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Record W4401933998 · doi:10.1021/acs.biomac.4c00489

Acid-Responsive Polymer Micelles for Targeted Delivery and Bioorthogonal Activation of Prodrug through Ru Catalyst in Tumor Cells

2024· article· en· W4401933998 on OpenAlexaff
Panpan Zhang, Leitao Zhang, Zhihao Wang, Qiuli Cheng, Wen‐Lan Wu, Junbo Li, Gaofeng Liang, Ravin Narain

Bibliographic record

VenueBiomacromolecules · 2024
Typearticle
Languageen
FieldChemistry
TopicClick Chemistry and Applications
Canadian institutionsUniversity of Alberta
FundersNational Natural Science Foundation of China-Henan Joint FundNatural Science Foundation of Henan ProvinceNational Natural Science Foundation of China
KeywordsProdrugBioorthogonal chemistryMicelleChemistryCatalysisCombinatorial chemistryPolymerDrug deliveryNanotechnologyBiochemistryOrganic chemistryClick chemistryMaterials science

Abstract

fetched live from OpenAlex

Bioorthogonal reactions present a promising strategy for minimizing off-target toxicity in cancer chemotherapy, yet a dependable nanoplatform is urgently required. Here, we have fabricated an acid-responsive polymer micelle for the specific delivery and activation of the prodrug within tumor cells through Ru catalyst-mediated bioorthogonal reactions. The decomposition of micelles, triggered by the cleavage of the hydrazone bond in the acidic lysosomal environment, facilitated the concurrent release of Alloc -DOX and the Ru catalyst within the cells. Subsequently, the uncaging process of Alloc -DOX was demonstrated to be induced by the high levels of glutathione within tumor cells. Notably, the limited glutathione inside normal cells prevented the conversion of Alloc -DOX into active DOX, thereby minimizing the toxicity toward normal cells. In tumor-bearing mice, this nanoplatform exhibited enhanced efficacy in tumor suppression while minimizing off-target toxicity. Our study provides an innovative approach for in situ drug activation that combines safety and effectiveness in cancer chemotherapy.

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)0.000
metaresearch head score (Gemma)0.000
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 score0.003
Threshold uncertainty score0.660

Codex and Gemma teacher scores by category

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 head0.012
GPT teacher head0.254
Teacher spread0.242 · 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

Citations7
Published2024
Admission routes1
Has abstractyes

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