Combinatorial and sequential delivery of gemcitabine and oseltamivir phosphate from implantable poly(D,L-lactic-co-glycolic acid) cylinders disables human pancreatic cancer cell survival
Bibliographic record
Abstract
Abstract: Combination therapies against multiple targets are currently being developed to prevent resistance to a single chemotherapeutic agent and to extirpate pre-existing resistance in heterogeneous cancer cells in tumors due to selective pressure from the single agent. Gemcitabine (GEM), a chemotherapeutic agent, is the current standard of care for patients with pancreatic cancer. Patients with pancreatic cancer receiving GEM have a low progression-free survival. Given the poor response rate to GEM, cancer cells are known to develop rapid resistance to this drug. Metronomic chemotherapy using combinatorial and sequential delivery systems are novel developmental approaches to disrupt tumor neovascularization, reduce systemic drug toxicity, and increase the sensitivity of chemotherapeutics in cancer. Here, implantable double-layered poly(D,L-lactic-co-glycolic acid) (PLGA) cylinders were engineered to sequentially release GEM in combination with oseltamivir phosphate (OP) over an extended time. Double-layered PLGA cylindrical implants loaded with these active hydrophilic drugs were fabricated with minimal loss of drugs during the formulation, enabling extensive control of drug loading and establishing uniform drug distribution throughout the polymer matrix. OP is used in the formulation because of its anticancer drug properties targeting mammalian neuraminidase 1 (Neu1) involved in multistage tumorigenesis. OP and GEM encapsulated in inner/outer GEM in /OP out or OP in /GEM out implantable PLGA double-layered cylinders displayed sustained near linear release over 30 days. OP and GEM released from the double-layered cylinders effectively reduced cell viability in pancreatic cancer cell line PANC1 and its GEM-resistant variant for up to 15 days. Keywords: pancreatic cancer, oseltamivir phosphate, gemcitabine, PLGA, chemoresistance
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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) | 0.000 | 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 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".