Preparation of parenteral nanocrystal suspensions of etoposide from the excipient free dry state of the drug to enhance in vivo antitumoral properties
Bibliographic record
Abstract
Nanoparticle technology in cancer chemotherapy is a promising approach to enhance active ingredient pharmacology and pharmacodynamics. Indeed, drug nanoparticles display various assets such as extended blood lifespan, high drug loading and reduced cytotoxicity leading to better drug compliance. In this context, organic nanocrystal suspensions for pharmaceutical use have been developed in the past ten years. Nanocrystals offer new possibilities by combining the nanoformulation features with the properties of solid dispersed therapeutic ingredients including (i) high loading of the active ingredient, (ii) its bioavailability improvement, and (iii) reduced drug systemic cytotoxicity. However, surprisingly, no antitumoral drug has been marketed as a nanocrystal suspension until now. Etoposide, which is largely used as an anti-cancerous agent against testicular, ovarian, small cell lung, colon and breast cancer in its liquid dosage form, has been selected to develop injectable nanocrystal suspensions designed to be transferred to the clinic. The aim of the present work is to provide optimized formulations for nanostructured etoposide solutions and validate by means of in vitro and in vivo evaluations the efficiency of this multiphase system. Indeed, the etoposide formulated as a nanosuspension by a bottom-up approach showed higher blood life span, reduced tumor growth and higher tolerance in a murine carcinoma cancer model. The results obtained are promising for future clinical evaluation of these etoposide nanosuspensions.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot 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.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".