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Record W4412055403 · doi:10.1080/17425247.2025.2531062

3D printed hollow microneedles: the latest innovation in drug delivery

2025· review· en· W4412055403 on OpenAlexaff
Mahmood Razzaghi, Mohsen Akbari

Bibliographic record

VenueExpert Opinion on Drug Delivery · 2025
Typereview
Languageen
FieldPharmacology, Toxicology and Pharmaceutics
TopicAdvancements in Transdermal Drug Delivery
Canadian institutionsUniversity of Victoria
Fundersnot available
KeywordsMaterials scienceDrug deliveryNanotechnology3d printedDrugBiomedical engineeringPharmacologyMedicine

Abstract

fetched live from OpenAlex

INTRODUCTION: Hollow microneedles (HMNs) offer a minimally invasive and highly efficient method for transdermal drug administration, overcoming the limitations of traditional delivery systems. AREAS COVERED: This review focuses on recent advancements in 3D-printed HMNs, highlighting their transformative potential in drug delivery applications. The integration of cutting-edge 3D printing technologies, such as stereolithography (SLA), digital light processing (DLP), and two-photon polymerization (2PP), has enabled the fabrication of complex, precise, and customizable microneedles (MNs). These innovations facilitate patient-specific applications, enhance drug bioavailability, and provide unparalleled control over dosage and delivery. Advances in biocompatible and biodegradable materials have further improved the safety and functionality of HMNs. Applications range from insulin delivery to biomarker sensing and theranostic systems, showcasing their versatility. EXPERT OPINION: 3D-printed HMNs are set to play an important role in improving personalized medicine and precision healthcare. By addressing fabrication and design issues, and using new materials, these devices are expected to change drug delivery systems and help develop new therapeutic and diagnostic platforms.

Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.

How this classification was reachedexpand

Full frame distilled prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.

metaresearch head score (Codex)0.002
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesMeta-epidemiology (narrow), Research integrity, Insufficient payload (model declined to judge)
Consensus categoriesMeta-epidemiology (narrow)
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: none
GenreCandidate signal: Review · Consensus signal: Review
Teacher disagreement score0.913
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0020.000
Meta-epidemiology (narrow)0.0010.001
Meta-epidemiology (broad)0.0020.001
Bibliometrics0.0020.002
Science and technology studies0.0010.000
Scholarly communication0.0000.000
Open science0.0020.001
Research integrity0.0010.004
Insufficient payload (model declined to judge)0.0010.001

Machine scores (provisional)

The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.

Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.

Opus teacher head0.117
GPT teacher head0.452
Teacher spread0.335 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; both teacher heads agree on what is shown here.

Study designNot applicable
Domainnot available
GenreReview

How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".

Quick stats

Citations5
Published2025
Admission routes1
Has abstractyes

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