Arora Pankhuri, Dixit Narendra M
Department of Chemical Engineering, Indian Institute of Science, Bangalore, India.
PLoS Comput Biol. 2009 Mar;5(3):e1000305. doi: 10.1371/journal.pcbi.1000305. Epub 2009 Mar 13.
New antiretroviral drugs that offer large genetic barriers to resistance, such as the recently approved inhibitors of HIV-1 protease, tipranavir and darunavir, present promising weapons to avert the failure of current therapies for HIV infection. Optimal treatment strategies with the new drugs, however, are yet to be established. A key limitation is the poor understanding of the process by which HIV surmounts large genetic barriers to resistance. Extant models of HIV dynamics are predicated on the predominance of deterministic forces underlying the emergence of resistant genomes. In contrast, stochastic forces may dominate, especially when the genetic barrier is large, and delay the emergence of resistant genomes. We develop a mathematical model of HIV dynamics under the influence of an antiretroviral drug to predict the waiting time for the emergence of genomes that carry the requisite mutations to overcome the genetic barrier of the drug. We apply our model to describe the development of resistance to tipranavir in in vitro serial passage experiments. Model predictions of the times of emergence of different mutant genomes with increasing resistance to tipranavir are in quantitative agreement with experiments, indicating that our model captures the dynamics of the development of resistance to antiretroviral drugs accurately. Further, model predictions provide insights into the influence of underlying evolutionary processes such as recombination on the development of resistance, and suggest guidelines for drug design: drugs that offer large genetic barriers to resistance with resistance sites tightly localized on the viral genome and exhibiting positive epistatic interactions maximally inhibit the emergence of resistant genomes.
新的抗逆转录病毒药物，如最近获批的HIV-1蛋白酶抑制剂替拉那韦和达芦那韦，对耐药性具有较大的遗传屏障，为避免目前的HIV感染治疗失败提供了有前景的武器。然而，新药的最佳治疗策略尚未确立。一个关键限制是对HIV克服较大遗传耐药屏障过程的了解不足。现有的HIV动态模型基于耐药基因组出现背后确定性力量的主导地位。相比之下，随机力量可能起主导作用，尤其是当遗传屏障较大时，并会延迟耐药基因组的出现。我们建立了一个在抗逆转录病毒药物影响下的HIV动态数学模型，以预测携带必要突变以克服药物遗传屏障的基因组出现的等待时间。我们应用我们的模型来描述体外连续传代实验中对替拉那韦耐药性的发展。对替拉那韦耐药性不断增加的不同突变基因组出现时间的模型预测与实验在数量上一致，表明我们的模型准确地捕捉了抗逆转录病毒药物耐药性发展的动态。此外，模型预测为潜在进化过程（如重组）对耐药性发展的影响提供了见解，并为药物设计提出了指导原则：对耐药性具有较大遗传屏障、耐药位点紧密定位在病毒基因组上且表现出最大正上位性相互作用的药物能最大程度地抑制耐药基因组的出现。
