Evidence map›Paper›PMID 39253438›Full record

ArticlebioRxiv : the preprint server for biology2024

Proteolysis targeting chimera extracellular vesicles for therapeutic development treating triple negative breast cancer.

Nina Erwin, Umasankar De, Yufeng Xiao, Lei Wang, Chandra Maharjan, Xiaoshu Pan, Nikee Awasthee, Guangrong Zheng, Daiqing Liao, Weizhou Zhang and 1 more

Abstract readPreprint
In one paragraph

Article in bioRxiv : the preprint server for biology, 2024. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.

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

0 citing papers in PubMed.

No citing paper in PubMed yet.

4 · The record

Corrections and comments

5 · Who and what money

Authors and funding

11 authors.

Nina Erwin
Umasankar De
Yufeng Xiao
Lei Wang
Chandra Maharjan
Xiaoshu Pan
Nikee Awasthee
Guangrong Zheng
Weizhou Zhang
Mei He

Funding

Proteolysis-targeting chimera against BCL-XL inhibits breast cancer metastasisR01CA260239 · NCI · UNIVERSITY OF FLORIDA · PI ZHANG, WEIZHOU · 2021 to 2025
$2.5M
NCI NIH HHS R01 CA260239
6 · The paper itself

Abstract

Proteolysis targeting chimeras (PROTACs) are an emerging targeted cancer therapy approach, but wide-spread clinical use of PROTAC is limited due to poor cell targeting and penetration, and instability in vivo. To overcome such issues and enhance the in vivo efficacy of PROTAC drugs, microfluidic droplet-based electroporation (µDES) was developed as a novel extracellular vesicle (EVs) transfection system, which enables the high-efficient PROTAC loading and effective delivery in vivo. Our previously developed YX968 PROTAC drug had shown the selectively degradation of HDAC3 and 8, which effectively suppresses the growth of breast tumor cell lines, including MDA-MB-231 triple negative breast cancer (TNBC) line, via dual degradation without provoking a global histone hyperacetylation. In this study, we demonstrated that µDES-based PROTAC loading in EVs significantly enhanced therapeutic function of PROTAC drug in vivo in the TNBC breast tumor mouse model. NSG mice with pre-established MDA-MB-231 tumors and treated with intraperitoneal injection of EVs for tumor inhibition study, which showed significantly higher HDAC 3 and 8 degradation efficiency and tumor inhibition than PROTAC only group. The liver, spleen, kidney, lung, heart, and brain were collected for safety testing, which exhibited improved toxicity. The EV delivery of PROTAC drug enhances drug stability and bioavailability in vivo, transportability, and drug targeting ability, which fills an important gap in current development of PROTAC therapeutic functionality in vivo and clinical translation. This novel EV-based drug transfection and delivery strategy could be applicable to various therapeutics for enhancing in vivo delivery, efficacy, and safety.

Identifiers

PMID39253438
PMCPMC11383279

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Registered trials

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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.