分析COVID-19患者体内Nirmatrelvir进入肺内衬液的情况:探索和理解抗病毒活性目标血浆浓度 292 纳克/毫升的独特视角

IF 3.1 4区 医学 Q3 IMMUNOLOGY
Wenjing Zhang, Lin Xia, Zhilong Yuan, Yang Jiao, Zhuo Wang
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引用次数: 0

摘要

背景:目前,申请人已为 nirmatrelvir 选择了目标血浆谷浓度,并根据该药物的分子量(499.54 道尔顿)、与人体血浆蛋白的结合率(69%)和体外抗病毒 EC90 值(181 nM)将其调整为 292 ng/mL。然而,目前对 EPIC-HR 研究入组患者病毒载量的暴露-效应关系(ER)分析并未显示出具有临床意义的 ER 趋势。鉴于肺部是 COVID-19 感染的主要部位,我们希望通过探索和分析 nirmatrelvir 在肺部的渗透特性来进一步了解这种暴露关系:探索并理解292纳克/毫升的血浆目标浓度在抗病毒活性中的作用:我们确定了2023年1月至2023年10月期间在长海医院呼吸重症监护室接受过尼马瑞韦/利托那韦治疗的所有2019年冠状病毒病重症患者。研究人员在服用尼尔马特韦(NMV)后,以用药前的谷值浓度采集血浆和支气管肺泡灌洗液样本。通过测定 NMV 的浓度,评估了血浆中 NMV 水平与上皮内衬液 (ELF) 之间的关系:血浆中的 NMV 水平与 ELF 之间存在明显关系(ELF = 0.4976*PLASMA- 204;R = 0.96),其斜率(0.4976)表明血液与 ELF 的药物渗透比为 2:1。等式中的负值表示 NMV 需要达到基线浓度才能穿透 ELF:血浆中的 NMV 水平与 ELF 之间的关系、低渗透性和负基线值表明,292 纳克/毫升的目标血浆浓度不足以产生抗病毒活性。这项研究提供了一个独特的视角,以探索和了解 EPIC-HR 登记患者中无临床意义的暴露-反应关系趋势。
本文章由计算机程序翻译,如有差异,请以英文原文为准。

Analysis of Nirmatrelvir Entry into Pulmonary Lining Fluid in Patients with COVID-19: A Unique Perspective to Explore and Understand the Target Plasma Concentration of 292 ng/mL in Antiviral Activity

Analysis of Nirmatrelvir Entry into Pulmonary Lining Fluid in Patients with COVID-19: A Unique Perspective to Explore and Understand the Target Plasma Concentration of 292 ng/mL in Antiviral Activity

Background

Currently, the applicant has chosen a target plasma trough concentration for nirmatrelvir, which is adjusted to 292 ng/mL based on the drug's molecular weight (499.54 Daltons), its binding to human plasma proteins (69%), and the in vitro antiviral EC90 value (181 nM). However, the current exposure-effect relationships (ER) analysis of viral load in patients enrolled in the EPIC-HR study has not revealed clinically significant trends in the ER. Given that the lungs are the primary site of COVID-19 infection, we aim to further understand this exposure relationship by exploring and analyzing the penetration characteristics of nirmatrelvir in the lungs.

Objectives

To explore and understand the target plasma concentration of 292 ng/mL in antiviral activity.

Methods

We identified all critically ill patients with coronavirus disease 2019 who received nirmatrelvir/ritonavir treatment in the respiratory intensive care unit of Changhai hospital between January 2023 and October 2023. Samples of plasma and bronchoalveolar lavage fluid were obtained at pre-dose trough concentrations after administration of nirmatrelvir (NMV). The relationship between NMV levels in plasma and the epithelial lining fluid (ELF) was assessed by determining concentrations of NMV.

Results

There was a significant relationship between NMV levels in plasma and ELF (ELF = 0.4976*PLASMA- 204; R = 0.96), with a correlation whose slope (0.4976) suggested that the blood-to-ELF ratio of drug penetration was 2:1. A negative value from the equation indicates that NMV requires to reach baseline concentration to penetrate the ELF.

Conclusions

The relationship between NMV levels in plasma and ELF with low permeability and a negative baseline value suggests that the target plasma concentration of 292 ng/mL is insufficient for antiviral activity. This study provides a unique perspective to explore and understand no clinically meaningful trend of exposure-response relationships in patients enrolled in EPIC-HR.

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来源期刊
Immunity, Inflammation and Disease
Immunity, Inflammation and Disease Medicine-Immunology and Allergy
CiteScore
3.60
自引率
0.00%
发文量
146
审稿时长
8 weeks
期刊介绍: Immunity, Inflammation and Disease is a peer-reviewed, open access, interdisciplinary journal providing rapid publication of research across the broad field of immunology. Immunity, Inflammation and Disease gives rapid consideration to papers in all areas of clinical and basic research. The journal is indexed in Medline and the Science Citation Index Expanded (part of Web of Science), among others. It welcomes original work that enhances the understanding of immunology in areas including: • cellular and molecular immunology • clinical immunology • allergy • immunochemistry • immunogenetics • immune signalling • immune development • imaging • mathematical modelling • autoimmunity • transplantation immunology • cancer immunology
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