Novel bioactive cationic cubosomes enhance the cytotoxic effect of paclitaxel against a paclitaxel resistant prostate cancer cell-line
Bibliographic record
Abstract
Hypothesis The study aimed to use molecular hybridization of a cationic lipid with a known pharmacophore to produce a bifunctional lipid having a cationic charge to enhance fusion with the cancer cell surface and biological activity via the pharmacophoric head group. Experiments The novel cationic lipid DMP12 [N-(2-(3-(3,4-dimethoxyphenyl) propanamido) ethyl)-N-dodecyl-N-methyldodecan-1-aminium iodide] was synthesised by conjugating 3-(3,4-dimethoxyphenyl) propanoic acid (or 3,4-dimethoxyhydrocinnamic acid) to twin 12 carbon chains bearing a quaternary ammonium group [N-(2-aminoethyl)-N-dodecyl-N-methyldodecan-1-aminium iodide]. The physicochemical and biological properties of DMP12 were investigated. Cubosome particles consisting of monoolein (MO) doped with DMP12 and paclitaxel were characterized using Small-angle X-ray Scattering (SAXS), Dynamic Light Scattering (DLS), and Cryo-Transmission Electron Microscopy (Cryo-TEM). Combination therapy using these cubosomes was assessed in vitro against the gastric (AGS) and prostate (DU-145 and PC-3) cancer cell lines using cytotoxicity assay. Findings Monoolein (MO) cubosomes doped with DMP12 were observed to be toxic against the AGS and DU-145 cell-lines at higher cubosome concentrations (≥100 µg/ml) but had limited activity against the PC-3 cell-line. However, combination therapy consisting of 5 mol% DMP12 and 0.5 mol% paclitaxel (PTX) significantly increased the cytotoxicity against the PC-3 cell-line which was resistant to either DMP12 or PTX individually. The results demonstrate that DMP12 has a prospective role as a bioactive excipient in cancer therapy.
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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".