MétaCan
Menu
Back to cohort
Record W2518518788

Development of highly efficacious hydrophobic paclitaxel prodrugs delivered in nanoparticles for fixed-ratio drug combination applications

2008· article· en· W2518518788 on OpenAlexaff
Steven M. Ansell, Sharon A. Johnstone, Paul Tardi, Lily Lo, Sherwin Xie, Yu Shu, Troy O. Harasym, Natashia Harasym, Laura Williams, David Bermudes, Barry D. Liboiron, Walid Saad, Robert K. Prud’homme, Lawrence D. Mayer

Bibliographic record

VenueCancer Research · 2008
Typearticle
Languageen
FieldMedicine
TopicCancer Treatment and Pharmacology
Canadian institutionsCelator Pharmaceuticals (Canada)
Fundersnot available
KeywordsPaclitaxelProdrugDrugPharmacokineticsPharmacologyDrug deliveryChemistryIn vivoBioavailabilityMicelleDrug carrierTargeted drug deliveryCancerAqueous solutionMedicineOrganic chemistry
DOInot available

Abstract

fetched live from OpenAlex

AACR Annual Meeting-- Apr 12-16, 2008; San Diego, CA 5734 In vitro evidence has revealed that many drug combinations can act either synergistically or antagonistically, depending on the exposed drug ratio. Since the pharmacokinetic behavior of the individual drugs cannot be controlled when administered in a conventional aqueous based cocktail, drug delivery systems must be utilized to maintain optimal drug ratios. We report here the development of nanoparticle delivery systems for hydrophobic drugs where plasma drug levels can be controlled in a manner that can be readily adapted to deliver drug combinations. Paclitaxel is a hydrophobic chemotherapeutic drug that is typically used in combination with other agents to treat breast, lung and ovarian cancer. Poor drug solubility necessitates its formulation into a mixture of Cremophor and ethanol. Toxicity associated with the use of Cremophor has resulted in formulation efforts using polymer micelles or nanoparticles with hydrophobic cores to serve as a drug reservoir. While paclitaxel can be efficiently solubilized in these delivery systems, the drug is rapidly cleared from circulation following injection. We have generated a series of hydrophobic paclitaxel prodrugs with the objective of enhancing and controlling drug circulation lifetime through changes in the hydrophobic lipid anchor composition. Paclitaxel prodrugs were stably incorporated into amphiphilic block copolymer nanoparticles and administered intravenously into mice. The nanoparticles were shown to have an elimination half-life of approximately 24 h in vivo . The rate at which the prodrug was released from the nanoparticles could be controlled by adjusting the hydrophobicity of the lipid anchor, resulting in release rates ranging from 1h to 24 h. The nanoparticle formulations could be stored stably at 4°C for several months. To evaluate the therapeutic activity of the various paclitaxel prodrugs, nanoparticle formulations were administered intravenously into mice bearing HT29 human colon xenograph tumors. As the plasma half-life and area under the curve of the prodrug increased, activity against HT29 tumors also increased. Paclitaxel prodrug nanoparticles more than doubled the time for HT29 tumors to reach 400 mg relative to commercial paclitaxel in Cremophor, when both treatments were administered at MTD using optimal treatment regimens. The paclitaxel prodrugs could be co-formulated with hydrophobic prodrug analogues of water-soluble agents such as doxorubicin and dual-drug nanoparticles maintained the two agents at the injected drug:drug ratio in the plasma for extended times after injection. Formulating hydrophobic prodrugs in nanoparticles provides a novel approach to co-deliver anticancer drug combinations with widely differing physicochemical properties and maintain optimal drug:drug ratios 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 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)0.0010.000

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 head0.092
GPT teacher head0.420
Teacher spread0.329 · 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

Citations5
Published2008
Admission routes1
Has abstractyes

Explore more

Same venueCancer ResearchSame topicCancer Treatment and PharmacologyFrench-language works237,207