Alan S. Perelson

Type I interferons (IFN) help clear a viral infection, but IFN levels can then decline to reset the antiviral state and avoid innate immune dysregulation. Such dysregulation can occur following infection with highly pathogenic and pandemic RNA viruses, such as H5N1 influenza A viruses or SARS-CoV-2. In spite of the strong innate immune response that these viruses induce, pandemic viruses replicate and spread successfully, suggesting that they can circumvent the effects of IFN. Innate immune activation is triggered when host cells detect viral RNA, especially defective viral genomes (DVGs). A strong IFN response (e.g. to DVGs or a highly immunogenic wild-type infection) must then decline through negative feedback involving the IFN receptor. How DVGs modulate this feedback remains poorly understood. To quantify positive and negative IFN regulation, we first performed in vitro experiments combining different doses of viral RNA and exogenous IFN, and measured subsequent IFN expression. We next developed an in vivo Bayesian computational model that captures the impact of influenza virus DVGs on both positive and negative IFN feedback. Competition between DVGs and virus is also considered. Finally, we calibrated this model on published in vitro and in vivo influenza virus infection data. Our model suggests that the cumulative IFN exposure of a host cell, rather than the instantaneous IFN level, shapes further IFN induction; this gives DVGs a dual role: beyond triggering IFN induction, they can also lower it through negative feedback. Our model further predicts that these effects are important locally, shaping the spatial dynamics of the response, allowing suppression of innate immune responses in neighbouring, uninfected cells. Through this model, we can explain two important biological observations: i) IFN levels can decline while virus and DVG levels continue to rise in tissue culture and mouse infections; and ii) pandemic viruses that produce high levels of immunogenic RNA molecules can successfully replicate through local innate immune suppression. Overall, we propose that heterogeneity within viral populations (including DVGs and other noncanonical viral RNAs) can modulate positive and negative feedback loops and shape infection outcomes. More broadly, our work may support studies into the therapeutic use of these viral RNA populations. Author summary: Humans and animals have to fight influenza virus and other infections without inducing immunopathology. After detection of an infection, the innate immune system produces type I interferon (IFN), a signal that starts a protective response. However, high levels of IFN and prolonged IFN expression can cause damage to tissues and worsen disease. Negative feedback is one route by which IFN production can stop, but its activation during viral infection remains poorly understood. A central question is why viruses can continue to replicate and spread despite a strong IFN response. We here investigated why IFN levels can begin to decline while virus levels continue to rise during influenza virus infection. This decline is most pronounced when the virus produces large amounts of defective viral genomes (DVGs). These defective genomes arise naturally during viral replication and are produced by many RNA viruses, including SARS-CoV-2, Ebola virus, and measles virus. Using cell-culture experiments, computational modelling, and fitting of our model to cell-culture and mouse data, we find that defective genomes contribute to both IFN activation and its negative feedback. Both the experiments and model support a mechanism in which IFN accumulation during infection suppresses its own production, and that this suppression can start earlier under strong DVG stimulation. At the local level, this can allow viral spread to continue even while overall IFN levels remain high. Overall, our findings reveal an additional level of interplay between defective viral genomes and the host innate immune system. They also suggest that the potential therapeutic use of defective genomes must be carefully calibrated to achieve an optimal antiviral response.

Bemnifosbuvir (BEM) and ruzasvir (RZR) are pan‐genotypic HCV polymerase (NS5B) and NS5A inhibitors, respectively. In a single‐arm study (NCT05904470), 215 treatment‐naïve, adherent participants with chronic HCV (with or without cirrhosis) received BEM 550 mg QD plus RZR 180 mg QD for 8 weeks. We employed a multiscale mathematical model fitted to the HCV RNA and ALT dynamics to quantify the antiviral activity and evaluate the modes of action of BEM and RZR. We found that after treatment initiation, the viral load (VL) decreased in a triphasic manner, with a very rapid first phase lasting −4 IU/mL) was 6.5 weeks for F0–F3 and 6.9 weeks for F4, with some variability between individuals. The population estimate of the effectiveness of the 8 weeks of treatment in blocking viral replication was 98.6% for F0–F3 and 99.4% for F4, while the estimate of the effectiveness in blocking viral assembly/secretion was 99.8% for F0–F3 and 99.0% for F4. We also found a treatment‐induced 70% enhancement of the rate of intracellular HCV RNA degradation. The estimated efficacy of therapy was similar across genotypes 1 to 4. Thus, our modeling supports the conclusions that BEM/RZR is pan‐genotypic, has high antiviral effectiveness and leads to rapid viral clearance in both cirrhotic and non‐cirrhotic patients.

Capsid assembly modulators (CAMs) are a promising class of antiviral treatments for hepatitis B virus (HBV) infection. Several CAMs have been evaluated in clinical trials but there is no simple method to estimate their in vivo antiviral effectiveness. We performed viral dynamics modeling of the intracellular and extracellular dynamics of HBV RNA, HBV DNA, and ALT during phase I trials of two CAMs, vebicorvir and ABI-H2158, which inhibit the encapsidation of pgRNA. Fitting our model to the data, we quantify the drug-induced percent inhibition of encapsidated pgRNA production, which we term their in vivo antiviral effectiveness. In both trials, the HBV RNA and HBV DNA declined in a biphasic manner during therapy. The model described these decays well and, by fitting the model to the data, we estimated the CAM effectiveness in each trial participant. Mathematical analysis of the model showed that the magnitude of the first phase of decline of HBV RNA and HBV DNA is explicitly related to CAM effectiveness. However, in the clinic, the end of the first phase may not be known due to sparse sampling. Using clinical trial simulations, we show that the HBV RNA and HBV DNA declines between baseline and day 14 of CAM monotherapy can be used to predict CAM effectiveness. We show that HBV RNA is a clinically relevant biomarker and that very short-term phase I clinical trials can be used to evaluate the in vivo effectiveness of new CAMs, thus reducing the danger of drug resistance developing in trial participants.

AZD5582 (AZD) is a latency reversing agent used to support the “shock-and-kill” strategy in HIV-1 cure research. Previous studies in ART-suppressed rhesus macaques have shown that AZD can promote reactivation of latently infected cells, resulting in 2–3 log increases in on-ART viral load and significant reductions in SIV reservoir size over 5–10 doses. To quantify the impact of AZD on the reservoir, we developed an ensemble of mechanistic viral dynamic models and fit them to longitudinal plasma SIV RNA and SIV CA-DNA data from 23 macaques treated with AZD in combination with other therapies. The aggregate predictions of the model ensemble recapitulate the reactivation patterns observed in both SIV RNA and SIV CA-DNA and provide robust estimates of key parameters associated with reactivated cells. We found that AZD reactivates approximately 25% of cells in the latent reservoir per dose, with a mean reactivation duration of about 5–6 days. Of the reactivated cells, 60–79% are eventually cleared, while the remainder enter a state refractory to AZD stimulation before returning to latency. Because of this refractory state, each consecutive weekly dose reactivates about 28% fewer cells than the previous one, an effect that could be more pronounced if the refractory period substantially exceeds the interval between doses. However, the duration of this refractory state remains uncertain. Altogether, our results suggest that AZD-reactivated cells are effectively cleared. Future work should focus on improving LRAs that safely reactivate a larger fraction of the latent reservoir. Furthermore, designing experiments with varying dosing schedules can help better quantify the duration of refractoriness, which will be important for informing optimal treatment schedules and maximizing the effect of LRAs.

Bemnifosbuvir (BEM) is a potent, pan‐genotypic inhibitor targeting the hepatitis C virus (HCV) NS5B polymerase. Its antiviral activity was evaluated in an ascending dose phase I clinical trial involving 30 patients treated once a day for 7 days. After treatment initiation, plasma HCV RNA declined in a biphasic manner with a mean reduction of 2.3 log ₁₀ IU/mL after 24 hours and 4.4 log ₁₀ IU/mL by day 7 for the highest dose. Alanine aminotransferase (ALT) also normalized in most patients. We employed a multiscale mathematical model fitted to the HCV RNA and ALT dynamics to quantify the antiviral activity and evaluate the modes of action of BEM. We found that models in which BEM only acted as a typical HCV RNA polymerase inhibitor and reduced the intracellular production of HCV RNA did not fit the data as well as models in which BEM had multiple modes of action, including suppressing viral assembly and secretion and enhancing intracellular HCV RNA degradation. BEM's effectiveness in inhibiting intracellular HCV RNA production increased with dose (150 mg/day: 88.2%, 300 mg/day: 98.8%, 600 mg/day: 99.5%), while inhibition of viral assembly and release was ~95% effective regardless of dose. We observed a dose‐dependent enhancement in the degradation of intracellular HCV RNA, with degradation rates 1.5‐fold higher in patients receiving 300 mg/day and 2.7‐fold higher in those receiving 600 mg/day than in patients receiving 150 mg/day. No significant differences in antiviral activity were detected between HCV genotypes 1b and 3 or between patients with and without compensated cirrhosis.

Multiscale mathematical models of hepatitis C infection have been instrumental in our understanding of direct acting antivirals. These models include the mechanisms driving intracellular viral production and explicitly model the intracellular concentration of viral RNA. However, incorporating proliferation of infected hepatocytes in these models can be subtle, as infected daughter cells inherit viral RNA from the proliferating mother cell. Here, we show how to incorporate this inheritance within a multiscale model of HCV infection. As in typical multiscale models of HCV infection, we show that this model is mathematically equivalent to a system of ordinary differential equations and perform bifurcation analysis of the resulting ODE demonstrating that proliferation of infected hepatocytes can lead to infection persistence, even if the basic reproduction number is less than 1.

Insights into HIV-1 pathogenesis have come from studies of viral dynamics. However, there is little information on viral dynamics in lentiviral infections in which viral replication is naturally controlled in a subset of infected individuals. We evaluated the decay of simian immunodeficiency virus (SIV) RNA and cell-associated SIV genomes in a nonhuman primate (NHP) model in which replication of an engineered SIV variant is naturally controlled by cellular immune responses in most infected animals. This variant lacks a trafficking motif in the gp41 cytoplasmic tail. A trajectory of control was evident by 21 days after infection. In animals with natural control, we observed similar biphasic decay of intact proviruses in blood and lymph nodes, at rates close to those in animals that failed to control the virus and were put on antiretroviral therapy (ART). Both natural control and ART effectively blocked viral evolution, but not persistence. Thus, in this NHP model, natural control can be nearly as effective as ART in controlling viral replication.

The prolonged viral shedding from the gastrointestinal tract is well documented for numerous pathogens, including SARS-CoV-2. However, the impact of prolonged viral shedding on epidemiological inferences using wastewater data is not yet fully understood. To gain a better understanding of this phenomenon at the population level, we extended a wastewater-based modeling framework that integrates viral shedding dynamics, viral load data in wastewater, case report data, and an epidemic model. Our results indicate that as an outbreak progresses, the viral load from recovered individuals gradually becomes predominant, surpassing that from the infectious population. This phenomenon leads to a dynamic relationship between model-inferred and reported daily incidence over the course of an outbreak. Sensitivity analyses on the duration and rate of viral shedding for recovered individuals reveal that accounting for this phenomenon can considerably advance prediction of transmission peak timing. Furthermore, extensive viral shedding from the recovered population toward the conclusion of an epidemic wave may overshadow viral signals from newly infected cases carrying emerging variants, which can delay the rapid recognition of emerging variants based on viral load. These findings highlight the necessity of integrating post-recovery viral shedding to enhance the accuracy and utility of wastewater-based epidemiological analysis.

Chronic hepatitis B virus (HBV) infection is strongly associated with increased risk of liver cancer and cirrhosis. While existing treatments effectively inhibit the HBV life cycle, viral rebound frequently occurs following treatment interruption. Consequently, functional cure rates of chronic HBV infection remain low and there is increased interest in a novel treatment modality, capsid assembly modulators (CAMs). Here, we develop a multiscale mathematical model of CAM treatment in chronic HBV infection. By fitting the model to participant data from a phase I trial of the first-generation CAM vebicorvir, we estimate the drug’s dose-dependent effectiveness and identify the physiological mechanisms that drive the observed biphasic decline in HBV DNA and RNA, and mechanistic differences between HBeAg-positive and negative infection. Finally, we demonstrate analytically and numerically that the relative change of HBV RNA more accurately reflects the antiviral effectiveness of a CAM than the relative change in HBV DNA.

In a subset of SARS-CoV-2-infected individuals treated with the antiviral nirmatrelvir-ritonavir, the virus rebounds following treatment. The mechanisms driving this rebound are not well understood. We used a mathematical model to describe the longitudinal viral load dynamics of 51 individuals treated with nirmatrelvir-ritonavir, 20 of whom rebounded. Target cell preservation, either by a robust innate immune response or initiation of N-R near the time of symptom onset, coupled with incomplete viral clearance, appears to be the main factor leading to viral rebound. Moreover, the occurrence of viral rebound is likely influenced by the time of treatment initiation relative to the progression of the infection, with earlier treatments leading to a higher chance of rebound. A comparison with an untreated cohort suggests that early treatments with nirmatrelvir-ritonavir may be associated with a delay in the onset of an adaptive immune response. Nevertheless, our model demonstrates that extending the course of nirmatrelvir-ritonavir treatment to a 10-day regimen may greatly diminish the chance of rebound in people with mild-to-moderate COVID-19 and who are at high risk of progression to severe disease. Altogether, our results suggest that in some individuals, a standard 5-day course of nirmatrelvir-ritonavir starting around the time of symptom onset may not completely eliminate the virus. Thus, after treatment ends, the virus can rebound if an effective adaptive immune response has not fully developed. These findings on the role of target cell preservation and incomplete viral clearance also offer a possible explanation for viral rebounds following other antiviral treatments for SARS-CoV-2. IMPORTANCE: Nirmatrelvir-ritonavir is an effective treatment for SARS-CoV-2. In a subset of individuals treated with nirmatrelvir-ritonavir, the initial reduction in viral load is followed by viral rebound once treatment is stopped. We show that the timing of treatment initiation with nirmatrelvir-ritonavir may influence the risk of viral rebound. Nirmatrelvir-ritonavir stops viral growth and preserves target cells but may not lead to full clearance of the virus. Thus, once treatment ends, if an effective adaptive immune response has not adequately developed, the remaining virus can lead to rebound. Our results provide insights into the mechanisms of rebound and can help develop better treatment strategies to minimize this possibility.