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

Gastine, Silke; Pang, Juanita; Boshier, Florencia A.T.; Carter, Simon J.; Lonsdale, Derrick; Cortina‐Borja, Mario; Hung, Ivan; Breuer, Judith; Kloprogge, Frank; Standing, Joseph F.

doi:10.1002/cpt.2223

Cited by 30 publications

(50 citation statements)

References 78 publications

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“…We also observed an increase in the proportion of patients with undetectable viral load compared to placebo, lopinavir-ritonavir or combination therapy (Table 4, Supplementary Figure 4). The effect was seen especially in those with higher baseline viral load, likely due to viral replication having slowed substantially in those with low viral load, limiting the potential for antivirals to inhibit replication [17, 18]. However, this may point towards efficacy in a group with the most potential to benefit.…”

Section: Discussionmentioning

confidence: 99%

“…In line with this, lopinavir-ritonavir monotherapy did not improve clinical outcomes in platform trials on hospitalised patients [15, 16]. However, viral dynamic modelling suggests that drugs with lower potency may nevertheless inhibit viral replication if started earlier [17, 18], and high-quality early treatment trials with lopinavir-ritonavir are lacking.…”

Section: Introductionmentioning

confidence: 99%

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Favipiravir, lopinavir-ritonavir or combination therapy (FLARE): a randomised, double blind, 2x2 factorial placebo-controlled trial of early antiviral therapy in COVID-19

Lowe

Brown

Chowdhury

et al. 2022

Preprint

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Background: Early antiviral treatment is effective for COVID-19 but currently available agents are expensive. Favipiravir is routinely used in many countries, but efficacy is unproven. Antiviral combinations have not been systematically studied. We aimed to evaluate the effect of favipiravir, lopinavir-ritonavir or the combination of both agents on SARS-CoV-2 viral load trajectory when administered early. Methods: We conducted a Phase 2, proof of principle, randomised, placebo-controlled, 2x2 factorial, double-blind trial of outpatients with early COVID-19 (within 7 days of symptom onset) at two sites in the United Kingdom. Participants were randomised using a centralised online process to receive: favipiravir (1800mg twice daily on Day 1 followed by 400mg four times daily on Days 2-7) plus lopinavir-ritonavir (400mg/100mg twice daily on Day 1, followed by 200mg/50mg four times daily on Days 2-7); favipiravir plus lopinavir-ritonavir placebo; lopinavir-ritonavir plus favipiravir placebo; or both placebos. The primary outcome was SARS-CoV-2 viral load at Day 5, accounting for baseline viral load. ClinicalTrials.gov: NCT04499677. Findings: Between 6 October 2020 and 4 November 2021, we recruited 240 participants. For the favipiravir+lopinavir-ritonavir, favipiravir+placebo, lopinavir-ritonavir+placebo and placebo-only arms, we recruited 61, 59, 60 and 60 participants and analysed 55, 56, 55 and 58 participants respectively who provided viral load measures at Day 1 and Day 5. In the primary analysis, the mean viral load in the favipiravir+placebo arm had decreased by 0.57 log10 (95% CI -1.21 to 0.07, p=0.08) and in the lopinavir-ritonavir+placebo arm by 0.18 log10 (95% CI -0.82 to 0.46, p=0.58) more than in the placebo arm at Day 5. There was no significant interaction between favipiravir and lopinavir-ritonavir (interaction coefficient term: 0.59 log10, 95% CI -0.32 to 1.50, p=0.20). More participants had undetectable virus at Day 5 in the favipiravir+placebo arm compared to placebo only (46.3% vs 26.9%, odds ratio (OR): 2.47, 95% CI 1.08 to 5.65; p=0.03). Adverse events were observed more frequently with lopinavir-ritonavir, mainly gastrointestinal disturbance. Favipiravir drug levels were lower in the combination arm than the favipiravir monotherapy arm. Interpretation: At the current doses, no treatment significantly reduced viral load in the primary analysis. Favipiravir requires further evaluation with consideration of dose escalation. Lopinavir-ritonavir administration was associated with lower plasma favipiravir concentrations.

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Section: Discussionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Favipiravir, lopinavir-ritonavir or combination therapy (FLARE): a randomised, double blind, 2x2 factorial placebo-controlled trial of early antiviral therapy in COVID-19

Lowe

Brown

Chowdhury

et al. 2022

Preprint

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“…Notwithstanding, similar patterns of clinical and virological response to remdesivir have been described in adults. 2 , 7 , 46 Moreover, both clinical and viral sequence data from the use of repurposed drugs to treat other severe RNA viral infections have shown similarities in adults and children. 10 , 38 Additionally, although the largest of its kind, our cohort is small, with interpatient clinical and genomic heterogeneity patterns restricting some of the conclusions that can be drawn.…”

Section: Discussionmentioning

confidence: 99%

Evolution of viral variants in remdesivir‐treated and untreated SARS‐CoV‐2‐infected pediatrics patients

Boshier

Pang

Penner

et al. 2021

Journal of Medical Virology

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Detailed information on intrahost viral evolution in SARS‐CoV‐2 with and without treatment is limited. Sequential viral loads and deep sequencing of SARS‐CoV‐2 from the upper respiratory tract of nine hospitalized children, three of whom were treated with remdesivir, revealed that remdesivir treatment suppressed viral load in one patient but not in a second infected with an identical strain without any evidence of drug resistance found. Reduced levels of subgenomic RNA during treatment of the second patient, suggest an additional effect of remdesivir on viral replication. Haplotype reconstruction uncovered persistent SARS‐CoV‐2 variant genotypes in four patients. These likely arose from within‐host evolution, although superinfection cannot be excluded in one case. Although our dataset is small, observed sample‐to‐sample heterogeneity in variant frequencies across four of nine patients suggests the presence of discrete viral populations in the lung with incomplete population sampling in diagnostic swabs. Such compartmentalization could compromise the penetration of remdesivir into the lung, limiting the drugs in vivo efficacy, as has been observed in other lung infections.

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“…When possible, we selected studies with concordant assay sensitivities and read-outs. The observational data used to constrain the model states in the plausible population are primarily from hospitalized COVID-19 patients since such datasets were more readily available and of higher quality at the time of model development; however, we used outpatient data when possible, to inform some of these states such as plasma IL-6 and the viral load [39,40]. Nevertheless, despite the challenges, the data were suitable for the generation of the plausible population given the initial goal of constraining the model to a physiologically realistic regime.…”

Section: Generating a Plausible Populationmentioning

confidence: 99%

A Quantitative Systems Pharmacology Model of the Pathophysiology and Treatment of COVID-19 Predicts Optimal Timing of Pharmacological Interventions

Rao

Musante

2021

Preprint

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A quantitative systems pharmacology (QSP) model of the pathogenesis and treatment of SARS-CoV-2 infection can streamline and accelerate the development of novel medicines to treat COVID-19. Simulation of clinical trials allows in silico exploration of the uncertainties of clinical trial design and can rapidly inform their protocols. We previously published a preliminary model of the immune response to SARS-CoV-2 infection. To further our understanding of COVID-19 and treatment we significantly updated the model by matching a curated dataset spanning viral load and immune responses in plasma and lung. We identified a population of parameter sets to generate heterogeneity in pathophysiology and treatment and tested this model against published reports from interventional SARS-CoV-2 targeting Ab and anti-viral trials. Upon generation and selection of a virtual population, we match both the placebo and treated responses in viral load in these trials. We extended the model to predict the rate of hospitalization or death within a population. Via comparison of the in silico predictions with clinical data, we hypothesize that the immune response to virus is log-linear over a wide range of viral load. To validate this approach, we show the model matches a published subgroup analysis, sorted by baseline viral load, of patients treated with neutralizing Abs. By simulating intervention at different timepoints post infection, the model predicts efficacy is not sensitive to interventions within five days of symptom onset, but efficacy is dramatically reduced if more than five days pass post-symptom onset prior to treatment.

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

Abstract: Systematic review and patient-level meta-analysis of SARS-CoV-2 viral 9 dynamics to model response to antiviral therapies 10

Cited by 30 publications

References 78 publications

Favipiravir, lopinavir-ritonavir or combination therapy (FLARE): a randomised, double blind, 2x2 factorial placebo-controlled trial of early antiviral therapy in COVID-19

Favipiravir, lopinavir-ritonavir or combination therapy (FLARE): a randomised, double blind, 2x2 factorial placebo-controlled trial of early antiviral therapy in COVID-19

Evolution of viral variants in remdesivir‐treated and untreated SARS‐CoV‐2‐infected pediatrics patients

A Quantitative Systems Pharmacology Model of the Pathophysiology and Treatment of COVID-19 Predicts Optimal Timing of Pharmacological Interventions

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