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Record W2037995511 · doi:10.1038/cddis.2014.413

Nanoencapsulation of ABT-737 and camptothecin enhances their clinical potential through synergistic antitumor effects and reduction of systemic toxicity

2014· article· en· W2037995511 on OpenAlexfundno aff
Daniela Schmid, Gavin E. Jarvis, François Fay, Donna M. Small, Michelle K. Greene, Joanna Majkut, Shaun Spence, Kirsty M. McLaughlin, Karen D. McCloskey, Patrick G. Johnston, Adrien Kissenpfennig, Daniel B. Longley, Christopher J. Scott

Bibliographic record

VenueCell Death and Disease · 2014
Typearticle
Languageen
FieldBiochemistry, Genetics and Molecular Biology
TopicCancer therapeutics and mechanisms
Canadian institutionsnot available
FundersMedical Research CouncilQueen's University BelfastQueen's UniversityCancer Research UK
KeywordsCamptothecinPharmacologyToxicityIn vivoChemistryTopoisomeraseApoptosisDrugEtoposideLeukopeniaSystemic administrationIn vitroMedicineChemotherapyBiologyBiochemistryInternal medicine

Abstract

fetched live from OpenAlex

The simultaneous delivery of multiple cancer drugs in combination therapies to achieve optimal therapeutic effects in patients can be challenging. This study investigated whether co-encapsulation of the BH3-mimetic ABT-737 and the topoisomerase I inhibitor camptothecin (CPT) in PEGylated polymeric nanoparticles (NPs) was a viable strategy for overcoming their clinical limitations and to deliver both compounds at optimal ratios. We found that thrombocytopenia induced by exposure to ABT-737 was diminished through its encapsulation in NPs. Similarly, CPT-associated leukopenia and gastrointestinal toxicity were reduced compared with the administration of free CPT. In addition to the reduction of dose-limiting side effects, the co-encapsulation of both anticancer compounds in a single NP produced synergistic induction of apoptosis in both in vitro and in vivo colorectal cancer models. This strategy may widen the therapeutic window of these and other drugs and may enhance the clinical efficacy of synergistic drug combinations.

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 score0.012
Threshold uncertainty score0.300

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 head0.009
GPT teacher head0.254
Teacher spread0.245 · 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

Citations55
Published2014
Admission routes1
Has abstractyes

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