Foam granulation: new developments in pharmaceutical solid oral dosage forms using twin screw extrusion machinery
Bibliographic record
Abstract
This paper investigates foam granulation in a twin screw extruder as a new continuous wet granulation technique for pharmaceutical powder drug formulations. Foamed aqueous binder has a reportedly lower soak-to-spread ratio than drop or spray liquid addition in batch granulation. This work demonstrates a twin screw extruder configuration for foam granulation and subsequently compares the new approach against liquid injection in the granulation of α-lactose monohydrate with a methylcellulose binder. Trials were conducted at high powder output rates (20-40 kg/h) and high screw speeds (220-320 RPM) with two screw configurations. Process stability improved with the new technique allowing granulation with less binder. The extruded mass maintained a low exit temperature, being insensitive to operating conditions unlike the liquid injection approach, where temperatures rose significantly as flow rate increased. The particle size distribution by foam granulation reflected a more uniformly wetted mass with larger granule growth noted even for conditions where dry powder exited by liquid injection. Other factors were found similar between the two binder delivery methods such as consumed mechanical energy, as well as fracture strength and compressibility of produced granules.
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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 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 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".