Evidence map›Paper›PMID 25851513›Full record

ArticleThe AAPS journal2015

Mathematical Models to Explore Potential Effects of Supersaturation and Precipitation on Oral Bioavailability of Poorly Soluble Drugs.

Mary S Kleppe, Kelly M Forney-Stevens, Roy J Haskell, Robin H Bogner

Abstract read
In one paragraph

Article in The AAPS journal, 2015. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 1 paper.

0numbers the graph read from it
0cells of the map it votes in
1citing 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

1 citing paper in PubMed.

  1. 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

4 authors.

Mary S KleppeDepartment of Pharmaceutical Sciences, School of Pharmacy, University of Connecticut, Storrs, Connecticut, 06269, USA.
Kelly M Forney-Stevens
Roy J Haskell
Robin H Bogner

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

Poorly soluble drugs are increasingly formulated into supersaturating drug delivery systems which may precipitate during oral delivery. The link between in vitro drug concentration profiles and oral bioavailability is under intense investigation. The objective of the present work was to develop closed-form analytical solutions that relate in vitro concentration profiles to the amount of drug absorbed using several alternate assumptions and only six parameters. Three parameters define the key features of the in vitro drug concentration-time profile. An additional three parameters focus on physiological parameters. Absorption models were developed based on alternate assumptions; the drug concentration in the intestinal fluid: (1) peaks at the same time and concentration as in vitro, (2) peaks at the same time as in vitro, or (3) reaches the same peak concentration as in vitro. The three assumptions provide very different calculated values of bioavailability. Using Case 2 assumptions, bioavailability enhancement was found to be less than proportional to in silico examples of dissolution enhancement. Case 3 assumptions lead to bioavailability enhancements that are more than proportional to dissolution enhancements. Using Case 1 predicts drug absorption amounts that fall in between Case 2 and 3. The equations developed based on the alternate assumptions can be used to quickly evaluate the potential improvement in bioavailability due to intentional alteration of the in vitro drug concentration vs. time curve by reformulation. These equations may be useful in making decisions as to whether reformulation is expected to provide sufficient bioavailability enhancement to justify the effort.

Indexed as

Drug Delivery SystemsModels, TheoreticalAdministration, OralBiological AvailabilityChemical PrecipitationChemistry, PharmaceuticalComputer SimulationHumansPharmaceutical PreparationsSolubilityPharmaceutical Preparations

Identifiers

PMID25851513
PMCPMC4476993

What OpenQuestion holds

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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.