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COVID-19合并肝病患者地塞米松的生理药代动力学建模

PBPK Modelling of Dexamethasone in Patients With COVID-19 and Liver Disease.

作者信息

Montanha Maiara Camotti, Cottura Nicolas, Booth Michael, Hodge Daryl, Bunglawala Fazila, Kinvig Hannah, Grañana-Castillo Sandra, Lloyd Andrew, Khoo Saye, Siccardi Marco

机构信息

Department of Pharmacology and Therapeutics, Molecular and Integrative Biology, Institute of Systems, University of Liverpool, Liverpool, United Kingdom.

出版信息

Front Pharmacol. 2022 Jan 28;13:814134. doi: 10.3389/fphar.2022.814134. eCollection 2022.

DOI:10.3389/fphar.2022.814134
PMID:35153785
原文链接:https://pmc.ncbi.nlm.nih.gov/articles/PMC8832977/
Abstract

The aim of the study was to apply Physiologically-Based Pharmacokinetic (PBPK) modelling to predict the effect of liver disease (LD) on the pharmacokinetics (PK) of dexamethasone (DEX) in the treatment of COVID-19. A whole-body PBPK model was created to simulate 100 adult individuals aged 18-60 years. Physiological changes (e.g., plasma protein concentration, liver size, CP450 expression, hepatic blood flow) and portal vein shunt were incorporated into the LD model. The changes were implemented by using the Child-Pugh (CP) classification system. DEX was qualified using clinical data in healthy adults for both oral (PO) and intravenous (IV) administrations and similarly propranolol (PRO) and midazolam (MDZ) were qualified with PO and IV clinical data in healthy and LD adults. The qualified model was subsequently used to simulate a 6 mg PO and 20 mg IV dose of DEX in patients with varying degrees of LD, with and without shunting. The PBPK model was successfully qualified across DEX, MDZ and PRO. In contrast to healthy adults, the simulated systemic clearance of DEX decreased (35%-60%) and the plasma concentrations increased (170%-400%) in patients with LD. Moreover, at higher doses of DEX, the AUC ratio between healthy/LD individuals remained comparable to lower doses. The exposure of DEX in different stages of LD was predicted through PBPK modelling, providing a rational framework to predict PK in complex clinical scenarios related to COVID-19. Model simulations suggest dose adjustments of DEX in LD patients are not necessary considering the low dose administered in the COVID-19 protocol.

摘要

本研究的目的是应用基于生理的药代动力学(PBPK)模型来预测肝脏疾病(LD)对治疗新冠肺炎时地塞米松(DEX)药代动力学(PK)的影响。创建了一个全身PBPK模型来模拟100名年龄在18至60岁的成年人。生理变化(如血浆蛋白浓度、肝脏大小、细胞色素P450表达、肝血流量)和门静脉分流被纳入LD模型。这些变化通过Child-Pugh(CP)分类系统来实现。DEX通过健康成年人口服(PO)和静脉注射(IV)给药的临床数据进行验证,同样,普萘洛尔(PRO)和咪达唑仑(MDZ)通过健康和LD成年人的PO和IV临床数据进行验证。随后,使用经过验证的模型来模拟不同程度LD患者(有或无分流)口服6 mg和静脉注射20 mg DEX的情况。PBPK模型在DEX、MDZ和PRO方面均成功验证。与健康成年人相比 LD患者中DEX的模拟全身清除率降低(35%-60%),血浆浓度升高(170%-400%)。此外,在较高剂量的DEX下,健康/LD个体之间的AUC比值与较低剂量时相当。通过PBPK模型预测了DEX在LD不同阶段的暴露情况,为预测与新冠肺炎相关的复杂临床场景中的PK提供了一个合理的框架。模型模拟表明,考虑到新冠肺炎方案中使用的低剂量,LD患者中DEX的剂量调整没有必要。

https://cdn.ncbi.nlm.nih.gov/pmc/blobs/1b67/8832977/3031445c51aa/fphar-13-814134-g002.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/1b67/8832977/b3318190d0c9/fphar-13-814134-g001.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/1b67/8832977/3031445c51aa/fphar-13-814134-g002.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/1b67/8832977/b3318190d0c9/fphar-13-814134-g001.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/1b67/8832977/3031445c51aa/fphar-13-814134-g002.jpg

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本文引用的文献

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Modeling Approach to Predict the Impact of Inflammation on the Pharmacokinetics of CYP2C19 and CYP3A4 Substrates.预测炎症对CYP2C19和CYP3A4底物药代动力学影响的建模方法
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