How Can Viral Dynamics Models Inform Endpoint Measures in Clinical Trials of Therapies for Acute Viral Infections?

Acute viral infections pose many practical challenges for the accurate assessment of the impact of novel therapies on viral growth and decay. Using the example of influenza A, we illustrate how the measurement of infection-related quantities that determine the dynamics of viral load within the human host, can inform investigators on the course and severity of infection and the efficacy of a novel treatment. We estimated the values of key infection-related quantities that determine the course of natural infection from viral load data, using Markov Chain Monte Carlo methods. The data were placebo group viral load measurements collected during volunteer challenge studies, conducted by Roche, as part of the oseltamivir trials. We calculated the values of the quantities for each patient and the correlations between the quantities, symptom severity and body temperature. The greatest variation among individuals occurred in the viral load peak and area under the viral load curve. Total symptom severity correlated positively with the basic reproductive number. The most sensitive endpoint for therapeutic trials with the goal to cure patients is the duration of infection. We suggest laboratory experiments to obtain more precise estimates of virological quantities that can supplement clinical endpoint measurements.

急性病毒感染对精准评估新型疗法对病毒增殖与清除的影响带来了诸多实际挑战。以甲型流感（influenza A）为例，我们阐释了如何通过测定决定人体宿主内病毒载量动态变化的感染相关量化指标，为研究者提供感染进程、严重程度以及新型疗法疗效的相关信息。我们采用马尔可夫链蒙特卡洛（Markov Chain Monte Carlo）方法，基于病毒载量数据估算了决定自然感染进程的关键感染相关量化指标数值。本次研究所用数据为罗氏（Roche）开展的奥司他韦（oseltamivir）临床试验中，志愿者攻毒研究阶段收集的安慰剂组病毒载量检测结果。我们针对每位患者计算了各量化指标的数值，并分析了指标间、症状严重程度与体温之间的相关性。个体间差异最大的指标为病毒载量峰值与病毒载量曲线下面积。症状总严重程度与基本再生数（basic reproductive number）呈正相关。以治愈患者为目标的治疗试验中，最灵敏的终点指标为感染持续时长。我们建议开展实验室实验，以获取更精准的病毒学量化指标估算值，从而辅助临床终点指标的测定。