Preuse/Poststerilization Integrity Testing (PUPSIT): To Do or Not to Do?
Bibliographic record
Abstract
In manufacture of heat labile sterile drug products, the final step involves filtration through sterilizing grade filters. It is the drug manufacturer's responsibility to check whether an integral filter has been used. One method used widely to check the integrity of a filter is the bubble point test. To confirm that the filter used is integral, the postuse integrity test is made obligatory by regulatory bodies. However, preuse/poststerilization integrity testing (PUPSIT) of filters remains debatable for the risks associated in its execution. Although PUPSIT is recommended by regulatory bodies, it poses a risk of compromising downstream sterility and involves high costs to mitigate such risks. This study highlights the impact of filter clogging on bubble point values with the consequent possibility of a nonintegral filter passing postuse integrity testing. The results clearly show an increase in postuse bubble point values, which can camouflage a possible flaw in sterilizing filters. The fluid streams 20% dextrose, 0.001% bentonite, paclitaxel, and 0.05% sodium hyaluronate were selected based not only on the commonality of their clogging propensity but also on the different nature of streams that influence the clogging of sterilizing filters. Paclitaxel is an injectable for oncotherapy, and 0.05% sodium hyaluronate is an ophthalmic. The study was conducted with 0.2 μm sterilizing filters from four different manufacturers. It was observed that some fluid streams show a significant increase in the postuse bubble point test values over the preuse bubble point values. This establishes the necessity of performing PUPSIT in certain cases based on the postfiltration shift in bubble point values. As part of the filter validation studies with specific drug products, additional testing should be carried out to establish the need for PUPSIT on a case-by-case basis.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.010 | 0.016 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.002 | 0.003 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.004 | 0.003 |
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".