Evidence map›Paper›PMID 35405765›Full record

ArticleBiopharmaceutics & drug disposition2022

Physiologically based pharmacokinetic modeling and simulations to inform dissolution specifications and clinical relevance of release rates on elagolix exposure.

Dwaipayan Mukherjee, Manoj S Chiney, Xi Shao, Tzuchi R Ju, Mohamad Shebley, Patrick Marroum

Abstract read
In one paragraph

Article in Biopharmaceutics & drug disposition, 2022. 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. Review
  3. 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

6 authors.

Dwaipayan MukherjeeClinical Pharmacology and Pharmacometrics, AbbVie, North Chicago, Illinois, USA.ORCID https://orcid.org/0000-0002-3918-3188
Manoj S ChineyClinical Pharmacology and Pharmacometrics, AbbVie, North Chicago, Illinois, USA.
Xi ShaoAnalytical Research and Development, AbbVie, North Chicago, Illinois, USA.
Tzuchi R JuAnalytical Research and Development, AbbVie, North Chicago, Illinois, USA.
Mohamad ShebleyClinical Pharmacology and Pharmacometrics, AbbVie, North Chicago, Illinois, USA.
Patrick MarroumClinical Pharmacology and Pharmacometrics, AbbVie, North Chicago, Illinois, USA.ORCID https://orcid.org/0000-0002-9954-3798

Funding

AbbVie N/A
6 · The paper itself

Abstract

The aim of this analysis was to use a physiologically based pharmacokinetic (PBPK) model to predict the impact of changes in dissolution rates on elagolix exposures and define clinically relevant acceptance criteria for dissolution. Varying in vitro dissolution profiles were utilized in a PBPK model to describe the absorption profiles of elagolix formulations used in Phase 3 clinical trials and for the to be marketed commercial formulations. Single dose studies of 200 mg elagolix formulations were used for model verification under fasted conditions. Additional dissolution scenarios were evaluated to assess the impact of dissolution rates on elagolix exposures. Compared to the Phase 3 clinical trial formulation, sensitivity analysis on dissolution rates suggested that a hypothetical scenario of ∼75% slower dissolution rate would result in 14% lower predicted elagolix plasma exposures, however, the predicted exposures are still within the bioequivalence boundaries of 0.8-1.25 for both C

Indexed as

Models, BiologicalAdministration, OralClinical Trials, Phase III as TopicComputer SimulationDrug LiberationHydrocarbons, FluorinatedPyrimidinesSolubilityTherapeutic EquivalencyelagolixHydrocarbons, FluorinatedPyrimidinesbiopharmaceuticsdissolution specificationsPBPK modeling

Identifiers

PMID35405765
PMCPMC9320978

What OpenQuestion holds

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LicenceCC BY-NC-ND
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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.