Resistance Suppression for Influenza Virus With Combination Chemotherapy

描述(申请人提供)：H5N1甲型流感病毒的出现是一个重要的警钟。我们早就应该看到由H5N1或其他甲型流感病毒引起的流感病毒的全球大流行。这种大流行可能在全世界造成大量死亡，并造成严重的发病率和经济混乱。重要的是要认识到，针对这种大流行病毒的最佳化疗对于降低随之而来的死亡率和发病率至关重要。在具体目标#1中，我们建议使用我们的新型中空纤维感染模型(HFIM)来证明我们可以快速选择对金刚烷胺或神经氨酸酶抑制剂耐药的流感病毒克隆，并且导致耐药性的突变将与自然发生的毒株相同。一旦该系统被验证为是临床选择耐药菌株的良好替代品，我们就可以利用我们的HFIM来实现特定的目标2，并确定这些药物作为单一疗法给予的最佳剂量和给药时间表，以优化病毒抑制和抑制耐药的出现。这将通过剂量范围和剂量分级实验来实现。很重要的一点是，单独确定抑制耐药性和抑制病毒周转的最佳剂量范围，因为药物之间的药理差异可能会导致在治疗期内的某些时间出现“药代动力学不匹配”。即使在联合化疗的情况下，这种不匹配的时间可能更容易出现耐药。因此，重要的是，任何组合中的每一种药物本身都是抑制耐药性的最佳或接近最佳的药物。在具体目标3中，我们将寻求优化药物组合以抑制耐药。在这方面几乎没有做过什么。我们开发了一种混合模型方法，可以同时描述这些抗病毒化合物组合对完全野生型病毒种群以及具有耐药性突变的病毒亚群的影响。正如我们实验室以前对细菌所做的工作所表明的那样，这些不同的病原体群体将受到药物压力的不同影响。我们的方法将是根据在特定目标2的单一治疗实验中开发的数据来设计联合治疗实验。然后，我们将进行16种不同药物剂量组合的联合治疗实验。所有这些数据(两种药物随时间的药物浓度，随时间对总病毒种群的影响，以及随时间对突变病毒种群的影响)将同时使用我们全新的数学种群混合模型进行联合建模。获得系统参数的稳健点估计将允许设计在抑制流感病毒耐药性的组合中进行优化的方案。我们早就应该迎来一场流感病毒的全球大流行，它可能造成严重破坏，造成相当大的死亡率、发病率和经济混乱。抗流感化疗对于保护我们免受这种大流行的影响至关重要。这项应用的目标是：1)证明我们的体外中空纤维系统产生抗药性流感病毒，这些抗药性病毒反映了在给予次优药物暴露时的临床情况；2)确定抑制流感病毒对神经氨酸酶抑制剂和金刚烷胺的抗药性的最佳药物暴露；3)确定联合使用这些药物以防止流感病毒出现抗药性的最佳方法。

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.