Viral Load Kinetics of Severe Acute Respiratory Syndrome Coronavirus 2 in Hospitalized Individuals With Coronavirus Disease 2019

Regan, James; Flynn, James P.; Rosenthal, Alexandra; Jordan, Hannah; Li, Yijia; Chishti, Rida; Giguel, Françoise; Corry, Heather; Coxen, Kendyll; Fajnzylber, Jesse; Gillespie, Elizabeth; Kuritzkes, Daniel R.; Hacohen, Nir; Goldberg, Marcia B.; Filbin, Michael R.; Yu, Xu G.; Baden, Lindsey R.; Ribeiro, Ruy M.; Perelson, Alan S.; Conway, Jessica M.; Li, Jonathan Z.; Abayneh, Betelihem A.; Allen, Patrick; Alter, Galit; Antille, Diane; Armstrong, Katrina; Balazs, Alejandro; Bals, Julia; Barbash, Max; Bartsch, Yannic C.; Boucau, Julie; Boyce, Siobhan; Braley, Joan; Branch, Kelley R.; Broderick, Katherine; Carney, Julia; Chan, Andrew; Chevalier, Josh; Chowdhury, Fatema Z.; Daley, George Q.; Davidson, Susan; Dougan, Michael; Drew, David A.; Einkauf, Kevin; Elliman, Ashley; Fallon, Jon; Fedirko, Liz; Finn, Kelsey; Flaherty, Keith T.; Flannery, Jeanne; Forde, Pamela; García-Broncano, Pilar; Gettings, Elise; Golan, David; Griffin, Amanda; Grimmel, Sheila; Grinke, Kathleen; Hall, Kathryn; Hartana, Ciputra Adijaya; Healy, Meg; Heller, Howard M.; Henault, Deborah; Holland, Grace; Jiang, Chenyang; Jilg, Nikolaus; Kapłonek, Paulina; Karpell, Marshall; Kayitesi, Chantal; Lam, Evan C.; LaValle, Vlasta; Lefteri, Kristina; Lian, Xiaodong; Lichterfeld, Mathias; Lingwood, Daniel; Liu, Huan; Liu, Jinqing; Lu, Yiting; Luthern, Sarah; Ly, Natasha; Marchewka, Jordan; Martino, Britanni; McNamara, Roseann; Michell, Ashlin; Millstrom, Ilan; Miranda, Noah; Nambu, Christian; Nelson, Susan C.; Noone, Marjorie; O’Callaghan, Claire; Ommerborn, Christine; Osborn, Mathew; Pacheco, Lois Chris; Phan, Nicole; Pillai, Shiv; Porto, Falisha A.; Rassadkina, Yelizaveta; Reissis, Alexandra; Rosenthal, Alex; Ruzicka, Francis F.; Ryan, Edward T.; Seiger, Kyra; Selleck, Kathleen; Sessa, Libera; Sharpe, Arlene; Sharr, Christianne; Shin, Sally; Singh, Nishant K.; Slaughenhaupt, Sue; Sheppard, Kimberly Smith; Sun, Weiwei; Sun, Xiaoming; Suschana, Elizabeth; Ticheli, Hannah; Trocha-Piechocka, Alicja; Wilson, Vivine; Wong, Colline; Worrall, Daniel; Zhu, Alex; Hill, Z; Demers, Edward; Judge, Kelly; Walker, Bruce D.; Lai, Peggy S.; Li, Jonathan; Ghebremichael, Musie

doi:10.1093/ofid/ofab153

Cited by 22 publications

(22 citation statements)

References 14 publications

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“…Nevertheless, our results do not indicate that remdesivir is ineffective, they indicate that the current dosing has room for improvement if toxicity allows. This is in line with clinical trials that report a shorter time to recovery [1,46], newer meta-analyses that support a slightly reduced mortality [7] and recent findings that the viral load declines more quickly in patients receiving remdesivir [11]. We therefore would like to argue for more dose-ranging and dose-fractionation trials.…”

Section: Discussionsupporting

confidence: 83%

“…These are observation studies, but one can also, with some difficulty, investigate the impact of remdesivir on SARS-CoV-2 viral replication in host. While a modeling study from the French COVID Cohort Investigators, and French Cohort Study groups suggested remdesivir did not improve SARS-CoV-2 viral clearance time [9], the sample size was small, animal [10] and larger human studies [11] do show accelerated viral decay in remdesivir-treated macaques and humans, respectively.…”

Section: Introductionmentioning

confidence: 99%

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Remdesivir to treat COVID-19: can dosing be optimized?

Conway

Wiesch

2021

Preprint

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The antiviral remdesivir has been approved by regulatory bodies such as EMA and FDA for the treatment of COVID-19. However, its efficacy is debated and toxicity concerns might limit the therapeutic range of this drug. Computational models that aid in balancing efficacy and toxicity would be of great help. Parametrizing models is difficult because the prodrug remdesivir is metabolized to its active form (RDV-TP) upon cell entry, which complicates dose-activity relationships. Here, we employ a computational model that allows predicting drug efficacy based on the binding affinity of RDV-TP for its target polymerase in SARS-CoV-2. We identify an optimal infusion rate to maximize remdesivir efficacy. We also assess drug efficacy in suppressing both wild-type and resistant strains, and thereby selection of resistance. Our results differ from predictions using prodrug dose-response curves (pseudo-EC50s). We expect that reaching 90% inhibition (EC90) is insufficient to suppress SARS-CoV-2 in lungs. While standard dosing mildly inhibits viral polymerase and therefore likely reduces morbidity, we also expect selection for resistant mutants for most realistic parameter ranges. To increase efficacy and safeguard against resistance, we recommend continuing remdesivir use with companion antivirals and/or with dosing regimens that substantially increase the levels of RDV-TP.

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Section: Discussionsupporting

confidence: 83%

Section: Introductionmentioning

confidence: 99%

Remdesivir to treat COVID-19: can dosing be optimized?

Conway

Wiesch

2021

Preprint

View full text Add to dashboard Cite

show abstract

“…The observation that there is room for improvement in the dosing regimen is in line with clinical trials that report a shorter time to recovery [1,48], newer meta-analyses that support a slightly reduced mortality [7] and recent findings that the viral load declines more quickly in patients receiving remdesivir [11]. We therefore would like to argue specifically for more dose-ranging and dose-fractionation trials.…”

Section: Discussionmentioning

confidence: 62%

“…A modeling study from the French COVID Cohort Investigators and French Cohort Study groups suggested that remdesivir did not improve SARS-CoV-2 viral clearance time [9]. But their sample size was small, and animal [10] and larger human studies [11] do show accelerated viral decay in remdesivir-treated macaques and humans, respectively.…”

Section: Introductionmentioning

confidence: 99%

Mathematical Modeling of Remdesivir to Treat COVID-19: Can Dosing Be Optimized?

Conway

Wiesch

2021

Pharmaceutics

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The antiviral remdesivir has been approved by regulatory bodies such as the European Medicines Agency (EMA) and the US Food and Drug administration (FDA) for the treatment of COVID-19. However, its efficacy is debated and toxicity concerns might limit the therapeutic range of this drug. Computational models that aid in balancing efficacy and toxicity would be of great help. Parametrizing models is difficult because the prodrug remdesivir is metabolized to its active form (RDV-TP) upon cell entry, which complicates dose–activity relationships. Here, we employ a computational model that allows drug efficacy predictions based on the binding affinity of RDV-TP for its target polymerase in SARS-CoV-2. We identify an optimal infusion rate to maximize remdesivir efficacy. We also assess drug efficacy in suppressing both wild-type and resistant strains, and thereby describe a drug regimen that may select for resistance. Our results differ from predictions using prodrug dose–response curves (pseudo-EC50s). We expect that reaching 90% inhibition (EC90) is insufficient to suppress SARS-CoV-2 in the lungs. While standard dosing mildly inhibits viral polymerase and therefore likely reduces morbidity, we also expect selection for resistant mutants for most realistic parameter ranges. To increase efficacy and safeguard against resistance, we recommend more clinical trials with dosing regimens that substantially increase the levels of RDV-TP and/or pair remdesivir with companion antivirals.

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“…These models typically predict viral peaks within the first four or five dpi. However, SARS-CoV-2 peak viral load occurs later in the lung than in the upper respiratory tract (URT) [38], and the patient data have complex dynamics that last 15 to 30 dpi (Fig 6). Although the viral peak may be reached earlier in the URT, in the lung, it may not be achieved before the adaptive immune response begins [17].…”

Section: Simcov Viral Loads Compared To Patient Datamentioning

confidence: 99%

Spatially distributed infection increases viral load in a computational model of SARS-CoV-2 lung infection

et al. 2021

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A key question in SARS-CoV-2 infection is why viral loads and patient outcomes vary dramatically across individuals. Because spatial-temporal dynamics of viral spread and immune response are challenging to study in vivo, we developed Spatial Immune Model of Coronavirus (SIMCoV), a scalable computational model that simulates hundreds of millions of lung cells, including respiratory epithelial cells and T cells. SIMCoV replicates viral growth dynamics observed in patients and shows how spatially dispersed infections can lead to increased viral loads. The model also shows how the timing and strength of the T cell response can affect viral persistence, oscillations, and control. By incorporating spatial interactions, SIMCoV provides a parsimonious explanation for the dramatically different viral load trajectories among patients by varying only the number of initial sites of infection and the magnitude and timing of the T cell immune response. When the branching airway structure of the lung is explicitly represented, we find that virus spreads faster than in a 2D layer of epithelial cells, but much more slowly than in an undifferentiated 3D grid or in a well-mixed differential equation model. These results illustrate how realistic, spatially explicit computational models can improve understanding of within-host dynamics of SARS-CoV-2 infection.

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Viral Load Kinetics of Severe Acute Respiratory Syndrome Coronavirus 2 in Hospitalized Individuals With Coronavirus Disease 2019

Cited by 22 publications

References 14 publications

Remdesivir to treat COVID-19: can dosing be optimized?

Remdesivir to treat COVID-19: can dosing be optimized?

Mathematical Modeling of Remdesivir to Treat COVID-19: Can Dosing Be Optimized?

Spatially distributed infection increases viral load in a computational model of SARS-CoV-2 lung infection

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