DCcov: Repositioning of drugs and drug combinations for SARS-CoV-2 infected lung through constraint-based modeling.
Ontology highlight
ABSTRACT:
SUBMITTER: Kishk A
PROVIDER: S-EPMC8536485 | biostudies-literature |
REPOSITORIES: biostudies-literature
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Project description:A new coronavirus called SARS-CoV-2 is rapidly spreading around the world. Over 16,558,289 infected cases with 656,093 deaths have been reported by July 29th, 2020, and it is urgent to identify effective antiviral treatment. In this study, potential antiviral drugs against SARS-CoV-2 were identified by drug repositioning through Virus-Drug Association (VDA) prediction. 96 VDAs between 11 types of viruses similar to SARS-CoV-2 and 78 small molecular drugs were extracted and a novel VDA identification model (VDA-RLSBN) was developed to find potential VDAs related to SARS-CoV-2. The model integrated the complete genome sequences of the viruses, the chemical structures of drugs, a regularized least squared classifier (RLS), a bipartite local model, and the neighbor association information. Compared with five state-of-the-art association prediction methods, VDA-RLSBN obtained the best AUC of 0.9085 and AUPR of 0.6630. Ribavirin was predicted to be the best small molecular drug, with a higher molecular binding energy of -6.39 kcal/mol with human angiotensin-converting enzyme 2 (ACE2), followed by remdesivir (-7.4 kcal/mol), mycophenolic acid (-5.35 kcal/mol), and chloroquine (-6.29 kcal/mol). Ribavirin, remdesivir, and chloroquine have been under clinical trials or supported by recent works. In addition, for the first time, our results suggested several antiviral drugs, such as FK506, with molecular binding energies of -11.06 and -10.1 kcal/mol with ACE2 and the spike protein, respectively, could be potentially used to prevent SARS-CoV-2 and remains to further validation. Drug repositioning through virus-drug association prediction can effectively find potential antiviral drugs against SARS-CoV-2.
| S-EPMC7525008 | biostudies-literature
Project description:SARS-CoV-2 has been widely spread around the world and COVID-19 was declared a global pandemic by the WHO. Limited clinically effective antiviral drugs are available now. The development of anti-SARS-CoV-2 drugs has become an urgent work worldwide. At present, potential therapeutic targets and drugs for SARS-CoV-2 are continuously reported, and many repositioning drugs are undergoing extensive clinical research, including remdesivir and chloroquine. On the other hand, structures of many important viral target proteins and host target proteins, including that of RdRp and Mpro were constantly reported, which greatly promoted structure-based drug design. This paper summarizes the current research progress and challenges in the development of anti-SARS-CoV-2 drugs, and proposes novel short-term and long-term drug research strategies.
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Project description:Since COVID-19 emerged in 2019, significant levels of suffering and disruption have been caused on a global scale. Although vaccines have become widely used, the virus has shown its potential for evading immunities or acquiring other novel characteristics. Whether current drug treatments are still effective for people infected with Omicron remains unclear. Due to the long development cycles and high expense requirements of de novo drug development, many researchers have turned to consider drug repositioning in the search to find effective treatments for COVID-19. Here, we review such drug repositioning and combination efforts towards providing better handling. For potential drugs under consideration, aspects of both structure and function require attention, with specific categories of sequence, expression, structure, and interaction, the key parameters for investigation. For different data types, we show the corresponding differing drug repositioning methods that have been exploited. As incorporating drug combinations can increase therapeutic efficacy and reduce toxicity, we also review computational strategies to reveal drug combination potential. Taken together, we found that graph theory and neural network were the most used strategy with high potential towards drug repositioning for COVID-19. Integrating different levels of data may further improve the success rate of drug repositioning.
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Project description:Antiviral drug development for coronavirus disease 2019 (COVID-19) is occurring at an unprecedented pace, yet there are still limited therapeutic options for treating this disease. We hypothesized that combining drugs with independent mechanisms of action could result in synergy against SARS-CoV-2, thus generating better antiviral efficacy. Using in silico approaches, we prioritized 73 combinations of 32 drugs with potential activity against SARS-CoV-2 and then tested them in vitro. Sixteen synergistic and eight antagonistic combinations were identified; among 16 synergistic cases, combinations of the US Food and Drug Administration (FDA)-approved drug nitazoxanide with remdesivir, amodiaquine, or umifenovir were most notable, all exhibiting significant synergy against SARS-CoV-2 in a cell model. However, the combination of remdesivir and lysosomotropic drugs, such as hydroxychloroquine, demonstrated strong antagonism. Overall, these results highlight the utility of drug repurposing and preclinical testing of drug combinations for discovering potential therapies to treat COVID-19.
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| S-SCDT-EMBOJ-2020-106267 | biostudies-other
Project description:Coronavirus disease 2019 (COVID-19) is the latest respiratory pandemic resulting from zoonotic transmission of severe acute respiratory syndrome-related coronavirus 2 (SARS-CoV-2). Severe symptoms include viral pneumonia secondary to infection and inflammation of the lower respiratory tract, in some cases causing death. We developed primary human lung epithelial infection models to understand responses of proximal and distal lung epithelium to SARS-CoV-2 infection. Differentiated air-liquid interface cultures of proximal airway epithelium and 3D organoid cultures of alveolar epithelium were readily infected by SARS-CoV-2 leading to an epithelial cell-autonomous proinflammatory response. We validated the efficacy of selected candidate COVID-19 drugs confirming that Remdesivir strongly suppressed viral infection/replication. We provide a relevant platform for studying COVID-19 pathobiology and for rapid drug screening against SARS-CoV-2 and future emergent respiratory pathogens.
2020-11-06 | GSE155518 | GEO
Project description:In a short time, the COVID-19 pandemic has left the world with over 25 million cases and staggering death tolls that are still rising. Treatments for SARS-CoV-2 infection are desperately needed as there are currently no approved drug therapies. With limited knowledge of viral mechanisms, a network controllability method of prioritizing existing drugs for repurposing efforts is optimal for quickly moving through the drug approval pipeline using limited, available, virus-specific data. Based on network topology and controllability, 16 proteins involved in translation, cellular transport, cellular stress, and host immune response are predicted as regulators of the SARS-CoV-2 infected cell. Of the 16, eight are prioritized as possible drug targets where two, PVR and SCARB1, are previously unexplored. Known compounds targeting these genes are suggested for viral inhibition study. Prioritized proteins in agreement with previous analysis and viral inhibition studies verify the ability of network controllability to predict biologically relevant candidates.
| S-EPMC7650805 | biostudies-literature
Project description:Computational systems biology plays a key role in the discovery of suitable antiviral targets. We designed a cell-specific, constraint-based modeling technique for severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2)-infected lungs. We used the gene sequence of the alpha variant of SARS-CoV-2 to build a viral biomass reaction (VBR). We also used the mass proportion of lipids between the viral biomass and its host cell to estimate the stoichiometric coefficients of viral lipids in the reaction. We then integrated the VBR, the gene expression of the alpha variant of SARS-CoV-2, and the generic human metabolic network Recon3D to reconstruct a cell-specific genome-scale metabolic model. An antiviral target discovery (AVTD) platform was introduced using this model to identify therapeutic drug targets for combating COVID-19. The AVTD platform not only identified antiviral genes for eliminating viral replication but also predicted side effects of treatments. Our computational results revealed that knocking out dihydroorotate dehydrogenase (DHODH) might reduce the synthesis rate of cytidine-5'-triphosphate and uridine-5'-triphosphate, which terminate the viral building blocks of DNA and RNA for SARS-CoV-2 replication. Our results also indicated that DHODH is a promising antiviral target that causes minor side effects, which is consistent with the results of recent reports. Moreover, we discovered that the genes that participate in the de novo biosynthesis of glycerophospholipids and ceramides become unidentifiable if the VBR does not involve the stoichiometry of lipids.
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Project description:Drug repositioning (also referred to as drug repurposing) is the method of exploring novel therapeutic indications for Food and Drug Administration-approved clinically implemented drugs. The unique strategy of drug repositioning is used to boost the drug development process since drug discovery is an expensive, arduous, cumbersome, and high-risk procedure. Recently, several pharmaceutical firms have used the drug repositioning technique in their drug discovery and development programs to develop new medications based on the identification of new therapeutic targets. This technique is extremely effective, saves time, is comparatively economical, and has a low chance of failure. Developing appropriate treatment measures to inhibit the spread of Coronavirus disease-2019 (COVID-19) is currently a top priority. As a result, several studies were conducted to build novel therapeutic molecules using diverse strategies of drug repurposing to discover drug candidates against COVID-19 infection that can act as substantial inhibitors against virus particles. By implementing virtual screening of drug libraries, it is possible to identify potential drugs through drug repurposing. A molecular docking approach and calculation of binding free energy are used to estimate binding affinity and drug–receptor interactions. Drug-repurposing methodologies can be divided into three categories: target-oriented, drug-oriented, and disease-oriented, based on the gathered data about the various physicochemical, pharmacokinetic and pharmacological features of a drug candidate. Using computational methods such as homology modeling and molecular similarity, this methodology aids in determining the binding interaction of drug molecules with the target protein of the virus. In this book chapter, we explore a typical set of currently utilized computational techniques for identifying repurposable drug molecules for COVID-19, as well as their supporting databases. We also assess promising drugs anticipated by computational approaches to drugs currently being evaluated in clinical trials. Moreover, we also examine the takeaways from the evaluated research efforts, such as how to competently combine bioinformatics tools with experimental work and suggest a fully integrated drug-repurposing approach to combat the deadly COVID-19 infection.
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