Introduction
The ongoing coronavirus pandemic has resulted in more than 7.4 million cases of infection and more than 418,000 deaths worldwide, including more than 115,000 deaths in the US alone as of 11-June-2020 [1,2]. The SARS-CoV-2 virus was subsequently isolated and the disease designated as COVID-19 [1,3,4].
According to the Siddiqui and Mehra typology of COVID-19 disease progression, three distinct phases are evident from the first stage being presented as mild phase and occurring immediately following infection and early disease [5]. During this phase, SARS-CoV-2 multiplies and engages with the host respiratory system. Stage 2 of the clinical progression involves moderate pulmonary involvement wherein infected subjects evidence early stages of viral pneumonia, with more pronounced cough and fever. Stage 3 is the severe form of infection where there is evidence of systemic hyperinflammation. Here, systemic inflammation markers are elevated. Patients progress to shock, vasoplegia, respiratory failure, and cardiopulmonary collapse. This is the phase with overall poor prognosis and recovery [5].
As of 11-June-2020, there were 1166 clinical interventional studies registered in clinicaltrials.gov with therapies targeting COVID-19. Due to the studies being on the pandemic frontlines, many of these studies are not appropriately designed randomized placebo-controlled clinical trials and often targeted patients with COVID-19 that are hospitalized and were diagnosed with severe form [6-9].
In many cases, exploration of treatment options for COVID-19 has been occurring without consideration of biological or pharmacological plausibility for the therapeutic to work, or the stage of infection the patient is in. We hypothesize that, considering not only the timing of intervention, but more importantly dose and schedule of these interventions, treatments given alone or in combination matched to the cell cycle of the virus and the purported windows of opportunity, will yield clinically significant reductions in viral loads and associated efficacy. This cell cycle dependency of treatment options forms the central premise of our investigations and is further conceptualized inFigure 1 .
There are various population level viral cell cycle models available in the literature. These vary by the complexity of the models, ranging from the most parsimonious target cell limited model to the most sophisticated yet complex multi-scale models that describe virus-host interactions [10-12]. These models were used to characterize a variety of viruses including HIV, HCV, and Influenza A. We believe that viral cell cycle in basic terms would be adequate to test the impact of the currently envisaged antiviral armamentarium. However, as models evolve, a fuller quantitative and systems pharmacology (QSP) model might provide a scaffold for the generation of testable hypotheses incorporating interventions impacting downstream host-inflammatory pathways. We consider our efforts as a parsimonious first module to inform and inspire more comprehensive QSP strategies, not only for COVID-19 but for emerging viruses in general.
In simple terms, the target cell-limited model integrates four entities: uninfected susceptible epithelial target cells (T ), latently infected cells (I1 ), productively infected cells (I2 ), and the virus load (V ) and is described by a system of nonlinear ordinary differential equations [13]. Given the timescale of the infection, we neglect target cell proliferation and natural death, and focus on the process of epithelial cell depletion (T ) by virus infection. When a virus interacts with an uninfected target cell at a defined infection rate, β, then the target cells will become infected (I1 ) and remain so during an incubation period. These cells, in turn, convert to productively infected cells (I2 ) at a rate,k . These cells then produce new virions (V ) with a defined production rate, ρ. Simultaneously, productively infected cells die at a certain rate, δ. Circulating virions are then cleared at a certain rate, c, from the body or go on to infect new cells as above. Based on the dynamics of the cell model and the associated mechanisms of actions of the currently experimented drugs for SARS-CoV-2 infection, we classify treatments to potentially affect one or more of the five different distinct check points in this model: β, k, ρ, δ, c (Figure 1 ). We describe a model-informed analytical framework that yields predictions on the most viable combinations of drugs matched by phase of clinical progression.