Evidence map›Paper›PMID 33720733›Full record

ArticleMolecular pharmaceutics2021

Physiologically Based Biopharmaceutics Modeling of Regional and Colon Absorption in Dogs.

Emma Eckernäs, Christer Tannergren

Open access · hybridAbstract read
In one paragraph

Article in Molecular pharmaceutics, 2021. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 5 papers.

0numbers the graph read from it
0cells of the map it votes in
5citing papers in PubMed
0.8field-weighted citation impact, top 34% 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

5 citing papers in PubMed, 10 citations in OpenAlex.

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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

2 authors at 1 institution in 1 country.

Emma EckernäsOral Product Development, Pharmaceutical Technology & Development, Operations, AstraZeneca, Gothenburg, S-431 83 Mölndal, Sweden.ORCID 0000-0002-0625-5616
Christer TannergrenOral Product Development, Pharmaceutical Technology & Development, Operations, AstraZeneca, Gothenburg, S-431 83 Mölndal, Sweden.
AstraZeneca (Sweden) · SE

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

Colon absorption is a key determinant for the successful development of modified-release (MR) formulations, and the risk that colon absorption may limit the in vivo performance of an MR product can be assessed early by various in vitro tests or by preclinical in vivo regional absorption studies in dogs. Mechanistic physiologically based biopharmaceutics modeling (PBBM) is becoming increasingly accepted to predict in vivo performance and guide formulation development; however, no evaluation of the ability to predict colon absorption has been performed. The purpose of this study was to investigate if regional and colon absorption of drugs in dogs could be predicted with sufficient accuracy using PBBM to enable the replacement of in vivo dog studies in the early assessment of colon absorption limitation risks. This was done by predicting the regional and colon absorption and plasma exposure of 14 drugs after administration to the dog colon according to an a priori approach using the in silico absorption models GI-Sim and GastroPlus. Predictive performance was primarily assessed by comparing observed and predicted plasma concentration-time profiles, AUC

Indexed as

Models, BiologicalAdministration, OralAnimalsArea Under CurveBiological AvailabilityBiopharmaceuticsChemistry, PharmaceuticalColonDelayed-Action PreparationsDogsIntestinal AbsorptionIntestinal MucosaModels, AnimalSolubilitySolutionsSuspensionsDelayed-Action PreparationsSolutionsSuspensionscolon absorptiondrug absorptionin silico predictionPBBMPBPKphysiologically based biopharmaceutics modeling

Identifiers

PMID33720733
PMCPMC8041381
OpenAlexW3136336889

What OpenQuestion holds

Textmetadata
LicenceCC BY
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.