High-Speed Tableting of High Drug-Loaded Tablets Prepared from Fluid-Bed Granulated Isoniazid
Bibliographic record
Abstract
The aim of this feasibility study was to investigate the possibility of producing industrial-scale relevant, robust, high drug-loaded (90.9%, w/w) 100 mg dose immediate-release tablets of isoniazid and simultaneously meet the biowaiver requirements. With an understanding of the real-life constrictions on formulation scientists during product development for the generic industry, this study was done considering a common set of excipients and manufacturing operations, as well as paying special attention to the industrial-scale high-speed tableting process as one of the most critical manufacturing operations. The isoniazid substance was not applicable for the direct compression method. Thus, the selection of granulation method was logically justified, and it was fluid-bed granulated with an aqueous solution of Kollidon® 25, mixed with excipients, and tableted with a rotary tablet press (Korsch XL 100) at 80 rpm (80% of the maximum speed) in the compaction pressure range 170–549 MPa monitoring of ejection/removal forces, tablet weight uniformity, thickness, and hardness. Adjusting the main compression force, the Heckel plot, manufacturability, tabletability, compactability, and compressibility profiles were analysed to choose the main compression force that resulted in the desirable tensile strength, friability, disintegration, and dissolution profile. The study showed that highly robust drug-loaded isoniazid tablets with biowaiver requirements compliance can be prepared with a common set of excipients and manufacturing equipment/operations incl. the industrial-scale high-speed tableting process.
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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".