Evidence map›Paper›PMID 35153785›Full record

ArticleFrontiers in pharmacology2022

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

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

Open access · goldAbstract read
In one paragraph

Article in Frontiers in pharmacology, 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 5 papers.

0numbers the graph read from it
0cells of the map it votes in
5citing papers in PubMed
1.1field-weighted citation impact, top 24% of its field
1 · What the graph read from it

What it found

Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.

The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.

2 · The registry

The trial behind it

Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.

Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.

3 · Its place in the literature

Who cites it

5 citing papers in PubMed, 8 citations in OpenAlex.

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  4. Review
  5. Review
4 · The record

Corrections and comments

PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.

5 · Who and what money

Authors and funding

10 authors at 1 institution in 1 country.

Maiara Camotti MontanhaDepartment of Pharmacology and Therapeutics, Molecular and Integrative Biology, Institute of Systems, University of Liverpool, Liverpool, United Kingdom.
Nicolas CotturaDepartment of Pharmacology and Therapeutics, Molecular and Integrative Biology, Institute of Systems, University of Liverpool, Liverpool, United Kingdom.
Michael BoothDepartment of Pharmacology and Therapeutics, Molecular and Integrative Biology, Institute of Systems, University of Liverpool, Liverpool, United Kingdom.
Daryl HodgeDepartment of Pharmacology and Therapeutics, Molecular and Integrative Biology, Institute of Systems, University of Liverpool, Liverpool, United Kingdom.
Fazila BunglawalaDepartment of Pharmacology and Therapeutics, Molecular and Integrative Biology, Institute of Systems, University of Liverpool, Liverpool, United Kingdom.
Hannah KinvigDepartment of Pharmacology and Therapeutics, Molecular and Integrative Biology, Institute of Systems, University of Liverpool, Liverpool, United Kingdom.
Sandra Grañana-CastilloDepartment of Pharmacology and Therapeutics, Molecular and Integrative Biology, Institute of Systems, University of Liverpool, Liverpool, United Kingdom.
Andrew LloydDepartment of Pharmacology and Therapeutics, Molecular and Integrative Biology, Institute of Systems, University of Liverpool, Liverpool, United Kingdom.
Saye KhooDepartment of Pharmacology and Therapeutics, Molecular and Integrative Biology, Institute of Systems, University of Liverpool, Liverpool, United Kingdom.
Marco SiccardiDepartment of Pharmacology and Therapeutics, Molecular and Integrative Biology, Institute of Systems, University of Liverpool, Liverpool, United Kingdom.
University of Liverpool · GB

Funding

Medical Research Council MR/V020498/1
6 · The paper itself

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.

Indexed as

COVID-19dexamethasoneliver diseasePBPK modellingpharmacokinetic (PK)

Identifiers

PMID35153785
PMCPMC8832977
OpenAlexW4225647102

What OpenQuestion holds

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LicenceCC BY
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Registered trials

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Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.