Analysis of solid-state transformations of pharmaceutical compounds using vibrational spectroscopy
Bibliographic record
Abstract
Abstract Objectives Solid-state transformations may occur during any stage of pharmaceutical processing and upon storage of a solid dosage form. Early detection and quantification of these transformations during the manufacture of solid dosage forms is important since the physical form of an active pharmaceutical ingredient can significantly influence its processing behaviour, including powder flow and compressibility, and biopharmaceutical properties such as solubility, dissolution rate and bioavailability. Key findings Vibrational spectroscopic techniques such as infrared, near-infrared, Raman and, most recently, terahertz pulsed spectroscopy have become popular for solidstate analysis since they are fast and non-destructive and allow solid-state changes to be probed at the molecular level. In particular, Raman and near-infrared spectroscopy, which require no sample preparation, are now commonly used coupled to fibreoptic probes and are able to characterise solid-state conversions in-line. Traditionally, uni- or bivariate approaches have been used to analyse spectroscopic data sets; however, recently the simultaneous detection of several solid-state forms has been increasingly performed using multivariate approaches where even overlapping spectral bands can be analysed. Summary This review discusses the applications of different vibrational spectroscopic techniques to detect and monitor solid-state transformations possible for crystalline polymorphs, hydrates and amorphous forms of pharmaceutical compounds. In this context, the theoretical basis of solid-state transformations and vibrational spectroscopy and common experimental approaches are described, including recent methods of data analysis.
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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 | 0.001 | 0.002 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".