Pharmacokinetic Consequences of PLGA Nanoparticles in Docetaxel Drug Delivery
Bibliographic record
Abstract
BACKGROUND: Cancer chemotherapy is accompanied with administration of highly potent cytotoxic agents in doses that can result in non-specific drug toxicity and side effects. Chemotherapeutic agents possess limitations such as lack of water solubility, high volume of distribution, poor bioavailability, narrow therapeutic indices, multi-drug resistance, etc. that raise serious matters of concern regarding drug's pharmaceutical and clinical aspects. However, application of nanoparticles in delivery of anti-cancer agents has been a popular approach to address these concerns. Poly (lactide-co-glycolide) (PLGA), a biocompatible/biodegradable FDA-approved polymer has been widely used as drug carrier to enhance pharmaceutical/therapeutic properties of anticancer agents, prolonging their circulation time, targeting cancer tissues or protecting the drug from rapid elimination/premature degradation. This favourably modifies drug's pharmacokinetics and pharmacodynamics. OBJECTIVE: This paper provides a general perspective on how association of docetaxel to PLGA nanoparticles potentially modifies pharmacokinetics and biodistribution profile of the anticancer agent. METHOD: A comprehensive literature search has been conducted and dedicated to compile most relevant and up-to-date material about pharmacokinetic consequences of PLGA nanoparticles in docetaxel drug delivery. RESULTS: A set of determinants are considered to be influential on biodistribution and fate of docetaxel and PLGA nanoparticles. These are attributed to physicochemical properties of PLGA polymer, docetaxel, nanoparticle, and the set of events imposed to the nanoparticles by the host body. CONCLUSION: Association of PLGA nanoparticles and docetaxel has demonstrated to modify the drug's pharmacokinetic and biodistribution profile.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot 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.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 0.000 |
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.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
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".