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.