In silico analysis suggests repurposing of ibuprofen for prevention and treatment of EBOLA virus disease

In Silico Screening of Natural Compounds for Candidates 5HT6 Receptor Antagonists against Alzheimer's Disease

Alzheimer's disease (AD), a devastating neurodegenerative disease, is the focus of pharmacological research. One of the targets that attract the most attention for the potential therapy of AD is the serotonin 5HT6 receptor, which is the receptor situated exclusively in CNS on glutamatergic and GABAergic neurons. The neurochemical impact of this receptor supports the hypothesis about its role in cognitive, learning, and memory systems, which are of critical importance for AD. Natural products are a promising source of novel bioactive compounds with potential therapeutic potential as a 5HT6 receptor antagonist in the treatment of AD dementia. The ZINC-natural product database was in silico screened in order to find the candidate antagonists of 5-HT6 receptor against AD. A virtual screening protocol that includes both short-and long-range interactions between interacting molecules was employed. First, the EIIP/AQVN filter was applied for in silico screening of the ZINC database followed by 3D QSAR and molecular docking. Ten best candidate compounds were selected from the ZINC Natural Product database as potential 5HT6 Receptor antagonists and were proposed for further evaluation. The best candidate was evaluated by molecular dynamics simulations and free energy calculations.

Effect of Ionic Strength on Ibuprofenate Adsorption on a Lipid Bilayer of Dipalmitoylphosphatidylcholine from Molecular Dynamics Simulations

In this work, the free energy change in the process of transferring ibuprofenate from the bulk solution to the center of a model of the dipalmitoylphosphatidylcholine bilayer at different NaCl concentrations was calculated. Two minima were found in the free energy profile: a local minimum, located in the vicinity of the membrane, and the global free energy minimum, found near the headgroup region. The downward shift of free energy minima with increasing NaCl concentration is consistent with the results of previous works. Conversely, the upward shift of the free energy maximum with increasing ionic strength is due to the competition of sodium ions and lipids molecules to coordinate with ibuprofenate and neutralize its charge. In addition, normal molecular dynamics simulations were performed to study the effects of the ibuprofenate on the lipid bilayer and in the presence of a high ibuprofenate concentration. The effect of ionic strength on the properties of the lipid bilayer and on lipid-drug interactions was analyzed. The area per lipid shrinking with increasing ionic strength, volume of lipids, and thickness of the bilayer is consistent with the experimental results. At a very high ibuprofenate concentration, the lipid bilayer dehydrates, and it consequently transforms into the gel phase, thus blocking the permeation.

Identification of SARS-CoV-2 Papain-like Protease (PLpro) Inhibitors Using Combined Computational Approach

In the current pandemic, finding an effective drug to prevent or treat the infection is the highest priority. A rapid and safe approach to counteract COVID-19 is in silico drug repurposing. The SARS-CoV-2 PLpro promotes viral replication and modulates the host immune system, resulting in inhibition of the host antiviral innate immune response, and therefore is an attractive drug target. In this study, we used a combined in silico virtual screening for candidates for SARS-CoV-2 PLpro protease inhibitors. We used the Informational spectrum method applied for Small Molecules for searching the Drugbank database followed by molecular docking. After in silico screening of drug space, we identified 44 drugs as potential SARS-CoV-2 PLpro inhibitors that we propose for further experimental testing.

A Revisit to the Research Updates of Drugs, Vaccines, and Bioinformatics Approaches in Combating COVID-19 Pandemic

A new strain of coronavirus severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) responsible for the coronavirus disease 2019 (COVID-19) pandemic was first detected in the city of Wuhan in Hubei province, China in late December 2019. To date, more than 1 million deaths and nearly 57 million confirmed cases have been recorded across 220 countries due to COVID-19, which is the greatest threat to global public health in our time. Although SARS-CoV-2 is genetically similar to other coronaviruses, i.e., SARS and Middle East respiratory syndrome coronavirus (MERS-CoV), no confirmed therapeutics are yet available against COVID-19, and governments, scientists, and pharmaceutical companies worldwide are working together in search for effective drugs and vaccines. Repurposing of relevant therapies, developing vaccines, and using bioinformatics to identify potential drug targets are strongly in focus to combat COVID-19. This review deals with the pathogenesis of COVID-19 and its clinical symptoms in humans including the most recent updates on candidate drugs and vaccines. Potential drugs (remdesivir, hydroxychloroquine, azithromycin, dexamethasone) and vaccines [mRNA-1273; measles, mumps and rubella (MMR), bacille Calmette-Guérin (BCG)] in human clinical trials are discussed with their composition, dosage, mode of action, and possible release dates according to the trial register of US National Library of Medicines (clinicaltrials.gov), European Union (clinicaltrialsregister.eu), and Chinese Clinical Trial Registry (chictr.org.cn) website. Moreover, recent reports on in silico approaches like molecular docking, molecular dynamics simulations, network-based identification, and homology modeling are included, toward repurposing strategies for the use of already approved drugs against newly emerged pathogens. Limitations of effectiveness, side effects, and safety issues of each approach are also highlighted. This review should be useful for the researchers working to find out an effective strategy for defeating SARS-CoV-2.

In vitro anti-influenza activity of in silico repurposed candidate drug cycrimine

BACKGROUND

Due to the limitations of current antiviral therapies because of drug resistance and the emergence of new circulating viral strains, novel effective antivirals are urgently needed. Results of the previous drug repurposing by virtual screening of DrugBank revealed the anticholinergic drug cycrimine as a possible inhibitor of the influenza virus infection.

METHODS

In this study we examined the potential antiviral activity of cycrimine in vitro.

RESULTS

The experimental results showed the anti-influenza activity of cycrimine against two different influenza A subtypes in cell culture.

CONCLUSIONS

The findings of this study suggest cycrimine as a potential therapeutic agent for influenza.

Ibuprofen, a traditional drug that may impact the course of COVID-19 new effective formulation in nebulizable solution

The traditional formulation of ibuprofen is poorly soluble in water, so the administered dose must be 10 times higher than the dose required for a therapeutic effect. The development of a hydrosoluble form of ibuprofen can be a strategy to reach a high concentration in the lungs by using modern inhalation devices. Therefore, the development of an inhalable formulation with high bioavailability in the lungs was the leitmotiv of our investigation. The hypertonic ibuprofen solution to be nebulized (NIH) presents great relevant characteristics: bactericidal, virucidal, mucolytic and has a known anti-inflammatory property. Bactericidal and virucidal effects are related to the physico-chemical properties of Na-ibuprofenate as an amphipathic molecule. It has the capability to insert into the bilayer membranes destabilizing the structure, altering its biological properties and avoiding the duplication or infection. Our preliminary results indicate that the presence of this high ionic strength solution reduces 10 times the amount of ibuprofen necessary to kill bacteria, but also the time to kill 1x10⁶ bacteria, from 4 h (in its absence) to only three minutes (in its presence). That was observed using Pseudomona aeruginosa, methicillin-resistant Staphylococcus aureus and Burkholderia cepacia. Also, "in vitro'' ibuprofen demonstrated virucidal activity against the so-called enveloped virus, a family that includes coronavirus strain (2019-nCoV). We observed too, the markedly reduced local inflammation in the airways after administering NIH lays on its ability to inhibit the enzyme cyclooxygenase and to markedly diminish reactive oxygen species (ROS). Other investigators also showed the importance of actin in the rapid spread of virus infection. Furthermore, reorganization of the actin filaments is a key step in lung inflammation induced by systemic inflammatory responses caused by SARS-CoV-2. These findings suggest that the interaction between actin proteins and S1 is involved in the 2019-nCoV infection and pathogenesis. Consequently, the possibility of interfering in this interaction could represent a valid hypothesis for the development of promising therapeutic and prevention strategies. In conclusion, we consider that treating people with COVID-19 with NIH may be beneficial and an opportunity to contribute for the current global health emergency.

Use of the informational spectrum methodology for rapid biological analysis of the novel coronavirus 2019-nCoV: prediction of potential receptor, natural reservoir, tropism and therapeutic/vaccine target

A novel coronavirus recently identified in Wuhan, China (SARS-CoV-2) has expanded the number of highly pathogenic coronaviruses affecting humans. The SARS-CoV-2 represents a potential epidemic or pandemic threat, which requires a quick response for preparedness against this infection. The present report uses the informational spectrum methodology to identify the possible origin and natural host of the new virus, as well as putative therapeutic and vaccine targets. The performed in silico analysis indicates that the newly emerging SARS-CoV-2 is closely related to severe acute respiratory syndrome (SARS)-CoV and, to a lesser degree, Middle East respiratory syndrome (MERS)-CoV. Moreover, the well-known SARS-CoV receptor (ACE2) might be a putative receptor for the novel virus as well. Actin protein was also suggested as a host factor that participates in cell entry and pathogenesis of SARS-CoV-2; therefore, drugs modulating biological activity of this protein (e.g. ibuprofen) were suggested as potential candidates for treatment of this viral infection. Additional results indicated that civets and poultry are potential candidates for the natural reservoir of the SARS-CoV-2, and that domain 288-330 of S1 protein from the SARS-CoV-2 represents promising therapeutic and/or vaccine target.

Use of the informational spectrum methodology for rapid biological analysis of the novel coronavirus 2019-nCoV: prediction of potential receptor, natural reservoir, tropism and therapeutic/vaccine target

A novel coronavirus recently identified in Wuhan, China (SARS-CoV-2) has expanded the number of highly pathogenic coronaviruses affecting humans. The SARS-CoV-2 represents a potential epidemic or pandemic threat, which requires a quick response for preparedness against this infection. The present report uses the informational spectrum methodology to identify the possible origin and natural host of the new virus, as well as putative therapeutic and vaccine targets. The performed in silico analysis indicates that the newly emerging SARS-CoV-2 is closely related to severe acute respiratory syndrome (SARS)-CoV and, to a lesser degree, Middle East respiratory syndrome (MERS)-CoV. Moreover, the well-known SARS-CoV receptor (ACE2) might be a putative receptor for the novel virus as well. Actin protein was also suggested as a host factor that participates in cell entry and pathogenesis of SARS-CoV-2; therefore, drugs modulating biological activity of this protein (e.g. ibuprofen) were suggested as potential candidates for treatment of this viral infection. Additional results indicated that civets and poultry are potential candidates for the natural reservoir of the SARS-CoV-2, and that domain 288-330 of S1 protein from the SARS-CoV-2 represents promising therapeutic and/or vaccine target.

Use of the informational spectrum methodology for rapid biological analysis of the novel coronavirus 2019-nCoV: prediction of potential receptor, natural reservoir, tropism and therapeutic/vaccine target

A novel coronavirus recently identified in Wuhan, China (SARS-CoV-2) has expanded the number of highly pathogenic coronaviruses affecting humans. The SARS-CoV-2 represents a potential epidemic or pandemic threat, which requires a quick response for preparedness against this infection. The present report uses the informational spectrum methodology to identify the possible origin and natural host of the new virus, as well as putative therapeutic and vaccine targets. The performed in silico analysis indicates that the newly emerging SARS-CoV-2 is closely related to severe acute respiratory syndrome (SARS)-CoV and, to a lesser degree, Middle East respiratory syndrome (MERS)-CoV. Moreover, the well-known SARS-CoV receptor (ACE2) might be a putative receptor for the novel virus as well. Actin protein was also suggested as a host factor that participates in cell entry and pathogenesis of SARS-CoV-2; therefore, drugs modulating biological activity of this protein (e.g. ibuprofen) were suggested as potential candidates for treatment of this viral infection. Additional results indicated that civets and poultry are potential candidates for the natural reservoir of the SARS-CoV-2, and that domain 288-330 of S1 protein from the SARS-CoV-2 represents promising therapeutic and/or vaccine target.

Use of the informational spectrum methodology for rapid biological analysis of the novel coronavirus 2019-nCoV: prediction of potential receptor, natural reservoir, tropism and therapeutic/vaccine target

A novel coronavirus recently identified in Wuhan, China (SARS-CoV-2) has expanded the number of highly pathogenic coronaviruses affecting humans. The SARS-CoV-2 represents a potential epidemic or pandemic threat, which requires a quick response for preparedness against this infection. The present report uses the informational spectrum methodology to identify the possible origin and natural host of the new virus, as well as putative therapeutic and vaccine targets. The performed in silico analysis indicates that the newly emerging SARS-CoV-2 is closely related to severe acute respiratory syndrome (SARS)-CoV and, to a lesser degree, Middle East respiratory syndrome (MERS)-CoV. Moreover, the well-known SARS-CoV receptor (ACE2) might be a putative receptor for the novel virus as well. Actin protein was also suggested as a host factor that participates in cell entry and pathogenesis of SARS-CoV-2; therefore, drugs modulating biological activity of this protein (e.g. ibuprofen) were suggested as potential candidates for treatment of this viral infection. Additional results indicated that civets and poultry are potential candidates for the natural reservoir of the SARS-CoV-2, and that domain 288-330 of S1 protein from the SARS-CoV-2 represents promising therapeutic and/or vaccine target.

Virtual Screen for Repurposing of Drugs for Candidate Influenza a M2 Ion-Channel Inhibitors

Influenza A virus (IAV) matrix protein 2 (M2), an ion channel, is crucial for virus infection, and therefore, an important anti-influenza drug target. Adamantanes, also known as M2 channel blockers, are one of the two classes of Food and Drug Administration-approved anti-influenza drugs, although their use was discontinued due to prevalent drug resistance. Fast emergence of resistance to current anti-influenza drugs have raised an urgent need for developing new anti-influenza drugs against resistant forms of circulating viruses. Here we propose a simple theoretical criterion for fast virtual screening of molecular libraries for candidate anti-influenza ion channel inhibitors both for wild type and adamantane-resistant influenza A viruses. After in silico screening of drug space using the EIIP/AQVN filter and further filtering of drugs by ligand based virtual screening and molecular docking we propose the best candidate drugs as potential dual inhibitors of wild type and adamantane-resistant influenza A viruses. Finally, guanethidine, the best ranked drug selected from ligand-based virtual screening, was experimentally tested. The experimental results show measurable anti-influenza activity of guanethidine in cell culture.

Antiviral Agents Against Ebola Virus Infection: Repositioning Old Drugs and Finding Novel Small Molecules

Ebola virus (EBOV) causes a deadly hemorrhagic syndrome in humans with mortality rate up to 90%. First reported in Zaire in 1976, EBOV outbreaks showed a fluctuating trend during time and fora long period it was considered a tragic disease confined to the isolated regions of the African continent where the EBOV fear was perpetuated among the poor communities. The extreme severity of the recent 2014-16 EBOV outbreak in terms of fatality rate and rapid spread out of Africa led to the understanding that EBOV is a global health risk and highlights the necessity to find countermeasures against it. In the recent years, several small molecules have been shown to display in vitro and in vivo efficacy against EBOV and some of them have advanced into clinical trials. In addition, also existing drugs have been tested for their anti-EBOV activity and were shown to be promising candidates. However, despite the constant effort addressed to identify anti-EBOV therapeutics, no approved drugs are available against EBOV yet. In this chapter, we describe the main EBOV life cycle steps, providing a detailed picture of the druggable viral and host targets that have been explored so far by different technologies. We then summarize the small molecules, nucleic acid oligomers, and antibody-based therapies reported to have an effect either in in silico, or in biochemical and cell-based assays or in animal models and clinical trials, listing them according to their demonstrated or putative mechanism of action.

A Systematic Review of Computational Drug Discovery, Development, and Repurposing for Ebola Virus Disease Treatment

Ebola virus disease (EVD) is a deadly global public health threat, with no currently approved treatments. Traditional drug discovery and development is too expensive and inefficient to react quickly to the threat. We review published research studies that utilize computational approaches to find or develop drugs that target the Ebola virus and synthesize its results. A variety of hypothesized and/or novel treatments are reported to have potential anti-Ebola activity. Approaches that utilize multi-targeting/polypharmacology have the most promise in treating EVD.

A simple method for calculation of basic molecular properties of nutrients and their use as a criterion for a healthy diet

Background: Healthy nutrition is vital for good health and well-being. Despite the important role of a healthy nutritional diet, recommendations for healthy eating remain elusive and are mainly based on general properties of nutrients. The present study proposes an improved characterization of the molecular characteristics of nutrients, which are important for biological functions and can be useful in describing a healthy diet. Methods: We investigated the electronic properties of some known nutrient ingredients. In this analysis, we used the average quasi valence number (AQVN) and the electron-ion interaction potential (EIIP), which are molecular descriptors that represent the basic electronic properties of organic molecules.   Results: Our results show that most nutrients can be represented by specific groups of organic compounds according to their basic electronic properties, and these differ from the vast majority of known chemicals. Based on this finding, we have proposed a simple criterion for the selection of food components for healthy nutrition. Discussion: Further studies on the electronic properties of nutrients could serve as a basis for better understanding of their biological functions.

Toremifene interacts with and destabilizes the Ebola virus glycoprotein

Ebola viruses (EBOVs) are responsible for repeated outbreaks of fatal infections, including the recent deadly epidemic in West Africa. There are currently no approved therapeutic drugs or vaccines for the disease. EBOV has a membrane envelope decorated by trimers of a glycoprotein (GP, cleaved by furin to form GP1 and GP2 subunits) which is solely responsible for host cell attachment, endosomal entry and membrane fusion1–7. GP is thus a primary target for the development of antiviral drugs. Here we report the first unliganded structure of EBOV GP, and complexes with an anticancer drug toremifene and the painkiller ibuprofen. The high-resolution apo structure gives a more complete and accurate picture of the molecule, and allows conformational changes introduced by antibody and receptor binding to be deciphered8–10. Unexpectedly both toremifene and ibuprofen bind in a cavity between the attachment (GP1) and fusion (GP2) subunits at the entrance to a large tunnel that links with equivalent tunnels from the other monomers of the trimer at the 3-fold axis. Protein-drug interactions, with both GP1 and GP2, are predominately hydrophobic. Residues lining the binding site are highly conserved amongst filoviruses except Marburg virus (MARV), suggesting that MARV may not bind these drugs. Thermal shift assays show up to a 14 °C decrease in protein melting temperature upon toremifene binding, while ibuprofen has only a marginal effect and is a less potent inhibitor. The results suggest that inhibitor binding destabilizes GP and triggers premature release of GP2, therefore preventing fusion between the viral and endosome membranes. Thus these complex structures reveal the mechanism of inhibition and may guide the development of more powerful anti-EBOV drugs.

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

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 EC50 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.

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

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.

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

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.