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Record W2582481970 · doi:10.2147/ijn.s121881

Docetaxel-loaded PLGA and PLGA-PEG nanoparticles for intravenous application: pharmacokinetics and biodistribution profile

2017· article· en· W2582481970 on OpenAlexafffund
Pedram Rafiei, Azita Haddadi

Bibliographic record

VenueInternational Journal of Nanomedicine · 2017
Typearticle
Languageen
FieldMaterials Science
TopicNanoparticle-Based Drug Delivery
Canadian institutionsUniversity of Saskatchewan
FundersNatural Sciences and Engineering Research Council of Canada
KeywordsDocetaxelBiodistributionPLGAPharmacokineticsPharmacologyDrug deliveryDrug carrierMaterials scienceChemistryDrugMedicineNanoparticleNanotechnologyCancerIn vitroInternal medicineBiochemistry

Abstract

fetched live from OpenAlex

Abstract: Docetaxel is a highly potent anticancer agent being used in a wide spectrum of cancer types. There are important matters of concern regarding the drug’s pharmacokinetics related to the conventional formulation. Poly(lactide- co -glycolide) (PLGA) is a biocompatible/biodegradable polymer with variable physicochemical characteristics, and its application in human has been approved by the United States Food and Drug Administration. PLGA gives polymeric nanoparticles with unique drug delivery characteristics. The application of PLGA nanoparticles (NPs) as intravenous (IV) sustained-release delivery vehicles for docetaxel can favorably modify pharmacokinetics, biofate, and pharmacotherapy of the drug in cancer patients. Surface modification of PLGA NPs with poly(ethylene glycol) (PEG) can further enhance NPs’ long-circulating properties. Herein, an optimized fabrication approach has been used for the preparation of PLGA and PLGA–PEG NPs loaded with docetaxel for IV application. Both types of NP formulations demonstrated in vitro characteristics that were considered suitable for IV administration (with long-circulating sustained-release purposes). NP formulations were IV administered to an animal model, and docetaxel’s pharmacokinetic and biodistribution profiles were determined and compared between study groups. PLGA and PEGylated PLGA NPs were able to modify the pharmacokinetics and biodistribution of docetaxel. Accordingly, the mode of changes made to pharmacokinetics and biodistribution of docetaxel is attributed to the size and surface properties of NPs. NPs contributed to increased blood residence time of docetaxel fulfilling their role as long-circulating sustained-release drug delivery systems. Surface modification of NPs contributed to more pronounced docetaxel blood concentration, which confirms the role of PEG in conferring long-circulation properties to NPs. Keywords: poly(lactide-co-glycolide), poly(lactide-co-glycolide)–poly(ethylene glycol), polymeric nanoparticles, docetaxel, emulsification solvent evaporation, sustained release, pharmacokinetics, biodistribution

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: Bench or experimental · Consensus signal: Bench or experimental
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.001
Threshold uncertainty score0.003

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0000.000

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 head0.016
GPT teacher head0.296
Teacher spread0.280 · 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 designBench or experimental
Domainnot available
GenreEmpirical

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

Citations285
Published2017
Admission routes2
Has abstractyes

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