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1.
Front Pharmacol ; 13: 840639, 2022.
Article in English | MEDLINE | ID: covidwho-1742239

ABSTRACT

Since the first reports from December 2019, COVID-19 caused an overwhelming global pandemic that has affected 223 countries, seriously endangering public health and creating an urgent need for effective drugs to treat SARS-CoV-2 infection. Currently, there is a lack of safe, effective, and specific therapeutic drugs for COVID-19, with mainly supportive and symptomatic treatments being administered to patients. The preferred option for responding to an outbreak of acute infectious disease is through drug repurposing, saving valuable time that would otherwise be lost in preclinical and clinical research, hastening clinical introduction, and lowering treatment costs. Alternatively, researchers seek to design and discover novel small-molecule candidate drugs targeting the key proteins in the life cycle of SARS-CoV-2 through an in-depth study of the infection mechanism, thus obtaining a number of candidate compounds with favorable antiviral effects in preclinical and clinical settings. There is an urgent need to further elucidate the efficacy and mechanism of action of potential anti-SARS-CoV-2 small-molecule drugs. Herein, we review the candidate small-molecule anti-SARS-CoV-2 drugs in ongoing clinical trials, with a major focus on their mechanisms of action in an attempt to provide useful insight for further research and development of small-molecule compounds against SARS-CoV-2 infection.

2.
EuropePMC; 2020.
Preprint in English | EuropePMC | ID: ppcovidwho-325295

ABSTRACT

Background: Identifying patients who may develop severe coronavirus disease 2019 (COVID-19) will facilitate personalized treatment and optimize the distribution of medical resources. Methods In this study, 590 COVID-19 patients during hospitalization were enrolled (Training set: n = 285;Internal validation set: n = 127;Prospective set: n = 178). After filtered by 2 machine learning methods in the training set, 5 out of 31 clinical features were selected into model building to predict the risk of developing severe COVID-19 disease. Multivariate logistic regression was applied to build the prediction nomogram and validated in 2 different sets. Receiver operating characteristic (ROC) analysis and decision curve analysis (DCA) were used to evaluate its performance. Results From 31 potential predictors in the training set, 5 independent predictive factors were identified and included in the risk score: C-reactive protein (CRP), Lactate dehydrogenase (LDH), Age, Charlson/Deyo comorbidity score (CDCS) and Erythrocyte sedimentation rate (ESR). Subsequently, we generated the nomogram based on the above features for predicting severe COVID-19. In the training cohort, the Area under curves (AUCs) were 0.822 (95% CI 0.765–0.875) and the internal validation cohort was 0.762 (95% CI 0.768–0.844). Further, we validated it in a prospective cohort with the AUCs of 0.705 (95% CI 0.627–0.778). The internally bootstrapped calibration curve showed favorable consistency between prediction by nomogram and actual situation. And DCA analysis also conferred high clinical net benefit. Conclusion In this study, our predicting model based on 5 clinical characteristics of COVID-19 patients will enable clinicians to predict the potential risk of developing critical illness and thus optimize medical management.

3.
Acta Pharmaceutica Sinica ; 56(7):1769-1777, 2021.
Article in Chinese | CAB Abstracts | ID: covidwho-1575862

ABSTRACT

As the main active compound of Stephania tetrandra S. Moore, tetrandrine (TET) has been used to treat silicosis for nearly 50 years. TET has clear therapeutic effect on pulmonary fibrosis and lung cancer. A recent study suggests that TET may inhibit the replication of SARS-CoV-2 by blocking the two-pore channel 2 (TPC2), revealing its potential as a natural medicine to treat COVID-19. To explore the material basis of TET targeting lung efficacy and its potential toxicity, available literatures related to the pharmacological activity on pulmonary, dosage, toxicity and pharmacokinetics of TET are systemically reviewed. The prospect and current problems of TET to be a therapeutic agent for COVID-19 are further investigated on this basis.

4.
MedComm (2020) ; 2(3): 381-401, 2021 Sep.
Article in English | MEDLINE | ID: covidwho-1513917

ABSTRACT

As of August 27, 2021, the ongoing pandemic of coronavirus disease 2019 (COVID-19), caused by severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2), has spread to over 220 countries, areas, and territories. Thus far, 214,468,601 confirmed cases, including 4,470,969 deaths, have been reported to the World Health Organization. To combat the COVID-19 pandemic, multiomics-based strategies, including genomics, transcriptomics, proteomics, and metabolomics, have been used to study the diagnosis methods, pathogenesis, prognosis, and potential drug targets of COVID-19. In order to help researchers and clinicians to keep up with the knowledge of COVID-19, we summarized the most recent progresses reported in omics-based research papers. This review discusses omics-based approaches for studying COVID-19, summarizing newly emerged SARS-CoV-2 variants as well as potential diagnostic methods, risk factors, and pathological features of COVID-19. This review can help researchers and clinicians gain insight into COVID-19 features, providing direction for future drug development and guidance for clinical treatment, so that patients can receive appropriate treatment as soon as possible to reduce the risk of disease progression.

5.
Innovation (N Y) ; 2(4): 100165, 2021 Nov 28.
Article in English | MEDLINE | ID: covidwho-1401933
8.
IEEE/ACM Trans Comput Biol Bioinform ; 18(4): 1290-1298, 2021.
Article in English | MEDLINE | ID: covidwho-1349906

ABSTRACT

An outbreak of COVID-19 that began in late 2019 was caused by a novel coronavirus(SARS-CoV-2). It has become a global pandemic. As of June 9, 2020, it has infected nearly 7 million people and killed more than 400,000, but there is no specific drug. Therefore, there is an urgent need to find or develop more drugs to suppress the virus. Here, we propose a new nonlinear end-to-end model called LUNAR. It uses graph convolutional neural networks to automatically learn the neighborhood information of complex heterogeneous relational networks and combines the attention mechanism to reflect the importance of the sum of different types of neighborhood information to obtain the representation characteristics of each node. Finally, through the topology reconstruction process, the feature representations of drugs and targets are forcibly extracted to match the observed network as much as possible. Through this reconstruction process, we obtain the strength of the relationship between different nodes and predict drug candidates that may affect the treatment of COVID-19 based on the known targets of COVID-19. These selected candidate drugs can be used as a reference for experimental scientists and accelerate the speed of drug development. LUNAR can well integrate various topological structure information in heterogeneous networks, and skillfully combine attention mechanisms to reflect the importance of neighborhood information of different types of nodes, improving the interpretability of the model. The area under the curve(AUC) of the model is 0.949 and the accurate recall curve (AUPR) is 0.866 using 10-fold cross-validation. These two performance indexes show that the model has superior predictive performance. Besides, some of the drugs screened out by our model have appeared in some clinical studies to further illustrate the effectiveness of the model.


Subject(s)
Antiviral Agents/pharmacology , COVID-19/drug therapy , COVID-19/virology , Drug Evaluation, Preclinical/methods , Neural Networks, Computer , SARS-CoV-2/drug effects , COVID-19/epidemiology , Computational Biology , Databases, Pharmaceutical/statistics & numerical data , Drug Development/methods , Drug Development/statistics & numerical data , Drug Evaluation, Preclinical/statistics & numerical data , Drug Repositioning/methods , Drug Repositioning/statistics & numerical data , Host Microbial Interactions/drug effects , Humans , Nonlinear Dynamics , Pandemics
9.
Brief Bioinform ; 22(6)2021 11 05.
Article in English | MEDLINE | ID: covidwho-1266105

ABSTRACT

Recent studies have demonstrated that the excessive inflammatory response is an important factor of death in coronavirus disease 2019 (COVID-19) patients. In this study, we propose a deep representation on heterogeneous drug networks, termed DeepR2cov, to discover potential agents for treating the excessive inflammatory response in COVID-19 patients. This work explores the multi-hub characteristic of a heterogeneous drug network integrating eight unique networks. Inspired by the multi-hub characteristic, we design 3 billion special meta paths to train a deep representation model for learning low-dimensional vectors that integrate long-range structure dependency and complex semantic relation among network nodes. Based on the representation vectors and transcriptomics data, we predict 22 drugs that bind to tumor necrosis factor-α or interleukin-6, whose therapeutic associations with the inflammation storm in COVID-19 patients, and molecular binding model are further validated via data from PubMed publications, ongoing clinical trials and a docking program. In addition, the results on five biomedical applications suggest that DeepR2cov significantly outperforms five existing representation approaches. In summary, DeepR2cov is a powerful network representation approach and holds the potential to accelerate treatment of the inflammatory responses in COVID-19 patients. The source code and data can be downloaded from https://github.com/pengsl-lab/DeepR2cov.git.


Subject(s)
COVID-19/drug therapy , Drug Repositioning , Inflammation/drug therapy , SARS-CoV-2/drug effects , Anti-Inflammatory Agents/chemistry , Anti-Inflammatory Agents/therapeutic use , COVID-19/complications , COVID-19/genetics , COVID-19/virology , Computational Biology , Deep Learning , Humans , Inflammation/complications , Inflammation/genetics , Inflammation/virology , Neural Networks, Computer , SARS-CoV-2/pathogenicity , Software , Transcriptome/drug effects , Transcriptome/genetics
11.
Virol Sin ; 35(6): 776-784, 2020 Dec.
Article in English | MEDLINE | ID: covidwho-1217480

ABSTRACT

The recent outbreak of novel coronavirus pneumonia (COVID-19) caused by a new coronavirus has posed a great threat to public health. Identifying safe and effective antivirals is of urgent demand to cure the huge number of patients. Virus-encoded proteases are considered potential drug targets. The human immunodeficiency virus protease inhibitors (lopinavir/ritonavir) has been recommended in the global Solidarity Trial in March launched by World Health Organization. However, there is currently no experimental evidence to support or against its clinical use. We evaluated the antiviral efficacy of lopinavir/ritonavir along with other two viral protease inhibitors in vitro, and discussed the possible inhibitory mechanism in silico. The in vitro to in vivo extrapolation was carried out to assess whether lopinavir/ritonavir could be effective in clinical. Among the four tested compounds, lopinavir showed the best inhibitory effect against the novel coronavirus infection. However, further in vitro to in vivo extrapolation of pharmacokinetics suggested that lopinavir/ritonavir could not reach effective concentration under standard dosing regimen [marketed as Kaletra®, contained lopinavir/ritonavir (200 mg/50 mg) tablets, recommended dosage is 400 mg/10 mg (2 tablets) twice daily]. This research concluded that lopinavir/ritonavir should be stopped for clinical use due to the huge gap between in vitro IC50 and free plasma concentration. Nevertheless, the structure-activity relationship analysis of the four inhibitors provided further information for de novel design of future viral protease inhibitors of SARS-CoV-2.


Subject(s)
Antiviral Agents/pharmacology , COVID-19/drug therapy , Coronavirus 3C Proteases/antagonists & inhibitors , Lopinavir/pharmacology , Ritonavir/pharmacology , SARS-CoV-2/drug effects , SARS-CoV-2/enzymology , Viral Protease Inhibitors/pharmacology , Animals , Antiviral Agents/chemistry , COVID-19/blood , COVID-19/virology , Cell Line , Chlorocebus aethiops , Coronavirus 3C Proteases/chemistry , Coronavirus 3C Proteases/metabolism , Drug Combinations , Humans , Lopinavir/blood , Male , Molecular Docking Simulation , Ritonavir/blood , Vero Cells , Viral Protease Inhibitors/chemistry
13.
EBioMedicine ; 62: 103125, 2020 Dec.
Article in English | MEDLINE | ID: covidwho-938894

ABSTRACT

BACKGROUND: The pharmacokinetics and appropriate dose regimens of favipiravir are unknown in hospitalized influenza patients; such data are also needed to determine dosage selection for favipiravir trials in COVID-19. METHODS: In this dose-escalating study, favipiravir pharmacokinetics and tolerability were assessed in critically ill influenza patients. Participants received one of two dosing regimens; Japan licensed dose (1600 mg BID on day 1 and 600 mg BID on the following days) and the higher dose (1800 mg/800 mg BID) trialed in uncomplicated influenza. The primary pharmacokinetic endpoint was the proportion of patients with a minimum observed plasma trough concentration (Ctrough) ≥20 mg/L at all measured time points after the second dose. RESULTS: Sixteen patients were enrolled into the low dose group and 19 patients into the high dose group of the study. Favipiravir Ctrough decreased significantly over time in both groups (p <0.01). Relative to day 2 (48 hrs), concentrations were 91.7% and 90.3% lower in the 1600/600 mg group and 79.3% and 89.5% lower in the 1800/800 mg group at day 7 and 10, respectively. In contrast, oseltamivir concentrations did not change significantly over time. A 2-compartment disposition model with first-order absorption and elimination described the observed favipiravir concentration-time data well. Modeling demonstrated that less than 50% of patients achieved Ctrough ≥20 mg/L for >80% of the duration of treatment of the two dose regimens evaluated (18.8% and 42.1% of patients for low and high dose regimen, respectively). Increasing the favipravir dosage predicted a higher proportion of patients reaching this threshold of 20 mg/L, suggesting that dosing regimens of ≥3600/2600 mg might be required for adequate concentrations. The two dosing regimens were well-tolerated in critical ill patients with influenza. CONCLUSION: The two dosing regimens proposed for uncomplicated influenza did not achieve our pre-defined treatment threshold.


Subject(s)
Amides , Influenza, Human/drug therapy , Oseltamivir , Pyrazines , Aged , Amides/administration & dosage , Amides/pharmacokinetics , Drug Therapy, Combination , Female , Humans , Influenza, Human/blood , Male , Middle Aged , Oseltamivir/administration & dosage , Oseltamivir/pharmacokinetics , Pyrazines/administration & dosage , Pyrazines/pharmacokinetics , Severity of Illness Index
14.
Organic Process Research & Development ; 24(9):1772-1777, 2020.
Article in English | Web of Science | ID: covidwho-880187

ABSTRACT

The bulk supply of the antiviral C-nucleoside analogue remdesivir is largely hampered by a low-yielding Cglycosylation step in which the base is coupled to the pentose unit. Here, we disclose a significantly improved methodology for this critical transformation. By utilizing diisopropylamine as a cost-effective additive, the addition reaction furnishes an optimal yield of 75% of the desired ribofuranoside adduct, representing the highest yield obtained thus far for this key step. The method proved suitable for hectogram scale synthesis without column chromatographic operations.

15.
Signal Transduct Target Ther ; 5(1): 240, 2020 10 15.
Article in English | MEDLINE | ID: covidwho-872677

ABSTRACT

The COVID-19 pandemic has emerged as a global health emergency due to its association with severe pneumonia and relative high mortality. However, the molecular characteristics and pathological features underlying COVID-19 pneumonia remain largely unknown. To characterize molecular mechanisms underlying COVID-19 pathogenesis in the lung tissue using a proteomic approach, fresh lung tissues were obtained from newly deceased patients with COVID-19 pneumonia. After virus inactivation, a quantitative proteomic approach combined with bioinformatics analysis was used to detect proteomic changes in the SARS-CoV-2-infected lung tissues. We identified significant differentially expressed proteins involved in a variety of fundamental biological processes including cellular metabolism, blood coagulation, immune response, angiogenesis, and cell microenvironment regulation. Several inflammatory factors were upregulated, which was possibly caused by the activation of NF-κB signaling. Extensive dysregulation of the lung proteome in response to SARS-CoV-2 infection was discovered. Our results systematically outlined the molecular pathological features in terms of the lung response to SARS-CoV-2 infection, and provided the scientific basis for the therapeutic target that is urgently needed to control the COVID-19 pandemic.


Subject(s)
Betacoronavirus/pathogenicity , Coronavirus Infections/genetics , Lung Injury/genetics , Pneumonia, Viral/genetics , Proteome/genetics , Proteomics/methods , Severe Acute Respiratory Syndrome/genetics , Aged , Autopsy , COVID-19 , Coronavirus Infections/metabolism , Coronavirus Infections/pathology , Coronavirus Infections/virology , Cytokines/genetics , Cytokines/metabolism , Female , Gene Expression Profiling , Gene Expression Regulation , Gene Ontology , Humans , Lung/metabolism , Lung/pathology , Lung/virology , Lung Injury/metabolism , Lung Injury/pathology , Lung Injury/virology , Male , Metabolic Networks and Pathways , Molecular Sequence Annotation , NF-kappa B/genetics , NF-kappa B/metabolism , Pandemics , Pneumonia, Viral/metabolism , Pneumonia, Viral/pathology , Pneumonia, Viral/virology , Proteome/metabolism , SARS-CoV-2 , Severe Acute Respiratory Syndrome/metabolism , Severe Acute Respiratory Syndrome/pathology , Severe Acute Respiratory Syndrome/virology , Severity of Illness Index , Signal Transduction
16.
Biomed Central; 2020.
Preprint | BioMed Central | ID: ppcovidwho-2276

ABSTRACT

Background: Since pneumonia caused by coronavirus disease 2019 (COVID-19) broke out in Wuhan, Hubei province, China, tremendous infected cases has risen all over the world attributed to high transmissibility. We managed to mathematically forecast the inflection point (IFP) of new cases in South Korea, Italy, and Iran, utilizing the transcendental model from Hubei and non-Hubei in China. Methods: We extracted data from reports released by the National Health Commission of the People's Republic of China (Dec 31, 2019 to Mar 5, 2020) and World Health Organization (Jan 20, 2020 to Mar 5, 2020) as the training set to deduce the arrival of the IFP of new cases in Hubei and non-Hubei on subsequent days and the data from Mar 6 to Mar 9 as validation set. New close contacts, newly confirmed cases, cumulative confirmed cases, non-severe cases, severe cases, critical cases, cured cases, and death data were collected and analyzed. Using this state transition matrix model, the horizon of the IFP of time (the rate of new increment reaches zero) could be predicted in South Korean, Italy, and Iran. Also, through this model, the global trend of the epidemic will be decoded to allocate international medical resources better and instruct the strategy for quarantine. Results: the optimistic scenario (non-Hubei model, daily increment rate of -3.87%), the relative pessimistic scenario (Hubei model, daily increment rate of -2.20%), and the relatively pessimistic scenario (adjustment, daily increment rate of -1.50%) were inferred and modeling from data in China. Matching and fitting with these scenarios, the IFP of time in South Korea would be Mar 6-Mar 12, Italy Mar 10-Mar 24, and Iran is Mar 10-Mar 24. The numbers of cumulative confirmed patients will reach approximately 20k in South Korea, 209k in Italy, and 226k in Iran under fitting scenarios, respectively. There should be room for improvement if these metrics continue to improve. In that case, the IFP will arrive earlier than our estimation. However, with the adoption of different diagnosis criteria, the variation of new cases could impose various influences in the predictive model. If that happens, the IFP of increment will be higher than predicted above. Conclusion: We can affirm that the end of the burst of the epidemic is still inapproachable, and the number of confirmed cases is still escalating. With the augment of data, the world epidemic trend could be further predicted, and it is imperative to consummate the assignment of global medical resources to manipulate the development of COVID-19. ### Competing Interest Statement The authors have declared no competing interest. ### Funding Statement The reported work was supported in part by research grants from the Natural Science Foundation of China (no. 81972393, 81772705, 31570775). ### Author Declarations All relevant ethical guidelines have been followed;any necessary IRB and/or ethics committee approvals have been obtained and details of the IRB/oversight body are included in the manuscript. Yes All necessary patient/participant consent has been obtained and the appropriate institutional forms have been archived. Yes I understand that all clinical trials and any other prospective interventional studies must be registered with an ICMJE-approved registry, such as ClinicalTrials.gov. I confirm that any such study reported in the manuscript has been registered and the trial registration ID is provided (note: if posting a prospective study registered retrospectively, please provide a statement in the trial ID field explaining why the study was not registered in advance). Yes I have followed all appropriate research reporting guidelines and uploaded the relevant EQUATOR Network research reporting checklist(s) and other pertinent material as supplementary files, if applicable. Yes The data that support the findings of this study are available in the National Health Commission of the People’s Republic of China, the Health Commission of Hubei Province, and the World Health Organization. <http://www.nhc.gov.cn/> <http://wjw.hubei.gov cn/> <https://www.who.int/emergencies/diseases/novel-coronavirus-2019/situation-reports/>

19.
ACS Infect Dis ; 6(9): 2524-2531, 2020 09 11.
Article in English | MEDLINE | ID: covidwho-695395

ABSTRACT

The discovery of novel drug candidates with anti-severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) potential is critical for the control of the global COVID-19 pandemic. Artemisinin, an old antimalarial drug derived from Chinese herbs, has saved millions of lives. Artemisinins are a cluster of artemisinin-related drugs developed for the treatment of malaria and have been reported to have multiple pharmacological activities, including anticancer, antiviral, and immune modulation. Considering the reported broad-spectrum antiviral potential of artemisinins, researchers are interested in whether they could be used to combat COVID-19. We systematically evaluated the anti-SARS-CoV-2 activities of nine artemisinin-related compounds in vitro and carried out a time-of-drug-addition assay to explore their antiviral mode of action. Finally, a pharmacokinetic prediction model was established to predict the therapeutic potential of selected compounds against COVID-19. Arteannuin B showed the highest anti-SARS-CoV-2 potential with an EC50 of 10.28 ± 1.12 µM. Artesunate and dihydroartemisinin showed similar EC50 values of 12.98 ± 5.30 µM and 13.31 ± 1.24 µM, respectively, which could be clinically achieved in plasma after intravenous administration. Interestingly, although an EC50 of 23.17 ± 3.22 µM was not prominent among the tested compounds, lumefantrine showed therapeutic promise due to high plasma and lung drug concentrations after multiple dosing. Further mode of action analysis revealed that arteannuin B and lumefantrine acted at the post-entry step of SARS-CoV-2 infection. This research highlights the anti-SARS-CoV-2 potential of artemisinins and provides leading candidates for anti-SARS-CoV-2 drug research and development.


Subject(s)
Antiviral Agents/pharmacology , Artemisinins/pharmacology , Betacoronavirus/drug effects , Coronavirus Infections/drug therapy , Coronavirus Infections/virology , Pneumonia, Viral/drug therapy , Pneumonia, Viral/virology , Animals , Antimalarials/pharmacology , COVID-19 , Chlorocebus aethiops , Drug Discovery , Drug Repositioning , Drugs, Chinese Herbal/pharmacology , Pandemics , SARS-CoV-2 , Vero Cells
20.
Future Med Chem ; 12(17): 1565-1578, 2020 09.
Article in English | MEDLINE | ID: covidwho-637735

ABSTRACT

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.


Subject(s)
Coronavirus Infections/drug therapy , Drug Repositioning , Pneumonia, Viral/drug therapy , Antiviral Agents/therapeutic use , Betacoronavirus/chemistry , Betacoronavirus/drug effects , COVID-19 , Clinical Trials as Topic , Humans , Pandemics , SARS-CoV-2 , Viral Proteins/chemistry , Viral Proteins/drug effects
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