Dynamic Nuclear Polarization Enhanced NMR Spectroscopy for Pharmaceutical Formulations
Bibliographic record
Abstract
Dynamic nuclear polarization (DNP) enhanced solid-state NMR spectroscopy at 9.4 T is demonstrated for the detailed atomic-level characterization of commercial pharmaceutical formulations. To enable DNP experiments without major modifications of the formulations, the gently ground tablets are impregnated with solutions of biradical polarizing agents. The organic liquid used for impregnation (here 1,1,2,2-tetrachloroethane) is chosen so that the active pharmaceutical ingredient (API) is minimally perturbed. DNP enhancements (ε) of between 40 and 90 at 105 K were obtained for the microparticulate API within four different commercial formulations of the over-the-counter antihistamine drug cetirizine dihydrochloride. The different formulations contain between 4.8 and 8.7 wt % API. DNP enables the rapid acquisition with natural isotopic abundances of one- and two-dimensional (13)C and (15)N solid-state NMR spectra of the formulations while preserving the microstructure of the API particles. Here this allowed immediate identification of the amorphous form of the API in the tablet. API-excipient interactions were observed in high-sensitivity (1)H-(15)N correlation spectra, revealing direct contacts between povidone and the API. The API domain sizes within the formulations were determined by measuring the variation of ε as a function of the polarization time and numerically modeling nuclear spin diffusion. Here we measure an API particle radius of 0.3 μm with a single particle model, while modeling with a Weibull distribution of particle sizes suggests most particles possess radii of around 0.07 μm.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.001 | 0.001 |
| 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 | 0.001 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".