Evidence map›Paper›PMID 33717263›Full record

ReviewOncology letters2021

Research update on the anticancer effects of buparlisib.

Jinshan Xing, Jun Yang, Yingjiang Gu, Jingyan Yi

Open access · diamondAbstract readReview
In one paragraph

Review in Oncology letters, 2021. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 15 papers.

0numbers the graph read from it
0cells of the map it votes in
15citing papers in PubMed
1.5field-weighted citation impact, top 18% 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

15 citing papers in PubMed, 24 citations in OpenAlex.

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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.

Jinshan XingDepartment of Neurosurgery, The Affiliated Traditional Chinese Medicine Hospital of Southwest Medical University, Luzhou, Sichuan 646000, P.R. China.
Jun YangDepartment of Neurosurgery, The Affiliated Traditional Chinese Medicine Hospital of Southwest Medical University, Luzhou, Sichuan 646000, P.R. China.
Yingjiang GuDepartment of Neurosurgery, The Affiliated Traditional Chinese Medicine Hospital of Southwest Medical University, Luzhou, Sichuan 646000, P.R. China.
Jingyan YiDepartment of Medical Cell Biology and Genetics, School of Basic Medical Sciences, Southwest Medical University, Luzhou, Sichuan 646000, P.R. China.
Southwest Medical University · CN

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

Buparlisib is a highly efficient and selective PI3K inhibitor and a member of the 2,6-dimorpholinopyrimidine-derived family of compounds. It selectively inhibits four isomers of PI3K, PI3Kα, PI3Kβ, PI3Kγ and PI3Kδ, by competitively binding the lipid kinase domain on adenosine 5'-triphosphate (ATP), and serves an important role in inhibiting proliferation, promoting apoptosis and blocking angiogenesis, predominantly by antagonizing the PI3K/AKT pathway. Buparlisib has been confirmed to have a clinical effect in patients with solid tumors and hematological malignancies. A global, phase II clinical trial with buparlisib and paclitaxel in head and neck squamous cell carcinoma has now been completed, with a manageable safety profile. Buparlisib currently has fast-track status with the United States Food and Drug Administration. The present review examined the biochemical structure, pharmacokinetic characteristics, preclinical data and ongoing clinical studies of buparlisib. The various mechanisms of influence of buparlisib in tumors, particularly in preclinical research, were summarized, providing a theoretical basis and direction for basic research on and clinical treatment with buparlisib.

Indexed as

apoptosisbuparlisibcancerdrug combinationPI3K

Identifiers

PMID33717263
PMCPMC7885152
OpenAlexW3126519615

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.