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Synthesis of a Gemcitabine Prodrug for Remote Loading into Liposomes and Improved Therapeutic Effect

2015· article· en· W2396647400 on OpenAlexafffund
Jonathan P. May, Elijus Undzys, Aniruddha Roy, Shyh‐Dar Li

Bibliographic record

VenueBioconjugate Chemistry · 2015
Typearticle
Languageen
FieldMaterials Science
TopicNanoparticle-Based Drug Delivery
Canadian institutionsUniversity of British ColumbiaOntario Institute for Cancer Research
FundersCanadian Institutes of Health ResearchProstate Cancer Foundation
KeywordsChemistryProdrugGemcitabineLiposomePharmacologyCombinatorial chemistryChemotherapyBiochemistryInternal medicine

Abstract

fetched live from OpenAlex

The chemotherapeutic gemcitabine was actively and stably loaded into lipid nanoparticles through the formation of a prodrug. Gemcitabine was chemically modified to increase the lipophilicity and introduce a weak base moiety for remote loading. Several derivatives were synthesized and screened for their potential to be good liposomal drug candidates for remote loading by studying their solubility, stability, cytotoxicity, and loading efficiency. Two morpholino derivatives of GEM (22 and 23) were chosen as the preferred prodrugs for this purpose as they possessed the best loading efficiencies (100% for drug-to-lipid ratio of 0.36 w/w). This is a considerable improvement over a passive loading strategy where typical loading efficiencies are on the order of ∼10-20% for a drug-to-lipid ratio of ∼0.01. Liposomes loaded with these two prodrugs were studied in an s.c. tumor model in vivo and showed improved therapeutic effect over free GEM (∼2-fold) and saline control (8- to 10-fold). This work demonstrates how chemical modification of a known hydrophilic drug can lead to improved loading, stability, and drug delivery in vivo.

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 distilled prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.

metaresearch head score (Codex)0.001
metaresearch head score (Gemma)0.001
Version: codex-gemma-dda1882f352aValidation 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 score0.004
Threshold uncertainty score0.717

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.001
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 head0.014
GPT teacher head0.246
Teacher spread0.232 · 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 teacher head, 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

Citations28
Published2015
Admission routes2
Has abstractyes

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