Evidence map›Paper›PMID 42064386›Full record

ArticleNano today2026

The landscape of nanomedical clinical trials.

Evin Gultepe, Raghnya Valluru, Nik Bear Brown, Srinivas Sridhar

Abstract read
In one paragraph

Article in Nano today, 2026. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 6 papers.

0numbers the graph read from it
0cells of the map it votes in
6citing 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

6 citing papers in PubMed.

  1. Review
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  6. 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

4 authors.

Evin GultepeNortheastern University, Boston, MA, United States.
Raghnya ValluruNortheastern University, Boston, MA, United States.
Nik Bear BrownNortheastern University, Boston, MA, United States.
Srinivas SridharNortheastern University, Boston, MA, United States.ORCID 0000-0003-1605-0734

Funding

CaNCURE: Cancer Nanomedicine Co-ops For Undergraduate Research ExperiencesR25CA174650 · NCI · NORTHEASTERN UNIVERSITY · PI SRINIVAS SRIDHAR · 2014 to 2026
$3.4M
NCI NIH HHS R25 CA174650
6 · The paper itself

Abstract

Nanotechnology has transformed healthcare, leading to the clinical adoption of numerous nanomedical products. To evaluate their clinical translation, we analyzed all trials registered on ClinicalTrials.gov using a novel nanomedicine lexicon developed through expert curation and generative AI. This approach identified 4114 nanomedical clinical trials (out of more than 500,000) forming the Nanomedical Clinical Trials (NanoCT) dataset. Our analysis reveals a 38 % rise in nanomedical trials in recent years. While oncology remains dominant (30 %), emerging applications-particularly in infectious diseases, driven by the rise of mRNA vaccines-demonstrate the field's expanding therapeutic scope. This diversification is further evidenced by the growing use of micelles, polymeric, and metallic nanoparticles, marking a shift from the dominance of liposomal formulations. Despite significant advancements, nanomedical trials account for only 0.8 % of all registered clinical trials, highlighting key translational challenges such as regulatory complexities, high production costs, and clinical design limitations. Addressing these barriers requires the establishment of a universally accepted nanomedical lexicon to enhance data harmonization, streamline regulatory pathways, and improve interdisciplinary communication. This comprehensive analysis provides critical insights into the trajectory of nanohealth, identifies obstacles to clinical translation, and outlines strategies to maximize its future impact in medicine.

Indexed as

AACTClinical trialsCovidNanohealthNanomedicalNanomedicineNanotherapeutics

Identifiers

PMID42064386
PMCPMC13127981

What OpenQuestion holds

Textmetadata
LicenceCC BY-NC-ND
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.