Rapid and simple flow injection analysis tandem mass spectrometric method for the quantification of melphalan in a lipid‐based drug delivery system
Bibliographic record
Abstract
RATIONALE: The use of the anticancer drug melphalan is limited due to its poor water solubility. To address this limitation, it is incorporated within a novel delivery system using β-cyclodextrin-gemini surfactants (18:1βCDg). METHODS: Herein, two fast and simple flow injection analysis/tandem mass spectrometric (FIA-MS/MS) methods are developed for the quantification of melphalan (Mel) within the drug delivery system so that the solubilization efficiency of the system can be assessed. FIA-MS/MS methods are developed using a triple quadrupole linear ion trap mass spectrometer, equipped with electrospray ionization (ESI) in the positive ion mode. A deuterated form of melphalan (melphalan-d8) was used as an internal standard (IS). The methods were validated according to the FDA guidance. RESULTS: A linearity in the range of 2-100 ng/mL and accuracy and precision below 15% were observed for all standard points and quality control samples. The intra- and inter-day variations and freeze-thaw stability were within the acceptable range according to the criteria set by regulatory guidelines. On the other hand, other stability measures, such as room temperature stability and long-term stability, did not meet the required guidelines in some cases, indicating the need for quick sample analysis upon preparation. Such a fact could have been overlooked if full method validation had not been performed. CONCLUSIONS: The developed methods were applied to determine the encapsulation/solubilization of the [18:1βCDg/Mel] delivery system. 18:1βCDg enhances the aqueous solubility of melphalan without the need for co-solvent. The highest melphalan solubility was observed at a melphalan18:1βCDg/Mel complex molar ratio of 2:1. This study demonstrated that a fast analysis for the purpose of quantifying a chemically unstable drug, such as melphalan, is feasible and important for the development of commercial dosage forms.
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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 | 0.002 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 0.001 |
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".