Disentangling the lifespans of hepatitis C virus‐infected cells and intracellular vRNA replication‐complexes during direct‐acting anti‐viral therapy

Abstract

The decay rate of hepatitis C virus (HCV)‐infected cells during therapy has been used to determine the duration of treatment needed to attain a sustained virologic response, but with direct‐acting anti‐virals (DAA), this rate has been difficult to estimate. Here, we show that it is possible to estimate it, by simultaneously analysing the viral load and alanine aminotransferase (ALT) kinetics during combination DAA therapy. We modelled the HCV RNA and ALT serum kinetics in 26 patients with chronic HCV genotype 1b infection, under four different sofosbuvir‐based combination treatments. In all patients, ALT decayed exponentially to a set point in the normal range by 1‐3 weeks after initiation of therapy. The model indicates that the ALT decay rate during the first few weeks after initiation of therapy reflects the death rate of infected cells, with an estimated median half‐life of 2.5 days in this patient population. This information allows independent estimation of the rate of loss of intracellular replication complexes during therapy. Our model also predicts that the final ALT set point is not related to the release of ALT by dying HCV‐infected cells. Using ALT data, one can separately obtain information about the rate of ‘cure’ of HCV‐infected cells versus their rate of death, something not possible when analysing only HCV RNA data. This information can be used to compare the effects of different DAA combinations and to rationally evaluate their anti‐viral effects.

Energy Requirements for Loss of Viral Infectivity

“… 2017 ; Cardozo et al. 2020 , 2017 ). Thus a better understanding of the mechanisms underlying loss of infectivity for different viruses is important for developing accurate viral kinetics models.…”

“…Most viral kinetics models assume exponential decay of virus (Baccam et al 2006;Perelson et al 1996;González-Parra and Dobrovolny 2015;González-Parra et al 2018), but recent studies suggest that this might not be an accurate assumption for all viruses (Beauchemin et al 2019;Ailavadi et al 2019;Bozkurt et al 2015). Incorrect modeling of the mechanism of viral decay could lead to erroneous predictions of viral time courses, particularly when using models to estimate antiviral efficacy from viral decay rates (Palmer et al 2017;Cardozo et al 2020Cardozo et al , 2017. Thus a better understanding of the mechanisms underlying loss of infectivity for different viruses is important for developing accurate viral kinetics models.…”

Incorporating Intracellular Processes in Virus Dynamics Models

“…This extended model showed that the observed differences across drug classes in the initial HIV RNA decline arise from increased death of infected cells when drugs enter productively infected cells post-integration, while, prior to integration, the half-lives of infected cells are similar to those of uninfected cells [116]. Simplifying the picture partway, Cardozo et al [46] included pre-and post-integration compartments, and used them to quantify the efficacy of raltegravir, a great first step in determining the potency of integrase inhibitors in HIV infections.…”

“…As modern antiviral drugs that target specific stages of the life cycle of different viruses are being developed, models considering intracellular events are also needed. They are essential tools for investigating the impact of antiviral therapies on viral replication and clearance [39,46]. Furthermore, they can provide guidance on potential treatment approaches, such as timing, frequency, and duration.…”

Significant Unresolved Questions and Opportunities for Bioengineering in Understanding and Treating COVID-19 Disease Progression

“… 10 , 151 , 284 These approaches have their roots in control theory, in which a predictive mathematical system model is updated in real-time based on measurements, and interventions are chosen to optimize the predicted behavior of the system. 60 Mathematical models exist for many of the phenomena described above, including within-host viral dynamics, 30 , 31 , 173 circadian rhythms, 11 , 201 and cytokine storm dynamics, 79 , 265 which could easily be adapted to match COVID-19 infection behavior. Experiments will need to be designed to collect data for model tuning and validation (a process known as system identification in control theory).…”