Population dynamics and evolution of drug resistance

描述(由申请人提供)：我建议进行数学和 HIV-1耐药性动态和演变的体外研究。这个 提案包括理论和实验项目，这些项目由 使用解释HIV-1重要特征的机制模型 病毒的生命周期。该提案的第一个目标是使用数学模型 评估将耐药病毒的选择降至最低的策略 活着。使用这些模型，我希望为 初次感染患者和患者的最佳治疗时间 接受有组织的治疗中断。第二个主要目标将是 改进HIV-1与HIV-1相互作用动力学的数学模型 体外靶细胞。这些模型将与以前的努力有所不同 因为它们将包括对病毒生命史参数的明确估计 并将在体外接受严格的测试。然后，这些模型将被 用来预测耐药与药物竞争的结果 体外敏感菌株，模型体外类似物的试验预测 并量化导致差异的生活史特征 病毒分离株在体外的复制能力。我也希望向 长期暴露在药物选择压力下的病毒群体问题 时间段会聚在同一自适应峰值上。我还将向 我们是否可以将病毒适应性的降低与病毒的变化联系起来？ 特定的动力学参数和病毒动力学的数学模型 可以解释为什么体外耐药性并不总是预测体内的耐药性。 对这些问题的回答可能会导致改进治疗策略 继续服用抗逆转录病毒药物的患者 抗药性。

DESCRIPTION (Provided by the applicant): I propose to conduct mathematical and in vitro studies of the dynamics and evolution of drug resistance in HIV-1. The proposal includes theoretical and experimental projects that are united by the use of mechanistic models that account for important features of the HIV-1 viral life cycle. The first goal of the proposal is to use mathematical models to evaluate strategies for minimizing selection for drug resistant virus in vivo. Using these models, I hope to generate theoretical predictions for the optimal time of treatment in primary infection patients and in patients undergoing structured treatment interruptions. The second major goal will be to improve mathematical models for the dynamics of interactions between HIV-1 and target cells in vitro. These models will be distinguished from previous efforts in that they will include explicit estimates for viral life history parameters and will be subjected to rigorous testing in vitro. These models will then be used to predict the results of competition between drug resistant and drug sensitive strains in vitro, test predictions of in vitro analogs of the models presented in part 1 and quantify life-history traits that underlie differences in the replicative capacity of viral isolates in vitro. I also hope to address the question of viral populations exposed to drug selection pressures for long periods of time converge on the same adaptive peak. I will also address the question of whether we can we relate reductions in viral fitness to changes in specific kinetic parameters and whether mathematical modeling of viral dynamics can explain why resistance in vitro does not always predict resistance in vivo. Answers to these questions may lead to improved treatment strategies for patients who continue to take antiretroviral drugs following the evolution of drug resistance.