Sample Extraction and Simultaneous Chromatographic Quantitation of Doxorubicin and Mitomycin C Following Drug Combination Delivery in Nanoparticles to Tumor-bearing Mice
Bibliographic record
Abstract
Combination chemotherapy is frequently used in the clinic for cancer treatment; however, associated adverse effects to normal tissue may limit its therapeutic benefit. Nanoparticle-based drug combination has been shown to mitigate the problems encountered by free drug combination therapy. Our previous studies have shown that the combination of two anticancer drugs, doxorubicin (DOX) and mitomycin C (MMC), produced a synergistic effect against both murine and human breast cancer cells in vitro. DOX and MMC co-loaded polymer-lipid hybrid nanoparticles (DMPLN) bypassed various efflux transporter pumps that confer multidrug resistance and demonstrated enhanced efficacy in breast tumor models. Compared to conventional solution forms, such superior efficacy of DMPLN was attributed to the synchronized pharmacokinetics of DOX and MMC and increased intracellular drug bioavailability within tumor cells enabled by the nanocarrier PLN. To evaluate the pharmacokinetics and bio-distribution of co-administered DOX and MMC in both free solution and nanoparticle forms, a simple and efficient multi-drug analysis method using reverse-phase high performance liquid chromatography (HPLC) was developed. In contrast to previously reported methods that analyzed DOX or MMC individually in the plasma, this new HPLC method is able to simultaneously quantitate DOX, MMC and a major cardio-toxic DOX metabolite, doxorubicinol (DOXol), in various biological matrices (e.g., whole blood, breast tumor, and heart). A dual fluorescent and ultraviolet absorbent probe 4-methylumbelliferone (4-MU) was used as an internal standard (I.S.) for one-step detection of multiple drug analysis with different detection wavelengths. This method was successfully applied to determine the concentrations of DOX and MMC delivered by both nanoparticle and solution approaches in whole blood and various tissues in an orthotopic breast tumor murine model. The analytical method presented is a useful tool for pre-clinical analysis of nanoparticle-based delivery of drug combinations.
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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".