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1.
EuropePMC; 2022.
Preprint in English | EuropePMC | ID: ppcovidwho-329088

ABSTRACT

Mutations continue to accumulate within the SARS-CoV-2 genome, and the ongoing epidemic has shown no signs of ending. It is critical to predict problematic mutations that may arise in clinical environments and assess their properties in advance to quickly implement countermeasures against future variant infections. In this study, we identified mutations resistant to remdesivir, which is widely administered to SARS-CoV-2-infected patients, and discuss the cause of resistance. First, we simultaneously constructed eight recombinant viruses carrying the mutations detected in in vitro serial passages of SARS-CoV-2 in the presence of remdesivir. Time course analyses of cellular virus infections showed significantly higher infectious titers and infection rates in mutant viruses than wild type virus under treatment with remdesivir. Next, we developed a mathematical model in consideration of the changing dynamic of cells infected with mutant viruses with distinct propagation properties and defined that mutations detected in in vitro passages canceled the antiviral activities of remdesivir without raising virus production capacity. Finally, molecular dynamics simulations of the NSP12 protein of SARS-CoV-2 revealed that the molecular vibration around the RNA-binding site was increased by the introduction of mutations on NSP12. Taken together, we identified multiple mutations that affected the flexibility of the RNA binding site and decreased the antiviral activity of remdesivir. Our new insights will contribute to developing further antiviral measures against SARS-CoV-2 infection.

2.
EuropePMC;
Preprint in English | EuropePMC | ID: ppcovidwho-327114

ABSTRACT

Appropriate isolation guidelines for COVID-19 patients are warranted. Currently, isolating for fixed time is adapted in most countries. However, given the variability in viral dynamics between patients, some patients may no longer be infectious by the end of isolation (thus they are redundantly isolated), whereas others may still be infectious. Utilizing viral test results to determine ending isolation would minimize both the risk of ending isolation of infectious patients and the burden due to redundant isolation of noninfectious patients. In our previous study, we proposed a computational framework using SARS-CoV-2 viral dynamics models to compute the risk and the burden of different isolation guidelines with PCR tests. In this study, we extend the computational framework to design isolation guidelines for COVID-19 patients utilizing rapid antigen tests. Time interval of tests and number of consecutive negative tests to minimize the risk and the burden of isolation were explored. Furthermore, the approach was extended for asymptomatic cases. We found the guideline should be designed considering various factors: the infectiousness threshold values, the detection limit of antigen tests, symptom presence, and an acceptable level of releasing infectious patients. Especially, when detection limit is higher than the infectiousness threshold values, more consecutive negative results are needed to ascertain loss of infectiousness. To control the risk of releasing of infectious individuals under certain levels, rapid antigen tests should be designed to have lower detection limits than infectiousness threshold values to minimize the length of prolonged isolation, and the length of prolonged isolation increases when the detection limit is higher than the infectiousness threshold values, even though the guidelines are optimized for given conditions.

3.
Nihon Yakurigaku Zasshi ; 157(1): 31-37, 2022.
Article in Japanese | MEDLINE | ID: covidwho-1609118

ABSTRACT

Remdesivir is a direct-acting antiviral agent that inhibits viral RNA synthesis developed by Gilead Sciences, Inc. in the United States. It has been shown to have antiviral activity against single-stranded RNA viruses, including coronaviruses, in cell culture systems and animal models, and has been developed as a therapeutic agent for Ebola virus infection since 2015. however, to date, it has not been approved in any country. A novel coronavirus infection (COVID-19) was identified in Wuhan, Hubei Province, China in Dec, 2019, and is a respiratory disease characterized by fever, cough, and dyspnea. In severe cases, it may cause serious pneumonia, multi-organ failure and death. Gilead Sciences, Inc. U.S. embarked on the development of COVID-19 as a therapeutic drug, using remdesivir, which has shown in vitro and in vivo antiviral activities against MERS-CoV and SARS-CoV, which are single-stranded RNA coronaviruses that cause Middle East respiratory syndrome (MERS) and severe acute respiratory syndrome (SARS). The in vitro antiviral activity of remdesivir against SARS-CoV-2, which causes COVID-19, was confirmed and clinical studies were initiated in February 2020. Based on the results of clinical studies conducted by the National Institute of Allergy and Infectious Diseases (NIAID) and Gilead Sciences, Inc. and experience of administration from a compassionate use, an exceptional approval system based on the "Pharmaceuticals and Medical Devices Act" was also approved in Japan as of May 7, 2020 for the indication of "infections caused by SARS-CoV-2." In this article, the background of the development and clinical results of remdesivir are described.


Subject(s)
COVID-19 , Hemorrhagic Fever, Ebola , Hepatitis C, Chronic , Adenosine Monophosphate/analogs & derivatives , Alanine/analogs & derivatives , Animals , Antiviral Agents/pharmacology , Antiviral Agents/therapeutic use , COVID-19/drug therapy , Hemorrhagic Fever, Ebola/drug therapy , Hepatitis C, Chronic/drug therapy , Humans , SARS-CoV-2
4.
Life Sci Alliance ; 4(10)2021 10.
Article in English | MEDLINE | ID: covidwho-1342114

ABSTRACT

The duration of viral shedding is determined by a balance between de novo infection and removal of infected cells. That is, if infection is completely blocked with antiviral drugs (100% inhibition), the duration of viral shedding is minimal and is determined by the length of virus production. However, some mathematical models predict that if infected individuals are treated with antiviral drugs with efficacy below 100%, viral shedding may last longer than without treatment because further de novo infections are driven by entry of the virus into partially protected, uninfected cells at a slower rate. Using a simple mathematical model, we quantified SARS-CoV-2 infection dynamics in non-human primates and characterized the kinetics of viral shedding. We counterintuitively found that treatments initiated early, such as 0.5 d after virus inoculation, with intermediate to relatively high efficacy (30-70% inhibition of virus replication) yield a prolonged duration of viral shedding (by about 6.0 d) compared with no treatment.


Subject(s)
Antiviral Agents/pharmacology , COVID-19/drug therapy , COVID-19/virology , Virus Shedding/drug effects , Adenosine Monophosphate/analogs & derivatives , Adenosine Monophosphate/pharmacology , Alanine/analogs & derivatives , Alanine/pharmacology , Animals , Lung/virology , Macaca mulatta , Models, Theoretical , Nose/virology , Pharynx/virology , SARS-CoV-2/drug effects , SARS-CoV-2/physiology , Time Factors , Viral Load/drug effects , Virus Replication/drug effects
5.
Elife ; 102021 07 27.
Article in English | MEDLINE | ID: covidwho-1328262

ABSTRACT

Since the start of the COVID-19 pandemic, two mainstream guidelines for defining when to end the isolation of SARS-CoV-2-infected individuals have been in use: the one-size-fits-all approach (i.e. patients are isolated for a fixed number of days) and the personalized approach (i.e. based on repeated testing of isolated patients). We use a mathematical framework to model within-host viral dynamics and test different criteria for ending isolation. By considering a fixed time of 10 days since symptom onset as the criterion for ending isolation, we estimated that the risk of releasing an individual who is still infectious is low (0-6.6%). However, this policy entails lengthy unnecessary isolations (4.8-8.3 days). In contrast, by using a personalized strategy, similar low risks can be reached with shorter prolonged isolations. The obtained findings provide a scientific rationale for policies on ending the isolation of SARS-CoV-2-infected individuals.


Subject(s)
COVID-19/epidemiology , COVID-19/virology , Patient Isolation , Practice Guidelines as Topic , Quarantine , SARS-CoV-2 , COVID-19/diagnosis , COVID-19/transmission , Humans , Models, Theoretical , Molecular Diagnostic Techniques , Pandemics , Patient Isolation/methods , Patient Isolation/standards , Precision Medicine/methods , Quarantine/methods , Quarantine/standards , SARS-CoV-2/physiology , Viral Load
6.
PLoS Med ; 18(7): e1003660, 2021 07.
Article in English | MEDLINE | ID: covidwho-1298077

ABSTRACT

BACKGROUND: Development of an effective antiviral drug for Coronavirus Disease 2019 (COVID-19) is a global health priority. Although several candidate drugs have been identified through in vitro and in vivo models, consistent and compelling evidence from clinical studies is limited. The lack of evidence from clinical trials may stem in part from the imperfect design of the trials. We investigated how clinical trials for antivirals need to be designed, especially focusing on the sample size in randomized controlled trials. METHODS AND FINDINGS: A modeling study was conducted to help understand the reasons behind inconsistent clinical trial findings and to design better clinical trials. We first analyzed longitudinal viral load data for Severe Acute Respiratory Syndrome Coronavirus 2 (SARS-CoV-2) without antiviral treatment by use of a within-host virus dynamics model. The fitted viral load was categorized into 3 different groups by a clustering approach. Comparison of the estimated parameters showed that the 3 distinct groups were characterized by different virus decay rates (p-value < 0.001). The mean decay rates were 1.17 d-1 (95% CI: 1.06 to 1.27 d-1), 0.777 d-1 (0.716 to 0.838 d-1), and 0.450 d-1 (0.378 to 0.522 d-1) for the 3 groups, respectively. Such heterogeneity in virus dynamics could be a confounding variable if it is associated with treatment allocation in compassionate use programs (i.e., observational studies). Subsequently, we mimicked randomized controlled trials of antivirals by simulation. An antiviral effect causing a 95% to 99% reduction in viral replication was added to the model. To be realistic, we assumed that randomization and treatment are initiated with some time lag after symptom onset. Using the duration of virus shedding as an outcome, the sample size to detect a statistically significant mean difference between the treatment and placebo groups (1:1 allocation) was 13,603 and 11,670 (when the antiviral effect was 95% and 99%, respectively) per group if all patients are enrolled regardless of timing of randomization. The sample size was reduced to 584 and 458 (when the antiviral effect was 95% and 99%, respectively) if only patients who are treated within 1 day of symptom onset are enrolled. We confirmed the sample size was similarly reduced when using cumulative viral load in log scale as an outcome. We used a conventional virus dynamics model, which may not fully reflect the detailed mechanisms of viral dynamics of SARS-CoV-2. The model needs to be calibrated in terms of both parameter settings and model structure, which would yield more reliable sample size calculation. CONCLUSIONS: In this study, we found that estimated association in observational studies can be biased due to large heterogeneity in viral dynamics among infected individuals, and statistically significant effect in randomized controlled trials may be difficult to be detected due to small sample size. The sample size can be dramatically reduced by recruiting patients immediately after developing symptoms. We believe this is the first study investigated the study design of clinical trials for antiviral treatment using the viral dynamics model.


Subject(s)
Antiviral Agents/therapeutic use , COVID-19/drug therapy , Randomized Controlled Trials as Topic , Sample Size , Humans , Models, Biological , SARS-CoV-2 , Treatment Outcome , Viral Load , Virus Replication , Virus Shedding
7.
J R Soc Interface ; 18(177): 20200947, 2021 04.
Article in English | MEDLINE | ID: covidwho-1194079

ABSTRACT

Viral tests including polymerase chain reaction (PCR) tests are recommended to diagnose COVID-19 infection during the acute phase of infection. A test should have high sensitivity; however, the sensitivity of the PCR test is highly influenced by viral load, which changes over time. Because it is difficult to collect data before the onset of symptoms, the current literature on the sensitivity of the PCR test before symptom onset is limited. In this study, we used a viral dynamics model to track the probability of failing to detect a case of PCR testing over time, including the presymptomatic period. The model was parametrized by using longitudinal viral load data collected from 30 hospitalized patients. The probability of failing to detect a case decreased toward symptom onset, and the lowest probability was observed 2 days after symptom onset and increased afterwards. The probability on the day of symptom onset was 1.0% (95% CI: 0.5 to 1.9) and that 2 days before symptom onset was 60.2% (95% CI: 57.1 to 63.2). Our study suggests that the diagnosis of COVID-19 by PCR testing should be done carefully, especially when the test is performed before or way after symptom onset. Further study is needed of patient groups with potentially different viral dynamics, such as asymptomatic cases.


Subject(s)
COVID-19 , SARS-CoV-2 , Humans , Polymerase Chain Reaction , Probability , Serologic Tests
8.
Proceedings for Annual Meeting of The Japanese Pharmacological Society ; 94(0):1-S07-2, 2021.
Article in English | J-STAGE | ID: covidwho-1145472
9.
PLoS Biol ; 19(3): e3001128, 2021 03.
Article in English | MEDLINE | ID: covidwho-1145480

ABSTRACT

The scientific community is focused on developing antiviral therapies to mitigate the impacts of the ongoing novel coronavirus disease 2019 (COVID-19) outbreak. This will be facilitated by improved understanding of viral dynamics within infected hosts. Here, using a mathematical model in combination with published viral load data, we compare within-host viral dynamics of SARS-CoV-2 with analogous dynamics of MERS-CoV and SARS-CoV. Our quantitative analyses using a mathematical model revealed that the within-host reproduction number at symptom onset of SARS-CoV-2 was statistically significantly larger than that of MERS-CoV and similar to that of SARS-CoV. In addition, the time from symptom onset to the viral load peak for SARS-CoV-2 infection was shorter than those of MERS-CoV and SARS-CoV. These findings suggest the difficulty of controlling SARS-CoV-2 infection by antivirals. We further used the viral dynamics model to predict the efficacy of potential antiviral drugs that have different modes of action. The efficacy was measured by the reduction in the viral load area under the curve (AUC). Our results indicate that therapies that block de novo infection or virus production are likely to be effective if and only if initiated before the viral load peak (which appears 2-3 days after symptom onset), but therapies that promote cytotoxicity of infected cells are likely to have effects with less sensitivity to the timing of treatment initiation. Furthermore, combining a therapy that promotes cytotoxicity and one that blocks de novo infection or virus production synergistically reduces the AUC with early treatment. Our unique modeling approach provides insights into the pathogenesis of SARS-CoV-2 and may be useful for development of antiviral therapies.


Subject(s)
Betacoronavirus/physiology , COVID-19/therapy , COVID-19/virology , Antiviral Agents/pharmacology , Antiviral Agents/therapeutic use , COVID-19/transmission , Coronavirus Infections/therapy , Coronavirus Infections/virology , Humans , Longitudinal Studies , Middle East Respiratory Syndrome Coronavirus/physiology , Models, Biological , SARS Virus/physiology , SARS-CoV-2/physiology , Viral Load/drug effects
10.
Epidemics ; 35: 100454, 2021 06.
Article in English | MEDLINE | ID: covidwho-1135321

ABSTRACT

The incubation period, or the time from infection to symptom onset, of COVID-19 has usually been estimated by using data collected through interviews with cases and their contacts. However, this estimation is influenced by uncertainty in the cases' recall of exposure time. We propose a novel method that uses viral load data collected over time since hospitalization, hindcasting the timing of infection with a mathematical model for viral dynamics. As an example, we used reported data on viral load for 30 hospitalized patients from multiple countries (Singapore, China, Germany, and Korea) and estimated the incubation period. The median, 2.5, and 97.5 percentiles of the incubation period were 5.85 days (95 % CI: 5.05, 6.77), 2.65 days (2.04, 3.41), and 12.99 days (9.98, 16.79), respectively, which are comparable to the values estimated in previous studies. Using viral load to estimate the incubation period might be a useful approach, especially when it is impractical to directly observe the infection event.


Subject(s)
COVID-19/transmission , Infectious Disease Incubation Period , Viral Load/statistics & numerical data , Adult , COVID-19/virology , China , Hospitalization , Humans , Male , Models, Theoretical , SARS-CoV-2
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