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Record W4379745401 · doi:10.1016/j.addr.2023.114950

Solid implantable devices for sustained drug delivery

2023· review· en· W4379745401 on OpenAlexfundno aff
Elizabeth Magill, Sara Demartis, Elisabetta Gavini, Andi Dian Permana, Muhammad Faris Adrianto, David Waite, Katie Glover, Camila J. Picco, Anna Korelidou, Usanee Detamornrat, Lalitkumar K. Vora, Linlin Li, Qonita Kurnia Anjani, Ryan F. Donnelly, Juan Domínguez‐Robles, Eneko Larrañeta

Bibliographic record

VenueAdvanced Drug Delivery Reviews · 2023
Typereview
Languageen
FieldPharmacology, Toxicology and Pharmaceutics
TopicAdvanced Drug Delivery Systems
Canadian institutionsnot available
FundersH2020 Marie Skłodowska-Curie ActionsEngineering and Physical Sciences Research CouncilHorizon 2020 Framework ProgrammeEuropean CommissionQueen's UniversityAgencia Estatal de InvestigaciónMinisterio de Ciencia, Innovación y UniversidadesQueen's University BelfastAcademy of Medical Sciences
KeywordsDrugDrug deliveryPharmacologyDrug administrationMedicineOral administrationRoute of administrationTargeted drug deliveryDosage formChemistry

Abstract

fetched live from OpenAlex

Implantable drug delivery systems (IDDS) are an attractive alternative to conventional drug administration routes. Oral and injectable drug administration are the most common routes for drug delivery providing peaks of drug concentrations in blood after administration followed by concentration decay after a few hours. Therefore, constant drug administration is required to keep drug levels within the therapeutic window of the drug. Moreover, oral drug delivery presents alternative challenges due to drug degradation within the gastrointestinal tract or first pass metabolism. IDDS can be used to provide sustained drug delivery for prolonged periods of time. The use of this type of systems is especially interesting for the treatment of chronic conditions where patient adherence to conventional treatments can be challenging. These systems are normally used for systemic drug delivery. However, IDDS can be used for localised administration to maximise the amount of drug delivered within the active site while reducing systemic exposure. This review will cover current applications of IDDS focusing on the materials used to prepare this type of systems and the main therapeutic areas of application.

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 machine prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.

metaresearch head score (Codex)0.000
metaresearch head score (Gemma)0.000
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: none
GenreCandidate signal: Review · Consensus signal: Review
Teacher disagreement score0.009
Threshold uncertainty score0.031

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0010.001
Bibliometrics0.0020.001
Science and technology studies0.0000.000
Scholarly communication0.0010.001
Open science0.0010.001
Research integrity0.0010.001
Insufficient payload (model declined to judge)0.0090.006

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 head0.184
GPT teacher head0.495
Teacher spread0.312 · 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; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
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

Citations127
Published2023
Admission routes1
Has abstractyes

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