Virtual screen for repurposing approved and experimental drugs for candidate inhibitors of EBOLA virus infection

Veljković, Veljko; Loiseau, Philippe M.; Figadère, Bruno; Glišić, Sanja; Veljković, Nevena; Perovic, Vladimir; Cavanaugh, David P.; Branch, Donald R.

doi:10.12688/f1000research.6110.1

Cited by 39 publications

(27 citation statements)

References 37 publications

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“…The binding site of ibuprofen on the Ebola virus GP proposed by Zhao and co-workers (10) differs from the binding site proposed in our study (9). In this article, we performed an additional docking analysis in order to explain possible reason for this discrepancy.…”

contrasting

confidence: 67%

“…In the case of the crystal structure 5jqb, ibuprofen is placed in the fusion region between GP1 and GP2 subunits, forming interactions with Arg64 (salt bridge), Val66 and Leu184 from GP1 and Met548 and Leu554 from GP2 (hydrophobic interactions) (10). The binding region is far from the one we previously predicted, and still not crystalographically isolated (9). Also, our homology modelling of GP1 suggested disordered structure of the binding region (Figure 1).…”

Section: Resultsmentioning

confidence: 78%

“…Also, our homology modelling of GP1 suggested disordered structure of the binding region (Figure 1). The reported intermolecular interactions from docking were with Thr 338, Ser 340, Gln 344 (hydrophilic interactions) and with Ala415 (hydrophobic interactions) (9). Therefore, binding of ibuprofen to this region is favoured by more hydrophilic interactions and a disordered structure that could be more prone to finer structural tuning of the binding site to ibuprofen than the one in the 5jqb structure suggested by Zhao and co-workers (10).…”

Section: Resultsmentioning

confidence: 91%

“…By detailed analysis of approved drugs located within EVIIS, including the docking analysis, we selected the non-steroidal anti-inflammatory drug ibuprofen as an inexpensive, widely accessible and minimally toxic candidate for prevention and treatment of Ebola virus disease (9). Later high resolution structural study confirmed binding of ibuprofen to the Ebola virus glycoprotein (GP) (10).…”

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confidence: 99%

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Ibuprofen as a template molecule for drug design against Ebola virus

et al. 2018

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The Ebola virus outbreak in West Africa 2015 and Congo 2017, point out an urgent need for development of drugs against this important pathogen. Previously, by repurposing virtual screening of 6438 drugs from DrugBank, ibuprofen was selected as a possible inhibitor of the Ebola virus infection. The results of an additional docking analysis as well as experimental results showing measurable anti-Ebola effect of ibuprofen in cell culture suggest ibuprofen as a promising molecular template for the development of drugs for treatment of the infection by Ebola virus.

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contrasting

confidence: 67%

Section: Resultsmentioning

confidence: 78%

Section: Resultsmentioning

confidence: 91%

mentioning

confidence: 99%

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Ibuprofen as a template molecule for drug design against Ebola virus

et al. 2018

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“…As stated before in perspectives by us 72 and others 2, 16, 20, 73 , the fact that approved drugs may be repurposed for other diseases should not be viewed as a negative aspect of the small molecules, belying undesirable target promiscuity 74 . Instead, we prefer to reference recently published crystallographic analyses 75 demonstrating that small molecules may bind multiple proteins in different types of binding sites and with distinct conformations to ultimately facilitate molecular repurposing.…”

Section: Discussionmentioning

confidence: 86%

Machine learning models identify molecules active against the Ebola virus in vitro

et al. 2016

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The search for small molecule inhibitors of Ebola virus (EBOV) has led to several high throughput screens over the past 3 years. These have identified a range of FDA-approved active pharmaceutical ingredients (APIs) with anti-EBOV activity in vitro and several of which are also active in a mouse infection model. There are millions of additional commercially-available molecules that could be screened for potential activities as anti-EBOV compounds. One way to prioritize compounds for testing is to generate computational models based on the high throughput screening data and then virtually screen compound libraries. In the current study, we have generated Bayesian machine learning models with viral pseudotype entry assay and the EBOV replication assay data. We have validated the models internally and externally. We have also used these models to computationally score the MicroSource library of drugs to select those likely to be potential inhibitors. Three of the highest scoring molecules that were not in the model training sets, quinacrine, pyronaridine and tilorone, were tested in vitro and had EC 50 values of 350, 420 and 230 nM, respectively. Pyronaridine is a component of a combination therapy for malaria that was recently approved by the European Medicines Agency, which may make it more readily accessible for clinical testing. Like other known antimalarial drugs active against EBOV, it shares the 4-aminoquinoline scaffold. Tilorone, is an investigational antiviral agent that has shown a broad array of biological activities including cell growth inhibition in cancer cells, antifibrotic properties, α7 nicotinic receptor agonist activity, radioprotective activity and activation of hypoxia inducible factor-1. Quinacrine is an antimalarial but also has use as an anthelmintic. Our results suggest data sets with less than 1,000 molecules can produce validated machine learning models that can in turn be utilized to identify novel EBOV inhibitors in vitro.

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The Role of AI in Drug Discovery

Abbas,

Rassam,

Karamshahi

et al. 2024

ChemBioChem

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The emergence of Artificial Intelligence (AI) in drug discovery marks a pivotal shift in pharmaceutical research, blending sophisticated computational techniques with conventional scientific exploration to break through enduring obstacles. This review paper elucidates the multifaceted applications of AI across various stages of drug development, highlighting significant advancements and methodologies. It delves into AI's instrumental role in drug design, polypharmacology, chemical synthesis, drug repurposing, and the prediction of drug properties such as toxicity, bioactivity, and physicochemical characteristics. Despite AI's promising advancements, the paper also addresses the challenges and limitations encountered in the field, including data quality, generalizability, computational demands, and ethical considerations. By offering a comprehensive overview of AI's role in drug discovery, this paper underscores the technology's potential to significantly enhance drug development, while also acknowledging the hurdles that must be overcome to fully realize its benefits.

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Virtual screen for repurposing approved and experimental drugs for candidate inhibitors of EBOLA virus infection

Cited by 39 publications

References 37 publications

Ibuprofen as a template molecule for drug design against Ebola virus

Ibuprofen as a template molecule for drug design against Ebola virus

Machine learning models identify molecules active against the Ebola virus in vitro

The Role of AI in Drug Discovery

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