Quantitative systems pharmacology (QSP) has been an important tool to project safety and efficacy of novel or repurposed therapies for the SARS‐CoV‐2 virus. Here, we present a QSP modeling framework to predict response to antiviral therapeutics with three mechanisms of action (MoA): cell entry inhibitors, anti‐replicatives, and neutralizing biologics. We parameterized three distinct model structures describing virus‐host interaction by fitting to published viral kinetics data of untreated COVID‐19 patients. The models were used to test theoretical behaviors and map therapeutic design criteria of the different MoAs, identifying the most rapid and robust antiviral activity from neutralizing biologic and anti‐replicative MoAs. We found good agreement between model predictions and clinical viral load reduction observed with anti‐replicative nirmatrelvir/ritonavir (Paxlovid®) and neutralizing biologics bamlanivimab and casirivimab/imdevimab (REGEN‐COV®), building confidence in the modeling framework to inform a dose selection. Finally, the model was applied to predict antiviral response with ensovibep, a novel DARPin therapeutic designed as a neutralizing biologic. We developed a new in silico measure of antiviral activity, area under the curve (AUC) of free spike protein concentration, as a metric with larger dynamic range than viral load reduction. By benchmarking to bamlanivimab predictions, we justified dose levels of 75, 225, and 600 mg ensovibep to be administered intravenously in a Phase 2 clinical investigation. Upon trial completion, we found model predictions to be in good agreement with the observed patient data. These results demonstrate the utility of this modeling framework to guide the development of novel antiviral therapeutics.

中文翻译：

---

SARS-CoV-2 感染的病毒动力学模型为药物发现、临床剂量和方案选择提供信息

定量系统药理学 (QSP) 一直是预测 SARS-CoV-2 病毒新型或重新用途疗法的安全性和有效性的重要工具。在这里，我们提出了一个 QSP 建模框架，用于预测对具有三种作用机制 (MoA) 的抗病毒治疗的反应：细胞进入抑制剂、抗复制剂和中和生物制剂。我们通过拟合已发表的未经治疗的 COVID-19 患者的病毒动力学数据，参数化了描述病毒与宿主相互作用的三种不同模型结构。这些模型用于测试理论行为并绘制不同 MoA 的治疗设计标准，从中和生物和抗复制 MoA 中识别出最快速、最强大的抗病毒活性。我们发现模型预测与抗复制尼马瑞韦/利托那韦 (Paxlovid®) 和中和生物制剂 bamlanivimab 和 casirivimab/imdevimab (REGEN-COV®) 观察到的临床病毒载量减少之间具有良好的一致性，从而建立了对模型框架的信心，以指导剂量选择。最后，该模型用于预测 ensovibep 的抗病毒反应，ensovibep 是一种新型 DARPin 治疗剂，设计为中和生物制剂。我们开发了一种新的计算机模拟抗病毒活性的测量，游离刺突蛋白浓度的曲线下面积（AUC），作为比病毒载量减少具有更大动态范围的指标。通过对 bamlanivimab 预测进行基准测试，我们证明在 2 期临床研究中静脉注射 ensovibep 的剂量水平为 75、225 和 600 mg。试验完成后，我们发现模型预测与观察到的患者数据非常一致。这些结果证明了该模型框架在指导新型抗病毒疗法的开发方面的实用性。