Increasing Doxorubicin Loading in Lipid-Shelled Perfluoropropane Nanobubbles via a Simple Deprotonation Strategy
Bibliographic record
Abstract
Drug delivery to solid tumors using echogenic nanobubbles (NBs) and ultrasound (US) has recently gained significant interest. The approach combines attributes of nanomedicine and the enhanced permeation and retention (EPR) effect with the documented benefits of ultrasound to improve tumor drug distribution and treatment outcomes. However, optimized drug loading strategies, the drug-carrying capacity of NBs and their drug delivery efficiency have not been explored in depth and remain unclear. Here, we report for the first time on the development of a novel deprotonated hydrophobic doxorubicin-loaded C3F8 nanobubble (hDox-NB) for more effective US-mediated drug delivery. In this study, the size distribution and yield of hDox-NBs were measured via resonant mass measurement, while their drug-loading capacity was determined using a centrifugal filter technique. In vitro acoustic properties including contrast-imaging enhancement, initial echogenic signal and decay were assessed and compared to doxorubicin hydrochloride loaded-NBs (Dox.HCl-NBs). In addition, in vitro therapeutic efficacy of hDox-NBs was evaluated by cytotoxicity assay in human ovarian cancer cells (OVCAR-3). The results showed that the hDox-NBs were small (300.7 ± 4.6 nm), and the drug loading content was significantly enhanced (2 fold higher) compared to Dox.HCl-NBs. Unexpectedly, the in vitro acoustic performance was also improved by inclusion of hDox into NBs. hDox-NB showed higher initial US signal and a reduced signal decay rate compared to Dox.HCl-NBs. Furthermore, hDox-NBs combined with higher intensity US exhibited an excellent therapeutic efficacy in human ovarian cancer cells as shown in a reduction in cell viability. These results suggest that hDox-NBs could be considered as a promising theranostic agent to achieve a more effective noninvasive US-mediated drug delivery for cancer treatment.
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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 | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| 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".