Evidence map›Paper›PMID 37376112›Full record

ReviewPharmaceutics2023

Anatomical Targeting of Anticancer Drugs to Solid Tumors Using Specific Administration Routes: Review.

Akira Saito, Joji Kitayama, Ryozo Nagai, Kenichi Aizawa

Open access · goldAbstract readReview
In one paragraph

Review in Pharmaceutics, 2023. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 13 papers.

0numbers the graph read from it
0cells of the map it votes in
13citing papers in PubMed
2.9field-weighted citation impact, top 9% 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

13 citing papers in PubMed, 14 citations in OpenAlex.

  1. Article
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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

4 authors at 1 institution in 1 country.

Akira SaitoDepartment of Surgery, Jichi Medical University, 3311-1 Yakushiji, Shimotsuke, Tochigi 329-0431, Japan.
Joji KitayamaDepartment of Surgery, Jichi Medical University, 3311-1 Yakushiji, Shimotsuke, Tochigi 329-0431, Japan.ORCID 0000-0002-5708-7130
Ryozo NagaiDepartment of Medicine, School of Medicine, Jichi Medical University, 3311-1 Yakushiji, Shimotsuke, Tochigi 329-0498, Japan.
Kenichi AizawaDivision of Translational Research, Clinical Research Center, Jichi Medical University Hospital, Tochigi, Tochigi 329-0498, Japan.
Jichi Medical University · JP

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

Despite remarkable recent progress in developing anti-cancer agents, outcomes of patients with solid tumors remain unsatisfactory. In general, anti-cancer drugs are systemically administered through peripheral veins and delivered throughout the body. The major problem with systemic chemotherapy is insufficient uptake of intravenous (IV) drugs by targeted tumor tissue. Although dose escalation and treatment intensification have been attempted in order to increase regional concentrations of anti-tumor drugs, these approaches have produced only marginal benefits in terms of patient outcomes, while often damaging healthy organs. To overcome this problem, local administration of anti-cancer agents can yield markedly higher drug concentrations in tumor tissue with less systemic toxicity. This strategy is most commonly used for liver and brain tumors, as well as pleural or peritoneal malignancies. Although the concept is theoretically reasonable, survival benefits are still limited. This review summarizes clinical results and problems and discusses future directions of regional cancer therapy with local administration of chemotherapeutants.

Indexed as

anticacncer drygintra-arterial routeintraperitoneal routeintrapleural routeintrathecal routeintratumor routethoracic duct infusion

Identifiers

PMID37376112
PMCPMC10301590
OpenAlexW4379536664

What OpenQuestion holds

Textmetadata
LicenceCC BY
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.