Comparison between nuclear magnetic resonance profiling and the source/sink approach for characterizing drug diffusion in hydrogel matrices
Bibliographic record
Abstract
In controlled drug delivery, the drug diffusion coefficient in a given matrix is a key factor for predicting its release rate. In this work, we compare (31)P nuclear magnetic resonance (NMR) profiling for obtaining the mutual-diffusion coefficient (D(m)(gel)) of a drug in hydrogel with results obtained from conventional source/sink experiments. Despite the fact that NMR profiling is a powerful approach for measuring transport properties, it is rarely used for characterizing drug diffusion in gel matrices in pharmaceutical sciences. This work provides an illustration of the applicability of this technique and highlights its advantages for studying drug release systems. The comparison with results obtained from the source/sink experiment clearly establishes the validity of the NMR profiling approach. Alendronate was used as a model drug while curdlan, a gel-forming bacterial polysaccharide, served as a model biomaterial. The determined (D(m)(gel)) value (5.6 ± 0.3 × 10(-10) m(2)/s) agrees with the one obtained from a conventional source/sink experiment (5.4 ± 0.5 × 10(-10) m(2)/s). In addition, the alendronate self-diffusion coefficients in solution (D(s)(sln)) and in the hydrogel (D(s)(gel)) were measured on the same system using pulse-field gradient (PFG) (31)P NMR. These supplementary parameters provided a more detailed characterization of the drug transport properties in the gel matrix.
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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 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| 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".