Application of Solid-State<sup>35</sup>Cl NMR to the Structural Characterization of Hydrochloride Pharmaceuticals and their Polymorphs
Bibliographic record
Abstract
Solid-state (35)Cl NMR (SSNMR) spectroscopy is shown to be a useful probe of structure and polymorphism in HCl pharmaceuticals, which constitute ca. 50% of known pharmaceutical salts. Chlorine NMR spectra, single-crystal and powder X-ray diffraction data, and complementary ab initio calculations are presented for a series of HCl local anesthetic (LA) pharmaceuticals and some of their polymorphs. (35)Cl MAS SSNMR spectra acquired at 21.1 T and spectra of stationary samples at 9.4 and 21.1 T allow for extraction of chlorine electric field gradient (EFG) and chemical shift (CS) parameters. The sensitivity of the (35)Cl EFG and CS tensors to subtle changes in the chlorine environments is reflected in the (35)Cl SSNMR powder patterns. The (35)Cl SSNMR spectra are shown to serve as a rapid fingerprint for identifying and distinguishing polymorphs, as well as a useful tool for structural interpretation. First principles calculations of (35)Cl EFG and CS tensor parameters are in good agreement with the experimental values. The sensitivity of the chlorine NMR interaction tensor parameters to the chlorine chemical environment and the potential for modeling these sites with ab initio calculations hold much promise for application to polymorph screening for a wide variety of HCl pharmaceuticals.
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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 | 0.000 | 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 | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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".