Evidence map›Paper›PMID 41696434›Full record

ReviewEXCLI journal2026

Development of nanoparticle-based Toll-like receptor agonists for respiratory immunotherapy.

Amisha S Raikar, Ananya Verma, Mayuri B Naik, Sweta M Prabhu, Keshav Raj Paudel, Kamal Dua

Abstract readReview
In one paragraph

Review in EXCLI journal, 2026. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 1 paper.

0numbers the graph read from it
0cells of the map it votes in
1citing 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

1 citing paper in PubMed.

  1. Review
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

6 authors.

Amisha S RaikarDepartment of Pharmaceutics- PES Rajaram and Tarabai Bandekar College of Pharmacy, Ponda, Goa, 403401, India.
Ananya VermaDepartment of Biotechnology- Amity Institute of Biotechnology, Amity University, Mumbai-Pune, Maharashtra, 410206, India.
Mayuri B NaikDepartment of Pharmacology- PES Rajaram and Tarabai Bandekar College of Pharmacy, Ponda, Goa, 403401, India.
Sweta M PrabhuDepartment of Pharmaceutics, Srinivas College of Pharmacy, Mangalore, Karnataka, 574143, India.
Keshav Raj PaudelNICM Health Research Institute and School of Science, Western Sydney University, Westmead, NSW, 2145, Australia.
Kamal DuaNICM Health Research Institute and School of Science, Western Sydney University, Westmead, NSW, 2145, Australia.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

Respiratory diseases are global health challenges demanding innovative immunotherapy solutions. Toll-like receptors (TLRs) play a crucial role in immune responses, making them attractive therapeutic targets. This review examines nanoparticle-based Toll-like receptor agonists as a promising approach for respiratory immunotherapy. Nanoparticles offer targeted drug delivery and sustained release, ideal for enhancing TLR agonist efficacy. This article explores TLRs' role in immunomodulation, nanoparticle applications, design considerations, preclinical efficacy, safety assessments, challenges, and future prospects. Promising results from animal studies suggest enhanced immunological responses and reduced inflammation compared to conventional treatments. Safety concerns are addressed with insights from toxicity studies. Challenges include regulatory hurdles and biocompatibility, with strategies proposed for optimization. Nanoparticle-based TLR agonists hold great potential to transform respiratory disease treatment, warranting further research and collaboration for successful clinical translation. See also the graphical abstract(Fig. 1).

Indexed as

nanoparticle-based immunotherapyrespiratory diseasesToll-like receptors (TLRs)

Identifiers

PMID41696434
PMCPMC12901955

What OpenQuestion holds

Textmetadata
LicenceCC BY
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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.