Evidence map›Paper›PMID 20399239›Full record

ReviewAdvanced drug delivery reviews2010

Development of recombinant cationic polymers for gene therapy research.

Brenda F Canine, Arash Hatefi

Abstract readReview
In one paragraph

Review in Advanced drug delivery reviews, 2010. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 14 papers.

0numbers the graph read from it
0cells of the map it votes in
14citing 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

14 citing papers in PubMed.

  1. Article
  2. Review
  3. Article
  4. Article
  5. Article
  6. Article
  7. Article
  8. Review
  9. Controlled release from recombinant polymers.Journal of controlled release : official journal of the Controlled Release Society · 2014
    Review
  10. Article
  11. Article
  12. Article
  13. Development of targeted recombinant polymers that can deliver siRNA to the cytoplasm and plasmid DNA to the cell nucleus.Journal of controlled release : official journal of the Controlled Release Society · 2011
    Article
  14. 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.

Brenda F CanineDepartment of Pharmaceutical Sciences, Center for Integrated Biotechnology, Washington State University, Pullman, 99164, USA.
Arash Hatefi

Funding

TRAINING IN BIOTECHNOLOGY: EMPHASIS ON PROTEIN CHEMISTRYT32GM008336 · NIGMS · WASHINGTON STATE UNIVERSITY · PI CALL, DOUGLAS R. · 1989 to 2023
$8.9M
NIGMS NIH HHS T-32 GM008336NIGMS NIH HHS T32 GM008336
6 · The paper itself

Abstract

Cationic polymers created through recombinant DNA technology have the potential to fill a void in the area of gene delivery. The recombinant cationic polymers to be discussed here are amino acid based polymers synthesized in E. coli with the purpose to not only address the major barriers to efficient gene delivery but offer safety, biodegradability, targetability and cost-effectiveness. This review helps the readers to get a better understanding about the evolution of recombinant cationic polymers; and the potential advantages that they could offer over viral and synthetic non-viral vectors for gene delivery. It also discusses some of the major challenges that must be addressed in future studies to turn recombinant polymers into clinically effective gene delivery systems. Recent advances with the biopolymer design suggest that this emerging new class of gene delivery systems has the potential to address some of the major barriers to efficient, safe and cost-effective gene therapy.

Indexed as

Amino Acid Transport Systems, BasicGenetic TherapyGene Transfer TechniquesPolymersRecombinant ProteinsAmino Acid Transport Systems, BasicPolymersRecombinant Proteins

Identifiers

PMID20399239
PMCPMC2939221

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.