Quantifying Disproportionation in Pharmaceutical Formulations with <sup>35</sup>Cl Solid-State NMR
Bibliographic record
Abstract
Reliable methods for the characterization of drug substances are critical for evaluating stability and bioavailability, especially in dosage formulations under varying storage conditions and usage. Such methods must also give information on the molecular identities and structures of drug substances and any potential byproducts of the formulation process, as well as providing a means of quantifying the relative amounts of these substances. For example, active pharmaceutical ingredients (APIs) are often formulated as ionic salts to improve the pharmaceutical properties of dosage forms; however, exposure of such formulations to elevated temperature and/or humidity can trigger the conversion of an ionic salt of an API to a neutral form with different properties, through a process known as disproportionation. It is particularly challenging to identify changes of pharmaceutical components in solid dosage formulations, which are complex heterogeneous mixtures of the API and excipient components (e.g., binders, disintegrants, and lubricants). In this study, we illustrate that ultra-wideline (UW) 35 Cl solid-state NMR (SSNMR) can be used to characterize the disproportionation reaction of pioglitazone HCl (PiogHCl) in mixtures with metallic stearate excipients. 35 Cl SSNMR can quantitatively detect the amount of PiogHCl in mixed samples within ±1 wt % and measure the degree of PiogHCl disproportionation in formulation samples stressed at high relative humidity and temperature. Unlike other methods used for characterizing disproportionation, our experiments directly probe the Cl – anions in both the intact salt and disproportionation products, revealing all of the chlorine-containing products in the solid-state chemical reaction without interfering signals from the formulation excipients.
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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 | 0.001 | 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 | 0.001 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".