Modeling Favipiravir Antiviral Efficacy Against Emerging Viruses: From Animal Studies to Clinical Trials

Abstract

In 2014, our research network was involved in the evaluation of favipiravir, an anti-influenza polymerase inhibitor, against Ebola virus. In this review, we discuss how mathematical modeling was used, first to propose a relevant dosing regimen in humans, and then to optimize its antiviral efficacy in a nonhuman primate (NHP) model. The data collected in NHPs were finally used to develop a model of Ebola pathogenesis integrating the interactions among the virus, the innate and adaptive immune response, and the action of favipiravir. We conclude the review of this work by discussing how these results are of relevance for future human studies in the context of Ebola virus, but also for other emerging viral diseases for which no therapeutics are available.

Figure 1

Top: Pharmacokinetic model for favipiravir in non‐human primates; bottom: predicted drug concentrations at D7 for three levels of dosing regimens and comparisons with drug half‐maximal effective concentration for various emerging viruses. CCHFV, Crimea Congo haemorrhagic fever virus; EBOV, Ebola virus; JUNV, Junin virus; LAV, Lassa fever virus; MARV, Marburg virus; RVFV, Rift valley fever virus; YFV, Yellow fever virus. Reproduced from ref. 8.

Figure 2

Survival obtained in 26 nonhuman primates infected with 1,000 focus forming units Ebola virus and treated with ascending doses of favipiravir. Left: Study design (note that treatment is administered 2 days prior to infection). Right: Survival curves. Reproduced from ref. 47.

Figure 3

Model structures used to described acute viral infection dynamics (a) “Target cell limited” model of viral dynamics considers the interaction between free virions (V) and three cell populations, namely target cells (T), infected cells in the eclipse phase (I₁) and productively infected cells (I₂). In this model, polymerase inhibitor, such as favipiravir, act by blocking viral replication from infected cells with efficacy noted ϵ. (b) Extended model to account for the role of cytokines, in particular IFNα, that can decrease the cell susceptibility to infection, modeled by a compartment of refractory cells (R). (c) Integrated model of Ebola virus (EBOV) infection. In addition to compartments presented in Figure 3 , the model also considers IL6 and TNF. In parallel, the cytokine release increases the apoptosis of nonspecific CD8 T‐cells (E₁), giving room for EBOV‐specific T‐cells (E₂) to grow and increase the elimination rate of actively infected cells. Cytokine levels increase the instantaneous rate of death, h(t). Modified from ref. 69.

Figure 4

Effect of an antiviral treatment on viral dynamics according to the timing of treatment initiation (left: day 0, middle: day 3; right: day 5) and the level antiviral effectiveness. Top: Model including an innate immune response (Eq. 3); bottom: target cell limited model (Eq. 2) according to the timing of treatment initiation. The following parameter values were used to generate these curves: T ₀ = 10⁹ cell/mL; c = 22/day, p = 1,000 virions/cell/day; V ₀ = 10⁻⁴ virions/mL, δ = 2/day, ϕ = 0.3 mL/pg/day, d = 1/day, q = 100 pg/cell/day, R ₀ = 6, and θ = 200 pg/mL. In the target cell limited model, q was set to 0.

Figure 5

Model predictions (median and 95% prediction interval) for the compartments of the integrated model given in Figure 3c in animals left untreated (black) or treated with favipiravir 180 mg/kg BID. ⁶⁹ The dots are the individual data in each compartment. Reproduced from ref. 69.

Figure 6

Median survival rate of n = 1,000 in silico macaques simulated with the integrated model given in Figure 3c depending on the day of treatment initiation, for various treatment potency.

Figure 7

Viral dynamic predictions in Zika infections. Left: Prophylactic treatment assuming two different levels of drug half‐maximal effective concentration (EC₅₀); right: effect of delayed treatment initiations on Zika dynamics.