Evidence map›Paper›PMID 20370696›Full record

ReviewCurrent computer-aided drug design2010

Bioavailability through PepT1: the role of computer modelling in intelligent drug design.

David W Foley, Jeyaganesh Rajamanickam, Patrick D Bailey, David Meredith

Abstract readReview
In one paragraph

Review in Current computer-aided drug design, 2010. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 10 papers.

0numbers the graph read from it
0cells of the map it votes in
10citing 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

10 citing papers in PubMed.

  1. Article
  2. Article
  3. Article
  4. Article
  5. Article
  6. Article
  7. Article
  8. Article
  9. Article
  10. 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.

David W FoleyFaculty of Natural Sciences, Keele University, Keele, Staffs ST5 5BG, UK.
Jeyaganesh Rajamanickam
Patrick D Bailey
David Meredith

Funding

Wellcome Trust 082051
6 · The paper itself

Abstract

In addition to being responsible for the majority of absorption of dietary nitrogen, the mammalian proton-coupled di- and tri-peptide transporter PepT1 is also recognised as a major route of drug delivery for several important classes of compound, including beta-lactam antibiotics and angiotensin-converting enzyme inhibitors. Thus there is considerable interest in the PepT1 protein and especially its substrate binding site. In the absence of a crystal structure, computer modelling has been used to try to understand the relationship between PepT1 3D structure and function. Two basic approaches have been taken: modelling the transporter protein, and modelling the substrate. For the former, computer modelling has evolved from early interpretations of the twelve transmembrane domain structure to more recent homology modelling based on recently crystallised bacterial members of the major facilitator superfamily (MFS). Substrate modelling has involved the proposal of a substrate binding template, to which all substrates must conform and from which the affinity of a substrate can be estimated relatively accurately, and identification of points of potential interaction of the substrate with the protein by developing a pharmacophore model of the substrates. Most recently, these two approaches have moved closer together, with the attempted docking of a substrate library onto a homology model of the human PepT1 protein. This article will review these two approaches in which computers have been applied to peptide transport and suggest how such computer modelling could affect drug design and delivery through PepT1.

Indexed as

Computer SimulationDrug DesignAnimalsBiological AvailabilityDipeptidesHumansPeptide Transporter 1Pharmaceutical PreparationsProtein BindingSymportersDipeptidesPeptide Transporter 1Pharmaceutical PreparationsSymporters

Identifiers

PMID20370696
PMCPMC3351990

What OpenQuestion holds

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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.