Evidence map›Paper›PMID 42801248›Full record

ReviewDrug design, development and therapy2026

Advances in Drug Delivery Systems for Breast Cancer: From Microenvironment Barriers and Smart Carriers to Clinical Translation Strategies.

Guiwen Liang, Chuli Zhu, Hualin Sun, Yanan Ji, Haiyan Jiang

Abstract readReview
In one paragraph

Review in Drug design, development and therapy, 2026. 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

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.

Guiwen Liang *Department of Emergency Medicine, Affiliated Hospital of Nantong University, Nantong, Jiangsu Province, People's Republic of China.
Chuli Zhu *Jiangsu Key Laboratory of Tissue Engineering and Neuroregeneration, Key Laboratory of Neuroregeneration of Ministry of Education, Co-Innovation Center of Neuroregeneration, Medical School of Nantong University, Nantong, Jiangsu Province, People's Republic of China.
Hualin SunJiangsu Key Laboratory of Tissue Engineering and Neuroregeneration, Key Laboratory of Neuroregeneration of Ministry of Education, Co-Innovation Center of Neuroregeneration, Medical School of Nantong University, Nantong, Jiangsu Province, People's Republic of China.ORCID 0000-0003-1889-1561
Yanan JiJiangsu Key Laboratory of Tissue Engineering and Neuroregeneration, Key Laboratory of Neuroregeneration of Ministry of Education, Co-Innovation Center of Neuroregeneration, Medical School of Nantong University, Nantong, Jiangsu Province, People's Republic of China.
Haiyan JiangDepartment of Emergency Medicine, Affiliated Hospital of Nantong University, Nantong, Jiangsu Province, People's Republic of China.ORCID 0009-0005-2271-6143

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

Breast cancer heterogeneity and its complex tumor microenvironment (TME) are key drivers of therapeutic failure and multidrug resistance (MDR). Drug delivery systems (DDS) provide an important approach, but their clinical translation remains limited by biological variability, formulation complexity, and manufacturing challenges. This review delineates the evolution of breast cancer DDS from passive carriers to increasingly functional and responsive platforms, focusing on three core dimensions. First, regarding decisive barriers, we dissect the constraints imposed by the three-dimensional collagen network, pH/glutathione (GSH) gradients, and tumor-associated macrophages (TAMs) on drug penetration. These factors not only act as physical and biochemical obstacles but can also be harnessed as endogenous triggers for responsive design. Second, in terms of diversified platforms, we critically evaluate liposomes, polymeric nanoparticles, hydrogels, extracellular vesicles, and antibody-drug conjugates (ADCs). We highlight the potential of solid lipid nanoparticles to address drug resistance, the development of carrier-free delivery enabled by prodrug self-assembly, and the clinical significance of ADCs (eg, T-DXd) in expanding treatment options for HER2-low breast cancer. Third, we examine inter- and intratumoral heterogeneity, metastatic site-specific delivery barriers in the brain, bone, and lung, the multilayered resistance network from efflux pumps to cancer stem cells, and the sequential barriers nanomedicines must traverse from systemic circulation to intracellular targets. We propose three strategic directions: personalized precision delivery integrating liquid biopsy, organoids, and functional imaging to support patient stratification and treatment adaptation; AI-assisted formulation optimization using machine learning to develop material-structure-function predictive models and support data-driven formulation design; and multi-responsive systems leveraging endogenous signals such as pH, GSH, and enzymes, together with exogenous stimuli including light, magnetism, and ultrasound, for spatiotemporally controlled combination therapy. This review summarizes key considerations from biological barriers to clinical translation, emphasizing that alignment of tumor subtype, metastatic site, microenvironmental characteristics, and patient-specific factors may improve the clinical applicability of drug delivery strategies.

Indexed as

Antineoplastic AgentsBreast NeoplasmsDrug Delivery SystemsTumor MicroenvironmentAnimalsDrug CarriersDrug Resistance, NeoplasmFemaleHumansNanoparticlesAntineoplastic AgentsDrug Carriersbreast cancerclinical translationdrug delivery systemsmultidrug resistancestimulus-responsive nanoparticlestumor microenvironment

Identifiers

PMID42801248
PMCPMC13615833

What OpenQuestion holds

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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.