Resistance Suppression for Influenza Virus With Combination Chemotherapy

联合化疗抑制流感病毒耐药性

• 批准号: 7640748
• 负责人: George Louis Drusano
• 金额: $ 65.14万
• 依托单位: ORDWAY RESEARCH INSTITUTE, INC.
• 依托单位国家: 美国
• 财政年份: 2008
• 资助国家: 美国
• 起止时间: 2008-07-01 至 2012-06-30
• 项目状态: 已结题
• 来源: https://reporter.nih.gov/project-details/7640748
• 关键词: Affect; Amantadine; Amantadine resistance; Anti-Bacterial Agents; Antifungal Agents; Antiviral Agents; Bacteria; Biological Models; Cells; Cessation of life; Characteristics; Child; Clinical; Collaborations; Combination Drug Therapy; Combined Modality Therapy; Data; Dose; Dose Fractionation; Drug Administration Schedule; Drug Combinations; Drug Exposure; Drug Kinetics; Drug resistance; Economics; Face; Fiber; Genomics; Goals; Human; In Vitro; Individual; Infection; Influenza; Influenza A Virus, H5N1 Subtype; Influenza A virus; Influenza Virus Infected Cells; Joint Dislocation; Laboratories; Link; Modeling; Morbidity - disease rate; Mutation; Neuraminidase inhibitor; Oseltamivir; Patients; Pharmaceutical Preparations; Pharmacologic Substance; Population; Prevention; Protocols documentation; Recombinants; Research; Research Institute; Research Project Grants; Resistance; Schedule; System; Time; Treatment Protocols; Vietnam; Viral; Virus; Virus Diseases; Virus Replication; Work; anti-influenza; carboxylate; chemotherapy; clinical application; design; fungus; influenza epidemic; influenzavirus; mortality; mutant; novel; pandemic disease; pandemic influenza; pathogen; pressure; prevent; research study; resistance mutation; resistant strain; treatment duration; viral resistance

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.

描述（由申请人提供）：H5N1 甲型流感病毒的出现敲响了警钟。由 H5N1 或其他甲型流感病毒引起的流感病毒全球大流行早就该发生了。这样的大流行可能会导致全世界大量死亡、重大发病和经济混乱。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，我们建议采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具有抗性的流感病毒克隆，并且赋予抗性的突变将与那些突变相同天然存在的菌株。一旦系统被验证为耐药菌株临床选择的良好替代品，我们就可以利用我们的 HFIM 来实现特定目标 #2，并确定这些药物作为单一疗法的最佳剂量和给药时间表，以优化病毒抑制并抑制抵抗的出现。这将通过剂量范围和剂量分割实验来完成。确定单独药物的耐药性抑制和病毒周转抑制的最佳剂量范围非常重要，因为药物之间的药理学差异可能会导致治疗期内某些时间的“药代动力学不匹配”。即使面对联合化疗，这种不匹配的时间也可能更容易出现耐药性。因此，对于任何组合中的每种药物来说，对于其自身的耐药性抑制来说，达到最佳或接近最佳的效果非常重要。在具体目标#3中，我们将追求优化药物组合以抑制耐药性。在这方面做得很少。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群体以及具有抗性突变的病毒亚群体的影响。正如我们实验室之前对细菌的研究表明，这些不同的病原体种群将受到药物压力组合的不同影响。我们的方法是根据特定目标 #2 的单一治疗实验中开发的数据来设计联合治疗实验。然后我们将使用十六种不同的药物剂量组合进行联合治疗实验。所有这些数据（两种药物随时间变化的药物浓度、随时间变化对总病毒群体的影响以及随时间变化对突变病毒群体的影响）将采用我们全新的数学群体混合模型同时进行联合建模。获得系统参数的稳健点估计将允许设计优化组合以抑制流感病毒耐药性的治疗方案。流感病毒的全球大流行早就该发生了，它可能会造成严重破坏，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。该应用的目标是 1) 证明我们的体外中空纤维系统产生耐药性流感病毒，该病毒反映了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药物的耐药性的最佳药物暴露金刚烷胺 金刚烷胺 3) 确定联合使用这些药物以防止流感病毒产生耐药性的最佳方法。