Elevation of Serum Cytokine Profiles and Liver Metabolomic Normalization in Early Convalescence of COVID-19 Patients

Lou, Yan; He, Xiaoying; Deng, Mingxia; Hu, Xiurong; Yang, Xi; Li, Lin; Hu, Ying; He, Lingjuan; Wang, Jiali; Zhang, Li; Zhao, Qiu; Lü, Xin; Qiu, Yunqing

doi:10.3389/fmed.2021.626633

Cited by 5 publications

(4 citation statements)

References 35 publications

(45 reference statements)

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“…It is assumed that CRP production is exclusively stimulated by IL‐6 per Liu et al 30 A constant increased production of TNFα and IL‐6 was assumed as described by zero‐order rate constants with degradation being first‐order leading to elevated steady‐state concentrations of these cytokines in COVID‐19. This assumption is in line with longitudinal monitoring indicating relatively constant concentrations of these cytokines in hospitalized patients with COVID‐19 18,31 . The PD part of the model is described by:

\begin{matrix} left \frac{dTNF}{normald t} goodbreak= k_{inTNF} \cdot ()(, 1 goodbreak- \frac{I_{maxDEX ()(, TNF)} \cdot C_{p}}{{IC}_{50 DEX ()(, TNF)} + C_{p}}) goodbreak- k_{outTNF} \cdot \\ left TNF; TNF ()(, 0) goodbreak= R_{0 TNF} \end{matrix}

\begin{matrix} left \frac{dIL 6}{normald t} goodbreak= k_{inIL 6} \cdot ()(, 1 goodbreak- \frac{I_{maxDEX ()(, IL 6)} \cdot C_{p}}{{IC}_{50 DEX ()(, IL 6)} + C_{p}}) goodbreak- k_{outIL 6} \cdot IL 6; \\ left IL 6 ()(, 0) goodbreak= R_{0 IL 6} \end{matrix}

\begin{matrix} left \frac{dCRP}{normald t} goodbreak= k_{inCRP} \cdot ()(, 1 goodbreak+ S_{IL 6 onCRP} \cdot IL 6) goodbreak- k_{outCRP} \cdot CRP; \\ left CRP ()(, 0) goodbreak= R_{0 CRP} \end{matrix}

where

k_{inTNF} <...

…”

Section: Methodsmentioning

confidence: 56%

“…This assumption is in line with longitudinal monitoring indicating relatively constant concentrations of these cytokines in hospitalized patients with COVID‐19. 18 , 31 The PD part of the model is described by:

where

, and

are zero‐order production rate constants and

, and

are first‐order disposition constants of the indicated mediators. The

and

reflect maximum inhibition of production by DEX and

and

are the concentrations resulting in 50% of the maximum inhibition of cytokine production.…”

Section: Methodsmentioning

confidence: 99%

“…This assumption is in line with longitudinal monitoring indicating relatively constant concentrations of these cytokines in hospitalized patients with COVID-19. 18,31 The PD part of the model is described by:…”

Section: Pd Model Of Dex Anti-inflammatory Effects In Patients With C...mentioning

confidence: 99%

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Anti‐inflammatory effects of dexamethasone in COVID‐19 patients: Translational population PK/PD modeling and simulation

Świerczek

Jusko

2023

Clinical Translational Sci

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Dexamethasone (DEX) given at a dose of 6 mg once‐daily for 10 days is a recommended dosing regimen in patients with coronavirus disease 2019 (COVID‐19) requiring oxygen therapy. We developed a population pharmacokinetic and pharmacodynamic (PopPK/PD) model of DEX anti‐inflammatory effects in COVID‐19 and provide simulations comparing the expected efficacy of four dosing regimens of DEX. Nonlinear mixed‐effects modeling and simulations were performed using Monolix Suite version 2021R1 (Lixoft, France). Published data for DEX PK in patients with COVID‐19 exhibited moderate variability with a clearance of about half that in healthy adults. No accumulation of the drug was expected even with daily oral doses of 12 mg. Indirect effect models of DEX inhibition of TNFα, IL‐6, and CRP plasma concentrations were enacted and simulations performed for DEX given at 1.5, 3, 6, and 12 mg daily for 10 days. The numbers of individuals that achieved specified reductions in inflammatory biomarkers were compared among the treatment groups. The simulations indicate the need for 6 or 12 mg daily doses of DEX for 10 days for simultaneous reductions in TNFα, IL‐6, and CRP. Possibly beneficial is DEX given at a dose of 12 mg compared to 6 mg. The PopPK/PD model may be useful in the assessment of other anti‐inflammatory compounds as well as drug combinations in the treatment of cytokine storms.

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\begin{matrix} left \frac{dTNF}{normald t} goodbreak= k_{inTNF} \cdot ()(, 1 goodbreak- \frac{I_{maxDEX ()(, TNF)} \cdot C_{p}}{{IC}_{50 DEX ()(, TNF)} + C_{p}}) goodbreak- k_{outTNF} \cdot \\ left TNF; TNF ()(, 0) goodbreak= R_{0 TNF} \end{matrix}

\begin{matrix} left \frac{dIL 6}{normald t} goodbreak= k_{inIL 6} \cdot ()(, 1 goodbreak- \frac{I_{maxDEX ()(, IL 6)} \cdot C_{p}}{{IC}_{50 DEX ()(, IL 6)} + C_{p}}) goodbreak- k_{outIL 6} \cdot IL 6; \\ left IL 6 ()(, 0) goodbreak= R_{0 IL 6} \end{matrix}

\begin{matrix} left \frac{dCRP}{normald t} goodbreak= k_{inCRP} \cdot ()(, 1 goodbreak+ S_{IL 6 onCRP} \cdot IL 6) goodbreak- k_{outCRP} \cdot CRP; \\ left CRP ()(, 0) goodbreak= R_{0 CRP} \end{matrix}

where

k_{inTNF} <...

…”

Section: Methodsmentioning

confidence: 56%

where

, and

are zero‐order production rate constants and

, and

are first‐order disposition constants of the indicated mediators. The

and

reflect maximum inhibition of production by DEX and

and

are the concentrations resulting in 50% of the maximum inhibition of cytokine production.…”

Section: Methodsmentioning

confidence: 99%

See 1 more Smart Citation

Anti‐inflammatory effects of dexamethasone in COVID‐19 patients: Translational population PK/PD modeling and simulation

Świerczek

Jusko

2023

Clinical Translational Sci

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“…From another aspect, metabolomics phenotyping might represent an important step toward personalized therapeutics in patients infected with COVID-19, because it could predict the antiviral drug efficacy ( Migaud et al., 2020a ; Migaud et al., 2020b ; Gómez-Cebrián et al., 2021 ). However, previous studies on the metabolomics characterization of COVID-19 mostly focused on the differences of infected populations ( Zhao et al., 2020 ; Lou et al., 2021 ; Wang et al., 2021 ). Thus, the molecular alterations in COVID-19 patients with different stages remain to be investigated, and such an effort will lead to a deep understanding of the detailed mechanisms underlying the different clinical stages of COVID-19…”

Section: Introductionmentioning

confidence: 99%

Sera Metabolomics Characterization of Patients at Different Stages in Wuhan Identifies Critical Biomarkers of COVID-19

Pan

Yuan

et al. 2022

Front. Cell. Infect. Microbiol.

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We have witnessed the 2-year-long global rampage of COVID-19 caused by the wide spread of severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2). However, knowledge about biomarkers of the entire COVID-19 process is limited. Identification of the systemic features of COVID-19 will lead to critical biomarkers and therapeutic targets for early intervention and clinical disease course prediction. Here, we performed a comprehensive analysis of clinical measurements and serum metabolomics in 199 patients with different stages of COVID-19. In particular, our study is the first serum metabolomic analysis of critical rehabilitation patients and critical death patients. We found many differential metabolites in the comparison of metabolomic results between ordinary, severe, and critical patients and uninfected patients. Through the metabolomic results of COVID-19 patients in various stages, and critical rehabilitation patients and critical death patients, we identified a series of differential metabolites as biomarkers, a separate queue and precise distinction, and predicted COVID-19 verification. These differentially expressed metabolites, included 1,2-di-(9Z,12Z-octadecadienoyl)-sn-glycero-3-phosphate, propylparaben, 20-hydroxyeicosatetraenoic acid, triethanolamine, chavicol, disialosyl galactosyl globoside, 1-arachidonoylglycerophosphoinositol, and alpha-methylstyrene, all of which have been identified for the first time as biomarkers in COVID-19 progression. These biomarkers are involved in many pathological and physiological pathways of COVID-19, for example, immune responses, platelet degranulation, and metabolism which might result in pathogenesis. Our results showed valuable information about metabolites obviously altered in COVID-19 patients with different stages, which could shed light on the pathogenesis as well as serve as potential therapeutic agents of COVID-19.

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Application of omics technology to combat the COVID‐19 pandemic

Yang

Yan²,

Zhong³

2021

MedComm

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As of August 27, 2021, the ongoing pandemic of coronavirus disease 2019 (COVID‐19), caused by severe acute respiratory syndrome coronavirus 2 (SARS‐CoV‐2), has spread to over 220 countries, areas, and territories. Thus far, 214,468,601 confirmed cases, including 4,470,969 deaths, have been reported to the World Health Organization. To combat the COVID‐19 pandemic, multiomics‐based strategies, including genomics, transcriptomics, proteomics, and metabolomics, have been used to study the diagnosis methods, pathogenesis, prognosis, and potential drug targets of COVID‐19. In order to help researchers and clinicians to keep up with the knowledge of COVID‐19, we summarized the most recent progresses reported in omics‐based research papers. This review discusses omics‐based approaches for studying COVID‐19, summarizing newly emerged SARS‐CoV‐2 variants as well as potential diagnostic methods, risk factors, and pathological features of COVID‐19. This review can help researchers and clinicians gain insight into COVID‐19 features, providing direction for future drug development and guidance for clinical treatment, so that patients can receive appropriate treatment as soon as possible to reduce the risk of disease progression.

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Elevation of Serum Cytokine Profiles and Liver Metabolomic Normalization in Early Convalescence of COVID-19 Patients

Cited by 5 publications

References 35 publications

Anti‐inflammatory effects of dexamethasone in COVID‐19 patients: Translational population PK/PD modeling and simulation

Anti‐inflammatory effects of dexamethasone in COVID‐19 patients: Translational population PK/PD modeling and simulation

Sera Metabolomics Characterization of Patients at Different Stages in Wuhan Identifies Critical Biomarkers of COVID-19

Application of omics technology to combat the COVID‐19 pandemic

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