Evidence map›Paper›PMID 22401638›Full record

ArticleImmunotherapy2012

Predicting the clinical efficacy and potential adverse effects of a humanized anticocaine monoclonal antibody.

Andrew B Norman, William J Ball

Open access · greenAbstract read
In one paragraph

Article in Immunotherapy, 2012. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 8 papers.

0numbers the graph read from it
0cells of the map it votes in
8citing papers in PubMed
0.7field-weighted citation impact, top 26% 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

8 citing papers in PubMed, 13 citations in OpenAlex.

  1. Review
  2. Article
  3. Structural analysis of free and liganded forms of the Fab fragment of a high-affinity anti-cocaine antibody, h2E2.Acta crystallographica. Section F, Structural biology communications · 2019
    Article
  4. Article
  5. Article
  6. Article
  7. Article
  8. Article
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 2 institutions in 1 country.

Andrew B NormanDepartment of Pharmacology & Cell Biophysics and Department of Psychiatry & Behavioral Neuroscience, College of Medicine, University of Cincinnati, Cincinnati, OH 45237-0506, USA. andrew.norman@uc.edu
William J Ball
University of Cincinnati · USUniversity of Cincinnati Medical Center · US

Funding

A Human Antibody as an Immunotherapy for Cocaine Abuse (DP1)DP1DA031386 · NIDA · UNIVERSITY OF CINCINNATI · PI NORMAN, ANDREW B · 2010 to 2014
$3.8M
NIDA NIH HHS DP1 DA031386NIDA NIH HHS DP1DA031386
6 · The paper itself

Abstract

The effects of a humanized monoclonal antibody (mAb) having high affinity and specificity for cocaine in animal models are reviewed. The mAb reduced the concentration of cocaine in the brain of mice after intravenous injection of cocaine. In addition, the mAb increased the concentration of cocaine required to reinstate cocaine self-administration. These effects may predict clinical efficacy of a passive immunotherapy for reducing the probability of cocaine-induced relapse. However, in the presence of the mAb, once cocaine self-administration was reinstated, the consumption rate of cocaine was increased. This effect is hypothesized to result from a pharmacokinetic/pharmacodynamic interaction. A humanized mAb should minimize adverse events related to the immunogenicity of the mAb protein, and the specificity for cocaine should avoid adverse events related to interactions with physiologically relevant endogenous proteins.

Indexed as

Anesthetics, LocalAntibodies, Monoclonal, HumanizedCocaineImmunization, PassiveAnimalsBrainCocaine-Related DisordersDisease Models, AnimalHumansMiceAnesthetics, LocalAntibodies, Monoclonal, HumanizedCocaine

Identifiers

PMID22401638
PMCPMC3321830
OpenAlexW2055505206

What OpenQuestion holds

Textmetadata
LicenceTDM
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.