The HIV Resistance Response Database Initiative (RDI), London, UK.
Instituto Ramón y Cajal de Investigación Sanitaria, Madrid, Spain.
J Antimicrob Chemother. 2018 Aug 1;73(8):2186-2196. doi: 10.1093/jac/dky179.
Optimizing antiretroviral drug combination on an individual basis can be challenging, particularly in settings with limited access to drugs and genotypic resistance testing. Here we describe our latest computational models to predict treatment responses, with or without a genotype, and compare their predictive accuracy with that of genotyping.
Random forest models were trained to predict the probability of virological response to a new therapy introduced following virological failure using up to 50 000 treatment change episodes (TCEs) without a genotype and 18 000 TCEs including genotypes. Independent data sets were used to evaluate the models. This study tested the effects on model accuracy of relaxing the baseline data timing windows, the use of a new filter to exclude probable non-adherent cases and the addition of maraviroc, tipranavir and elvitegravir to the system.
The no-genotype models achieved area under the receiver operator characteristic curve (AUC) values of 0.82 and 0.81 using the standard and relaxed baseline data windows, respectively. The genotype models achieved AUC values of 0.86 with the new non-adherence filter and 0.84 without. Both sets of models were significantly more accurate than genotyping with rules-based interpretation, which achieved AUC values of only 0.55-0.63, and were marginally more accurate than previous models. The models were able to identify alternative regimens that were predicted to be effective for the vast majority of cases in which the new regimen prescribed in the clinic failed.
These latest global models predict treatment responses accurately even without a genotype and have the potential to help optimize therapy, particularly in resource-limited settings.
在药物和基因型耐药性检测有限的情况下，针对个体优化抗逆转录病毒药物组合可能颇具挑战性。本研究描述了我们最新的计算模型，以预测有无基因型时的治疗反应，并比较其预测准确性与基因型检测的准确性。
采用随机森林模型，利用 50000 例无基因型的治疗转换事件（TCE）和 18000 例包含基因型的 TCE，训练预测病毒学失败后新治疗方案的病毒学应答概率。采用独立数据集评估模型。本研究检验了放宽基线数据时间窗、使用新的过滤器排除可能不依从的病例以及在系统中添加马拉维若、替诺福韦和艾维雷格韦对模型准确性的影响。
标准和放宽基线数据窗的无基因型模型的受试者工作特征曲线下面积（AUC）分别为 0.82 和 0.81。新非依从性过滤器的基因型模型 AUC 值为 0.86，无过滤器的 AUC 值为 0.84。与基于规则解释的基因型检测相比，这两组模型均显著更准确（基因型检测 AUC 值为 0.55-0.63），且略优于之前的模型。这些模型能够识别替代方案，对于新方案在临床上失败的绝大多数病例，预测这些替代方案可能有效。
即使没有基因型，这些最新的全球模型也能准确预测治疗反应，有可能帮助优化治疗，特别是在资源有限的环境中。
