Evidence map›Paper›PMID 22781694›Full record

ReviewNature biotechnology2012

Modeling and predicting clinical efficacy for drugs targeting the tumor milieu.

Mallika Singh, Napoleone Ferrara

Abstract readReview
PubMed Publisher
In one paragraph

Review in Nature biotechnology, 2012. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 59 papers.

0numbers the graph read from it
0cells of the map it votes in
59citing 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

59 citing papers in PubMed.

  1. First experience of 18F-alfatide in lung cancer patients using a new lyophilized kit for rapid radiofluorination.Journal of nuclear medicine : official publication, Society of Nuclear Medicine · 2013
    Trial
  2. Review
  3. Review
  4. Article
  5. Article
  6. Metabolic Reprogramming in Tumor Endothelial Cells.International journal of molecular sciences · 2022
    Review
  7. Article
  8. Article
  9. Review
  10. Hepatocellular carcinoma.Nature reviews. Disease primers · 2021
    Review
  11. Article
  12. Article
  13. Article
  14. Article
  15. Vascular changes in tumors resistant to a vascular disrupting nanoparticle treatment.Journal of controlled release : official journal of the Controlled Release Society · 2017
    Article
  16. Review
  17. Article
  18. Mouse models of metastasis: progress and prospects.Disease models & mechanisms · 2017
    Review
  19. Article
  20. 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.

Mallika SinghNovartis Institutes for Biomedical Research, Emeryville, California, USA. mallika.singh@novartis.com
Napoleone Ferrara

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

Disappointing results from most late-stage clinical trials of cancer therapeutics indicate a need for improved and more-predictive animal tumor models. This insufficiency of models, combined with the advent of a class of drugs that target the tumor microenvironment rather than the tumor cell, presents new challenges for designing and interpreting preclinical efficacy studies. A comparison of the clinical efficacy of anti-angiogenic drugs with their corresponding preclinical studies over the past two decades offers many lessons that can inform and improve the design of experiments in existing mouse models. In addition, technological and logistical advances in mouse models of human cancer over the past five years have the potential to increase the clinical translatability of animal studies.

Indexed as

Disease Models, AnimalNeoplasmsNeovascularization, PathologicAngiogenesis InhibitorsAnimalsCell Transformation, NeoplasticClinical Trials as TopicDrug Evaluation, PreclinicalHumansMiceAngiogenesis Inhibitors

Identifiers

PMID22781694

What OpenQuestion holds

Textmetadata
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.