Evidence map›Paper›PMID 40343691›Full record

ReviewDrug delivery and translational research2025

A framework for conducting clinical trials involving 3D printing of medicines at the point-of-care.

Carlos Javier Parramon-Teixido, Lucía Rodríguez-Pombo, Abdul W Basit, Anna Worsley, Carme Cañete-Ramírez, Carmen Alvarez-Lorenzo, Maria Josep Cabañas-Poy, Alvaro Goyanes

Abstract readReview
In one paragraph

Review in Drug delivery and translational research, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 9 papers.

0numbers the graph read from it
0cells of the map it votes in
9citing 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

9 citing papers in PubMed.

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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

8 authors.

Carlos Javier Parramon-TeixidoPharmacy Department, Vall d'Hebron Hospital Universitari, Vall d'Hebron Barcelona Hospital Campus, Barcelona, Spain.ORCID 0000-0001-8023-3979
Lucía Rodríguez-PomboDepartamento de Farmacología, Farmacia y Tecnología Farmacéutica, I+D Farma (GI-1645), Facultad de Farmacia, Instituto de Materiales (iMATUS) and Health Research Institute of Santiago de Compostela (IDIS), Universidade de Santiago de Compostela, Santiago de Compostela, 15782, Spain.ORCID 0000-0003-1028-2737
Abdul W BasitDepartment of Pharmaceutics, UCL School of Pharmacy, University College London, 29-39 Brunswick Square, London, WC1N 1AX, UK.ORCID 0000-0002-5368-6603
Anna WorsleyFABRX Ltd, Henwood House, Henwood, Ashford, Kent, TN24 8DH, UK.
Carme Cañete-RamírezPharmacy Department, Vall d'Hebron Hospital Universitari, Vall d'Hebron Barcelona Hospital Campus, Barcelona, Spain.ORCID 0009-0002-9849-1495
Carmen Alvarez-LorenzoDepartamento de Farmacología, Farmacia y Tecnología Farmacéutica, I+D Farma (GI-1645), Facultad de Farmacia, Instituto de Materiales (iMATUS) and Health Research Institute of Santiago de Compostela (IDIS), Universidade de Santiago de Compostela, Santiago de Compostela, 15782, Spain. carmen.alvarez.lorenzo@usc.es.ORCID 0000-0002-8546-7085
Maria Josep Cabañas-PoyPharmacy Department, Vall d'Hebron Hospital Universitari, Vall d'Hebron Barcelona Hospital Campus, Barcelona, Spain. mjosep.cabanas@vallhebron.cat.ORCID 0000-0003-2724-2715
Alvaro GoyanesDepartamento de Farmacología, Farmacia y Tecnología Farmacéutica, I+D Farma (GI-1645), Facultad de Farmacia, Instituto de Materiales (iMATUS) and Health Research Institute of Santiago de Compostela (IDIS), Universidade de Santiago de Compostela, Santiago de Compostela, 15782, Spain. a.goyanes@ucl.ac.uk.ORCID 0000-0002-3378-6797

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

The integration of 3D printing (3DP) technologies into personalized medicine manufacture at the point-of-care is garnering significant interest due to its potential to create tailored drug products with precise dosages and other unique attributes. Both preclinical and clinical studies have demonstrated promising outcomes, including pharmacokinetic bioequivalence, improved patient acceptability, enhanced adherence, and the ability to produce consistent, reproducible dosage forms with accurate drug distribution. Some compounding pharmacies around the world are already incorporating 3DP into standard practice for simpler therapeutic treatments. However, further clinical evaluation is required for more complex treatments, such as multi-drug polypills. Conducting clinical trials involving 3DP technologies presents several challenges, including navigating evolving regulatory frameworks, addressing ethical and legal concerns, and complying with new point-of-care manufacturing guidelines. Although regulatory agencies are beginning to adapt their policies to accommodate 3DP, the absence of a comprehensive framework still creates uncertainty for pharmacists and healthcare providers. This article explores the planning and execution of clinical trials involving 3D printed medicines, with a focus on regulatory barriers, patient recruitment, compliance, and the integration of specialized equipment and expertise. It also discusses the implementation of 3DP for personalized drug manufacturing within hospital settings and offers guidance for obtaining clinical trial approval from the Spanish Agency for Medicine and Health Products (AEMPS). By providing these insights and recommendations, this article aims to support international harmonization and facilitate the adoption of 3DP technologies in clinical trials globally.

Indexed as

Clinical Trials as TopicDrug CompoundingPoint-of-Care SystemsPrinting, Three-DimensionalHumansPrecision Medicine3D printingPaediatricsPersonalized medicationsPrinted pharmaceuticals and formulationsQuality controlRegulatory

Identifiers

PMID40343691
PMCPMC12350472

What OpenQuestion holds

Textmetadata
LicenceCC BY
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.