Evidence map›Paper›PMID 29280349›Full record

ArticleCPT: pharmacometrics & systems pharmacology2018

Translational model to predict pulmonary pharmacokinetics and efficacy in man for inhaled bronchodilators.

Ramon Hendrickx, Eva Lamm Bergström, David L I Janzén, Markus Fridén, Ulf Eriksson, Ken Grime, Douglas Ferguson

Abstract read
In one paragraph

Article in CPT: pharmacometrics & systems pharmacology, 2018. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 13 papers.

0numbers the graph read from it
0cells of the map it votes in
13citing 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

13 citing papers in PubMed.

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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

7 authors.

Ramon HendrickxDMPK, Respiratory, Inflammation, and Autoimmunity, IMED Biotech Unit, AstraZeneca, Gothenburg, Sweden.
Eva Lamm BergströmDMPK, Respiratory, Inflammation, and Autoimmunity, IMED Biotech Unit, AstraZeneca, Gothenburg, Sweden.
David L I JanzénDMPK, Cardiovascular and Metabolic Diseases, IMED Biotech Unit, AstraZeneca, Gothenburg, Sweden.
Markus FridénDMPK, Respiratory, Inflammation, and Autoimmunity, IMED Biotech Unit, AstraZeneca, Gothenburg, Sweden.
Ulf ErikssonEarly Clinical Development, IMED Biotech Unit, AstraZeneca, Gothenburg, Sweden.
Ken GrimeDMPK, Respiratory, Inflammation, and Autoimmunity, IMED Biotech Unit, AstraZeneca, Gothenburg, Sweden.
Douglas FergusonDMPK, Oncology, IMED Biotech Unit, AstraZeneca, Boston, Massachusetts, USA.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

Translational pharmacokinetic (PK) models are needed to describe and predict drug concentration-time profiles in lung tissue at the site of action to enable animal-to-man translation and prediction of efficacy in humans for inhaled medicines. Current pulmonary PK models are generally descriptive rather than predictive, drug/compound specific, and fail to show successful cross-species translation. The objective of this work was to develop a robust compartmental modeling approach that captures key features of lung and systemic PK after pulmonary administration of a set of 12 soluble drugs containing single basic, dibasic, or cationic functional groups. The model is shown to allow translation between animal species and predicts drug concentrations in human lungs that correlate with the forced expiratory volume for different classes of bronchodilators. Thus, the pulmonary modeling approach has potential to be a key component in the prediction of human PK, efficacy, and safety for future inhaled medicines.

Indexed as

Administration, InhalationAdministration, IntravenousAnimalsBronchodilator AgentsDogsDrug Evaluation, PreclinicalForced Expiratory VolumeHumansLungMaleModels, AnimalModels, BiologicalRatsRats, Sprague-DawleyBronchodilator Agents

Identifiers

PMID29280349
PMCPMC5869554

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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.