Modeling the emergence of viral resistance for SARS-CoV-2 during treatment with an anti-spike monoclonal antibody (preprint)

The COVID-19 pandemic has led to over 760 million cases and 6.9 million deaths worldwide. To mitigate the loss of lives, emergency use authorization was given to several anti-SARS-CoV-2 monoclonal antibody (mAb) therapies for the treatment of mild-to-moderate COVID-19 in patients with a high risk of progressing to severe disease. Monoclonal antibodies used to treat SARS-CoV-2 target the spike protein of the virus and block its ability to enter and infect target cells. Monoclonal antibody therapy can thus accelerate the decline in viral load and lower hospitalization rates among high-risk patients with susceptible variants. However, viral resistance has been observed, in some cases leading to a transient viral rebound that can be as large as 3-4 orders of magnitude. As mAbs represent a proven treatment choice for SARS-CoV-2 and other viral infections, evaluation of treatment-emergent mAb resistance can help uncover underlying pathobiology of SARS-CoV-2 infection and may also help in the development of the next generation of mAb therapies. Although resistance can be expected, the large rebounds observed are much more difficult to explain. We hypothesize replenishment of target cells is necessary to generate the high transient viral rebound. Thus, we formulated two models with different mechanisms for target cell replenishment (homeostatic proliferation and return from an innate immune response anti-viral state) and fit them to data from persons with SARS-CoV-2 treated with a mAb. We showed that both models can explain the emergence of resistant virus associated with high transient viral rebounds. We found that variations in the target cell supply rate and adaptive immunity parameters have a strong impact on the magnitude or observability of the viral rebound associated with the emergence of resistant virus. Both variations in target cell supply rate and adaptive immunity parameters may explain why only some individuals develop observable transient resistant viral rebound. Our study highlights the conditions that can lead to resistance and subsequent viral rebound in mAb treatments during acute infection.

How reliable are estimates of key parameters in viral dynamic models? (preprint)

Mathematical models of viral infection have been developed and fit to data to gain insight into disease pathogenesis for a number of agents including HIV, hepatitis C and B virus. However, for acute infections such as influenza and SARS-CoV-2, as well as for infections such as hepatitis C and B that can be acute or progress to being chronic, viral load data are often collected after symptoms develop, usually around or after the peak viral load. Consequently, we frequently lack data in the exponential phase of viral growth, i.e., when most transmission events occur. Missing data may make estimation of the time of infection, the infectious period, and parameters in viral dynamic models, such as the cell infection rate, difficult. Here, we evaluated the reliability of estimates of key model parameters when viral load data prior to the viral load peak is missing. We estimated the time from infection to peak viral load by fitting non-linear mixed models to a dataset with frequent viral RNA measurements, including pre-peak. We quantified the reliability of estimated infection times, key model parameters, and the time to peak viral load. Although estimates of the time of infection are sensitive to the quality and amount of available data, other parameters important in understanding disease pathogenesis, such as the loss rate of infected cells, are less sensitive. We find a lack of data in the exponential growth phase underestimates the time to peak viral load by several days leading to a shorter predicted exponential growth phase. On the other hand, having an idea of the time of infection and fixing it, results in relatively good estimates of dynamical parameters even in the absence of early data.

An explanation for SARS-CoV-2 rebound after Paxlovid treatment (preprint)

In a fraction of SARS-CoV-2 infected individuals treated with the oral antiviral Paxlovid, the virus rebounds following treatment. The mechanism driving rebound is not understood. Here, we show that viral dynamic models based on the hypothesis that Paxlovid treatment near the time of symptom onset halts the depletion of target cells, but may not fully eliminate the virus, which can lead to viral rebound. We also show that the occurrence of viral rebound is sensitive to model parameters, and the time treatment is initiated, which may explain why only a fraction of individuals develop viral rebound. Finally, the models are used to test the therapeutic effects of two alternative treatment schemes. These findings also provide a possible explanation for rebounds following other antiviral treatments for SARS-CoV-2.

Bamlanivimab reduces nasopharyngeal SARS-CoV-2 RNA levels but not symptom duration in non-hospitalized adults with COVID-19 (preprint)

Importance: The antiviral activity and efficacy of anti-SARS-CoV-2 monoclonal antibody (mAb) therapies to accelerate recovery from COVID-19 is important to define. Objective: To determine safety and efficacy of the mAb bamlanivimab to reduce nasopharyngeal (NP) SARS-CoV-2 RNA levels and symptom duration. Design: ACTIV-2/A5401 is a randomized, blinded, placebo-controlled platform trial. Two dose cohorts were enrolled between August 19 and November 17, 2020 for phase 2 evaluation: in the first, participants were randomized 1:1 to bamlanivimab 7000 mg versus placebo, and in the second to bamlanivimab 700 mg versus placebo. Randomization was stratified by time from symptom onset ([≤] or >5 days) and risk of progression to severe COVID-19 ('higher' vs 'lower'). Setting: Multicenter trial conducted at U.S. sites. Participants: Non-hospitalized adults [≥]18 years of age with positive SARS-CoV-2 antigen or nucleic acid test within 7 days, [≤]10 days of COVID-19 symptoms, and with oxygen saturation [≥]92% within 48 hours prior to study entry. Intervention: Single infusion of bamlanivimab (7000 or 700 mg) or placebo. Main Outcomes and Measures: Detection of NP SARS-CoV-2 RNA at days 3, 7, 14, 21, and 28, time to improvement of all of 13 targeted COVID-19 symptoms by daily self-assessment through day 28, and grade 3 or higher treatment emergent adverse events (TEAEs) through day 28. Secondary measures included quantitative NP SARS-CoV-2 RNA, all-cause hospitalizations and deaths (composite), area under the curve of symptom scores from day 0 through day 28, plasma bamlanivimab concentrations, plasma and serum inflammatory biomarkers, and safety through week 24. Results: Ninety-four participants were enrolled to the 7000 mg cohort and 223 to the 700 mg cohort and initiated study intervention. The proportion meeting protocol criteria for 'higher' risk for COVID-19 progression was 42% and 51% for the 7000 and 700 mg cohort, respectively. Median time from symptom onset at study entry for both cohorts was 6 days. There was no difference in the proportion with undetectable NP SARS-CoV-2 RNA at any post-treatment timepoints (risk ratio compared to placebo, 0.82-1.05 for 7000 mg dose [overall p=0.88] and 0.81-1.21 for 700 mg dose [overall p=0.49]), time to symptom improvement (median of 21 vs 18.5 days, p=0.97, for 7000 mg bamlanivimab vs placebo and 24 vs 20.5 days, p=0.08, for 700 mg bamlanivimab vs placebo), or grade 3+ TEAEs with either dose compared to placebo. Median NP SARS-CoV-2 RNA levels were lower at day 3 and C-reactive protein, ferritin, and fibrinogen levels significantly reduced at days 7 and 14 for bamlanivimab 700 mg compared to placebo, with similar trends observed for bamlanivimab 7000 mg. Viral decay modeling supported more rapid decay with bamlanivimab compared to placebo. Conclusions and Relevance: Treatment with bamlanivimab 7000 mg and 700 mg was safe and compared to placebo led to more rapid reductions in NP SARS-CoV-2 RNA and inflammatory biomarkers, but did not decrease time to symptom improvement. The clinical utility of mAbs for outcomes other than hospitalizations and deaths is uncertain. Trial Registration: ClinicalTrials.gov Identifier: NCT04518410

A simple model of COVID-19 explains disease severity and the effect of treatments (preprint)

Considerable effort was made to better understand why some people suffer from severe COVID-19 while others remain asymptomatic. This has led to important clinical findings; people with severe COVID-19 generally experience persistently high levels of inflammation, slower viral load decay, display a dysregulated type-I interferon response, have less active natural killer cells and increased levels of neutrophil extracellular traps. How these findings are connected to the pathogenesis of COVID-19 remains unclear. We propose a mathematical model that sheds light on this issue. The model focuses on cells that trigger inflammation through molecular patterns: infected cells carrying pathogen-associated molecular patterns (PAMPs) and damaged cells producing damage-associated molecular patterns (DAMPs). The former signals the presence of pathogens while the latter signals danger such as hypoxia or the lack of nutrients. Analyses show that SARS-CoV-2 infections can lead to a self-perpetuating feedback loop between DAMP expressing cells and inflammation. It identifies the inability to quickly clear PAMPs and DAMPs as the main contributor to hyperinflammation. The model explains clinical findings and the conditional impact of treatments on disease severity. The simplicity of the model and its high level of consistency with clinical findings motivate its use for the formulation of new treatment strategies.

Emergence of SARS-CoV-2 Resistance with Monoclonal Antibody Therapy (preprint)

Resistance mutations to monoclonal antibody (mAb) therapy has been reported, but in the non-immunosuppressed population, it is unclear if in vivo emergence of SARS-CoV-2 resistance mutations alters either viral replication dynamics or therapeutic efficacy. In ACTIV-2/A5401, non-hospitalized participants with symptomatic SARS-CoV-2 infection were randomized to bamlanivimab (700mg or 7000mg) or placebo. Treatment-emergent resistance mutations were significantly more likely detected after bamlanivimab 700mg treatment than placebo (7% of 111 vs 0% of 112 participants, P=0.003). There were no treatment-emergent resistance mutations among the 48 participants who received bamlanivimab 7000mg. Participants with emerging mAb resistant virus had significantly higher pre-treatment nasopharyngeal and anterior nasal viral load. Intensive respiratory tract viral sampling revealed the dynamic nature of SARS-CoV-2 evolution, with evidence of rapid and sustained viral rebound after emergence of resistance mutations, and worsened symptom severity. Participants with emerging bamlanivimab resistance often accumulated additional polymorphisms found in current variants of concern/interest and associated with immune escape. These results highlight the potential for rapid emergence of resistance during mAb monotherapy treatment, resulting in prolonged high level respiratory tract viral loads and clinical worsening. Careful virologic assessment should be prioritized during the development and clinical implementation of antiviral treatments for COVID-19.

In vivo kinetics of SARS-CoV-2 infection and its relationship with a person's infectiousness (preprint)

The within-host viral kinetics of SARS-CoV-2 infection and how they relate to a person's infectiousness are not well understood. This limits our ability to quantify the impact of interventions on viral transmission. Here, we develop data-driven viral dynamic models of SARS-CoV-2 infection and estimate key within-host parameters such as the infected cell half-life and the within-host reproductive number. We then develop a model linking viral load (VL) to infectiousness, showing that infectiousness increases sub-linearly with VL. We show that the logarithm of the VL in the upper respiratory tract (URT) is a better surrogate of infectiousness than the VL itself. Using data on VL and the predicted infectiousness, we further incorporated data on antigen and reverse transcription polymerase chain reaction (RT-PCR) tests and compared their usefulness in detecting infection and preventing transmission. We found that RT-PCR tests perform better than antigen tests assuming equal testing frequency; however, more frequent antigen testing may perform equally well with RT-PCR tests at a lower cost, but with many more false-negative tests. Overall, our models provide a quantitative framework for inferring the impact of therapeutics and vaccines that lower VL on the infectiousness of individuals and for evaluating rapid testing strategies.

Kinetics of SARS-CoV-2 infection in the human upper and lower respiratory tracts and their relationship with infectiousness (preprint)

SARS-CoV-2 is a human pathogen that causes infection in both the upper respiratory tract (URT) and the lower respiratory tract (LRT). The viral kinetics of SARS-CoV-2 infection and how they relate to infectiousness and disease progression are not well understood. Here, we develop data-driven viral dynamic models of SARS-CoV-2 infection in both the URT and LRT. We fit the models to viral load data from patients with likely infection dates known, we estimated that infected individuals with a longer incubation period had lower rates of viral growth, took longer to reach peak viremia in the URT, and had higher chances of presymptomatic transmission. We then developed a model linking viral load to infectiousness. We found that to explain the substantial fraction of transmissions occurring presymptomatically, the infectiousness of a person should depend on a saturating function of the viral load, making the logarithm of the URT viral load a better surrogate of infectiousness than the viral load itself. Comparing the roles of target-cell limitation, the innate immune response, proliferation of target cells and spatial infection in the LRT, we found that spatial dissemination in the lungs is likely to be an important process in sustaining the prolonged high viral loads. Overall, our models provide a quantitative framework for predicting how SARS-CoV-2 within-host dynamics determine infectiousness and represent a step towards quantifying how viral load dynamics and the immune responses determine disease severity.

Rethinking antiviral effects for COVID-19 in clinical studies: early initiation is key to successful treatment (preprint)

Development of an effective antiviral drug for COVID-19 is a global health priority. Although several candidate drugs have been identified through in vitro and in vivo models, consistent and compelling evidence for effective drugs from clinical studies is limited. The lack of evidence could be in part due to heterogeneity of virus dynamics among patients and late initiation of treatment. We first quantified the heterogeneity of viral dynamics which could be a confounder in compassionate use programs. Second, we demonstrated that an antiviral drug is unlikely to be effective if initiated after a short period following symptom onset. For accurate evaluation of the efficacy of an antiviral drug for COVID-19, antiviral treatment should be initiated before or soon after symptom onset in randomized clinical trials.

Predicted success of prophylactic antiviral therapy to block or delay SARS-CoV-2 infection depends on the targeted mechanism (preprint)

Repurposed drugs that are immediately available and safe to use constitute a first line of defense against new viral infections. Despite limited antiviral activity against SARS-CoV-2, several drugs are being tested as medication or as prophylaxis to prevent infection. Using a stochastic model of early phase infection, we find that a critical efficacy above 87% is needed to block viral establishment. This can be improved by combination therapy. Below the critical efficacy, establishment of infection can sometimes be prevented, most effectively with drugs blocking viral entry into cells or enhancing viral clearance. Even when a viral infection cannot be prevented, antivirals delay the time to detectable viral loads. This delay flattens the within-host viral dynamic curve, possibly reducing transmission and symptom severity. Thus, antiviral prophylaxis, even with reduced efficacy, could be efficiently used to prevent or alleviate infection in people at high risk.

Timing of antiviral treatment initiation is critical to reduce SARS-Cov-2 viral load (preprint)

We modeled the viral dynamics of 13 untreated patients infected with SARS-CoV-2 to infer viral growth parameters and predict the effects of antiviral treatments. In order to reduce peak viral load by more than 2 logs, drug efficacy needs to be greater than 80% if treatment is administered after symptom onset; an efficacy of 50% could be sufficient if treatment is initiated before symptom onset. Given their pharmacokinetic/pharmacodynamic properties, current investigated drugs may be in a range of 20-70% efficacy. They may help control virus if administered very early, but may not have a major effect in severe patients.