Evidence map›Paper›PMID 26453161›Full record

ReviewJournal of drug targeting2015

Smart approaches to glucose-responsive drug delivery.

Matthew J Webber, Daniel G Anderson

Abstract readReview
In one paragraph

Review in Journal of drug targeting, 2015. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 29 papers.

0numbers the graph read from it
0cells of the map it votes in
29citing 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

29 citing papers in PubMed.

  1. Review
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  11. Review
  12. Design and development of insulin microneedles for diabetes treatment.Drug delivery and translational research · 2022
    Review
  13. Review
  14. Article
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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

2 authors.

Matthew J Webbera David H. Koch Institute for Integrative Cancer Research, Massachusetts Institute of Technology , Cambridge , MA , USA .
Daniel G Andersona David H. Koch Institute for Integrative Cancer Research, Massachusetts Institute of Technology , Cambridge , MA , USA .

Funding

Array development of anti-inflammatory peptoid-graft polymers for islet deliveryF32DK101335 · NIDDK · MASSACHUSETTS INSTITUTE OF TECHNOLOGY · PI WEBBER, MATTHEW · 2013 to 2015
$159k
NIDDK NIH HHS F32 DK101335NIDDK NIH HHS F32DK101335
6 · The paper itself

Abstract

A grand challenge in the field of "smart" drug delivery has been the quest to create formulations that can sense glucose and respond by delivering an appropriate dose of insulin. This approach, referred to as the "fully synthetic pancreas", envisions closed-loop insulin therapy. The strategies for incorporating glucose sensing into formulations can be broadly categorized into three subsets: enzymatic sensing, natural glucose-binding proteins and synthetic molecular recognition. Here, we highlight some examples of each of these approaches. The challenges remaining en route to the realization of closed-loop insulin therapy are substantial, and include improved response time, more authentic fidelity in glycemic control, improved biocompatibility for delivery materials and assurance of both safety and efficacy. The ubiquitous existence of glucose, combined with the unstable and toxic properties of insulin, further compound efforts towards the generation of a fully synthetic pancreas. However, given the growing incidence of both type-1 and type-2 diabetes, there is significant potential impact from the realization of such an approach on improving therapeutic management of the disease.

Indexed as

Drug Delivery SystemsAnimalsBlood GlucoseDiabetes Mellitus, Type 1Diabetes Mellitus, Type 2Drug DesignGlucoseHumansHypoglycemic AgentsInsulinBlood GlucoseGlucoseHypoglycemic AgentsInsulinArtificial pancreasdiabetesinsulinsmart drug delivery

Identifiers

PMID26453161
PMCPMC5450160

What OpenQuestion holds

Textmetadata
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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.