In Situ Characterization of Waters of Hydration in a Variable-Hydrate Active Pharmaceutical Ingredient Using <sup>35</sup>Cl Solid-State NMR and X-ray Diffraction
Bibliographic record
Abstract
Variable-hydrate active pharmaceutical ingredients (APIs) are known to form thermodynamically and kinetically stabilized solid phases over a continuous range of nonstoichiometric hydration levels. Some of these forms can be problematic in the production of solid dosage forms (e.g., tablets and capsules), where manufacturing processes can induce changes in the hydration level of the API, resulting in transformations to undesirable solid phases that may affect product quality. In order to improve the development of variable-hydrate APIs for commercial use, reliable methods must be developed to not only measure the hydration levels, but also to probe the influences of water molecules on the molecular-level structures of APIs within dosage formulations. In this study, we examine a Genentech development compound, GNE-A, which is a hydrochloride (HCl) salt of an API that exhibits variable-hydrate behavior. Using a combination of 35 Cl solid-state NMR (SSNMR), variable-relative humidity (RH) powder X-ray diffraction (PXRD), thermogravimetric analysis, and dispersion-corrected plane-wave density functional theory (DFT-D2*) calculations, we reveal the local and long-range structural effects of water under different storage and processing conditions. 35 Cl SSNMR spectra are particularly sensitive to the presence of water and reveal distinct anionic Cl – environments in the hydrated and dehydrated forms of the HCl API. Our data demonstrate that complete dehydration of the material is surprisingly difficult, even with repeated drying cycles. Finally, 35 Cl SSSNMR is shown to be very useful for probing the local structural environments of Cl – ions in tablets processed using either wet or dry granulation, since there are no interfering signals from the complex array of excipient molecules present in the formulation.
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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 | 0.000 |
| 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 | 0.000 |
| 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".