Evidence map›Paper›PMID 38570601›Full record

ArticleActa pharmacologica Sinica2024

PBPK-PD model for predicting morphine pharmacokinetics, CNS effects and naloxone antagonism in humans.

Rui-Jing Mu, Tian-Lei Liu, Xiao-Dong Liu, Li Liu

Open access · greenAbstract read
In one paragraph

Article in Acta pharmacologica Sinica, 2024. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 4 papers.

0numbers the graph read from it
0cells of the map it votes in
4citing papers in PubMed
1.6field-weighted citation impact, top 18% 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

4 citing papers in PubMed, 6 citations in OpenAlex.

  1. Review
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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

4 authors at 1 institution in 1 country.

Rui-Jing MuDepartment of Pharmacology, College of Pharmacy, China Pharmaceutical University, Nanjing, 210009, China.
Tian-Lei LiuDepartment of Pharmacology, College of Pharmacy, China Pharmaceutical University, Nanjing, 210009, China.
Xiao-Dong LiuDepartment of Pharmacology, College of Pharmacy, China Pharmaceutical University, Nanjing, 210009, China. xdliu@cpu.edu.cn.
Li LiuDepartment of Pharmacology, College of Pharmacy, China Pharmaceutical University, Nanjing, 210009, China. liulee@cpu.edu.cn.
China Pharmaceutical University · CN

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

Morphine and morphine-6-glucuronide (M6G) produce central nervous system (CNS) effects by activating mu-opioid receptors, while naloxone is used mainly for the reversal of opioid overdose, specifically for the fatal complication of respiratory depression, but also for alleviating opioid-induced side effects. In this study we developed a physiologically-based pharmacokinetic-pharmacodynamic (PBPK-PD) model to simultaneously predict pharmacokinetics and CNS effects (miosis, respiratory depression and analgesia) of morphine as well as antagonistic effects of naloxone against morphine. The pharmacokinetic and pharmacodynamic parameters were obtained from in vitro data, in silico, or animals. Pharmacokinetic and pharmacodynamic simulations were conducted using 39 and 36 clinical reports, respectively. The pharmacokinetics of morphine and M6G following oral or intravenous administration were simulated, and the PBPK-PD model was validated using clinical observations. The E

Indexed as

Models, BiologicalMorphineNaloxoneNarcotic AntagonistsAdministration, IntravenousAdministration, OralAdultAnalgesics, OpioidAnimalsCentral Nervous SystemComputer SimulationFemaleHumansMaleMorphine DerivativesAnalgesics, OpioidMorphinemorphine-6-glucuronideMorphine DerivativesNaloxoneNarcotic AntagonistsanalgesiamiosismorphinenaloxonePBPK-PD modelrespiratory depression

Identifiers

PMID38570601
PMCPMC11272789
OpenAlexW4393861142

What OpenQuestion holds

Textmetadata
Read underepoch 390

Registered trials

None linked

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.