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Antiviral combinations for severe influenza.

ABSTRACT: Observational data suggest that the treatment of influenza infection with neuraminidase inhibitors decreases progression to more severe illness, especially when treatment is started soon after symptom onset. However, even early treatment might fail to prevent complications in some patients, particularly those infected with novel viruses such as the 2009 pandemic influenza A H1N1, avian influenza A H5N1 virus subtype, or the avian influenza A H7N9 virus subtype. Furthermore, treatment with one antiviral drug might promote the development of antiviral resistance, especially in immunocompromised hosts and critically ill patients. An obvious strategy to optimise antiviral therapy is to combine drugs with different modes of action. Because host immune responses to infection might also contribute to illness pathogenesis, improved outcomes might be gained from the combination of antiviral therapy with drugs that modulate the immune response in an infected individual. We review available data from preclinical and clinical studies of combination antiviral therapy and of combined antiviral-immunomodulator therapy for influenza. Early-stage data draw attention to several promising antiviral combinations with therapeutic potential in severe infections, but there remains a need to substantiate clinical benefit. Combination therapies with favourable experimental data need to be tested in carefully designed aclinical trials to assess their efficacy.

SUBMITTER: Dunning J

PROVIDER: S-EPMC7164787 | biostudies-literature | 2014 Dec

REPOSITORIES: biostudies-literature

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Antiviral combinations for severe influenza.

Dunning Jake J Baillie J Kenneth JK Cao Bin B Hayden Frederick G FG

The Lancet. Infectious diseases 20140908 12

Observational data suggest that the treatment of influenza infection with neuraminidase inhibitors decreases progression to more severe illness, especially when treatment is started soon after symptom onset. However, even early treatment might fail to prevent complications in some patients, particularly those infected with novel viruses such as the 2009 pandemic influenza A H1N1, avian influenza A H5N1 virus subtype, or the avian influenza A H7N9 virus subtype. Furthermore, treatment with one an ...[more]

PMID: 25213733

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Project description:The multiple circulating human influenza A virus subtypes coupled with the perpetual genomic mutations and segment reassortment events challenge the development of effective therapeutics. The capacity to drug most RNAs motivates the investigation on viral RNA targets. 123,060 segment sequences from 35,938 strains of the most prevalent subtypes also infecting humans-H1N1, 2009 pandemic H1N1, H3N2, H5N1 and H7N9, were used to identify 1,183 conserved RNA target sequences (≥15-mer) in the internal segments. 100% theoretical coverage in simultaneous heterosubtypic targeting is achieved by pairing specific sequences from the same segment ("Duals") or from two segments ("Doubles"); 1,662 Duals and 28,463 Doubles identified. By combining specific Duals and/or Doubles to form a target graph wherein an edge connecting two vertices (target sequences) represents a Dual or Double, it is possible to hedge against antiviral resistance besides maintaining 100% heterosubtypic coverage. To evaluate the hedging potential, we define the hedge-factor as the minimum number of resistant target sequences that will render the graph to become resistant i.e. eliminate all the edges therein; a target sequence or a graph is considered resistant when it cannot achieve 100% heterosubtypic coverage. In an n-vertices graph (n ≥ 3), the hedge-factor is maximal (= n- 1) when it is a complete graph i.e. every distinct pair in a graph is either a Dual or Double. Computational analyses uncover an extensive number of complete graphs of different sizes. Monte Carlo simulations show that the mutation counts and time elapsed for a target graph to become resistant increase with the hedge-factor. Incidentally, target sequences which were reported to reduce virus titre in experiments are included in our target graphs. The identity of target sequence pairs for heterosubtypic targeting and their combinations for hedging antiviral resistance are useful toolkits to construct target graphs for different therapeutic objectives.

Dataset Information

Antiviral combinations for severe influenza.

Publications

Antiviral combinations for severe influenza.

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