Snedecor Sonya J
Department of Biostatistics, University of California, P.O. Box 951772, Los Angeles, CA 90095-1772, USA.
Bull Math Biol. 2005 Nov;67(6):1315-32. doi: 10.1016/j.bulm.2005.02.004.
Recent advances in the chemotherapy of HIV infection have been successful in delaying the progression of disease in many patients and are responsible for the decline in HIV-related deaths in the United States. Yet, there are many patients who fail to maintain suppressed viral loads on treatment. Means to extend the utility of currently available drugs include developing improved ways to assess the therapeutic impact of drug-resistant variants. A mathematical model to incorporate the presence of resistance mutations with either primary or secondary classifications is created as a means to explore the association between phenotypic resistance and duration of viral response to therapy. The model, which includes phenotypic and genotypic resistance information for each viral mutant, is presented here with a simplified five-codon genome. However, as additional experimental data and computational resources become available future users may adapt the model to be larger and more accurate. Secondary analyses suggest that, in this model, the resistance phenotypes of the strains with an intermediate number of mutations are the primary determinants of both the total duration of viral suppression with a single treatment and the difference between the durations of suppression of the forward and reverse sequential administrations of two treatments. These findings imply that a model including the resistance phenotype and in vivo response of genotypically-resistant viral strains may lead to a priori prediction of successful anti-HIV drug selection for an individual harboring drug-resistant virus.
人类免疫缺陷病毒（HIV）感染化疗方面的最新进展已成功延缓了许多患者疾病的进展，并导致美国与HIV相关的死亡人数下降。然而，仍有许多患者在治疗过程中无法维持病毒载量被抑制的状态。扩展现有药物效用的方法包括开发更好的方式来评估耐药变异体的治疗影响。构建一个将耐药突变的存在纳入一级或二级分类的数学模型，以此来探索表型耐药与病毒对治疗反应持续时间之间的关联。该模型包含每个病毒突变体的表型和基因型耐药信息，这里以一个简化的五密码子基因组呈现。然而，随着更多实验数据和计算资源的可得，未来的用户可以使该模型变得更大且更准确。二次分析表明，在这个模型中，具有中等突变数量的毒株的耐药表型是单次治疗中病毒抑制总持续时间以及两种治疗的正向和反向顺序给药抑制持续时间差异的主要决定因素。这些发现意味着，一个包含耐药表型和基因型耐药病毒株体内反应的模型可能会为先验预测携带耐药病毒个体成功选择抗HIV药物提供帮助。
