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Record W2148910549 · doi:10.3390/molecules13051156

Prodrugs in Cardiovascular Therapy

2008· review· en· W2148910549 on OpenAlexafffund
Marinella G. Sandros, Chady B. Sarraf, Maryam Tabrizian

Bibliographic record

VenueMolecules · 2008
Typereview
Languageen
FieldImmunology and Microbiology
TopicMacrophage Migration Inhibitory Factor
Canadian institutionsMcGill University
FundersNatural Sciences and Engineering Research Council of CanadaCanadian Institutes of Health Research
KeywordsProdrugDrugPharmacologyAdeptChemistryMedicine

Abstract

fetched live from OpenAlex

Prodrugs are biologically inactive derivatives of an active drug intended to solve certain problems of the parent drug such as toxicity, instability, minimal solubility and non-targeting capabilities. The majority of drugs for cardiovascular diseases undergo first-pass metabolism, resulting in drug inactivation and generation of toxic metabolites, which makes them appealing targets for prodrug design. Since prodrugs undergo a chemical reaction to form the parent drug once inside the body, this makes them very effective in controlling the release of a variety of compounds to the targeted site. This review will provide the reader with an insight on the latest developments of prodrugs that are available for treating a variety of cardiovascular diseases. In addition, we will focus on several drug delivery methodologies that have merged with the prodrug approach to provide enhanced target specificity and controlled drug release with minimal side effects.

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: Not applicable · Consensus signal: none
GenreCandidate signal: Review · Consensus signal: Review
Teacher disagreement score0.003
Threshold uncertainty score0.011

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0020.001
Science and technology studies0.0000.001
Scholarly communication0.0010.001
Open science0.0010.001
Research integrity0.0010.002
Insufficient payload (model declined to judge)0.0030.004

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 head0.035
GPT teacher head0.278
Teacher spread0.243 · 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 designNot applicable
Domainnot available
GenreReview

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

Citations9
Published2008
Admission routes2
Has abstractyes

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