scholarly journals Word embedding mining for SARS-CoV-2 and COVID-19 drug repurposing

F1000Research ◽  
2020 ◽  
Vol 9 ◽  
pp. 585
Author(s):  
Finn Kuusisto ◽  
David Page ◽  
Ron Stewart
Keyword(s):  
De Novo ◽  
Drug Repurposing ◽  
Ground Truth ◽  
Biomedical Literature ◽  
Word Embedding ◽  
Electronic Health Record Data ◽  
Global Pandemic ◽  
Rapid Spread ◽  
Approved Drugs ◽  
Future Work

Background: The rapid spread of illness and death caused by the severe respiratory syndrome coronavirus 2 (SARS-CoV-2) and its associated coronavirus disease 2019 (COVID-19) demands a rapid response in treatment development. Limitations of de novo drug development, however, suggest that drug repurposing is best suited to meet this demand. Methods: Due to the difficulty of accessing electronic health record data in general and in the midst of a global pandemic, and due to the similarity between SARS-CoV-2 and SARS-CoV, we propose mining the extensive biomedical literature for treatments to SARS that may also then be appropriate for COVID-19. In particular, we propose a method of mining a large biomedical word embedding for FDA approved drugs based on drug-disease treatment analogies. Results: We first validate that our method correctly identifies ground truth treatments for well-known diseases. We then use our method to find several approved drugs that have been suggested or are currently in clinical trials for COVID-19 in our top hits and present the rest as promising leads for further experimental investigation. Conclusions: We find our approach promising and present it, along with suggestions for future work, to the computational drug repurposing community at large as another tool to help fight the pandemic. Code and data for our methods can be found at https://github.com/finnkuusisto/covid19_word_embedding.

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 Accelerating the Discovery of the Beyond Rule of Five Compounds That Have High Affinities Toward SARS-CoV-2 Spike RBD

2021 ◽  
Author(s):  
Abd Al-Aziz Abu-Saleh ◽  
Arpita Yadav ◽  
Raymond A. Poirier
Keyword(s):  
Molecular Dynamics ◽  
Molecular Dynamics Simulations ◽  
Rational Design ◽  
De Novo ◽  
Focal Point ◽  
Docking Studies ◽  
Global Pandemic ◽  
Approved Drugs ◽  
Dynamics Simulations ◽  
Binding Energy Calculations

The battle against SARS-CoV-2 coronavirus is the focal point for the global pandemic that has affected millions of lives worldwide. The need for effective and selective therapeutics for the treatment of the disease caused by SARS-CoV-2 is critical. Herein, we performed computational de novo design incorporating molecular docking studies, molecular dynamics simulations, absolute binding energy calculations, and steered molecular dynamics simulations for the discovery of potential compounds with high affinity towards SARS-CoV-2 spike RBD. By leveraging ZINC15 database, a total of 1282 in-clinical and FDA approved drugs were filtered out from nearly 0.5 million protomers of relatively large compounds (MW > 500, and LogP ≤ 5). Our results depict plausible mechanistic aspects related to the blockage of SARS-CoV-2 spike RBD by the top hits discovered. We found that the most promising candidates, namely, ZINC95628821, ZINC95617623, and ZINC261494658, strongly bind to the spike RBD and interfere with the human ACE2 receptor. These findings accelerate the rational design of selective inhibitors targeting the spike RBD protein of SARS-CoV-2.

scholarly journals Computational Determination of Toxicity Risks Associated with a Selection of Approved Drugs having Demonstrated Activity Against COVID- 19

2021 ◽  
Author(s):  
Maral Aminpour ◽  
Williams Miranda-Delgado ◽  
Soren Wacker ◽  
Sergey Noskov ◽  
Michael Houghton ◽  
...  
Keyword(s):  
Late Phase ◽  
Severe Pneumonia ◽  
Toxicity Assessment ◽  
Global Pandemic ◽  
Rapid Spread ◽  
Essential Knowledge ◽  
Approved Drugs ◽  
In Silico Models ◽  
Selection Of

Abstract BackgroundThe emergence and rapid spread of SARS-CoV-2 (severe acute respiratory syndrome coronavirus 2) at late 2019 has caused a devastating global pandemic of the severe pneumonia-like disease coronavirus disease 2019 (COVID-19). Although vaccines have been and are being developed, they are not accessible to everyone and not everyone can receive these vaccines. Also, it typically takes more than 10 years until a new therapeutic agent is approved for usage. Therefore, repurposing of known drugs can lend itself well as a key approach for significantly expediting the development of new therapies for COVID-19.MethodsWe have been incorporated machine learning-based computational tools and in silico models into the drug discovery process to predict Adsorption, Distribution, Metabolism, Excretion, and Toxicity (ADMET) profiles of 90 potential drugs for COVID-19 treatment identified from two independent studies mainly with the purpose of mitigating late-phase failures because of inferior pharmacokinetics and toxicity. ResultsHere, summarized the cardiotoxicity and general toxicity profiles of 90 potential drugs for COVID-19 treatment and summarize the risks of repurposing and propose a stratification of patients accordingly. We shortlist a total of five compounds based on their non-toxic properties.ConclusionIn summary, this manuscript aims to provide a potentially useful source of essential knowledge on toxicity assessment of 90 compounds for health care practitioners and researchers to find off-label alternatives for the treatment for COVID-19. The majority of the molecules discussed in this manuscript have already moved into clinical trials and thus their known pharmacological and human safety profiles are expected to facilitate a fast track preclinical and clinical assessment for treating COVID-19.

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.

scholarly journals Integrated Docking and Enhanced Sampling Based Selection of Repurposing Drugs for SARS-CoV-2 by Targeting Host Dependent Factors

2020 ◽  
Author(s):  
Amit Kumawat ◽  
Sadanandam Namsani ◽  
Debabrata Pramanik ◽  
Sudip Roy ◽  
Jayant K. Singh
Keyword(s):  
Regulatory Protein ◽  
Drug Repurposing ◽  
Md Simulations ◽  
Free Energy Calculations ◽  
High Throughput Analysis ◽  
Drug Candidates ◽  
Global Pandemic ◽  
Therapeutic Uses ◽  
Approved Drugs ◽  
Fda Approved Drugs

Since the onset of global pandemic, the most focused research currently in progress is the development of vaccine candidates and clinical trials of existing FDA approved drugs for other relevant diseases, in order to repurpose them for the COVID-19. Here, we investigate the drug repurposing strategies to counteract the coronavirus infection which involves several potential targetable host proteins involved in viral replication and disease progression. We report the high throughput analysis of literature-derived repurposing drug candidates that can be used to target the genetic regulators known to interact with viral proteins based on experimental and interactome studies. In this work we have performed integrated molecular docking followed by molecular dynamics (MD) simulations and free energy calculations through an expedite insilico process where the number of screened candidates reduces sequentially at every step based on physicochemical information. We elucidate that in addition to the pre-clinical and FDA approved drugs that targets specific regulatory proteins, a range of chemical compounds (Nafamostat, Chloramphenicol, Ponatinib) binds to the other gene transcription and translation regulatory protein with higher affinity and may harbour potential for therapeutic uses.<br>

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 Expert-Augmented Computational Drug Repurposing Identified Baricitinib as a Treatment for COVID-19

2021 ◽  
Vol 12 ◽  
Author(s):  
Daniel P. Smith ◽  
Olly Oechsle ◽  
Michael J. Rawling ◽  
Ed Savory ◽  
Alix M.B. Lacoste ◽  
...  
Keyword(s):  
Visual Analytics ◽  
Drug Repurposing ◽  
Standard Of Care ◽  
Biomedical Literature ◽  
Biological Research ◽  
Knowledge Graph ◽  
Neglected Diseases ◽  
Biomedical Knowledge ◽  
Computational Tools ◽  
Approved Drugs

The onset of the 2019 Severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) pandemic necessitated the identification of approved drugs to treat the disease, before the development, approval and widespread administration of suitable vaccines. To identify such a drug, we used a visual analytics workflow where computational tools applied over an AI-enhanced biomedical knowledge graph were combined with human expertise. The workflow comprised rapid augmentation of knowledge graph information from recent literature using machine learning (ML) based extraction, with human-guided iterative queries of the graph. Using this workflow, we identified the rheumatoid arthritis drug baricitinib as both an antiviral and anti-inflammatory therapy. The effectiveness of baricitinib was substantiated by the recent publication of the data from the ACTT-2 randomised Phase 3 trial, followed by emergency approval for use by the FDA, and a report from the CoV-BARRIER trial confirming significant reductions in mortality with baricitinib compared to standard of care. Such methods that iteratively combine computational tools with human expertise hold promise for the identification of treatments for rare and neglected diseases and, beyond drug repurposing, in areas of biological research where relevant data may be lacking or hidden in the mass of available biomedical literature.

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.

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