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Record W2166014214 · doi:10.2174/187152006778699121

Pharmacokinetics and Pharmacodynamics of Lipidic Nano-Particles in Cancer

2006· review· en· W2166014214 on OpenAlex

Why this work is in the frame

A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.

affAt least one author lists a Canadian institution in the pinned OpenAlex snapshot.
fundA Canadian funder is recorded on the work.

Bibliographic record

VenueAnti-Cancer Agents in Medicinal Chemistry · 2006
Typereview
Languageen
FieldComputer Science
TopicComputational Drug Discovery Methods
Canadian institutionsUniversity of Alberta
FundersCanadian Institutes of Health Research
KeywordsPharmacodynamicsPharmacokineticsCancerNano-PharmacologyMedicineInternal medicineMaterials scienceComposite material

Abstract

fetched live from OpenAlex

Nanoscale drug delivery systems (DDS) are used to circumvent some of the non-ideal properties of conventional anticancer chemotherapy drugs. Manipulation of the physical properties of DDS provides improved control over the pharmacokinetics (PK) and pharmacodynamics (PD) of the encapsulated drugs relative to free drugs. Liposomes are the archetypical nanoscale DDS and the first of these received clinical approval in 1990. DOXIL, liposomal doxorubicin, was the first commercially available liposomal anticancer drug (1995). It has an enhanced circulation half-life compared to the free drug because of its surface-grafted polyethylene glycol coating. DOXIL passively targets solid tumors, and once the liposomes localize in the tumor interstitial space, the cytotoxic drug is slowly released within the tumor. Liposomes can act as sustained release delivery system and manipulation of properties such as, liposome diameter, drug release rate, bioavailability and dosing schedule can significantly impact the therapeutic outcome of the liposomal drugs. This review will focus on how alteration of these properties can impact the therapeutic efficacy and side effect profiles of DDS.

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.

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)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesMeta-epidemiology (narrow)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Other design · Consensus signal: none
GenreCandidate signal: Review · Consensus signal: Review
Teacher disagreement score0.972
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0000.001
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0010.000
Research integrity0.0000.001
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 head0.078
GPT teacher head0.440
Teacher spread0.362 · 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