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

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

doi:10.1002/cpt.2223

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.

Highlights

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
Individual patient-level data were extracted from 45 articles reporting viral loads and/or polymerase chain reaction (PCR) cycle threshold (Ct) values with time since symptom onset
Data from all major sampling sites showed, that: following symptom onset in most patients, upper respiratory tract viral load has peaked and is declining, whereas in the lower respiratory tract viral load peaks 2–3 days after symptom onset; virus is detectable in stool for at least 2 weeks in 75% of individuals, and virus is detected in low levels in blood, urine, ocular secretions, and breast milk (Figure 2)

Summary

Introduction

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. Since February 2020, case reports and case series of patient-level viral dynamics have been published, some of which report dosing of antiviral drugs.[8] Clinical trials of antivirals and their association with viral load are beginning to read out.[9] large pragmatic trials of repurposed monotherapy antivirals have yet to find a clearly effective agent.[10] At this crucial juncture, it is vital to develop a pharmacodynamic modeling framework that can be used to describe the natural history of SARS-CoV-2 viral dynamics, make initial estimates on antiviral efficacy of agents used to date, and to design and evaluate phase II trials using viral load as a biomarker.

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