Evidence map›Paper›PMID 41588942›Full record

ArticleCurrent drug delivery2026

Advancements in 3D Printing and Nanomaterials for Personalized Drug Delivery.

M Arjun Gokulan, J Narayanan

Abstract read
In one paragraph

Article in Current drug delivery, 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. Article
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

2 authors.

M Arjun GokulanDepartment of Pharmacology, SRM College of Pharmacy, Faculty of Medicine and Health Sciences, SRM Institute of Science and Technology, Kattankulathur, Chengalpattu- 603 203, Tamil Nadu, India.ORCID 0000-0003-4253-7001
J NarayananDepartment of Pharmacology, SRM College of Pharmacy, Faculty of Medicine and Health Sciences, SRM Institute of Science and Technology, Kattankulathur, Chengalpattu- 603 203, Tamil Nadu, India.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

3D printing, also known as additive manufacturing, has transformed drug delivery by enabling the development of complex, patient-specific dosage forms and implantable systems tailored to individual therapeutic needs. This review explores the convergence of 3D printing technologies with nanomaterials in the fabrication of advanced drug delivery systems and biomedical implants. Key 3D printing techniques, including Fused Deposition Modeling (FDM), Stereolithography (SLA), Direct Energy Deposition (DED), and electrospinning, are discussed alongside their material compatibilities, such as polymers, metals, ceramics, and composites. Nanomaterials-like dendrimers, liposomes, polymeric nanoparticles, carbon nanotubes, and exosomes-are critically examined for their roles in enhancing drug stability, targeted delivery, and controlled release. The paper highlights innovative drug delivery strategies, including polypills, gastro-floating tablets, and compartmentalized dosage systems, enabled by precise 3D printing. Additionally, recent advancements in 3D-printed drugeluting implants for localized therapy in cancer and infectious diseases are presented. These systems demonstrate prolonged release profiles, biocompatibility, and mechanical properties resembling those of human tissue. Despite scaling and regulatory challenges, the future of this technology lies in the integration of smart materials, surface-modified nanoparticles, and AI-assisted design, paving the way for decentralized, personalized, and sustainable medical solutions.

Indexed as

3D printingbioprintingdrug deliveryFDMimplantsnanomaterialspersonalized medicinestereolithography.

Identifiers

PMID41588942
PMCPMC13621375

What OpenQuestion holds

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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.