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

Silke Gastine, Juanita Pang, Florencia A T Boshier, Simon J Carter, Dagan O Lonsdale, Mario Cortina-Borja, Ivan F N Hung, Judy Breuer, Frank Kloprogge, Joseph F Standing, Silke Gastine, Juanita Pang, Florencia A T Boshier, Simon J Carter, Dagan O Lonsdale, Mario Cortina-Borja, Ivan F N Hung, Judy Breuer, Frank Kloprogge, Joseph F Standing

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.

Conflict of interest statement

The authors declared no competing interests for this work. As an Associate Editor of Clinical Pharmacology and Therapeutics, Joseph F. Standing was not involved in the review or decision process for this paper.

© 2021 The Authors. Clinical Pharmacology & Therapeutics published by Wiley Periodicals LLC on behalf of American Society for Clinical Pharmacology and Therapeutics.

Figures

Figure 1
Figure 1
Preferred Reporting Items for Systematic Reviews and Meta‐Analyses (PRISMA) diagram detailing the systematic search results.
Figure 2
Figure 2
Model‐predicted viral load trajectories at each sample site studied. Black lines are the median predictions, with shaded areas representing the 95% prediction interval. The percentage of samples that are predicted to be below a typical limit of detection (10 copies/mL) are given in two‐daily time bins on each plot. [Colour figure can be viewed at wileyonlinelibrary.com]
Figure 3
Figure 3
Multivariable Cox proportional hazard results on all drug quality 1 and drug quality 2 data from respiratory and stool/rectal sampling sites. Adjusted hazard ratios exceeding 1 indicate virus being more likely to become undetectable. LOD, limit of detection.
Figure 4
Figure 4
Visual predictive checks for the nonlinear mixed‐effects model fitted to viral load data to each sampling site. For each site a plot of model simulations compared with observations is given for both the continuous data (upper) and the fraction of samples below the limit of detection (lower). Black circles are observed viral loads, purple shaded area is the 95% prediction interval of the simulated 2.5th and 97.5th percentile for comparison with the observed 2.5 and 97.5th percentile (dashed lines). The blue shaded area is the 95% prediction interval of the 50th percentile to compare with the continuous black line. In the lower plot, the observed proportion of samples below the lower limit of detection (LLOD) are shown as a black line and compared with the 95% prediction interval of the model predicted proportion of samples below the LLOD (green shaded area). [Colour figure can be viewed at wileyonlinelibrary.com]
Figure 5
Figure 5
Simulated viral load trajectories. Simulations with a dummy population equally distributed between 50 and 100 years, and equal male/female ratio were performed for each scenario. Drugs were started at day 1 (blue), day 3 (orange), day 7 (green), or day 10 (red) post symptom onset. Mean black line and error bars represent simulations of the dummy population without drug treatment. Colored mean lines and error bars represent the respective drug regimen. Percentage values represent expected proportion of samples below the limit of detection for no drug (black) vs. drug therapy (colored) at each timepoint. [Colour figure can be viewed at wileyonlinelibrary.com]

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