Evidence map›Paper›PMID 24691448›Full record

ArticlePloS one2014

A structure-based model for predicting serum albumin binding.

Katrina W Lexa, Elena Dolghih, Matthew P Jacobson

Abstract read
In one paragraph

Article in PloS one, 2014. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 21 papers.

0numbers the graph read from it
0cells of the map it votes in
21citing 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

21 citing papers in PubMed.

  1. Pharmaceutics · 2025
    Article
  2. Review
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  7. Effect of Adrenocorticotropic Hormone Stimulation on Ischemiamodified Albumin LevelsJournal of clinical research in pediatric endocrinology · 2024
    Article
  8. Deciphering albumin-directed drug delivery by imaging.Advanced drug delivery reviews · 2022
    Review
  9. Article
  10. Article
  11. 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

3 authors.

Katrina W LexaDepartment of Pharmaceutical Chemistry, University of California San Francisco, San Francisco, California, United States of America.
Elena DolghihDepartment of Pharmaceutical Chemistry, University of California San Francisco, San Francisco, California, United States of America.
Matthew P JacobsonDepartment of Pharmaceutical Chemistry, University of California San Francisco, San Francisco, California, United States of America.

Funding

UNDERSTANDING AND IMPROVING SMALL MOLECULE INHIBITORS OF PRION REPLICATIONP01AG021601 · NIA · UNIVERSITY OF CALIFORNIA, SAN FRANCISCO · PI PRUSINER, STANLEY B · 2003 to 2013
$31.9M
Characterizing Natural Product Permeability and BioactivityF32GM100619 · NIGMS · UNIVERSITY OF CALIFORNIA, SAN FRANCISCO · PI LEXA, KATRINA WALDEN · 2012 to 2014
$150k
NIA NIH HHS AG021601NIA NIH HHS P01 AG021601NIGMS NIH HHS F32 GM100619NIGMS NIH HHS GM100619
6 · The paper itself

Abstract

One of the many factors involved in determining the distribution and metabolism of a compound is the strength of its binding to human serum albumin. While experimental and QSAR approaches for determining binding to albumin exist, various factors limit their ability to provide accurate binding affinity for novel compounds. Thus, to complement the existing tools, we have developed a structure-based model of serum albumin binding. Our approach for predicting binding incorporated the inherent flexibility and promiscuity known to exist for albumin. We found that a weighted combination of the predicted logP and docking score most accurately distinguished between binders and nonbinders. This model was successfully used to predict serum albumin binding in a large test set of therapeutics that had experimental binding data.

Indexed as

Models, MolecularProtein ConformationAlgorithmsBinding SitesHumansLigandsProtein BindingSerum AlbuminStructure-Activity RelationshipLigandsSerum Albumin

Identifiers

PMID24691448
PMCPMC3972100

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.