Folate receptor targeted nanoparticles containing niraparib and doxorubicin as a potential candidate for the treatment of high grade serous ovarian cancer
Bibliographic record
Abstract
Combination chemotherapy is an established approach used to manage toxicities while eliciting an enhanced therapeutic response. Delivery of drug combinations at specific molar ratios has been considered a means to achieve synergistic effects resulting in improvements in efficacy while minimizing dose related adverse drug reactions. The benefits of this approach have been realized with the FDA approval of Vyxeos®, the first liposome formulation to deliver a synergistic drug combination leading to improved overall survival against standard of care. In the current study, we demonstrate the synergistic potential of the PARP inhibitor niraparib and doxorubicin for the treatment of ovarian cancer. Through in vitro screening in a panel of ovarian cancer cell lines, we find that niraparib and doxorubicin demonstrate consistent synergy/additivity at the majority of evaluated molar ratio combinations. Further to these findings, we report formulation of a nanoparticle encapsulating our identified synergistic combination. We describe a rational design process to achieve highly stable liposomes that are targeted with folate to folate-receptor-alpha, which is known to be overexpressed on the surface of ovarian cancer cells. With this approach, we aim to achieve targeted delivery of niraparib and doxorubicin at a pre-determined synergistic molar ratio via increased receptor-mediated endocytosis.
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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 | 0.000 | 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) | 0.001 | 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".