Systematic Review and Patient‐Level Meta‐Analysis of SARS‐CoV‐2 Viral Dynamics to Model Response to Antiviral Therapies

  1. Silke Gastine
  2. Juanita Pang
  3. Florencia A.T. Boshier
  4. Simon J. Carter
  5. Dagan O. Lonsdale
  6. Mario Cortina‐Borja
  7. Ivan F.N. Hung
  8. Judy Breuer
  9. Frank Kloprogge
  10. Joseph F. Standing

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Abstract

Severe acute respiratory syndrome‐coronavirus 2 (SARS‐CoV‐2) viral loads change rapidly following symptom onset, so to assess antivirals it is important to understand the natural history and patient factors influencing this. We undertook an individual patient‐level meta‐analysis of SARS‐CoV‐2 viral dynamics in humans to describe viral dynamics and estimate the effects of antivirals used to date. This systematic review identified case reports, case series, and clinical trial data from publications between January 1, 2020, and May 31, 2020, following Preferred Reporting Items for Systematic Reviews and Meta‐Analyses guidelines. A multivariable Cox proportional hazards (Cox‐PH) regression model of time to viral clearance was fitted to respiratory and stool samples. A simplified four parameter nonlinear mixed‐effects (NLME) model was fitted to viral load trajectories in all sampling sites and covariate modeling of respiratory viral dynamics was performed to quantify time‐dependent drug effects. Patient‐level data from 645 individuals (age 1 month to 100 years) with 6,316 viral loads were extracted. Model‐based simulations of viral load trajectories in samples from the upper and lower respiratory tract, stool, blood, urine, ocular secretions, and breast milk were generated. Cox‐PH modeling showed longer time to viral clearance in older patients, men, and those with more severe disease. Remdesivir was associated with faster viral clearance (adjusted hazard ratio (AHR) = 9.19, P  < 0.001), as well as interferon, particularly when combined with ribavirin (AHR = 2.2, P =  0.015; AHR = 6.04, P =  0.006). Combination therapy should be further investigated. A viral dynamic dataset and NLME model for designing and analyzing antiviral trials has been established.

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  1. Version published to 10.1002/cpt.2223
  2. ScreenIT

    SciScore for 10.1101/2020.08.20.20178699: (What is this?)

    Please note, not all rigor criteria are appropriate for all manuscripts.

    Table 1: Rigor

    Institutional Review Board Statementnot detected.
    Randomizationnot detected.
    Blindingnot detected.
    Power AnalysisComparisons of the sample size required to detect a significant difference in the proportion of undetectable virus between antiviral and no treatment were made after 7 days of treatment with a 90% power and alpha level of p<0.05 for antivirals starting at Days 1, 3 and 7 post symptom onset.
    Sex as a biological variableA dummy population of 5100 subjects with ages uniformly distributed across 50 to 100 years, consisting of an equal ratio of males and females was created.

    Table 2: Resources

    Software and Algorithms
    SentencesResources
    Hence, PubMed, EMBASE, medRxiv, and bioRxiv were searched with a date range of 1/1/2020 to 31/5/2020.
    PubMed
    suggested: (PubMed, RRID:SCR_004846)
    EMBASE
    suggested: (EMBASE, RRID:SCR_001650)
    bioRxiv
    suggested: (bioRxiv, RRID:SCR_003933)

    Results from OddPub: Thank you for sharing your code and data.

    Results from LimitationRecognizer: We detected the following sentences addressing limitations in the study:
    Despite this potential limitation, we found similar agents (combinations including interferons and ribavirin) to those identified in the primary analysis of time-to viral clearance. The major limitation of our work is the lack of clinical trial data and lack of data on potentially important re-purposing agents such as favipiravir and nitazoxanide and that only one of the authors of a major clinical trial agreed to share their data9. Through applying quality assessment criteria on drug history and assay reporting, pre-specifying our analysis in our protocol and PROSPERO registration before undertaking Cox proportional hazards and NLME modelling we aimed to reduce possible bias in the heterogenous data available. Whilst we were able to extract a limited common demographics set, particularly in the high-quality data subset (age, sex, disease severity, antiviral drug histories), our data may be limited by other non-antiviral medications that were not fully reported in the included papers. Furthermore, as many of our included papers were on patients with mild or no symptoms and only contained data on one patient reported to have died, we were unable to study associations of viral load and mortality. Viral load measured by PCR is not necessarily infectious virus, and recently it has been shown that only in samples above 107 copies/mL can SARS-CoV-2 be cultured29. Therefore, our data should preferably be used to study viral trajectories in relation to antiviral therapy rather than t...

    Results from TrialIdentifier: No clinical trial numbers were referenced.

    Results from Barzooka: We did not find any issues relating to the usage of bar graphs.

    Results from JetFighter: We did not find any issues relating to colormaps.

    Results from rtransparent:
    • Thank you for including a conflict of interest statement. Authors are encouraged to include this statement when submitting to a journal.
    • Thank you for including a funding statement. Authors are encouraged to include this statement when submitting to a journal.
    • No protocol registration statement was detected.

    About SciScore

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  3. Version published to 10.1101/2020.08.20.20178699 on medRxiv