Division of Applied Regulatory Science, Office of Clinical Pharmacology, Office of Translational Sciences, Center for Drug Evaluation and Research, US Food and Drug Administration, Silver Spring, Maryland, USA.
Department of Mechanical Engineering, University of Maryland, College Park, Maryland, USA.
Clin Pharmacol Ther. 2022 Oct;112(4):882-891. doi: 10.1002/cpt.2686. Epub 2022 Jun 29.
With the ongoing global pandemic of coronavirus disease 2019 (COVID-19), there is an urgent need to accelerate the traditional drug development process. Many studies identified potential COVID-19 therapies based on promising nonclinical data. However, the poor translatability from nonclinical to clinical settings has led to failures of many of these drug candidates in the clinical phase. In this study, we propose a mechanism-based, quantitative framework to translate nonclinical findings to clinical outcome. Adopting a modularized approach, this framework includes an in silico disease model for COVID-19 (virus infection and human immune responses) and a pharmacological component for COVID-19 therapies. The disease model was able to reproduce important longitudinal clinical data for patients with mild and severe COVID-19, including viral titer, key immunological cytokines, antibody responses, and time courses of lymphopenia. Using remdesivir as a proof-of-concept example of model development for the pharmacological component, we developed a pharmacological model that describes the conversion of intravenously administered remdesivir as a prodrug to its active metabolite nucleoside triphosphate through intracellular metabolism and connected it to the COVID-19 disease model. After being calibrated with the placebo arm data, our model was independently and quantitatively able to predict the primary endpoint (time to recovery) of the remdesivir clinical study, Adaptive Covid-19 Clinical Trial (ACTT). Our work demonstrates the possibility of quantitatively predicting clinical outcome based on nonclinical data and mechanistic understanding of the disease and provides a modularized framework to aid in candidate drug selection and clinical trial design for COVID-19 therapeutics.
随着 2019 年冠状病毒病（COVID-19）的全球大流行，加速传统药物开发进程迫在眉睫。许多研究基于有前途的非临床数据确定了潜在的 COVID-19 疗法。然而，从非临床到临床环境的转化性差导致许多候选药物在临床阶段失败。在这项研究中，我们提出了一种基于机制的定量框架，将非临床发现转化为临床结果。该框架采用模块化方法，包括用于 COVID-19 的计算机疾病模型（病毒感染和人类免疫反应）和 COVID-19 疗法的药理学组件。该疾病模型能够复制 COVID-19 轻度和重度患者的重要纵向临床数据，包括病毒滴度、关键免疫细胞因子、抗体反应以及淋巴细胞减少的时间过程。我们使用瑞德西韦作为药理学组件模型开发的概念验证示例，开发了一种药理学模型，该模型描述了静脉内给予瑞德西韦作为前药通过细胞内代谢转化为其活性代谢物核苷三磷酸，并将其与 COVID-19 疾病模型连接。在与安慰剂组数据进行校准后，我们的模型能够独立且定量地预测瑞德西韦临床试验（ACTT）的主要终点（恢复时间）。我们的工作表明，基于非临床数据和对疾病的机制理解，定量预测临床结果是有可能的，并提供了一个模块化框架，以帮助候选药物选择和 COVID-19 治疗的临床试验设计。
