Background A key research priority for developing an HIV cure strategy is to define the viral dynamics and biomarkers associated with sustained post-treatment control. The ability to predict the likelihood of sustained post-treatment control or non-control could minimize the time off antiretroviral therapy (ART) for those destined to not control and anticipate longer periods off ART for those destined to control. Methods Mathematical modeling and machine learning were used to characterize virologic predictors of long-term virologic control using viral kinetics data from several studies in which participants interrupted ART. Predictors of post-ART outcomes were characterized using data accumulated from the time of treatment interruption, replicating real-time data collection in a clinical study, and classifying outcomes as either post-treatment control (plasma viremia ≤400 copies/mL at 2 of 3 time points for ≥24 weeks) or non-control. Results Potential predictors of virologic control were the time to rebound, the rate of initial rebound, and the peak plasma viremia. We found that people destined to be non-controllers could be identified within 3 weeks of rebound (prediction scores: accuracy, 80%; sensitivity, 82%; specificity, 71%). Conclusions Given the widespread use of analytic treatment interruption in cure-related trials, these predictors may be useful to increase the safety of analytic treatment interruption through the early identification of people who are unlikely to become post-treatment controllers.

中文翻译：

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早期病毒动力学预测分析治疗中断后 HIV 治疗后控制


背景 制定 HIV 治愈策略的一个关键研究重点是确定与持续治疗后控制相关的病毒动力学和生物标志物。预测持续治疗后控制或非控制可能性的能力可以最大限度地减少那些注定无法控制的人的抗逆转录病毒治疗 （ART） 的停止时间，并预期那些注定要控制的人会更长时间地停止 ART。方法 使用数学建模和机器学习来表征长期病毒学控制的病毒学预测因子，使用来自参与者中断 ART 的几项研究的病毒动力学数据。使用从治疗中断时间积累的数据来表征 ART 后结果的预测因子，复制临床研究中的实时数据收集，并将结果分类为治疗后对照 （血浆病毒血症 ≤400 拷贝/mL，在 3 个时间点中的 2 个时间点 ≥24 周）或非对照。结果 病毒学控制的潜在预测因子是反弹时间、初始反弹率和血浆病毒血症峰值。我们发现，注定是非控制者的人可以在反弹后 3 周内被识别出来（预测评分：准确性，80%;敏感性，82%;特异性，71%）。结论 鉴于分析治疗中断在治愈相关试验中的广泛使用，这些预测因子可能有助于通过早期识别不太可能成为治疗后控制者的人来提高分析治疗中断的安全性。