin Repurposing for COVID-19

2020 ◽  
Author(s):  
Matthew Groves ◽  
Alexander Domling ◽  
Angel Jonathan Ruiz Moreno ◽  
Atilio Reyes Romero ◽  
Constantinos Neochoritis ◽  
...  
Keyword(s):  
Drug Discovery ◽  
Small Molecules ◽  
Drug Class ◽  
De Novo ◽  
Drug Repurposing ◽  
Therapeutic Modality ◽  
Antidiabetic Drug ◽  
Short Term ◽  
Computational Screening ◽  
Approved Drugs

<i>De novo</i> drug discovery of any therapeutic modality (e.g. antibodies, vaccines or small molecules) historically takes years from idea/preclinic to the market and it is therefore not a short-term solution for the current SARS-CoV-2 pandemic. Therefore, drug repurposing – the discovery novel indication areas for already approved drugs - is perhaps the only approach able to yield a short term relieve. Here we describe computational screening results suggesting that certain members of the drug class of gliptins are inhibitors of the two SARS-CoV-2 proteases 3CLpro and PLpro. The oral bioavailable antidiabetic drug class of gliptins are safe and have been introduced clinically since 2006 and used by millions of patients since then. Based on our repurposing hypothesis the nitrile containing gliptins deserve further investigation as potential anti-COVID19 drugs.

Coronavirus Disease 2019: Virology and Drug Targets

2020 ◽  
Vol 20 ◽  
Author(s):  
Praveen Thaggikuppe Krishnamurthy
Keyword(s):  
High Throughput Screening ◽  
Drug Targets ◽  
Vaccine Development ◽  
Antiviral Drug ◽  
Drug Repurposing ◽  
Structural Proteins ◽  
Current Status ◽  
Approved Drugs ◽  
Viral Target ◽  
Fda Approved Drugs

: The Coronavirus Disease 2019, a pandemic caused by novel Severe Acute Respiratory Syndrome Coronavirus 2 (SARS-CoV-2), is seriously affecting global health and the economy. As the vaccine development takes time, the current research is focused on repurposing FDA approved drugs against the viral target proteins. This review discusses the current understanding of SARS-CoV-2 virology, its target structural proteins (S- glycoprotein), non-structural proteins (3- chymotrypsin-like protease, papain-like protease, RNA-dependent RNA polymerase, and helicase) and accessory proteins, drug discovery strategies (drug repurposing, artificial intelligence, and high-throughput screening), and the current status of antiviral drug development.

scholarly journals An integrated drug repurposing strategy for the rapid identification of potential SARS-CoV-2 viral inhibitors

2020 ◽  
Author(s):  
Alfonso Trezza ◽  
Daniele Iovinelli ◽  
Filippo Prischi ◽  
Annalisa Santucci ◽  
Ottavia Spiga
Keyword(s):  
Host Cell ◽  
Viral Entry ◽  
In Silico ◽  
De Novo ◽  
Drug Repurposing ◽  
Rapid Identification ◽  
Spike Protein ◽  
Effective Vaccine ◽  
Docking Simulations ◽  
Approved Drugs

Abstract The Coronavirus disease 2019 (COVID-19) is an infectious disease caused by the severe acute respiratory syndrome–coronavirus 2 (SARS-CoV-2). The virus has rapidly spread in humans, causing the ongoing Coronavirus pandemic. Recent studies have shown that, similarly to SARS-CoV, SARS-CoV-2 utilises the Spike glycoprotein on the envelope to recognise and bind the human receptor ACE2. This event initiates the fusion of viral and host cell membranes and then the viral entry into the host cell. Despite several ongoing clinical studies, there are currently no approved vaccines or drugs that specifically target SARS-CoV-2. Until an effective vaccine is available, repurposing FDA approved drugs could significantly shorten the time and reduce the cost compared to de novo drug discovery. In this study we attempted to overcome the limitation of in silico virtual screening by applying a robust in silico drug repurposing strategy. We combined and integrated docking simulations, with molecular dynamics (MD), Supervised MD (SuMD) and Steered MD (SMD) simulations to identify a Spike protein – ACE2 interaction inhibitor. Our data showed that Nilotinib and Imatinib bind the receptor-binding domain of the Spike protein with high affinity and prevent ACE2 interaction.

scholarly journals Virtual High Throughput Screening Based Prediction of Potential Drugs for COVID-19

2020 ◽  
Author(s):  
Sekhar Talluri
Keyword(s):  
High Throughput ◽  
High Throughput Screening ◽  
Viral Infections ◽  
Genomic Sequence ◽  
Reproduction Number ◽  
Three Dimensional ◽  
Drug Repurposing ◽  
Dimensional Structure ◽  
Main Protease ◽  
Approved Drugs

SARS-CoV-2 is a betacoronavirus that was first identified during the Wuhan COVID-19 epidemic in 2019. It was listed as a potential global health threat by WHO due to high mortality, high basic reproduction number and lack of clinically approved drugs and vaccines for COVID-19. The genomic sequence of the virus responsible for COVID-19, as well as the experimentally determined three dimensional structure of the Main protease (Mpro) are available. The reported structure of the target Mpro was utilized in this study to identify potential drugs for COVID-19 using virtual high throughput screening. The results of this study confirm earlier preliminary reports based on studies of homologs that some of the drugs approved for treatment of other viral infections also have the potential for treatment of COVID-19. Approved anti-viral drugs that target proteases were ranked for potential effectiveness against COVID-19 and novel candidates for drug repurposing were identified.

scholarly journals A High-Throughput Screening Approach To Repurpose FDA-Approved Drugs for Bactericidal Applications againstStaphylococcus aureusSmall-Colony Variants

mSphere ◽  
2018 ◽  
Vol 3 (5) ◽  
Author(s):  
Ryan P. Trombetta ◽  
Paul M. Dunman ◽  
Edward M. Schwarz ◽  
Stephen L. Kates ◽  
Hani A. Awad
Keyword(s):  
High Throughput ◽  
Bactericidal Activity ◽  
High Throughput Screening ◽  
Drug Repurposing ◽  
Growth Patterns ◽  
Content Type ◽  
Airway Infections ◽  
Approved Drugs ◽  
Conventional Antibiotics ◽  
Fda Approved Drugs

ABSTRACTDrug repurposing offers an expedited and economical route to develop new clinical therapeutics in comparison to traditional drug development. Growth-based high-throughput screening is concomitant with drug repurposing and enables rapid identification of new therapeutic uses for investigated drugs; however, this traditional method is not compatible with microorganisms with abnormal growth patterns such asStaphylococcus aureussmall-colony variants (SCV). SCV subpopulations are auxotrophic for key compounds in biosynthetic pathways, which result in low growth rate. SCV formation is also associated with reduced antibiotic susceptibility, and the SCV’s ability to revert to the normal cell growth state is thought to contribute to recurrence ofS. aureusinfections. Thus, there is a critical need to identify antimicrobial agents that are potent against SCV in order to effectively treat chronic infections. Accordingly, here we describe adapting an adenylate kinase (AK)-based cell death reporter assay to identify members of a Food and Drug Administration (FDA)-approved drug library that display bactericidal activity againstS. aureusSCV. Four library members, daunorubicin, ketoconazole, rifapentine, and sitafloxacin, exhibited potent SCV bactericidal activity against a stableS. aureusSCV. Further investigation showed that sitafloxacin was potent against methicillin-susceptible and -resistantS. aureus, as well asS. aureuswithin an established biofilm. Taken together, these results demonstrate the ability to use the AK assay to screen small-molecule libraries for SCV bactericidal agents and highlight the therapeutic potential of sitafloxacin to be repurposed to treat chronicS. aureusinfections associated with SCV and/or biofilm growth states.IMPORTANCEConventional antibiotics fail to successfully treat chronic osteomyelitis, endocarditis, and device-related and airway infections. These recurring infections are associated with the emergence of SCV, which are recalcitrant to conventional antibiotics. Studies have investigated antibiotic therapies to treat SCV-related infections but have had little success, emphasizing the need to identify novel antimicrobial drugs. However, drug discovery is a costly and time-consuming process. An alternative strategy is drug repurposing, which could identify FDA-approved and well-characterized drugs that could have off-label utility in treating SCV. In this study, we adapted a high-throughput AK-based assay to identify 4 FDA-approved drugs, daunorubicin, ketoconazole, rifapentine, and sitafloxacin, which display antimicrobial activity againstS. aureusSCV, suggesting an avenue for drug repurposing in order to effectively treat SCV-related infections. Additionally, this screening paradigm can easily be adapted for other drug/chemical libraries to identify compounds bactericidal against SCV.

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