Evidence map›Paper›PMID 28952558›Full record

ReviewBioengineering (Basel, Switzerland)2017

Personalised 3D Printed Medicines: Which Techniques and Polymers Are More Successful?

Andrea Alice Konta, Marta García-Piña, Dolores R Serrano

Abstract readReview
In one paragraph

Review in Bioengineering (Basel, Switzerland), 2017. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 75 papers.

0numbers the graph read from it
0cells of the map it votes in
75citing 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

75 citing papers in PubMed.

  1. Article
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  6. Construction ofRegenerative biomaterials · 2026
    Article
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15 more citing papers are in PubMed but not listed here.

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

3 authors.

Andrea Alice KontaDepartamento de Farmacia y Tecnología Farmacéutica, Facultad de Farmacia, Universidad Complutense de Madrid, Plaza Ramóny Cajal s/n, 28040 Madrid, Spain. akonta@ucm.es.
Marta García-PiñaDepartamento de Farmacia y Tecnología Farmacéutica, Facultad de Farmacia, Universidad Complutense de Madrid, Plaza Ramóny Cajal s/n, 28040 Madrid, Spain. martga08@ucm.es.
Dolores R SerranoDepartamento de Farmacia y Tecnología Farmacéutica, Facultad de Farmacia, Universidad Complutense de Madrid, Plaza Ramóny Cajal s/n, 28040 Madrid, Spain. drserran@ucm.es.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

The interindividual variability is an increasingly global problem when treating patients from different backgrounds with diverse customs, metabolism, and necessities. Dose adjustment is frequently based on empirical methods, and therefore, the chance of undesirable side effects to occur is high. Three-dimensional (3D) Printed medicines are revolutionsing the pharmaceutical market as potential tools to achieve personalised treatments adapted to the specific requirements of each patient, taking into account their age, weight, comorbidities, pharmacogenetic, and pharmacokinetic characteristics. Additive manufacturing or 3D printing consists of a wide range of techniques classified in many categories but only three of them are mostly used in the 3D printing of medicines: printing-based inkjet systems, nozzle-based deposition systems, and laser-based writing systems. There are several drawbacks when using each technique and also the type of polymers readily available do not always possess the optimal properties for every drug. The aim of this review is to give an overview about the current techniques employed in 3D printing medicines, highlighting their advantages, disadvantages, along with the polymer and drug requirements for a successful printing. The major application of these techniques will be also discussed.

Indexed as

3D printingdrug deliveryFDMpersonalised medicinepolymersSLA

Identifiers

PMID28952558
PMCPMC5746746

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.