Evidence map›Paper›PMID 19381841›Full record

ReviewThe AAPS journal2009

Mechanistic approaches to predicting oral drug absorption.

Weili Huang, Sau Lawrence Lee, Lawrence X Yu

Open access · bronzeAbstract readReview
In one paragraph

Review in The AAPS journal, 2009. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 55 papers.

0numbers the graph read from it
0cells of the map it votes in
55citing papers in PubMed
6.8field-weighted citation impact, top 2% 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

55 citing papers in PubMed, 180 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

3 authors at 1 institution in 1 country.

Weili HuangOffice of Generic Drugs, Food and Drug Administration, 7519 Standish Place, Rockville, Maryland 20855, USA.
Sau Lawrence Lee
Lawrence X Yu
United States Food and Drug Administration · US

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

Modeling and simulation of oral drug absorption have been widely used in drug discovery, development, and regulation. Predictive absorption models are used to determine the rate and extent of oral drug absorption, facilitate lead drug candidate selection, establish formulation development strategy, and support the development of regulatory policies. This review highlights the development of recent drug absorption models including dispersion and compartmental models. The compartmental models include the compartmental absorption and transit model; Grass model; gastrointestinal transit absorption model; advanced compartmental absorption and transit model; and advanced dissolution, absorption, and metabolism model. Compared to the early absorption models, the above models developed or extended since the mid-1990s have demonstrated greatly improved predictive performance by accounting for multiple factors such as drug degradation, gastric emptying, intestinal transit, first-pass metabolism, and intestinal transport. For future model development, more heterogeneous features of the gastrointestinal tract (villous blood flow, metabolizing enzymes, and transporters), food effects, and drug-drug interactions should be fully characterized and taken into consideration. Moreover, predicting population inter- and intravariability in oral drug absorption can be useful and important for the evaluation of clinical safety and efficacy of drugs. Establishing databases and libraries that contain accurate pharmaceutical and pharmacokinetic information for commercialized and uncommercialized drugs may also be helpful for model development and validation.

Indexed as

Intestinal AbsorptionAnimalsBody Fluid CompartmentsForecastingHumansModels, StatisticalPharmaceutical PreparationsPharmaceutical Preparations

Identifiers

PMID19381841
PMCPMC2691458
OpenAlexW2062761938

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.