Scale-up of a Pharmaceutical Roller Compaction Process Using a Joint-Y Partial Least Squares Model
Bibliographic record
Abstract
Garcia-Munoz et al. [Garcia-Munoz, S.; Kourti, T.; MacGregor, J. F. Chemom. Intell. Lab. Syst. 2005, 79, 101–114] proposed a new latent variable regression methodology, joint-Y partial least squares (JYPLS), for product transfer between plants. In this paper, this method is used for product scale-up from a type of laboratory-scale roller compactor, a Fitzpatrick IR220, to a type of full-scale roller compactor, a Fitzpatrick IR520, in the pharmaceutical industry. A JYPLS model is first built with the data set collected from historical experiments on these two types of compactors. The JYPLS model relates API mass fraction, excipient mass factions, and roller compaction process measurements to ribbon properties. A constrained optimization is then formulated to invert the JYPLS model to find the key process settings of the Fitzpatrick IR520 to make the same quality of ribbon using the same raw materials formulation as the ribbon that had been produced on the Fitzpatrick IR220.
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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 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| 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".