Speaker
Description
The use of antiviral drugs to treat SARS-CoV-2 infection has been widely extended and several antiviral treatments have been tested, involving different modes of action (MOAs). These drugs may be either newly developed treatments or repurposed drugs and, though a positive effect may be observed from the application of these treatments, it may not always be clear what is the driving mechanism underneath. Moreover, testing these drugs involves great amounts of human, animal and material resources that limit the obtained results. The use of mathematical models is imperative to reduce these costs, to assess possible strategies of application of the treatments, and to uncover unknown behaviors.
One of the most widely used SARS-CoV-2 antiviral treatments is Paxlovid (nirmatrelvir/ritonavir), which is known to reduce viral replication. Here we present a methodology to validate this behavior and to find other possible hidden modes of action of the treatment. We consider a classical TIV model and clinical data from patients infected with the Omicron variant, both patients treated with Paxlovid and untreated patients. Given these data, we explore what parameters can capture the effect of the treatment and, hence, potential hidden modes of action. Furthermore, taking into account that there is some delay between administration of a drug and its effect, we also explore what this delay is when Paxlovid is administered to treat the infection with Omicron.
