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

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>

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Identification of saquinavir as a potent inhibitor of dimeric SARS-CoV2 main protease through MM/GBSA

10.21203/rs.3.rs-27406/v1 ◽

2020 ◽

Author(s):

Bello Martiniano ◽

Martínez-Muñoz Alberto ◽

Balbuena-Rebolledo Irving

Keyword(s):

Md Simulation ◽

Potent Inhibitor ◽

Drug Repurposing ◽

Md Simulations ◽

Generalized Born ◽

Main Protease ◽

Promising Target ◽

Approved Drugs ◽

Potential Inhibitors ◽

Fda Approved Drugs

Abstract Among targets selected for studies aimed to identify potential inhibitors against COVID-19, SARS-CoV2 main proteinase (Mpro) is highlighted. Mpro is indispensable for virus replication, and is a promising target of potential inhibitors of COVID-19. Recently, monomeric SARS-CoV2 Mpro, drug repurposing and docking methods have facilitated the identification of several potential inhibitors. Results were refined through the assessment of dimeric SARS-CoV2 Mpro, which represents the functional state of enzyme. Docking and molecular dynamics (MD) simulations combined with molecular mechanics/generalized Born surface area (MM/GBSA) studies indicated that dimeric Mpro most significantly impacts binding affinity tendency compared with the monomeric state, which suggesting that dimeric state is most useful when performing studies aimed to identify drugs targeting Mpro. In this study, we extend previous research by performing docking and MD simulation studies coupled with an MM/GBSA approach to assess binding of dimeric SARS-CoV2 Mpro to 12 FDA-approved drugs (darunavir, indinavir, saquinavir, tipranavir, diosmin, hesperidin, rutin, raltegravir, velpatasvir, ledipasvir, rosuvastatin and bortezomib), which were identified as the best candidates for treatment of COVID-19 in some previous dockings studies involving monomeric SARS-CoV2 Mpro. This analysis identified saquinavir as a potent inhibitor of dimeric SARS-CoV2 Mpro, therefore, the compound may have clinical utility against COVID-19.

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Structural Basis of Antisickling Effects of Selected FDA Approved Drugs: A Drug Repurposing Study

Current Computer - Aided Drug Design ◽

10.2174/1573409914666180129163711 ◽

2018 ◽

Vol 14 (2) ◽

pp. 106-116 ◽

Cited By ~ 1

Author(s):

Olujide O. Olubiyi ◽

Maryam O. Olagunju ◽

James O. Oni ◽

Abidemi O. Olubiyi

Keyword(s):

Drug Repurposing ◽

Structural Basis ◽

Approved Drugs ◽

Fda Approved Drugs

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Screening of an FDA-Approved Library for Novel Drugs against Y. pestis

Antibiotics ◽

10.3390/antibiotics10010040 ◽

2021 ◽

Vol 10 (1) ◽

pp. 40

Author(s):

David Gur ◽

Theodor Chitlaru ◽

Emanuelle Mamroud ◽

Ayelet Zauberman

Keyword(s):

Drug Repurposing ◽

Mortality Rates ◽

Systematic Screening ◽

Antibiotic Resistant ◽

Gram Negative ◽

Therapy Initiation ◽

Novel Drugs ◽

Resistant Strains ◽

Approved Drugs ◽

Fda Approved Drugs

Yersinia pestis is a Gram-negative pathogen that causes plague, a devastating disease that kills millions worldwide. Although plague is efficiently treatable by recommended antibiotics, the time of antibiotic therapy initiation is critical, as high mortality rates have been observed if treatment is delayed for longer than 24 h after symptom onset. To overcome the emergence of antibiotic resistant strains, we attempted a systematic screening of Food and Drug Administration (FDA)-approved drugs to identify alternative compounds which may possess antibacterial activity against Y. pestis. Here, we describe a drug-repurposing approach, which led to the identification of two antibiotic-like activities of the anticancer drugs bleomycin sulfate and streptozocin that have the potential for designing novel antiplague therapy approaches. The inhibitory characteristics of these two drugs were further addressed as well as their efficiency in affecting the growth of Y. pestis strains resistant to doxycycline and ciprofloxacin, antibiotics recommended for plague treatment.

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

In Silico Drug Design ◽

10.1016/b978-0-12-816125-8.00021-3 ◽

2019 ◽

pp. 625-648 ◽

Cited By ~ 2

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

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

10.21203/rs.2.12791/v4 ◽

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 .

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

10.20944/preprints202004.0062.v1 ◽

2020 ◽

Cited By ~ 4

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.

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