Evidence map›Paper›PMID 34959445›Full record

ReviewPharmaceutics2021

Intra-Articular Drug Delivery for Osteoarthritis Treatment.

Yifeng Cao, Yifeng Ma, Yi Tao, Weifeng Lin, Ping Wang

Open access · goldAbstract readReview
In one paragraph

Review in Pharmaceutics, 2021. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 41 papers.

0numbers the graph read from it
0cells of the map it votes in
41citing papers in PubMed
9.9field-weighted citation impact, top 1% 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

41 citing papers in PubMed, 86 citations in OpenAlex.

  1. Trial
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  10. Synthesis, characterisation, andFrontiers in drug delivery · 2026
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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

5 authors at 2 institutions in 2 countries.

Yifeng CaoCollege of Pharmaceutical Science, Zhejiang University of Technology, Hangzhou 310014, China.ORCID 0000-0002-1352-5709
Yifeng MaCollege of Pharmaceutical Science, Zhejiang University of Technology, Hangzhou 310014, China.
Yi TaoCollege of Pharmaceutical Science, Zhejiang University of Technology, Hangzhou 310014, China.
Weifeng LinDepartment of Molecular Chemistry and Materials Science, Weizmann Institute of Science, Rehovot 76100, Israel.ORCID 0000-0003-1416-974X
Ping WangCollege of Pharmaceutical Science, Zhejiang University of Technology, Hangzhou 310014, China.
Zhejiang University of Technology · CNWeizmann Institute of Science · IL

Funding

National Key R&D Program of China 2017YFE0130100Science Technology Department of Zhejiang Province 2017C04009
6 · The paper itself

Abstract

Osteoarthritis (OA) is the most prevalent degenerative joint disease affecting millions of people worldwide. Currently, clinical nonsurgical treatments of OA are only limited to pain relief, anti-inflammation, and viscosupplementation. Developing disease-modifying OA drugs (DMOADs) is highly demanded for the efficient treatment of OA. As OA is a local disease, intra-articular (IA) injection directly delivers drugs to synovial joints, resulting in high-concentration drugs in the joint and reduced side effects, accompanied with traditional oral or topical administrations. However, the injected drugs are rapidly cleaved. By properly designing the drug delivery systems, prolonged retention time and targeting could be obtained. In this review, we summarize the drugs investigated for OA treatment and recent advances in the IA drug delivery systems, including micro- and nano-particles, liposomes, and hydrogels, hoping to provide some information for designing the IA injected formulations.

Indexed as

developing disease-modifying OA drugs (DMOADs)drug delivery systemsintra-articular (IA) injectionosteoarthritis (OA)

Identifiers

PMID34959445
PMCPMC8703898
OpenAlexW4200084832

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.