Resistance Suppression for Influenza Virus With Combination Chemotherapy

联合化疗抑制流感病毒耐药性

• 批准号: 8097991
• 负责人: George Louis Drusano
• 金额: $ 55.84万
• 依托单位: UNIVERSITY OF FLORIDA
• 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 2013-12-31
• 项目状态: 已结题
• 来源: https://reporter.nih.gov/project-details/8097991
• 关键词: Adamantane; 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; Dislocations; Dose; Dose Fractionation; Drug Administration Schedule; Drug Combinations; Drug Exposure; Drug Kinetics; Drug resistance; Economics; Fiber; Genomics; Goals; Human; In Vitro; Individual; Infection; Influenza; Influenza A Virus, H5N1 Subtype; Influenza A virus; Influenza Virus Infected Cells; Laboratories; Link; Modeling; Morbidity - disease rate; Mutation; Neuraminidase inhibitor; Oseltamivir; Patients; Pharmaceutical Preparations; Pharmacologic Substance; Population; Prevention; Protocols documentation; Recombinants; Regimen; Research; Research Institute; Research Project Grants; Resistance; Schedule; System; Time; 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流感的出现病毒是一个关键的唤醒呼叫。我们为由H5N1或其他一些流感病毒引起的流感病毒全球流行病而逾期。这样的大流行可能会在全球造成大量死亡，重大发病率和经济破坏。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。在特定的目标＃1中，我们建议采用新型的空心纤维感染模型（HFIM），以证明我们可以迅速选择对金胺碱或神经氨基酶抑制剂具有耐药性的流感流感病毒克隆，并且赋予耐药性的突变将与自然发生的菌株相同。一旦对系统进行了验证，它是抗性分离株的临床选择的良好替代物，我们就可以利用HFIM追求特定的目标＃2，并确定这些作为单一疗法的药物的最佳剂量和给药时间表来优化病毒抑制并抑制抵抗力的出现。这将通过剂量范围和剂量分馏实验来实现。重要的是要鉴定仅针对药物抑制抗性抑制和病毒周转抑制的最佳剂量范围，因为在治疗期内的某些时候，药物学之间的药理差异可能会允许“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允许抵抗出现。因此，对于任何组合，对于每种药物来说，都是最佳或近乎最佳的抗性抑制作用。在特定的目标＃3中，我们将追求优化药物以抑制抑制。在这方面几乎没有完成。我们已经开发了一种混合模型方法，该方法将允许同时描述这些抗病毒化合物对完全野生型病毒种群以及具有抗性突变的病毒亚群的结合。正如我们对细菌实验室的先前工作所表明的那样，这些不同的病原体种群将受到混合压力的差异影响。我们的方法是从特定目标2的单一疗法实验中开发的数据设计组合疗法实验。然后，我们将使用16种不同的药物剂量组合进行组合疗法实验。所有这些数据（两种药物的药物浓度，随着时间的推移对总病毒群体的影响以及对突变病毒种群随时间的影响的影响）将同时使用我们完全新颖的数学种群混合模型共同建模。获得系统参数的可靠点估计值将允许设计在抑制流感病毒抗性的组合中已优化的方案。我们为全球流感病毒大流行而逾期逾期，可能造成严重破坏，导致相当大的死亡率，发病率和经济脱位。抗炎性化疗对于保护自己免受这种大流行至关重要。该应用的目标是：1）证明我们的体外空心纤维系统会产生抗性流感病毒，反映临床情况时，当次级药物暴露时2）确定最佳的药物暴露，以抑制流感病毒的耐药性，从而抑制了对神经酶抑制剂的抗药性和障碍者的最佳影响，以防止这些抗肿瘤的影响。 抵抗的。