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Sildenafil Enhances the Anticancer Activity of Paclitaxel in an ABCB1-Mediated Multidrug Resistance Xenograft Mouse Model

2014· article· en· W2122563400 on OpenAlexvenueno aff
Kamlesh Sodani, Amit K. Tiwari, Chun‐Ling Dai, Alaa H. Abuznait, Atish Patel, Zhijie Xiao, Charles R. Ashby, Amal Kaddoumi, Liwu Fu, Zhe‐Sheng Chen

Bibliographic record

VenueJournal of cancer research updates · 2014
Typearticle
Languageen
FieldMedicine
TopicDrug Transport and Resistance Mechanisms
Canadian institutionsnot available
Fundersnot available
KeywordsPaclitaxelEffluxPharmacologyIn vivoSildenafilMultiple drug resistanceTransporterATP-binding cassette transporterP-glycoproteinIn vitroChemistryMedicineBiologyCancerInternal medicineBiochemistryAntibiotics

Abstract

fetched live from OpenAlex

The overexpression of ATP-binding cassette (ABC) transporters can produce multidrug resistance (MDR) in cancer cells. Previous in vitro studies from our group reported that sildenafil significantly inhibits the efflux function of the ABCB1/P-glycoprotein transporter in vitro. This investigation examined the effect of sildenafil on the ABCB1 transporter-mediated MDR in vivo. A nude mouse ABCB1 overexpressing-xenograft model was used to examine the effect of sildenafil in vivo. The concentration of paclitaxel in tumors and plasma was analyzed using high performance liquid chromatography (HPLC). Sildenafil attenuated tumor growth synergistically, and this occurred without significant weight loss or other overt phenotypic changes. The action of sildenafil can be attributed to the inhibition of the ABCB1-mediated drug efflux, thereby increasing the concentration of paclitaxel in ABCB1-overexpressing tumors. The potentiation of the pharmacologic action of paclitaxel by sildenafil suggests that it may be useful in treating cancers that overexpress the ABCB1 transporter.

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 score0.002
Threshold uncertainty score0.006

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0010.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.001
Insufficient payload (model declined to judge)0.0020.000

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 head0.045
GPT teacher head0.396
Teacher spread0.351 · 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

Citations1
Published2014
Admission routes1
Has abstractyes

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Same venueJournal of cancer research updates→Same topicDrug Transport and Resistance Mechanisms→French-language works237,207→