Resistance Suppression for Influenza Virus With Combination Chemotherapy

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DESCRIPTION (provided by applicant): The advent of H5N1 influenza A Virus is a critical wake up call. We are overdue for a global pandemic of Influenza Virus caused by H5N1 or some other influenza A virus. Such a pandemic could cause a very large number of deaths worldwide and major morbidity and economic disruption. It is important to recognize that optimal chemotherapy directed at such a pandemic virus is critical to reduce the attendant mortality and morbidity. In Specific Aim #1, we propose to employ our novel hollow fiber infection model (HFIM) to demonstrate that we can rapidly select Influenza Virus clones that are resistant to either adamantine or neuraminidase inhibitors and that the mutations conferring resistance will be the same as those of naturally- occurring strains. Once the system is validated that it is a good surrogate for the clinical selection of resistant isolates, we can employ our HFIM to pursue Specific Aim #2, and identify the optimal dose and schedule of administration of these agents given as monotherapy to optimize viral suppression and suppress the emergence of resistance. This will be accomplished through dose ranging and dose fractionation experiments. It is important to identify optimal dose ranges for resistance suppression and viral turnover suppression for drugs alone, as pharmacological differences between agents may allow "pharmacokinetic mismatching" at certain times within the treatment period. Such mismatched times may be more permissive for resistance emergence, even in the face of combination chemotherapy. Therefore, it is important for each drug in any combination to be optimal or near-optimal for resistance suppression on its own. In Specific Aim #3, we will pursue optimizing the drugs in combination for resistance suppression. Little has been done in this regard. We have developed a mixture model approach that will allow simultaneous description of the effect of these antiviral compounds in combination on both the fully wild-type viral population as well as the viral subpopulation with resistance mutations. As previous work from our laboratory with bacteria has shown, these different pathogen populations will be differentially affected by the drug pressure in combination. Our approach will be to design combination therapy experiments from data developed in the monotherapy experiments of Specific Aim #2. We will then perform combination therapy experiments with sixteen different combinations of drug doses. All these data (drug concentrations over time for both drugs, the effect on the total viral population over time, and the effect on the mutant viral population over time) will be simultaneously co-modeled employing our completely novel mathematical population mixture model. Obtaining robust point estimates of system parameters will allow design of regimens that are optimized in the combination for Influenza Virus resistance suppression. We are well overdue for a global pandemic of Influenza virus that could wreak havoc, causing considerable mortality, morbidity and economic dislocation. Anti-influenza chemotherapy is critical in protecting ourselves from such a pandemic. The goals of this application are to 1) demonstrate that our in vitro hollow fiber system produces resistant Influenza Virus that reflect the clinical circumstance when suboptimal drug exposures are given 2) identify optimal drug exposures that suppress resistance by Influenza Virus to a neuraminidase inhibitor and the adamantine amantadine 3) identify the best ways to use these agents in combination to prevent Influenza virus from emerging resistant.