Evidence map›Paper›PMID 38973651›Full record

ArticleJournal of clinical pharmacology2024

Labetalol Dosing in Pregnancy: PBPK/PD and CYP2C19 Polymorphisms.

Xiaomei I Liu, Dionna J Green, John van den Anker, Joaquin Calderon, Homa Ahmadzia, Gilbert J Burckart, André Dallmann

Abstract read
PubMed Full text at PMC Publisher
In one paragraph

Article in Journal of clinical pharmacology, 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
–field-weighted citation impact
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.

  1. Review
  2. Article
  3. Review
  4. Current approaches and advances in placental toxicology.Trends in endocrinology and metabolism: TEM · 2026
    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

7 authors.

Xiaomei I LiuDivision of Clinical Pharmacology, Children's National Hospital, Washington, DC, USA.
Dionna J GreenOffice of Pediatric Therapeutics, US Food and Drug Administration, Silver Spring, MD, USA.
John van den AnkerDivision of Clinical Pharmacology, Children's National Hospital, Washington, DC, USA.
Joaquin CalderonDivision of Maternal-Fetal Medicine, Department of OB/Gyn, George Washington University, Washington, DC, USA.
Homa AhmadziaDivision of Maternal-Fetal Medicine, Department of OB/Gyn, George Washington University, Washington, DC, USA.
Gilbert J BurckartOffice of Clinical Pharmacology, US Food and Drug Administration, Silver Spring, MD, USA.ORCID 0000-0003-2005-9301
André DallmannBayer HealthCare SAS, Loos, France, on behalf of: Pharmacometrics/Modeling & Simulation, Research & Development, Pharmaceuticals, Bayer, AG, Germany.ORCID 0000-0003-1108-5719

Funding

Intramural FDA HHS FD999999U.S. Food and Drug Administration
6 · The paper itself

Abstract

As detailed information on the pharmacokinetics (PK) of labetalol in pregnant people are lacking, the aims of this study were: (1) to build a physiologically based PK (PBPK) model of labetalol in non-pregnant individuals that incorporates different CYP2C19 genotypes (specifically, *1/*1, *1/*2 or *3, *2/*2, and *17/*17); (2) to translate this model to the second and third trimester of pregnancy; and (3) to combine the model with a previously published direct pharmacodynamic (PD) model to predict the blood pressure lowering effect of labetalol in the third trimester. Clinical data for model evaluation was obtained from the scientific literature. In non-pregnant populations, the mean ratios of simulated versus observed peak concentration (C

Indexed as

Cytochrome P-450 CYP2C19LabetalolPolymorphism, GeneticAllelesBlood PressureClinical Studies as TopicFemaleGenotypeHumansMalePregnancyCYP2C19 protein, humanCytochrome P-450 CYP2C19LabetalolCYP2C19 polymorphismslabetalolpharmacodynamicsphysiologically based pharmacokineticspregnancy

Identifiers

PMID38973651
PMCPMC13185004

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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.