Quantitative Macro-Raman Spectroscopy on Microparticle-Based Pharmaceutical Dosage Forms
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Bibliographic record
Abstract
Quantitative macro-Raman spectroscopy was applied to the analysis of the bulk composition of pharmaceutical drug powders. Powders were extracted from seven commercial lactose-carrier-based dry-powder inhalers: Flixotide 50, 100, 250, and 500 μg/dose (four concentrations of fluticasone propionate) and Seretide 100, 250, and 500 μg/dose (three concentrations of fluticasone propionate, each with 50 μg/dose salmeterol xinafoate ). Also, a carrier-free pressurized metered-dose inhaler of the same combination product, Seretide 50 (50 μg fluticasone propionate and 25 μg salmeterol xinafoate per dose) was tested. The applicability of a custom-designed dispersive macro-Raman instrument with a large sample volume of 0.16 μL was tested to determine the composition of the multicomponent powder samples. To quantify the error caused by sample heterogeneity, a Monte Carlo model was developed to predict the minimum sample volume required for representative sampling of potentially heterogeneous samples at the microscopic level, characterized by different particle-size distributions and compositions. Typical carrier-free respirable powder samples required a minimum sample volume on the order of 10(-4) μL to achieve representative sampling with less than 3% relative error. In contrast, dosage forms containing non-respirable carriers (e.g., lactose) required a sample volume on the order of 0.1 μL for representative measurements. Error analysis of the experimental results showed good agreement with the error predicted by the simulation.
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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 | 0.001 |
| Insufficient payload (model declined to judge) | 0.000 | 0.002 |
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 it